20070207Application.pdf

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IDAHO~POWER(V
An IDACORP Company
Barton L. Kline
Senior Attorney
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February 6, 2007
Jean D. Jewell , Secretary
Idaho Public Utilities Commission
472 West Washington Street
P. O. Box 83720
Boise, Idaho 83720-0074
Re:Case No. IPC-07-
In the Matter of Idaho Power Company s Petition to Increase the
Published Rate Eligibility Cap for Wind Powered Small power
Production Facilities; and
To Eliminate the 90%/110% Performance Band for Wind Powered
Small Power Production Facilities
Dear Ms. Jewell:
Please find enclosed for filing an original and seven (7) copies of Idaho Power
Company s Petition for the above-referenced matter.
I would appreciate it if you would return a stamped copy of this transmittal letter in
the enclosed self-addressed , stamped envelope.
Barton L. Kline
BLK:sh
Enclosures
O. Box 70 (83707)
1221 W. Idaho St.
Boise, ID 83702
BARTON L. KLINE , ISB # 1526
MONICA B. MOEN , ISB # 5734
Idaho Power Company
1221 West Idaho Street
P. O. Box 70
Boise , Idaho 83707
Telephone: [phone]
FAX Telephone: [phone]
bkline (Q) idahopower.com
mmoen (Q) idahopower.com
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Attorneys for Idaho Power Company
Express Mail Address
1221 West Idaho Street
Boise, Idaho 83702
BEFORE THE IDAHO PUBLIC UTILITIES COMMISSION
IN THE MATTER OF IDAHO POWER
COMPANY'S PETITION TO INCREASE
THE PUBLISHED RATE ELIGIBILITY CAP
FOR WIND POWERED SMALL POWER
PRODUCTION FACILITIES; and
TO ELIMINATE THE 90%/110%
PERFORMANCE BAND FOR WIND
POWERED SMALL POWER PRODUCTIONFACILITIES
CASE NO. IPC-07-
PETITION
Idaho Power Company ("Idaho Power" or the "Company ), pursuant to RP 053
hereby requests that the Idaho Public Utilities Commission ("Commission ) issue its
order:
PETITION, Page 1
Raising the cap on entitlement to published avoided cost rates for
intermittent wind powered small power production facilities that are qualifying facilities
OFs ) under Sections 201 and 210 of the Public Utility Regulatory Policies Act of 1978
PURPA") from the current level of 100 kW to 10,000 average kWs per month ("
average MWs/mo" or "10 aMW"); and
Reducing the published avoided cost rates applicable to intermittent wind
powered OFs to compensate for the increase in system costs due to wind variability.
The Company s proposed new published avoided cost rates are set out in Attachment 2
enclosed with this Petition; and
Authorizing Idaho Power to purchase state-of-the-art wind forecasting
services that will provide Idaho Power with forecasts of wind conditions in those
geographic areas in which wind generation resources are located. The order should
further provide that OFs will reimburse the Company for their share of the on-going cost
of the wind forecasting service. and
Authorizing the Company to include a "mechanical availability guarantee
in all contracts with new intermittent wind powered OF resources. The mechanical
availability guarantee would require wind powered OFs to demonstrate monthly that
except for scheduled maintenance and events of force majeure the OF wind project
was physically capable and available to generate at full output during 85% of the hours
in the month.
If the Commission orders the changes to the published rates presented in
Attachment 2 , authorizes the acquisition and funding of the wind forecasting services
and authorizes the inclusion of mechanical availability guarantees in future contracts for
PETITION, Page 2
purchases of energy from intermittent wind powered OFs, Idaho Power proposes that
the Commission remove the requirement that the 90%/110% performance band be
included in new contracts for energy purchases from intermittent wind powered OFs.
This Petition is based on the following:
BACKGROUND
On June 17, 2005, in Case No. IPC-05-, Idaho Power filed a petition with the
Commission requesting a temporary suspension from the Company s obligation under
Sections 201 and 210 of PURPA and various Commission orders, to enter into new
contracts to purchase energy generated by wind powered OFs. Following a public
hearing and oral argument on August 4 , 2005 , the Commission entered Interlocutory
Order No. 29839. The Interlocutory Order did not approve a temporary suspension but
instead , reduced the published rate eligibility cap for intermittent OF wind projects from
10 average MW/mo to 100 kW and required individual contract negotiations for wind
OFs larger than 100 kW. Order No. 29839 also established grandfathering criteria for
OF wind projects that were in various stages of negotiation with Idaho Power at the time
Order No. 29839 was issued.
On August 23, 2005 in Order No. 29872 , the Commission designated
Interlocutory Order No. 29839 as final Order No. 29851 to allow parties to seek
reconsideration and appeal of the Interlocutory Order. Subsequently, Order No. 29872
denied the petitions and cross-petitions for reconsideration of final Order No. 29851 filed
by Windland Incorporated , Idaho Power and the Commission Staff and established the
right of aggrieved parties to appeal all final and interlocutory orders previously issued in
Case No. IPC-05-22 to the Idaho Supreme Court. No appeals were filed.
PETITION, Page 3
II.REDUCTION OF PUBLISHED AVOIDED COST RATES TO COMPENSATE
FOR INCREASED COSTS DUE TO WIND VARIABILITY
In Interlocutory Order No. 29839 (Final Order No. 29851), the Commission found
that wind generation presents operational integration costs to a utility different from
other PURPA qualified resources. (Order No. 29839 p. 8). The Commission also found
that the unique supply characteristics of wind generation and the related integration
costs provide a basis for adjustment of the published avoided cost rates , a calculated
figure that may be different for each utility. (Order No. 29839 p. 8). In the IPC-O5-
case , Idaho Power advised the Commission that it intended to perform a study to
quantify the additional costs it would incur directly related to purchasing a significant
amount of wind generation ("the wind integration study" or "the study ). The Company
further advised the Commission that, upon completion of the study, the Company would
provide it to the Commission for its consideration.
To assist Idaho Power in preparing the study, the Company retained the services
of EnerNex Corporation. EnerNex retained WindLogics , Inc. to assist by developing the
historical wind speed data set for the study. Both consultants are acknowledged as
experts in their respective fields, analysis and preparation of wind integration studies
(EnerNex) and atmospheric modeling and analysis (WindLogics).Idaho Power
distributed a peer review draft of the study to a number of entities that are considering
similar wind integration issues on a regional basis. These entities were given the
opportunity to provide the Company with a review of the methodology used in the peer
review draft of this study.1 Based on comments received from the peer review group
The peer review participants were Avista, BPA, Grant County PUD, National Renewable Energy
Laboratory, NorthWestern Energy, Oak Ridge National Laboratory, PacifiCorp, Puget Sound Energy,
Renewable Northwest Project, Seattle City Light, and two independent consultants.
PETITION, Page 4
and with further refinements performed by the Company, the methodology was finalized
and the final study prepared. A copy of the final study is enclosed as Attachment 1 to
this Petition.
In Order No. 29839, the Commission found that because of its variability,
intermittent wind resources cause Idaho Power to incur additional costs to include wind
resources in its portfolio. Attachment 1 quantifies some of the additional costs the
Commission was referring to in Order No. 29839. These additional costs reduce the
savings that the published rates assume the Company can obtain by purchasing OF
resources rather than generating the same amount of power from the surrogate avoided
resource ("SAR") used to set Idaho Power s avoided costs.For this reason , the
published OF rates do not reflect the actual costs Idaho Power can avoid by purchasing
energy from intermittent OF wind resources and are therefore in violation of PURPA
requirements. The study confirms that avoided cost rates paid to intermittent wind
powered OF resources must be reduced to be in compliance with PURPA.
The Company is concerned that the study, which generally will be referred to as
Idaho Power s "Wind Integration Study" might leave the incorrect impression that the
study covers all costs of integrating wind generation.In fact , a number of potentially
significant costs associated with the integration of wind resources are not addressed in
the wind integration study. Attachment 1 entitled "Operational Impacts of Integrating
Wind Generation into Idaho Power s Existing Resource Portfolio" identifies a number of
the costs not addressed in the study. The study only addresses the additional costs the
Company will incur in providing the additional up and down regulating reserves
necessary to integrate or "firm" the wind generation without a reduction in reliability.
PETITION , Page 5
The study assumes that the Company s Hells Canyon Complex is used as the primary
resource to provide the "firming" role. The study also describes the role that market
prices play in determining the additional costs the Company will incur as a result of
making additional market purchases during heavy load hours and additional sales
during light load hours. The additional purchases and sales are necessary because of
the changes in Hells Canyon Complex operations required to provide the additional up
and down regulating reserves required to integrate various amounts of wind energy.
The study does not consider or attempt to quantify other critical wind integration
cost issues such as how the firming requirements of wind will affect operations and
maintenance costs of the Company s low cost hydro system To a significant degree
those wind integration impacts cannot be analyzed and quantified without a reasonable
period of experience with actual operations after wind resources are added.
In addition , the study does not consider the impacts of the integration of
substantial wind generating resources on the Company s transmission system , either
internal or interconnecting to other utilities or regions or the infrastructure investment
levels necessary to support the growth in wind generation demands on the system.
Furthermore, the study does not consider the fact that, once the number of developed
wind projects reaches a certain level, the firming availability of the Company
hydroelectric resources will have been exhausted and the firming requirements for
additional resources of any kind will have to come from new, and much more expensive
back-up sources such as coal and gas-fired plants.
PETITION , Page 6
Based on the modeling described in the study, Idaho Power has computed the
additional costs it estimates it will incur as a result of incorporating varying amounts of
energy produced by intermittent wind generators. Those costs are shown in Figure 1
below:
Figure (MMS1)
$25.
IOAliO POWER COST IN DOUARS PER MWh Of WIND ($IMWlI)
COST BASED OK2001lEVEUZEO PURPARATE 1$62.171'MWh)
AVG '%AVG (SiMWh)
34111 1S.S:J%S91S
"'0 1K&7%$1172
94111 2:5.75%$16J&
12Gt1
$2000
$1 ~oo
'$11.12lh1'lM1
$'IO.l2MM1
81000 .. -$10:050,-"""."'0
..,...
$500 ~ AVERAGE.
3RI) ORDER UNE fmED TO ,t.YERAGE
$0.
300 6QO 900 100 500
\WID P8IETRATIOI~U:Vn.
Based on the average costs ($/MWh) shown on Figure 1 , Idaho Power has
computed the amounts that it believes should be deducted from the published rates so
that the published rates will more accurately reflect Idaho Power s avoided costs. The
Company currently has signed contracts or commitments to develop 384
(nameplate capacity) of wind generation. Idaho Power proposes that the prospective
cost reduction be set at $10.72 per MW hour which is the midpoint between the cost
PETITION, Page 7
associated with the current contract amount of 384 MW and the additional costs that will
be incurred at a 20% penetration level , 600 MW. The cost at this midpoint level (492
MW) is $10.72 per MW hour.Idaho Power requests that , in conjunction with
increase in the cap on entitlement to published rates , the Commission order a reduction
in the published avoided cost prices to be paid to intermittent wind energy OFs in the
amount of $10.72/MWh. This deduction is reflected in the published chart amount set
out in Attachment 2.2 3
III.WIND INTEGRATION IS A DYNAMIC PROCESS
The wind integration study makes it clear that there is still a great deal of
uncertainty surrounding the ultimate impact and cost of adding large amounts of
intermittent wind generation to the Company s resource portfolio. As Idaho Power gains
experience with integrating wind generation into its resource portfolio, it will update the
study and present the results of the update to the Commissioners.Idaho Power
anticipates that an update will be completed on or before the time that the Company
expects to have 600 MW of intermittent wind in its energy portfolio.
2 The Company has contracts with some wind resource developers in which the OFs deliver energy that
has been "firmed" by a third-party. Projects such as these do not impose the costs on Idaho Power
described in Attachment 1 and, as such, the published avoided cost prices for firm wind would not be
reduced to address these variability costs.
3 The Commission has previously determined that OF projects that are expected to generate more than
10 aMW each month are not entitled to receive the published rates. Instead, the purchase rates for these
larger projects are individually negotiated based on avoided costs determined by use of the AURORA
system dispatch model. This use of the AURORA model to determine avoided costs for large OFs is
commonly referred to as the IRP methodology. Individually negotiated rates for larger wind-powered OFs
will also need to be adjusted to address the costs of integrating wind resources. The IRP methodology
does not automatically make that adjustment.
PETITION , Page 8
IV.ELIMINATION OF THE 90%/110% PERFORMANCE BAND
As the Commission is well aware, developers of wind powered OFs have been
particularly critical of the inclusion in their contracts of the 90%/110% performance band
provisions that have been a part of all recent OF contracts in Idaho. Idaho Power
believes that inclusion of the 90%/110% performance band provisions in OF contracts
has been effective in (1) promoting more accurate estimates of monthly energy
deliveries and (2) more closely aligning the value of OF generation with the published
avoided cost rates. This alignment is crucial because the published rates are based on
the projected costs and operating characteristics of a firm , dispatchable, combined cycle
combustion turbine. That being said , Idaho Power is prepared to recommend that the
Commission eliminate the requirement that the 90%/110% performance band be
included in energy purchase contracts involving intermittent wind powered OFs
provided that the following three criteria are met.
Wind QFS Will Fund Their Share of Wind Forecasting Services.
During the course of the preparation of the wind integration study, Idaho Power
has become more familiar with the state-of-the-art wind forecasting services that are
now available. While these forecasting services are not inexpensive and certainly have
their limitations , particularly the inability to provide long-term forecasts, the use of these
forecasting services by utilities with large blocks of wind generation in their resource
portfolios has become more common.
As a result, Idaho Power proposes that it be authorized to purchase the services
of wind forecasting providers that will deliver to Idaho Power state-of-the-art forecasts of
wind conditions in the specific geographic areas in which the Company s wind
PETITION, Page 9
resources are located. Idaho Power believes that it is appropriate that the cost of the
wind forecasting service be shared among all purchased and owned wind generation
resources included in Idaho Power s resource portfolio.4 Idaho Power is confident that
the mechanics of cost sharing can be mutually agreed upon by the parties in settlement
discussions.
Wind QFs Will Provide A Mechanical Availability Guarantee.
Idaho Power is aware that the concept of substituting a mechanical availability
guarantee ("MAG") for the 90%/110% performance band has been considered and
rejected by the Commission. Idaho Power is not proposing that a MAG be considered a
one-for-one replacement for the 90%/110% performance band. Instead , Idaho Power is
proposing that the MAG be only one of three criteria to be satisfied as a condition
precedent to elimination of the 90%/110% performance band. Idaho Power proposes
that the mechanical availability guarantee require wind OFs to demonstrate each month
that, except for scheduled maintenance and force majeure events, the wind project was
physically capable of generating at full output during 85% of the hours in the month.
Failure to comply with the mechanical availability guarantee would result in the payment
of liquidated damages.
Intermittent Wind QFs Will Be Paid Lower Rates.
The third condition for the elimination of the 90%/110% performance band for
wind OFs is the adoption by the Commission of reduced published OF rates that
recognize the additional costs Idaho Power will incur as it acquires increasingly large
4 Horizon Wind has already agreed to provide this forecasting service for the Telocaset project at its
expense.
PETITION, Page 10
amounts of wind generation. Idaho Power s proposed changes to the published rates
for the first 600 MW of new wind powered OFs are set out in Attachment 2.
If the Commission is willing to accept Idaho Power s proposals regarding the
reductions of the published rates, the acquisition and funding of wind forecasting
services and the inclusion of a mechanical availability guarantee in wind OF contracts
Idaho Power would recommend that future energy purchase contracts with wind
powered OFs not be required to include the 90%/110% performance band provisions.
RECOMMENDED PROCEDURE FOR PROCESSING THIS PETITION
Idaho Power has posted this pleading and the two Attachments on its website.
has sent e-mails to all of the parties that participated in Case No. IPC-05-22 and the
entities that participated in the workshops that followed the issuance of Order No. 29839
advising them of the filing and providing a link to the website. If the Commission
concurs , Idaho Power proposes to expeditiously schedule and conduct at least one
workshop in which this Petition and the two Attachments can be discussed with the
parties that participated in the prior proceedings and workshops. Such a Commission
sanctioned workshop process could facilitate settlement discussions at an early stage of
the case.
At any time , parties could request a pre-hearing conference or otherwise request
that the Commission make a determination as to how to proceed with this case either by
submitting written comments or by the conducting of a technical hearing with pre-filed
testimony and discovery.
Based on all of the foregoing, Idaho Power respectfully requests that the
Commission issue its order.
PETITION, Page 11
Raising the cap on entitlement to published avoided cost rates for
intermittent wind powered small power production facilities that are qualifying facilities
under Sections 201 and 210 of the Public Utility Regulatory Policies Act of 1978 from
the current level of 100 kW to 10 average MW/mo; and
Reducing the published avoided cost rates applicable prospectively to
intermittent wind powered OFs to compensate for the increase in the Company costs
due to wind variability. The Company s proposed new published avoided cost rates for
up to 600 MW of wind powered OFs are set out in Attachment 2; and
Authorizing Idaho Power to purchase state-of-the-art wind forecasting
services that will provide Idaho Power with forecasts of wind conditions in those
geographic areas in which the Company s wind generation resources are located. The
order should further provide that wind powered OFs will reimburse the Company for
their share of the cost of the wind forecasting service; and
Authorizing Idaho Power to require the inclusion of mechanical
availability guarantee in all new contracts to purchase energy from wind powered OFs.
In conjunction with the Commission approval of Idaho Power s requests
set out in Paragraphs 2 , 3 and 4 above , authorizing the Company to eliminate the
requirement that the 90%/110% performance band be included in new OF wind
contracts.
6. Such other relief as the Commission may deem appropriate.
. ;\-1
Respectfully submitted this day of Februa
BART N L. KLINE
Attorney for Idaho Power Company
PETITION, Page 12
BEFORE THE IDAHO PUBLIC UTiliTIES COMMISSION
CASE NO. IPC-O7-
IDAHO POWER COMPANY
ATTACHMENT
Operational Impacts of Integrating
Wind Generation into Idaho Power
Existing Resource Portfolio
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IPC- E-07 -
Idaho Power s Perspective on the Operational
Impacts of Integrating Wind Generation
Consistent with its jurisdiction under the Public Utility Regulatory Policies Act of 1978
(PURP A), the Idaho Public Utilities Commission ("Commission ) has ordered Idaho Power
Company ("Idaho Power" or "Company ) to offer power purchase contracts including standard
avoided cost purchase rates ("published rates ) for energy generated by QFs with an output of 10
aMW or less. In 2005, due largely to federal tax incentives and favorable PURPA rates, a largenumber of wind project developers came to Idaho Power requesting PURPA contracts containing
the published rates. Because of the uncertainty associated with integrating this large volume of
wind generation on its system, Idaho Power sought temporary relief from PURPA requirements
until the impact of wind integration could be more fully studied ("Wind Integration Study" or
Study ). The Commission granted this relief by temporarily reducing the cap on entitlement to
published rates from 10 000 average kW/month (10 aMW) to 100 kW for PURPA wind projects.
The objective of this study is to assess the operational impacts Idaho Power must manage in
order to maintain system reliability as wind generation is added to its resource portfolio. The
intermittent nature of wind generation requires Idaho Power to have resources available to
increase or decrease generation on short notice in order to keep the interconnected power system
balanced. While Idaho Power s hydroelectric power plants are well suited for performing this
function, the study shows that adding large amounts of wind generation to the Company
system will increase the cost of supplying power to all of Idaho Power s customers.
As a public utility, Idaho Power is focused on providing reliable, low-cost energy to its
customers. The integration of wind generation will increase overall costs relative to current
operational practices, and could potentially impact reliability if not properly managed. This
study quantifies the cost associated with integrating wind generation without sacrificing
reliability. More specifically, this study quantifies the amount of up and down regulating
reserves Idaho Power needs to maintain for various amounts of wind generation in order to
maintain its current level of compliance with NERC Control Performance Standard 2, or CPS2.
What is the cost of integrating wind generation into Idaho Power s resource portfolio?" As a
result of performing this study, Idaho Power knows that the cost it has determined in this study
will need to be refined based on experience gained in future day-to-day operations. This is a
dynamic problem and the answer will change over time.
There are a number of impacts associated with the integration of wind generation that are not
accounted for or quantified in this study. They include:
Interconnection Costs: Wind generation interconnection costs or the costs of any internal
transmission system "backbone" upgrades necessary to integrate increasing levels of
wind generation were not investigated in this study. The study also does not account for
the less optimal use of firm transmission capacity by intermittent wind generation.
Changes in Electricity Price Levels: Future fluctuations in the overall level of wholesale
market prices for electricity are not accounted for in the study. Market prices will impact
Idaho Power Company February 2007
the overall cost of integrating wind as Idaho Power utilizes its transmission system to
access markets to purchase and sell electricity.
Changes in Heavy-Load/Light-Load Spread: It is a very real possibility that the spread
between heavy-load and light-load prices will increase as additional wind generation is
integrated throughout the Pacific Northwest. As the spread between heavy-load and
light-load prices increases, the cost of integrating wind generation will increase.
Curtailment Strategies: The impact on the cost of integrating wind if Idaho Power has the
ability to curtail wind generation is not considered in this analysis. Without the ability to
curtail wind generation, Idaho Power must maintain sufficient downward regulating
capacity in order to account for sudden increases in wind generation. The ability to
curtail wind generation in real time might reduce the amount of regulating capacity
required and subsequently reduce integration costs. However, such interruptability also
decreases the value of wind as a resource available to meet firm loads.
Improvements in Wind Forecasting: This study does not account for improvement in
wind forecasting methods. This analysis utilizes a persistence forecast.
Reduction in Geographic Diversity: This study anticipates a certain amount of geographic
diversity in wind resources for each of the levels of wind generation modeled. The
impact of wind integration on Idaho Power s system could be substantially different if the
actual geographic diversity of wind resources is substantially greater or less than
anticipated in this study.
Regional Wind Integration Efforts: The impacts associated with any future regional wind
integration efforts, like ACE Diversity Interchange (ADI), regulation sharing or other
regional strategies to reduce or share the costs of integrating wind resources in the Pacific
Northwest.
Changes in the Regional Market: The impact of enhancements in the regional wholesale
electricity market was not within the scope of the study. Current power trading practices
require a Balancing Authority to maintain considerable flexibility in its generating fleet
for responding to within-hour variability. These practices may evolve in the future such
that the electricity market provides an option for absorbing some of wind generation
short-term variability and uncertainty.
Reductions in Operational Flexibility: The study does not evaluate the effects of more
rigorous operating constraints occurring as a result of the relicensing of the Company
Hells Canyon Complex. However, it is clearly recognized that the costs to manage wind
generation will increase if the new FERC license for the Hells Canyon Complex
stipulates a reduction in operational flexibility.
The increased regulating reserves maintained for wind generation have the effect of constraining
hydro operations at the Company s Hells Canyon Complex. This study utilizes scenario-basedmodeling to estimate the costs of this effect. Essentially, this approach arrives at a cost estimate
based on the comparison of paired model simulations - one in which wind generation is input in
Idaho Power Company February 2007
its actual or variable hourly time series, and a second in which wind is input to the model in flat
daily blocks with energy equivalent to the variable wind generation. For the variable wind
simulation, an increased regulating reserve requirement is imposed based on analysis of load and
wind generation data. The regulating reserve requirement defined for the flat wind case is based
on characteristics observed within the load data alone. Because of expected cost correlations
with hydrologic conditions and wind penetration levels, three study years (1998, 2000, and 2005)
and four wind penetration levels (300 MW, 600 MW, 900 MW, and 1 200 MW) were
investigated. The selected study years correspond approximately to low-case (2005), medium-
case (2000), and high-case (1998) hydrologic conditions.
The results of the modeling, with costs expressed as a percentage of Mid-Columbia wholesale
electricity market prices, are given in the following figure. Results for the highest wind
penetration level (1,200 MW) are not included in the average result, because that amount of wind
overwhelms the ability of the system as represented by the modeling software (Vista DSS) to
consistently maintain the required amount of regulating reserve. Problems meeting requested
reserve levels are particularly severe under the low-case hydrology simulations. Consequently,
the result for the 900 MW penetration level for 2005 is also removed from the calculated
average. The average line shown in the graph below uses the data points from the study (which
are represented on the graph) where the Vista DSS modeling indicates the Hells Canyon
Complex AGC constraint violations are deemed acceptable. Because they reflect an operation
that is not as constrained as requested, AGC constraint violations have the effect of reducing the
integration cost at the expense of reliability, which is unacceptable given reliability standards.
The 900 MW and 1 200 MW data points for 2005 are therefore excluded from the average point
calculations at the respective penetration levels.
IDAHO POWER COST AS PERCENTAGE OF MID-COLUMBIA MARKET
40.
35.
30.
300
600
900
1201
1998 2000 2005 AVG
11.60% 16.60% 18.40% 15.53%
17.10% 22.90% 16.00% 18.67%
21.90% 29.60% 25.75%
25.10% 29.80%
25.
------...
20.
15.
10.-+-STUDYYEAR 1998 --STUDYYEAR 2000
-.!r- STUDY YEAR 2005 -AVERAGE
300 600 900
WIND PENETRATION LEVEL
200 500
Idaho Power Company February 2007
The observed reserve deficiencies indicate that the system represented by the modeling software
is unable at higher penetration levels to consistently provide the regulating reserve necessary to
manage wind, while honoring hydraulic constraints and the current regulatory requirements of
the Hells Canyon Complex. Furthermore, while Idaho Power considers its actual hydroelectric
operations to be optimized for all practical purposes, these operations are undoubtedly
conservative in comparison to the "perfectly" optimized operations computed by the Vista DSSmodel. Consequently, it is the Company s view that operations in practice would be severely
strained in attempting to provide reserves for wind at the higher penetration levels, and that adegradation of system reliability would likely result. At some point, Idaho Power will exhaustits ability to integrate wind with its existing resources and other, most likely more expensive
methods will have to be found.
The figure below presents Idaho Power s cost estimate (in $/MWh of wind generation) for
modifying its operations to accommodate wind generation for a range of penetration levels. The
Company currently has signed contracts or commitments to develop 384 MW (nameplate
capacity) of wind generation. Evidence from the modeling performed as part of this
investigation suggests that reserve requirements at higher wind penetration levels ultimately
overwhelm the current system s reserve capacity. Idaho Power believes the upper limit on theamount of wind generation that can be integrated on its system (without major investment in
additional resources) lies between 600 and 900 MW. It should be noted that 600 MW of wind
generation corresponds to a penetration level of approximately 20% (according to the convention
of expressing penetration level as percentage of peak system load), which is an ambitious level
of development by current standards.
$25.
IDAHO POWER COST IN DOLLARS PER MWh OF WIND ($/MWh)
COST BASED ON 2007 LEVELIZED PURPA RATE ($62.77/MWh)
MIl AVG %AVG ($iMWhl
300 15.53%$9.
600 18.67%$11.
900 25.75%$16.
1200
$11.72IMWh
$20.
$1500
--------------------------------------------
$10.12JMWh
$10.00 ---S10:05IMM1
-m---- -- -
o,f-
o,f-
$5.0 AVERAGE
3RD ORDER LINE FITTED TO AVERAGE
$0.
300 600 900 1,200 500
WlIID PEliETRATlOli LEVEL
Idaho Power Company February 2007
To arrive at a single cost estimate to account for system impacts due to wind integration, IdahoPower proposes the use of the estimated cost at the midpoint of wind development between the
current committed level (384 MW) and the 20% penetration level (600 MW). The cost at this
midpoint level (492 MW) is estimated via a 3rd order line fitted to the study results. This cost
($10.72/MWh of wind) is illustrated in the preceding figure. Idaho Power further proposes this
cost of wind integration" be applied as a deduction to current PURP A rates for all proposed
wind development in the near-term unless the developer is willing to purchase ancillary services
to firm the output from the project.
As a part of filing this study, Idaho Power is proposing the cap on PURPA wind projects be
raised back to the previous level of 10 000 average kW/month until the total amount of wind
generation on its system reaches 600 MW. Due to the uncertainties involved, Idaho Power plans
to continue studying the actual impacts as additional wind resources begin to interconnect to its
system at the end of2007. In the event the actual impacts of wind generation are significantly
different than presented in this study, it may be necessary for Idaho Power to request a
reinstatement of the 100 k W cap prior to reaching a penetration level of 600 MW in order to
preserve the reliability of its system.
Idaho Power supports society s desire to have future energy supplies come from clean
renewable energy sources. Renewable , emission-free electricity production has been a part of
our company s history for over 90 years and wind power will be an important part of continuing
that legacy. As Idaho Power gains actual experience with greater amounts of wind generation on
its system, it will be necessary to revisit and update this analysis. The issues surrounding the
integration of wind generation on interconnected power systems are numerous and complex.
The following study provides a first step toward understanding those issues.
Idaho Power Company February 2007
Idaho Power Company February 2007
Operational Impacts of Integrating Wind
Generation into Idaho Power s Existing
Resource Portfolio
Prepared by:
f n e 1111UtO~POWER~
An IDACORP company
EnerNex Corporation
170C Market Place Boulevard
Knoxville, Tennessee 37922
Tel: [phone] ext. 149
FAX: [phone]
bobz(g)enernex.com
www.enernex.com
Idaho Power Company
o. Box 70
Boise, Idaho 83707
February, 2007
Ta ble of Contents
Section 1 Executive Summary.....................................................................................
Developing Wind Generation Profiles
....................................................................................
Assessing Wind Generation Impacts on Real-Time Operation of the Idaho Power CompanySystem................................................................................................................................
Simulating Annual Operations of the Idaho Power Company System with Wind Generation......... 3
Results................................................................................................................................
Discussion.................................................................................. ............... .............. .... ........ 5
Conclusions...................................................................
"""""""
................................. ....... 6
Section 2 Introduction........... .... ...... ........... ....... ............. ........ ....... ......... ........ .... ........ 9
Why a "Wind Integration" Study? .......................................................................................... 9
About Idaho Power.............................................................................................................. 9
Characteristics of Wind Generation...................................
""""""""" .................................
Section 3 Overview of Utility System Operations .....................................................
Regulatory Requirements.....
"""""'"
........................................................... ....................... 13
Operational Planning and Reserves......................................................................................
Section 4 Wind Integration Study Methodology.......................................................
Study Methodology.................................. .................................... ...................................... 19
Simulating the Idaho Wind Resource ...................................................................................
Wind Generation Forecasting Error ...................................................................................... 21
Impacts of Wind Generation Within the Hour.......................................................................
Hourly Dispatch Simulations............................................. ................................................... 22
Section 5 Simulating the Idaho Wind Resource .......................................................
Objective...........................................................................................................................
Approach........................................................................................................................... 25
Developing Wind Generation Profiles........ ........................................................................... 30
Characteristics of the Wind Generation Model for the Study....... ..,......................................... 31
Section 6 Impacts of Wind Generation Within the Hour ..........................................
Analysis of High-Resolution Load and Wind Data................................................................... 37
Analysis of 10-Minute Load and Wind Data....... .................... ........... .... ....................... .......... 40
Combining Reserve Components......................................................................................... 42
Page
Section 7 Hourly Dispatch Simulations.....................................................................
Operational Considerations for the Idaho Power System ........................................................
The Vista Decision Support System.......................................
"""""""""""
........ ................. 46
Review of Study Design................................................................................................. .....
Study Limitations..... ....................................................... ................................................... 48
Results..............................................................................................................................
Sum mary and Conclusions.
................ ............... .................. ......... ......... ....
Summary of Study Findings................................................. ............................................... 55
Discussion................................................ .......................... ......................................... """ 56
Section 8
Conclusions .......................................... ....................... ...................................................... 58
Section 9 References ................................................................................................
Appendix A Developing Wind Generation Profiles from Wind Speed Data ..................
Appendix B Estimating Incremental Reserve Requirements using Load and WindGeneration Data ...........................................................................................................
Appendix C Idaho Wind Resource Map ........................................................................ 77
Appendix D Day-Ahead Wind Generation Forecast Error .............................................
Appendix E Vista Bus Configuration .............................................................................
Appendix F Wholesale Electricity Market Price Data ...................................................
Page ii
Table of Figures
Figure 1. Wind generation as a fraction of hourly load for calendar year 2000......................... 2
Figure 2. NERC reliability regions and balancing authority areas ...........................................
Figure 3. NERC CPS2 equation..............................................................
"""""""""""""" .
Figure 4. Components of operating reserve....................................................................... .
Figure 5. Scope of MM5 meteorological simulation model used to synthesize wind speed
data for study years. Outer and inner grids depicted ..............................................................
Figure 6. Idaho portion of MM5 model illustrating inner grid and data extraction points..........
Figure 7. Oregon portion of MM5 model showing inner grids and extraction points.................
Figure 8. Two weeks of typical winter Idaho Power load from CY2000 and wind
generation from model for study......................................................................................... .
Figure 9. Two weeks of typical summer Idaho Power load from calendar year 2000 and
wind generation from model for study............................. ................................................... ..
Figure 10. Production duration curve by year for 300 MW scenario .........................................
Figure 11. Production duration curve by year for 600 MW scenario .........................................
Figure 12. Production duration curve by year for 900 MW scenario .........................................
Figure 13. Production duration curve by year for 1200 MW scenario .......................................
Figure 14. Wind generation as a fraction of hourly load for calendar year 2000 ......................
Figure 15. Distribution of hourly production changes - calendar year 2000.............................
Figure 16. Distribution of hourly changes in Idaho Power load from calendar year 2000..........
Figure 17. High-resolution (30-see.) load data samples used for regulation analysis .................
Figure 18. Extracting the regulation characteristic.................................................................
Figure 19. "Regulation characteristic" of Idaho Power load.....................................................
Figure 20. Distribution of Idaho Power load variations from 20-minute rolling average .............
Figure 21. Observed Idaho Power system load, July 8 2005 - 10:00-11:00 ............................41
Figure 22. Required bi-directional reserve versus wind penetration level - based on 2005
Idaho Power s system load and wind data .............................................................................
Figure 23. Illustration of variable and flat wind energy deliveries............................................
Figure 24. Computed Hells Canyon Complex hourly generation August 6-, 2000 - 900
MW wind penetration level.................................................................................................. .
Figure 25. Absolute wind generation integration costs ($/MWh of delivered wind) as a
function of penetration level for three historical years with varying water conditions.....
""""" ..
Page iii
Figure 26. Wind generation integration cost as a percentage of the average market
energy price for three historical years with varying water conditions........................................
Figure 27. Turbine power curve used for calculating generation data from wind speed
measurements....................................................................................................................
Figure 28. Empirical "power curve" for wind plant from measured values ................................
Figure 29. Wind plant "power curve" calculated from 10-minute wind speed values .................
Figure 30. Measured wind generation vs. that from simple calculation (wind speed and
single turbine power curve)................................................................................................. .
Figure 31. Measured and calculated plant power curves ........................................................
Figure 32. Exponential modification of measured wind speed .................................................
Figure 33. Measured and modified calculated plant power curves ...........................................
Figure 34. Comparison of measured wind generation to that calculated with wind speed
modification........................................................................................................................
Figure 35. Comparison of simple versus modified method for calculating wind generation
from wind speed data................................................... ...................................................... .
Figure 36. Illustration of load following rule for load alone. ....................................................
Figure 37. One-hour variability of wind production as a function of production level.................
Figure 38. Average net load, actual net load, and load following "bands" for three-dayperiod
Figure 39. Average net load, actual net load, and load following "bands" for another
three-day period................. ............. .................... ...............
....................................... .........
Page
Table of Tables
Table 1.
Table 2.
Table 3.
Table 4.
Table 5.
Table 6.
Table 7.
Table 8.
Table 9.
Table 10.
Table 11.
Table 12.
Table 13.
Table 14.
$132
Table 15.
Table 16.
Table 17.
Load
Total bi-directional reserve........................... ........................ ................................ 3
Summary Vista DSS results with AGC constraint violation data................................. 5
Idaho Power hydro and thermal plants - generating capacity .................................
Partial list of 2006 CPS2 Bounds for WECC Balancing Authorities............................. 16
Assignment of extraction points to wind generation scenarios.................................
Annual capacity factor by scenario from wind generation model for study................
Estimated wind generation impacts on high-resolution reserve ...............................40
Bi-directional reserve to cover 98% of 10-minute deviations to within LlO ................
Total bi-directional reserve ..................................................................................
Operation of Hells Canyon Complex for study years...............................................
Wind integration costs ($/MWh of delivered wind)..
"""""'" """'" """""""""" .....
Wind integration costs as percentage of annual market energy price.......................
Summary Vista DSS results with AGC constraint violation data................................ 52
Integration cost summary for calendar year 2000-Average Market Price ::
Vista DSS results using historical Mid-C prices .......................................................
Statistics for load following and regulation profiles.................................................
MWh (over-) or under- forecast for year 2000; HL-Heavy Load, LL;-Light
.........................................................................................................................
Table 18. Monthly average of Idaho Power s purchase and sales prices for heavy andlight load firm products..................................
.................................................................... ..
Table 19. Difference between the day ahead and real-time for 1 MWh transaction..................
Table 20. Applying the above transaction to the total forecast error for the 300 MW
penetration level................................................................................................................ .
Page
Page
Section 1 EXECUTIVE Su M MARY
Variability and uncertainty are the two attributes of wind generation that underlie mostof the concerns related to power system operations and reliability. In day-aheadplanning, whether it be for conventional unit commitment or offering generation into anenergy market, forecasts of the demand for the next day will drive the process. In real-time operations, generating resources must be maneuvered to match the ever-changing
demand pattern. To the extent that wind generation adds to this variability anduncertainty, the challenge of meeting demand at the lowest cost while maintainingsystem reliability is increased.
The primary focus of this study is to determine how the real-time operation of IdahoPowers Hells Canyon Complex would be impacted by the addition of significant
amounts of wind generation. Enough has been learned about the behavior oflarge
amounts of wind generation - in either a few large plants consisting of many dozens to
hundreds of turbines , or an equivalent number of turbines spread out over a largegeographic area - to state that the impacts of wind generation uncertainty and
variability on the bulk power system are primarily economic, and manifested inincreased system costs. These costs are a consequence of the additional controllablegeneration capacity that must be allocated to manage the incremental variability of theBalancing Authority area due to wind generation, and the increased uncertainty thatmust be dealt with in operations planning.
The wind integration study ordered by the IPUC evaluates the operational and financialimpacts of four levels of wind generation on three differing hydro condition years. The
four wind generation levels are 300MW , 600MW, 900MW and 1 ,200MW. The threeyears are 1998, 2000 and 2005 which represent high, median and low hydro conditionsrespectively. The objective of the study is to evaluate the changes in operations and the
resultant costs that wind variability and uncertainty introduces into the system at thefour generation levels for each hydro year selected.
The major tasks in preparing the study consisted of:
1. Gathering wind data and building wind generation profiles.
2. Gathering and analyzing current generation and load data without wind.
3. Analyzing combined wind and load data and determining operational changes.
4. Modeling operational changes to determine economic impacts.
5. Evaluating the results,
DEVELOPING WIND GENERATION PROFILES
The study required detailed wind data from numerous locations over several years.Wind speed data of sufficient coverage and temporal resolution for constructing thechronological wind generation profiles is not available. Detailed meteorological
simulations for the study years were developed for this analysis, The wind resource in
Idaho Power service area is distributed across the southern third of the state (an Idaho
Page
wind resource map from the National Renewable Energy Laboratory (NREL) is includedin the appendices). The wind generation scenarios considered for this study assess
possible development projects throughout this area, along with potential projects in
eastern Oregon.
The meteorological simulations employ a three-dimensional, physics-based model of theatmosphere based on the MM5 mesoscale model. When used for weather forecasting,
the MM5 model is initialized with all known and available information about the current
state of the atmosphere. This includes data from meteorological stations , balloonsoundings, sensors on commercial aircraft, and satellite data. The model is then run
forward through time , and variables relevant to the forecast such as temperature
humidity, wind speed, etc. are saved periodically for locations of interest.
While the number of hours over the year at the highest wind generation production
levels is limited, the amount of wind generation relative to system load over any periodmay still be significant, Figure 1 shows the amount of hourly wind generation relative to
system load, sorted in descending order. This "wind generation penetration curveshows that for over 4 000 hours in calendar year 2000, the amount of wind generationis greater than 5% (for the 300 MW scenario) to 20% (for 1 200 MW of installedcapacity) of the hourly system load. At the extreme, wind generation can be as high as25% (for 300 MW) to almost 80% (for 1200 MW) of the system load.
100
;...
1000 2000 3000 4000 5000 6000 7000 8000 9000
Number of Hours
300 MW
600 MW
900 MW
1200MW
Figure 1.Wind generation as a fraction of hourly load for calendar year 2000
ASSESSING WIND GENERATION IMPACTS ON REAL-TIME OPERATION OF THE IDAHO
POWER S SYSTEM
The study evaluates the operational changes wind's characteristics would have on IdahoPowers current system. It is therefore important to understand the current operations
and Balancing Authority compliance to be able to evaluate how wind generation
changes the operations to maintain those compliance levels. Hourly load and hydro
Page 2
data were obtained from Idaho Power records for 1998 , 2000 and 2005. The 1998 and2000 loads are scaled to 2005.
Operationally, Idaho Power s generating resources must have the flexibility during thecourse of an hour to manage:
Variability in load and wind , and
Differences between forecast and actual load and wind.
Maintaining this flexibility is essential in assuring system reliability and compliancewith NERC performance standards (CPS1 and CPS2). Idaho Power presently maintainsthis operational flexibility to respond to unexpected and/or variable load conditions.With increased variability and short-term uncertainty due to wind generation, therequired operational flexibility will also need to increase to maintain current levels system control performance.
Using wind generation time-series data developed for this study and load data collected
from Idaho Power Company archives , incremental requirements due to wind generationwere determined for three categories of reserves:
Regulating reserve - minute-by-minute requirement - about 1 % of the installedwind generation capacity
Load following capability - hourly load following
Additional Operating Reserves - during the hour flexibility to cover deviationsbetween the forecast and actual control area demand with their own supply
resources. Wind generation adds to this short-term uncertainty, therebyincreasing operating reserve.
Results of this analysis for the no wind case and the four penetration levels are shown
in Table 1.
Table 1.Total bi-directional reserve
Incremental % of Installed Wind
Total System Bi- Reserve Due to GenerationWind Penetration directional Reserve Wind Capacity
0 MW (Load Only)
300 MW
600 MW
900 MW
1200 MW
48.3 MW
72.5 MW
102,6 MW
146.3 MW
205.3 MW
24.2 MW
54,3 MW
98.0 MW
157.1 MW
00%
10%
10.90%
13.10%
SIMULATING ANNUAL OPERATIONS OF IDAHO POWER S SYSTEM WITH WIND
GENERATION
While there is no formal or rigorous definition
, "
integration cost" is the term used todescribe the economic impact of wind generation variability and uncertainty on theutility company charged with accepting and delivering that energy. The term applies to
the operational time frame , which comprises the real-time management of conventional
generating units and the short-term planning for demand over the coming day or days.
Page 3
The Vista DSS Model is a hydro optimization program that simulates the operatingcharacteristics of Idaho Power s system. The model has detailed generating unitdefinitions, a simplified bus level transmission architecture and hourly inputs for hydro
inflows , loads , electricity prices, reserve requirements and energy contracts. Thissoftware is capable of optimizing generation scheduling for the Hells Canyon Complex
while observing hydraulic, transmission and regulatory constraints on the system. The
generation scheduling computed by the Vista DSS Model for the Hells Canyon hydrofacilities includes generation from other Idaho Power resources as well as off-systemmarket transactions.
The economic consequences of managing wind generation are computed through an
analytical procedure that replicates an hourly simulation of real-time operation
To elicit the integration costs attributable to wind generation, a reference or base case isdeveloped where wind generation is stripped of the attributes responsible for integrationcost - variability and uncertainty. This transformation of wind generation results in thedelivery of a precisely-known amount of energy in a way that imposes the minimum
burden on planning and real-time operations. This has been interpreted to be a flatblock of known energy for the day, although the amount can vary day-to-day. Economicmetrics from this case are compared to one where wind generation exhibits variabilityand uncertainty, and requires that additional generating capacity be set aside for itsmanagement. The difference between the cases, then, is attributed to wind generationintegration cost.
RESULTS
Integration costs determined via comparison of cases with variable wind and equivalent
wind energy delivery by a resource with no uncertainty or variability are summarized inTable 2. Simulations for calendar year 2000 revealed very high integration costs, whichafter additional analysis were determined to be a function of the anomalous market
prices that were the result of the California power crisis.
The hourly simulations with Vista DSS show a strong correlation between wind
generation integration cost and the market prices for electric energy. When normalizedby the average annual market price, the integration cost curves for calendar year 1998
and calendar year 2005 are track more consistently.
Page
Table 2.Summary Vista DSS results with AGC constraint violation data
Vista Results Using Historical
Mid-C prices as Benchmark
study
year
penetration
level (MW)
cost
per
MWh
wind
Annual Vista Vista
Mid-cost as AGC Res AGC Res
avg Constraint Constraint
energy enel'Violations Violations
price price Flat Case Var Case
1998 300 $3.$27.11.
1998 600 $4.73 $27.17.
1998 900 $6.$27.21.10219981200$6.$27.25.319
2000 300 $21.89 $132.16.
2000 600 $30.$132.22.
2000 900 $39.$132.29.37320001200$39.40 $132.29.0592005300$10.$58.18.4%
2005 600 $9.$58.16.
2005 900 $10.$58.18.79220051200$8.$58.14.132
It should again be noted that the focus of this study was the increased cost of IdahoPowers daily operations due to the variability and uncertainty of wind generation. In allof the simulations, wind generation was treated as a "must run" resource, and the pricepaid per MWh of wind energy was not considered. The integration cost is simply the
difference in the end-of-year operating economics between a case where wind generation
is "ideal" and another where it exhibits the normal variability and uncertainty.
DISCUSSION
Previous studies of this type have found that "integration costs" for wind generation asdefined here can be sensitive to the assumptions made. In addition, there are alwaysuncertainties about the future, in terms of physical characteristics such a load levelsand resource capabilities or institutional constructs which ultimately dictate how thepower system must be operated. These uncertainties all have potential to influence the
economics associated with managing a variable and uncertain resource.
It is appropriate , therefore , to recount some of the assumptions made to guide the
analysis as well as other uncertainties that could affect integration cost. These include:
Relicensing of Hydro Power Projects. In its long-term resource planning and forthis study, Idaho Power is assuming that no reduction of the available capacity
or operational flexibility of the Hells Canyon Complex will result from therelicensing process.
Effect of Load Growth. Peak load in the Idaho Power Company service territory is
growing twice as fast as the annual energy requirement. Going forward , thenthis growth will lead to higher ramp rate requirements in the summertime andless available hydro capacity for managing the system. The cost of reserves
would then likely increase, which could increase the integration cost for wind.
Page 5
Market Prices in the Pacific Northwest. The cost for managing wind generation isrelated to the market prices for energy, as shown in the analysis, and especiallyby the analysis for calendar year 2000 with the very high market prices. It was
assumed for this study that the addition of wind generation in Idaho would notinfluence market prices, so that historical profiles could be used to represent
other companies in the region. As more wind is considered and eventually
developed in the Pacific Northwest, this assumption would not be correct. It isdifficult to even conjecture how significant wind throughout the region (and even
the interconnection) would influence energy prices, but it is almost a certaintythat there would be significant effects.
Market Structure and Operating Agreements in the Pacific Northwest. A majorfinding of this study was how the structure for in-the-day transactions withother utilities leads to an increased requirement for operating reserves with windgeneration. As more utilities in the region ponder how the effects of windgeneration can be managed , there is a possibility that operating agreements
which seek to utilize geographic diversity and large aggregates of demand could
substantially reduce integration costs. Possible changes were not included inthis study.
Improvements in Wind Generation Forecasting. Over time, improvements in windforecasting could reduce integration costs.
Transmission Limitations. In addition to constraining the hourly operation of
Idaho Power s system , transmission limitations can affect the provision anddelivery of ancillary services. For example low-cost regulating resources in the
region may not have accessible transmission capacity to Idaho Power s system.
Nature of Wind Generation Development in Southern Idaho. The wind generationscenarios constructed for this study are well distributed across the wind
resource areas in the southern half of Idaho. Geographic diversity has adramatic impact on the aggregate variability in the operational time frames. Ifactual wind generation development in Idaho is more concentrated in just one or
two of the most favorable areas, the variability and uncertainty of the aggregate
generation would be higher than what was considered in the study, almost
certainly increasing integration costs.
Modeling of Reserve Requirements. In the hourly dispatch simulations, it wasassumed that the total operating reserves were the same for each hour of theyear. By using an average , the reserve amounts used are high when wind
generation is low or zero, and short of what would be required during periods of
substantial wind generation.
CONCLUSIONS
The previous uncertainties aside , there are substantial conclusions that can be drawnfrom the work reported here. These include:
The costs identified in this study are the opportunity cost and generator loading
inefficiencies introduced by adding operating reserves. These operating reserves
are needed to cover the increased variability and uncertainty of Idaho Power
Balancing Authority Area demand with wind.
Page
In general, incremental operating reserves required for wind generation fall into
three major categories: 1) regulating reserve; 2) load following reserve; and 3)
additional reserves to cover the expected short-term wind generation forecasterror over the next hour. Due to the prevalence of hydroelectric generation, thecurrent practice in the Pacific Northwest essentially combines regulating andload following reserve since they are generally provided by the same
hydroelectric units.
Wind integration costs are sensitive to hydro conditions and AGC constraintviolations. AGC constraint violations are correlated to hydro conditions as
shown in Table 2. Although more violations occurred at the higher penetration
levels in all scenarios it was especially pronounced for 2005 which was the low
water year studied. The Hells Canyon Complex did not have the fuel supply to
manage the 900 MW and 1 200 MW wind scenarios.
The results also suggest integration costs are partly a function of the disparity
between heavy and light load pricing; given that one of the primary effects of the
enhanced reserve requirement is an increase in light load generation and a
decrease in heavy load generation.
Page
Page
Section 2
INTRODUCTION
WHY A "WIND INTEGRATION" STUDY?
The objective of this study is to assess the costs that could be incurred by Idaho Powerin modifying its operations at the Hells Canyon Complex for "integrating" orincorporating wind energy onto its system. The intennittent and unpredictable nature ofwind generation requires a utility to have generating resources available which can
increase or decrease generation on short notice in order to keep the interconnected
power system balanced. While hydroelectric power plants are well suited for performing
this function , there are operational impacts and costs associated with operating Idaho
Power hydroelectric plants in a manner that maintains reliability and facilitates
integration of energy from wind generation facilities.
Under the Public Utility Regulatory Policies Act of 1978 (PURPA), Idaho Power isrequired to offer independent developers a power purchase contract based on a
standard avoided cost rate for a qualifying facility with an output of 10 MW or less. Duelargely to federal tax incentives and favorable PURPA rates, a large number of windproject developers came to Idaho Power in 2005 requesting PURPA contracts. Becauseof uncertainty in integrating this large volume of wind generation on its system, IdahoPower sought temporary relief from PURPA requirements until the impact of windintegration could be more fully studied. The Idaho Public Utilities Commission granted
this relief by temporarily reducing the PURPA cap of 10 aMW to 100 kW for PURPA windprojects.
ABOUT IDAHO POWER
Idaho Power Company is an investor-owned public utility responsible for the generation
purchase, transmission, distribution and sale of electric energy in a 24 000 square milearea in southern Idaho and eastern Oregon, At the end of 2006, Idaho Power suppliedelectricity to 471 779 general business customers and had 1 916 full-time employees.
Idaho Power is somewhat unique when compared to other Northwest utilities in that its
system load peaks in the summertime due primarily to the combined effects of irrigationpumping and air conditioning load. In July of 2006, Idaho Power set a new record of084 MW for peak system load. Idaho Power s wintertime peak load record of 2 459MW was set in December of 1998.
Idaho Power relies heavily on hydroelectric power for its generating needs and is one of
the nation s few investor-owned utilities with a predominantly hydroelectric generating
base. The Company has 3 087 MW of installed generation, comprised of 1 708 MW of
hydroelectric generation (nameplate capacity) and 1 379 MW of thermal generation. In a
typical year, 53 percent of Idaho Power s generation comes from its hydroelectricresources and 47 percent from its thermal resources.
Page 9
Idaho Power operates 18 hydroelectric projects located on the Snake River and its
tributaries in southern Idaho. Under median water conditions, these facilities provideannual generation of approximately 970 aMW, or 8.5 million MWh. The backbone ofIdaho Power s hydroelectric system is the Hells Canyon Complex which is located in the
Hells Canyon reach of the Snake River. The Hells Canyon Complex consists of the
Brownlee, Oxbow, and Hells Canyon projects. In a nonnal water year, these threeprojects generate 5.84 million MWh, or 667 aMW, which constitutes approximately 69percent of Idaho Power s annual hydroelectric generation. With Brownlee Reservior
active reservoir storage volume of nearly one million acre-feet, the Hells CanyonComplex provides a major portion of Idaho Power s peaking and load-followingcapability.
In addition to its hydroelectric resources, Idaho Power owns a share of three coal-firedresources. These include the Jim Bridger plant (Idaho Power share of nameplatecapacity is 771 MW) located in southern Wyoming, the Valmy plant (284 MW) innorthern Nevada, and the Boardman plant (56 MW) in northern Oregon. These facilitiesare all operated as base load resources.
Idaho Power also owns and operates two natural gas-fired plants which include theDanskin plant (90 MW nameplate capacity) and the Bennett Mountain plant (173 MW).Both plants are located in Mountain Home, Idaho and are typically operated to meet
system peak-hour loads during the summertime. In addition to these two facilities, inDecember 2006 the Idaho Public Utilities Commission granted authorization for Idaho
Power to proceed with its plan to construct an additional 170 MW natural gas-fired unitat the site of the existing Danskin plant. The new unit is scheduled to be completed and
on-line in 2008. For the purposes of this report, it is important to note these naturalgas-fired resources do not have quick start capability,
A complete listing of Idaho Power s generation resources is shown in Table 1. This table
includes the new gas-fired unit at the Danskin plant in 2008 , an upgrade to theShoshone Falls Hydroelectric Project scheduled for 2010, and an existing 5 MW diesel-fired plant located in Salmon, Idaho.
Page
Table 3.Idaho Power hydro and thermal plants .. generating capacity
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CHARACTERISTICS OF WIND GENERATION
The nature of the "fuel" supply for wind generation distinguishes it from more
traditional means of producing electric energy. For wind generation, the fuel supply andmotive force are wind. More specifically the electric power output of a wind turbinedepends on the speed of the wind passing over its blades. The effective speed, which is afunction of the wind speed across the swept area of the wind turbine rotor, can varyfrom second to second and determines the amount of power produced at any instant.
Terrain, topography, nearby turbines, local and regional weather patterns, and seasonaland annual climate variations are just a few of the factors that can influence theelectrical output variability of a wind turbine generator.
A typical individual wind turbine is small with respect to the load and other supplyresources in a region. It is the cumulative effect of a number of turbines that is ofprimary interest with respect to impacts of wind generation to the transmission gridand system operations. Wind generation facilities that connect directly to the
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transmission grid often employ large numbers of individual wind turbine generators
with a total nameplate generation on par with other more conventional utility scalegenerating plants. A wind project is comprised of individual wind turbine generators
that are usually spread out over a significant geographical area. This geographicdispersion has the effect of exposing each turbine to a slightly different fuel supply andresults in a "smoother" energy output. The effect of physical separation between wind
projects over larger geographical areas is an important consideration when evaluatingthe impact of wind generation. The primary impact of geographic dispersion is thecombined output of multiple wind projects will be less variable (as a percentage of total
output) than for each project individually.
Variability of energy production is not confined to wind generation. Hydro plants
depend on streamflows that can vary from year to year and seasonally. Coal fired plants
experience de-rations and unplanned outages , natural gas-fired generators can besubject to supply disruptions or storage limitations, and cogeneration plants may varytheir electric power production in response to demands for steam rather than thewishes of the power system operators. Idaho Power is experienced in handling these
types of variability in energy production. However, the effects of the variability anduncertainty in energy production of wind generation require special consideration.
Another important aspect of wind generation is the uncertainty in the amount and
timing of future energy production. All generation resources have an element of
uncertainty in future energy output, but not to the degree of a wind generationresource. Uncertainty of output and the inability to dispatch the plant is a primary and
differentiating characteristic of wind energy production.
Because wind generation is driven by the same physical phenomena that control the
weather, the uncertainty associated with a prediction of future generation output in a
future hour or even next hour is significant. In addition, the expected accuracy of anyprediction will degrade as the time horizon is extended, such that a prediction for thenext hour will almost always be more accurate than a prediction for the same hourtomorrow.
The combination of production variability and relatively high uncertainty of prediction
makes it difficult, at present, to "fit" wind generation into established practices for
power system operations and short-term planning and scheduling. In order to
understand the methodology and results presented in this report, it is necessary tohave a basic understanding of the regulatory requirements, operational constraintsand related terminology used in the electric utility industry. Section 3 presents an
overview of utility system operations relevant to this study.
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Section 3
OVERVIEW OF UTILITY SYSTEM OPERATIONS
Interconnected power systems are extremely complex and consist of tens of thousands
of individual elements. The equipment responsible for their control must continually
adjust the supply of electric energy to meet the combined and ever-changing electricdemand or load of the system s users. In order to provide a high degree of reliability,there are a host of constraints and objectives that govern how the system is operated.The operational limitations of individual network elements - generators, transmissionlines, substations - must be honored at all times. The capabilities of each of theseelements must be utilized in a fashion to provide the required high levels of
performance and reliability at a low overall cost.
Operating the power system involves more than adjusting the combined output of thesupply resources to meet the load. Maintaining reliability and acceptable performance
requires operators:
Regulate voltage throughout the system within prescribed limits
Regulate the system frequency (60 Hz in the U.
Maintain the system in a state where it is able to withstand and recover fromunplanned failures or losses of major elements
The frequency of the system and the voltage regulation are the fundamental
performance indices for the system. High interconnected power system reliability is a
consequence of maintaining the system in a secure state - a state where the loss of anyelement will not lead to cascading outages of other equipment - at all times.
REGULATORY REQUIREMENTS
The Federal Energy Regulatory Commission (FERC) and the North American ElectricReliability Council (NERC) are the two primary regulatory agencies charged withestablishing and enforcing the rules and procedures for the operation of interconnected
electric power systems in the United States. In general terms, the FERC regulates thetransmission of electricity while the NERC is more concerned with system operation andreliability.
The NERC has established geographical reliability regions, each of which containsnumerous Balancing Authority Areas. A Balancing Authority Area consists of
generators , loads, and defined and monitored transmission ties to neighboring areas.
The NERC defines the terms Balancing Authority and Balancing Authority Area as:
BALANCING AUTHORITY - An entity that integrates resource plans ahead of time, andmaintains load-interchange-generation balance within its metered boundary and
supports system frequency in real-time.
BALANCING AUTHORITY AREA - An electrical system bounded by interconnection (tie-line)metering and telemetry. It controls generation (and controllable loads) directly to
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maintain its interchange schedule with other Balancing Authorities and contributes to
frequency regulation of the interconnection.
Throughout the U., the operation of interconnected electric power systems is
accomplished through the coordinated actions of over 100 individual Balancing
Authorities. Within each Balancing Authority Area, the supply of electric energy iscontinuously adjusted to balance the requirements of loads and to maintain scheduled
sales or purchases of energy from other Balancing Authorities. Idaho Power s BalancingAuthority Area is located within the Western Electricity Coordinating Council (WECC)reliability region as shown in Figure 1.
ECAR
SERC
-- - - - - -
'DynallllCall~' Controlled Genemtlon ERCOT As of January 1, 2005
Figure 2.NERC reliability regions and balancing authority areas
In order to maintain system reliability and track energy transactions between Balancing
Authorities, it is necessary to monitor the performance of each Balancing Authority.
This is accomplished for individual Balancing Authority by calculating the Area Control
Error, or ACE, according to the following equation:
ACE = (NIA - NIs) - lOB (FA - Fs) - IME
Where:
NIA =
NIs =
the sum of the actual interchange with other Balancing Authorities
the total scheduled interchange with other Balancing Authorities
the actual frequency of the interconnectionFA =
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Fs =the scheduled frequency of the interconnection; this is usually 60 Hz
although there are times when the scheduled frequency is slightly aboveor below the nominal value to affect what is known as "time errorcorrection
13 =the Balancing Authority frequency bias, reflecting the fact that load willchange with frequency
metering error, which will be neglected for the purposes of the remaining
discussion
IME =
ACE is computed automatically by each Balancing Authority every few seconds. Theadequacy of generation adjustments by the Balancing Authority operators is gauged bytwo metrics that use ACE as an input, The first metric, Control Performance Standard
, or CPS 1 , uses ACE values averaged over a 1 minute period. It is a measure of howthe Balancing Authority is helping to support and manage the frequency of the entire
interconnection. If the interconnection frequency is low, it signifies that there is moreload than generation. If during that time a particular Balancing Authority has a
negative ACE , it is contributing to this frequency depression. Conversely, if ACE werepositive during that period, over-generation in the Balancing Authority is helping to
restore the interconnection frequency,
In order to maintain CPS 1 requirements, Balancing Authorities schedule and operate
generating resources to not only produce electric energy but also to provide the
flexibility required to regulate system frequency, follow the aggregate system load as ittrends up or down, and provide reserve capacity in the case of a generating unit or tie
line failure. This function is commonly referred to as automatic generation control or
AGC.
The CPSI "score" for Balancing Authorities is based on performance over a rolling 12-
month period. This score must be greater than 100% (an artifact of the equations usedto compute the compliance factor). Maintaining adequate generating capacity on AGC isa major factor in complying with CPS
The second metric is Control Performance Standard 2, or CPS2. It utilizes the average often consecutive I-minute ACE values, Over each ten-minute period, the ten-minuteaverage ACE for a Balancing Authority must be within specific bounds, known as 110.These bounds are unique for each Balancing Authority and are based generally on size.CPS2 bounds for 2006 for selected Balancing Authorities in the WECC, including IdahoPower, are shown in Table 4.
The CPS2 metric is tabulated monthly. To comply with CPS2 requirements, 90% ormore of the ten-minute average ACE values must be within the designated 110 boundsfor the Balancing Authority, as indicated by the equation in Figure 3. The "UnavailablePeriods" term accounts for intervals where communication problems prevent the
collection of information. Minimum acceptable performance allows over 400 violations
per month or 14.4 violations per day. Most Balancing Authorities keep their CPS2
scores in the mid 90% range.
AVGJO-minute (ACE):;;
CPS2 Violations"","'h . J 00(Total Periods'M",h - Unavailable Periods"","'h
Figure 3.NERC CPS2 equation
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Balancing Authority compliance with NERC performance standards is defined as a
combination of CPS 1 and CPS2 scores:
In compliance: CPS1 ;:. 100%and CPS2 ;:. 90%
Out of compliance: CPS1 00( 100%CPS2 00( 90%
Maintaining compliance with the NERC control performance standards requires
sufficient generating capacity be available and capable of being controlled or
dispatched , to compensate for fluctuations in Balancing Authority demand.
Table 4.Partial list of 2006 CPS2 Bounds for WECC Balancing Authorities
2006 CPS2 Bounds
Est. Peak Freq. Bias BiasJLoad Biasffotal l10 Variable
Demand (MW)(MW/.1Hz)
(%)
Bias (%)(MW)Bias?
WECC-NWPP
Alberta Elec1r;c System Operator 11.11.114 1.14 58.Avista Corp.132 21.1.00 UJ4 25,BonneYilie Power Administration 039 110- 700n 1.68 67.VariableBritish Columbia Transmission Corporation 11,462 114 - 250n .Q3 59.VariableIdaho Power Company 3.446 1.45 38.NorthWestern Energy 549 111 123 0",3 23.PaciliCorp-East 137 1.01 46.PaciliCorp-West 1159 -68 44.PortJand General Electric Company 000 1.25 2,44 38,PUD No, 1 of Chelan County 614 27.PUD No.1 of Douglas County 291 14.PUD No.2 of Grant County 550 1.22 27,Puget Sound Energy 1134 3B.Seattle Department of lighting 760 -40 HI6 34.Sierra Pacific Power Company 956 19.24.Tacoma Po....er 973 1.85 23.
Western Area Po....er Administration - Upper 115 1.74Great Plai"s West
WECC-NWPP Totals:897 851 41,
Source: ftp: / /www.nerc.com/pub I sysl all updll ocl opmanl CPS2Bounds 2006.pdf
OPERATIONAL PLANNING AND RESERVES
Idaho Power uses sophisticated strategies and tools for deploying its generating
resources in a way to serve load reliably and at the lowest cost. Forecasts of demand
over the next day to several days are the starting point for optimization processes thatdetermine which resources should be committed to operation, and how they should bescheduled to serve forecast load. Current and forecast water conditions also play an
important role in planning at Idaho Power due to the substantial amount of energyproduced from its hydroelectric projects. The control and reliability needs of the system
along with limitations of the generating units themselves, constrain this optimizationproblem.
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The variability and uncertainty of wind generation complicates this problem in several
ways:
Short-term variations in wind generation (minute by minute to tens of minutes)
necessitate the reservation of additional generating capacity to compensate forshort-term fluctuation in wind generation. In general, this reserve capacitycannot be used to serve load.
Wind generation varies in accordance with meteorological patterns. These
patterns often do not align with the daily load patterns. Production from wind
projects may be low during the late afternoon when system load is at its highest
or may be high during the overnight hours when the system load is near daily
minimums and the value of energy is the lowest.
Errors in wind generation forecasts can increase the overall uncertainty for unit
commitment and scheduling. Since the daily operating plans are developed andoptimized using forecast data, actual load and wind generation that significantlydepart from forecasts will cause the plan to be less than optimal, implying thatthe cost to serve the load will be higher.
For Idaho Power, a major objective of the operational planning process is to optimize the
use of water on an hourly, daily, monthly and seasonal basis. This optimization
considers many factors and among them are market and transmission conditions, loadforecasts, reserve and regulatory requirements and base loading thermal units.
Hydroelectric projects are well suited for providing the additional operational flexibilitynecessary to integrate wind resources because of their ability to store or shape the flowof water. To provide the operational flexibility necessary to accommodate theuncertainty and variability of wind generation, hydroelectric generators are loadeddifferently and water is used differently than it would have been if wind generation was
not present on the system. In effect the operation is de-optimized to accommodate windgeneration. While the hydro system has the operational flexibility to do this there is anassociated cost that comes with de-optimizing the use of available water. This
operational flexibility comes in the form of having hydroelectric generators available
that are capable of increasing or decreasing generation in real time as the output from
wind projects varies.
In the hourly time frame, Balancing Authority operators are primarily focused on
meeting hour-to-hour changes in loads by adjusting the set point of their fleet of
generation assets. These set point adjustments , or ramps, typically occur during a 20minute interval from 10 minutes before through 10 minutes after each hour. Because
electricity is traded on an hourly basis in the Pacific Northwest, Balancing Authorityoperators must establish a set point for each hour that will position the system so thatgenerating units on AGC can follow the full range of system load variation expected forthe next hour. This process is ongoing 24-hours per day, seven days a week.
During the within-hour time frame between set point adjustments, Balancing Authorityoperators are focused almost exclusively on reliability. Questions of economicoptimization are secondary to the challenge of managing system frequency.
accomplish this, operators must ensure that the system is carrying sufficient operating
reserves.
The Energy Management System (EMS) is the technical core of modern BalancingAuthorities. It consists of hardware , software , communications, and telemetry tomonitor the real-time performance of the Balancing Authority and make adjustments to
generating units and other network components to achieve operating performanceobjectives. A number of these adjustments happen very quickly without the intervention
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of human operators. Others, however, are made in response to decisions by individualscharged with monitoring the perfonnance of the system. The following tenus are used todescribe various operations within a Balancing Authority and are important to gain an
understanding of basic utility operations:
Automatic Generation Control (AGC)-the EMS continuously monitors ACE andcontrols certain generating units to quickly respond to real-time resource and system
load fluctuations.
Load Following-increasing or decreasing generation in response to system load by
using generating units on AGC, non-AGC units, and units identified as contingencyreserve.
Operating Reserve-spinning and non-spinning generating capacity available toprovide sufficient reserves to allow the Balancing Authority to meet NERC control
perfonnance standards (CPS 1 & CPS2). Operating reserve consists of both regulatingreserve and contingency reserve as shown in the Figure 4.
Spinning and Non-Spinning Reserve-reserve capacity able to respond within tenminutes to resource and system load fluctuations, the loss of a generating unit, or lossof a major transmission network element.
Regulating Reserve-required amount of on-line generating capacity on AGC toimmediately respond to fluctuations in system load.
Contingency Reserve-required amount of spinning and non-spinning reservegenerating capacity able to respond within ten minutes to resource and system load
fluctuations, the loss of a generating unit, or loss of a major transmission networkelement.
Operating Reserve
Regulating Reserve Contingency Reserve
(immediately responsive)(spinning and non-spinning)
Figure 4.Components of operating reserve
Interconnected power systems are extremely complex, especially to the uninitiated. Anunderstanding of the regulatory and operational requirements and tenninology
presented in this section are necessary to understand the methodology of this study
which is presented in Section 4.
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Section 4
WIND INTEGRATION STUDY METHODOLOGY
While there is no fonnal or rigorous definition
, "
integration cost" is the term used todescribe the economic impact of wind generation variability and uncertainty on theutility company charged with accepting and delivering that energy. The tenn applies to
the operational time frame , which comprises the real-time management of conventional
generating units and the short-tenn planning for demand over the coming day or days.As evaluated in this study, the term does not include costs related to transmission
upgrades required to deliver wind generation to serve load or for off system sales.
A chronological operations simulation methodology has become the de-facto standardanalytical approach for wind integration studies, This framework utilizes synchronized
hourly load and wind generation patterns , and mimics the scheduling and real-timeoperation activities for the company or area of interest. For this study, Idaho Power
used the Synexus Global Vista Decision Support SystemTM (Vista DSS) to assess theimpacts of wind generation on the real-time operation of its system.
The Vista DSS is a hydro optimization program that simulates the operating
characteristics of Idaho Power s system. The model has detailed generating unit
definitions, a simplified bus level transmission architecture and hourly inputs for hydro
inflows, loads, electricity prices, reserve requirements and energy contracts. This
software is capable of optimizing generation scheduling for the Hells Canyon Complex
while observing hydraulic, transmission, and regulatory constraints. The generation
scheduling computed by the Vista DSS for the Hells Canyon hydro facilities includes
generation from other Idaho Power resources as well as off-system market transactions,
STUDY METHODOLOGY
Seasonal water conditions playa critical role in Idaho Power s ability to utilize its fleet ofhydroelectric resources. Because of this, three different water condition years were
modeled for this study: 1998 (a good water year), 2000 (a normal water year), and 2005
(a poor water year). In addition to varying water conditions, the amount of windgeneration on Idaho Power s system, or "penetration level " was modeled for fourdifferent cases: 300 MW , 600 MW, 900 MW, and 1 200 MW.
This study evaluates the changes in operations and the resultant costs that windvariability and uncertainty introduces into Idaho Power s system at the four levels ofwind penetration for each of the three water years modeled. Two Vista DSS runs wereneeded to evaluate each wind penetration level for each water condition. The first run
or "flat wind case " modeled wind generation in flat blocks to simulate a predictable
resource. The second run , or "variable wind case " modeled wind generation with itsinherent unpredictability and variability. The difference between the value of these runsis the basis of determining the cost to integrate wind.
The flat wind case wind generation was calculated as the average wind energy for a day
and was determined by summing 24 hours of wind generation and dividing by 24. This
Page 19
average energy is then applied to each hour during that day resulting in a 24-hour flatblock of energy, which removes the variability of wind for that day. The second runincorporates the actual (hourly variable) wind output and the required additional
operating reserves necessary to maintain a consistent level of system control
performance (CPSI and CPS2).
The wind integration cost per MWh is calculated as the difference between the dollarvalue of the total annual generation from the flat wind case run valued at market and
that of the total annual generation from the actual wind run, divided by the total windenergy produced during the year in MWh. This process was completed for each wind
penetration level and water year which resulted in a total of 24 Vista DSS simulationsto complete the analysis.
Because the value of generation in anyone year depends on market prices occurring
during that year, the resultant integration costs (in $/MWh) are not directly
comparable. To overcome this, the integration costs in dollars per MWh were convertedto a percentage of market price, which allows a level of comparability between the three
years at the differing penetration levels.
The remainder of this section provides details on the topics addressed and steps taken to
complete this study which include:
Gathering wind data and building wind generation profiles
Accounting for wind generation forecasting errors
Determining the impact of wind generation within the hour
Completing hourly dispatch simulations to determine economic impacts
SIMULATING THE IDAHO WIND RESOURCE
The key to the approach used in this study is assembling the chronological data sets
which drive the analysis. While load data can usually be extracted from archives
obtaining the companion wind generation data has been problematic. Availablehistorical data from meteorological stations and other sources generally cover only ahandful of locations in a region, and may be of hourly resolution at best. For an
integration study, it is critical to appropriately characterize the temporal variability of
wind generation, since that attribute is responsible for a significant share of theintegration costs. For studies considering a wind development scenario distributedacross an entire region, the available data is usually not adequate as a basis for
synthesizing wind generation data.
Meteorological simulation techniques have been used in several recent integrationstudies, and have now become the preferred source of chronological wind speed data forassessing wind generation impacts on power system operations. The approach provides
flexibility as to the historical year or years to be simulated, and allows a very largenumber of "proxy" towers or points in the computer model grid where wind speed data
is to be extracted and saved as the simulation progresses. These simulations also allowthe dimensions of the grids in the atmospheric model to be reduced substantially in
areas with good wind resource. The high spatial and temporal resolution of the
simulation model provides a good quantification of spatial and geographic diversity
effects that are so critical to the aggregate behavior of large wind generation scenarios.
The wind speed data from the meteorological simulations is converted to wind
generation data through empirical transforms of wind speed to wind generation data
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developed in earlier wind integration projects. This process employs a modified windturbine power curve; since each wind speed record in the simulation data represents
more than one wind turbine. The techniques used here have been validated against
actual wind project production profiles in earlier studies. Details of this process are
presented in Appendix A.
Hourly simulations are intended to mimic the actual scheduling and real-timeoperational practices of Idaho Power. The objective here is to "pretend" that windgeneration was in operation for the years of interest, and using existing operating rulesand practices, determine how the system would have been operated. It is important to
note the objective of this analysis is not to determine the "best" way to operate with
wind generation - this will be determined over time as experience with wind generation
is gained. It is also important to note that the wind is assumed to be delivered as ausable resource to Idaho Power without regard to potentially costly transmission.
WIND GENERATION FORECASTING ERROR
An attribute of wind generation that results in increased integration costs is the
uncertainty of wind generation and the inability to accurately forecast its output. The
error associated with forecasting wind generation declines as the forecast horizon is
shortened.
Generation capacity, in addition to the required operating reserves, may be caITied toaccount for errors in short-term forecasts of Balancing Authority demand for the next
hour to several hours. Wind generation will obviously have an impact on this
uncertainty, and could potentially lead operators to increase the available amount of
operating reserve carried for load following. A persistence forecast - assuming that windgeneration next hour or the hour after that will be the same as for the current hour - isnearly as accurate as other currently available forecasting methods. Future expectedimprovements in forecasting techniques could reduce the amount of operating reserves
Idaho Power would need to integrate wind generation.
IMPACTS OF WIND GENERATION WITHIN THE HOUR
The main objective of this study is to determine how the real-time operation of IdahoPowers Balancing Authority would be impacted by the addition of significant wind
generation. An analysis combining Idaho Power s load and the simulated wind
generation data determines the requirements for regulating and load following
capabilities necessary to maintain system reliability and regulatory compliance. Thefindings from the load and wind analysis become "inputs" to the later analytical
processes involving the Vista DSS. The Vista DSS then simulates the operational
changes of the hydro units necessary to provide the additional regulating and load
following capabilities and monetize these changes.
The approach for analysis of the intra-hour impacts of wind generation is based on
straightforward mathematical and statistical analysis of Idaho Power s BalancingAuthority Area demand (i.e. load or load net wind generation). The incremental
requirements for regulation capacity and load following capability are determined by
comparing various metrics of the load by itself (and the present capacity and
capabilities allocated to perform these services) to the combination of load and wind
generation.
High resolution load and wind data are used in this analysis. For the load, achronological record of load for extended periods (e.g. a day or a week) at a time
Page 2
resolution of 30 seconds has been used. The simulated wind generation is in 5 minute
intervals, which is adequate for much of the analysis. It is not adequate , however tocharacterize the higher frequency fluctuations that would influence the calculation of
one-minute ACE (and CPSl); here , data from very high resolution measurements ofwind project output compiled by the National Renewable Engineering Lab (NREL) hasbeen used as a proxy.
Generation must also be controlled in real-time to compensate for slightly longer-termvariations in the Balancing Authority Area demand. The effect of wind generation on thenet variations can be assessed with an approach similar to that used in the regulation
analysis. Statistical distributions of changes in Balancing Authority Area demand withand without wind generation can be developed to illustrate the influence of windgeneration on the net volatility over this time interval. While the load exhibits a larger
trend pattern (steadily increasing over the interval in the morning, and falling in the
evening), there is still significant variability in the load by itself.
The deviations in wind generation are less patterned. When combined with the load
however, the net effects on the changes over this interval (which of course will dependon the ratio of load to wind generation) are slight. Closer inspection of the statistical
distributions, however, shows that the number of events at the extremes of thedistribution is higher. The implication is that wind generation will increase the number
of high swings in load over short intervals.
The consequences of the effects of wind generation on the Balancing Authority energy
balancing volatility will depend on a number of factors. One interpretation of the highernumber of extreme changes is that the number of violations of CPS2 (ten-minute ACE
less than the LIO for the Balancing Authority) will increase if no additional generation is
dispatched to respond to these deviations. The consequence of the intra-hour andshort-term impacts of wind generation are carried forward to the hourly analysis as
additional capacity that must be reserved to manage wind generation in real-time.
HOURLY DISPATCH SIMULATIONS
The economic consequences of managing wind generation are computed through
analytical procedure that replicates operational planning activities intertwined with an
hourly simulation of real-time operation. To elicit the integration costs attributable to
wind generation, a reference or flat wind case is developed where wind generation is
stripped of the attributes responsible for integration cost - variability and uncertainty.
This transformation of wind generation results in the delivery of a precisely-knownamount of energy in a way that imposes the minimum burden on planning and real-time operations. This has been interpreted to be a flat block of known energy for the
day, although the amount can vary day-to-day.
Discussions of the appropriateness of such a reference resource have been ongoing.Most of these have lead to a consensus or recognition that such a definition is a likelyand appropriate baseline for establishing integration costs. In reality, it roughly equates
to an "as-available" energy contract with a third-party where advance notification ofoperating levels (for day-ahead scheduling) is part of the contractual requirements,
Maintenance outages reduce unit availability and have an impact on operational
flexibility. For the purposes of this study, typical maintenance schedules for the HellsCanyon Complex were simulated. Scheduled maintenance outages for the flat wind and
variable wind cases were identical for all of the Vista DSS simulations.
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The flat wind case (or flat daily wind energy scenario) is evaluated with the Vista DSS.The analytical procedure determining the value of generation in the flat wind case
includes:
Wind generation is input as a flat block of energy corresponding to the averagedaily energy generated from wind resources.
Wind generation in the flat wind case has no hourly variability during a 24 -hourperiod. The amount of wind generation to be delivered is also perfectly known forthe hours it is delivered throughout the day.
The additional capacity relegated to operating reserves for managing wind
generation s short-term variability and uncertainty is not required for the flat
wind case. Contingency reserves equal to 5% of wind generation are held for the
flat wind case simulations.
The analytical procedure for determining the value of generation in the variable wind
case is evaluated in a manner similar to that used for the flat wind case , with thefollowing differences:
For the level of wind penetration under consideration, determine the incrementalcapacity requirements for contingency reserves and regulating reserve based on
an analysis of the high-resolution and 10 minute wind and load data. Bring
these capacity amounts forward to the Vista DSS analyses (i.e. insure that thiscapacity is not used to serve load or to fulfill out-of-area sales).
Configure the Vista DSS with actual hourly load and wind generation profiles for
the period (day, week, etc.
Execute the Vista DSS for the period (day, week, etc.), letting the program logicadjust for the actual load and wind generation.
Log metrics from each case.
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Page 24
Section 5
SIMULATING THE IDAHO WIND RESOURCE
OBJECTIVE
The analysis of wind generation impacts on system operation is based on a simulation
of Idaho Power s scheduling and dispatch operations over an extended chronological
period. The primary inputs to this simulation process are chronological profiles of
system load , wind generation, and market prices for energy purchases and sales. Loadand market price data can be extracted from archives, but acquiring the wind
generation data is much more problematic.
Recent studies have shown that a high-fidelity, long-term, chronological representation of windgeneration is perhaps the most critical element of this type of study. For large wind generation
development scenarios, it is very important that the effects of spatial and geographic diversity
be neither under- or over-estimated. The modeling approach employed by EnerNex and
WindLogics to address these important issues has been used in at least six previous windintegration studies, and is now considered the de-facto standard method.
ApPROACH
Since wind speed data of sufficient coverage and temporal resolution for constructing
the chronological wind generation profiles is not available, meteorological simulations of
historical years are used to synthesize chronological hub-height wind speed data at thelocations of interest for the study. The wind resource in Idaho Power s service area isdistributed across the southern third of the state (an Idaho wind resource map from theNational Renewable Energy Laboratory is included in the appendices). The wind
generation scenarios considered for this study must consider possible developmentprojects throughout this area, along with potential projects in eastern Oregon.
The meteorological simulations employ a three-dimensional, physics-based model of the
atmosphere based on the MM5 mesoscale model. The Pennsylvania State
University I National Center for Atmospheric Research (PSU INCAR) mesoscale model(known as MM5) is a limited-area, nonhydrostatic, terrain-following, sigma-coordinatemodel designed to simulate or predict mesoscale atmospheric circulation. The model is
supported by several pre- and post-processing programs, which are referred tocollectively as the MM5 modeling system (http:j jwww.mmm.ucar.edu/mm5j). Thisprognostic regional atmospheric model is capable of resolving meteorological features
that are not well represented in coarser-grid simulations from the standard weatherprediction models run by the National Centers for Environmental Prediction (NCEP).The MM5 was run in a configuration utilizing two grids as shown in Figure 5. This
telescoping" two-way nested grid configuration allowed for the greatest resolution in
the area of interest with coarser grid spacing employed where the resolution of small
mesoscale meteorological phenomena were not as important. This methodology wascomputationally efficient while still providing the necessary resolution for accurate
representation of the meteorological scales of interest within the inner grid.
Page 25
Regional Perspective
(Modeling Grid 2 with Inner Grids 3 &
h~r ,
, Ti
- n
- "
:;:i +:-
, '
'R "
- ( ,
- / f
. :
ffiftt1ft:~)m;j
j-
Figure 5, Scope of MM5 meteorological simulation model used to synthesize wind speed data forstudy years. Outer and inner grids depicted
When used for weather forecasting, the MM5 model is initialized with all known andavailable information about the current state of the atmosphere. This includes datafrom meteorological stations, balloon soundings , sensors on commercial aircraft, andsatellite data. The model is then run forward through time, and variables relevant to theforecast such as temperature , humidity, wind speed, etc. are saved periodically forlocations of interest.
The MM5 simulations for this study differ from the standard weather forecastingapplication in some important ways:
Page 26
1. As described previously, the spatial resolution of the model grid is increased in
areas of interest, down to a two-dimensional grid size of 3 km2 rather than the20 km2 or more in the base model. The vertical resolution of the base model
which consists of 33 layers from the surface up to the troposphere (with the
spacing between layers smaller near the surface than at altitude), is retained.
2. Wind speed, air density, temperature , and pressure data from the vertical layercorresponding to commercial turbine hub-heights are saved at five-minuteintervals (rather than hourly) in each of the inner grids designated as a site forpotential wind generation development. These extraction points are sometimes
referred to as a "proxy tower " since they mimic the function of a meteorologicalstation that is usually mounted on towers.
3. The simulation is run for historical years, rather than starting at the presentand extending into the future.
This last point is critical. The state of the atmosphere over the entire period of the
simulations for this study, rather than just the starting point as in weather forecasting,is known to some degree. This knowledge is contained in an archive of observation data
known as the "assimilation data set." With respect to the accuracy of the meteorologicalsimulations for this study, the assimilation data set provides a critical advantage:instead of a single initialization of the model with current observations, continuousupdating of the simulation as it progresses with information from the assimilation data
set allows the historical simulation to "track" the weather that actually occurred. Bymatching wind data developed in this way with electric load patterns from the same
period (via archival data), any correlation that may exist between wind generation and
load behavior due to common meteorological drivers can be captured.
Re-simulation of the regional weather in this way, along with the increased spatial and
temporal resolution comes with a price in terms of computer time and storage. The
MM5 simulations for this study required several weeks of continuous computing timeon a bank of 36 parallel processors. Several gigabytes of storage were also required tohold the chronological wind speed and other meteorological data from each of the modelextraction points.
Idaho Power s constructed a list of potential projects and provided location information
for each. Extraction points were identified in the MM5 meteorological simulation modelto correspond to each of these project areas , as shown in Figure 6 and Figure 7. Thelocation and size of the individual wind sites were determined by Idaho Power based on:
Wind developer information on prospective wind locations, and
A geographically diverse wind portfolio at all penetration levels incorporatingknown QF (qualifying facility) projects.
It is important to remember that Idaho Power does not have control of where theindependent producers site their wind turbines. It is likely that the actual wind will beless geographically diverse at all penetration levels. This may significantly impact the
variability of the wind portfolio. All turbines are modeled at 1.5 MW nameplate capacity.
A summary of the wind locations for which 5-minute simulated wind data was collected
is found in Table 5. Columns containing data indicate a location selected under that
penetration level with the total nameplate capacity modeled at that site. Four
penetration levels were evaluated: 300 MW, 600 MW 900 MW and
Page 27
200MW.
Grid 3 (Idaho) with Detailed Data Extraction Points
~_'-. ~-
"tl-~I
f'
\ -
J(~ --1-b ItV
""""'-,
CF Conlr;ll;: Pt",jl!Cl~ (Ip.:-; ~N;t ;:""911
. RFP s.::ale W11a Sires
Ctrer Re:;.artllO Of S~s
('f
Figure 6.Idaho portion of MM5 model illustrating inner grid and data extraction points
Grid 4 (Eastern Oregon) with Detailed Data Extraction Points
I -
'0-
~ '\; "
I ''-..... a"""""",,
tT .; +t.: J -
i::t- i, 4+
% -
Ij,"", . RFP Sl:aIe W'nd Sje
Figure 7,Oregon portion of MM5 model showing inner grids and extraction points
Page 28
Mea eiOive ", Q 30 60, 0 '~'\.to Boroll ;'Jeol /;'J0,1l~ Site West to
MW 1\'1'1'-/ IviW LUO r\,W
East
TableS.
West
West
West
West
West
West
West
East
East
East
East
East
East
East
East
East
East
East
East
East
East
East
East
East
East
East
East
East
East
East
East
East
East
East
East
East
East
East
East
East
East
West
West
West
Assignment of extraction points to wind generation scenarios
Fossil Gulch
Tuana Gulch
Pilgrim Stage
Thousand Springs
Oregon Trails
Salmon Falls
Notch Butte
Milner Dam
Burley Butte
Golden Valley
Lava Beds
10.10.10.10.
10.10.10.10.
10.10.10.10.
10.10.10.10.
10.10.10.10.
10.10.10.10.
10.10.10.10.
Ammon
Ammon
Parker
Parker
Ammon
Ammon
Ammon
Ammon
Ammon
Ammon
Basalt
Basalt
Basalt
Basalt
Basalt
Basalt
Rockland
Rockland
Rockland
Rockland
Rockland
Rockland
Rockland
Rockland
Pomerelle
Pomerelle
Pomerelle
Pomerelle
Pomerelle
Pomerelle
lviajic lvit
Majic Mt
Salmon Falls
Page
Reiative 0 . A:ea 300 600 900 1'1, B r I Nea"Na,Tle Site W~stto fiW VW MW 'LOOMW10 0,0,1 East , I
West
West
West
West
West
West
West
West
West
West
West
East
East
East
East
East
Salmon Falls
Salmon Falls
Glenns Ferry
Glenns Ferry
Glenns Ferry
Glenns Ferry
Mt Home
Mt Home
Mt Home
Mt Home
Mt Home
Geiger 1
Geiger 2
Geiger 3
Geiger 4
Schwendiman
Farms
Windy Pass
Tennessee Mt
Glenns Ferry
Glenns Ferry
Glenns Ferry
Majic Wind
Cassia Gulch
Cassia Farm
Glenns Ferry
Glenns Ferry
Glenns Ferry
Glenns Ferry
1.0
1.0
1.0
1.0
10.
10.
10.
10.
East 10,10.
West 10.
West 10.10.
West 10.10.
West 10.10.
West
West
West 10.10.10.10.
West 10.
West 10.
West 10.
West 10.
Oregon
Oregon
Oregon
Oregon
Oregon
Not included in totals above
Montana Horse Shoe Bend 11.
Bend
Montana Arrow Rock 11.19.19.19.19.
Flat
DEVELOPING WIND GENERATION PROFILES
Meteorological simulations for creating chronological wind speed data have greatlyenhanced the value of wind integration studies. It is not possible , however, to generatea separate wind speed profile for each turbine in the wind generation scenario with the
MM5 simulation model. Each profile, therefore, must represent a number of turbines
located in the general vicinity of the model extraction point.
Page
The procedure used for transforming wind speed profiles from the MMS simulations towind generation profiles is described in Appendix A.
CHARACTERISTICS OF THE WIND GENERATION MODEL FOR THE STUDY
The purpose of the simulations and calculations previously described in this section is
to develop a high-fidelity, long-term chronological record of wind production for the
proposed scenarios and for the selected historical periods. An overview of the wind
generation output, which forms the foundation for the wind integration study, is
presented below.
Figure 8 and Figure 9 each depict two weeks of wind generation and Idaho Power
system load from calendar year 2000. It's worth noting in these illustrations that thewind generation scenarios are not simply scaled from a single chronological record, andthat during the winter period shown in Figure 8, wind generation in the larger scenariosamounts to a significant fraction of the Idaho Power load.
3000
2700
2400
2100
1800
1500
1200
900
600
Day ( of CY2000j
IPC Load
Wind Generation - 300 MW
Wind Generation - 600 MW
Wind Generation - 900 MW
Wind Generation - 1200
Figure 8. Two weeks of typical winter Idaho Power load from CY2000 and wind generation frommodel for study
Page 3
3000
2700
2400
2100
1800
1500
1200
900
600
172 174 176 178 180 182 184
Day ( of CY2000j
IPC Load
---
-- Wind Generation - 300 MW
Wind Generation - 600 MW
Wind Generation - 900 MW
Wind Generation - 1200
Figure 9. Two weeks of typical summer Idaho Power load from calendar year 2000 and windgeneration from model for study
The annual capacity factor for each wind generation level and each year of the
meteorological simulation is shown in Table 6.
Table 6.Scenario CY 1998 CY2000 CY2005
Annual capacity factor by scenario from wind generation model for study
300 MW
600 MW
900 MW
1,200 MW
25.60%
29.50%
28.70%
28,60%
26.70%
30.20%
29.00%
28.70%
23.50%
27.60%
27.10%
27,00%
Production duration" curves for each scenario and simulation year are shown in Figure
10 through Figure 13. These charts show the number of hours over the year where the
hourly average wind generation exceeds the value on the vertical axis. As the scenario
size grows (and more wind turbines over more geographical area are included in theaggregation), there is a slight decrease in the slope of the curves in the mid-range of
production. The number of hours at very high production levels also declines. Note thatthe nameplate production for the aggregate wind generation is reached only in the 300MW scenario.
Page 32
D....
1500 3000 4500 6000 7500
# of Hours
CY1998 - 300 MW Scenario
CY2000 - 300 MW Scenario
CY2005 - 300 MW Scenario
Figure 10. Production duration curve by year for 300 MW scenario
:;::
0::
1500 3000 4500 6000 7500
# of Hours
CY1998 - 600 MW Scenario
CY2000 - 600 MW Scenario
---
CY2005 - 600 MW Scenario
Figure 11. Production duration curve by year for 600 MW scenario
Page
9000
9000
0::
1500 3000 4500 6000 7500
# of Hours
CY 1998 - 900 MW Scenario
CY2000 - 900 MW Scenario
--- CY2005 - 900 MW Scenario
Figure 12. Production duration curve by year for 900 MW scenario
0::
1500 3000 4500 6000 7500
# of Hours
CY1998 - 1200 MW Scenario
CY2000 - 1200 MW Scenario
----
- CY2005 - 1200 MW Scenario
Figure 13. Production duration curve by year for 1200 M W scenario
Page 34
9000
9000
While the number of hours over the year at the highest wind generation production
levels is limited, the amount of wind generation relative to system load over any periodcan also be an indication of impacts on system operation. Figure 14 shows the amount
of hourly wind generation relative to system load, sorted in descending order. Thiswind generation penetration curve" shows that for over 4 000 hours in calendar year2000, the amount of wind generation is greater than 5% (for the 300 MW scenario) to20% (for 1 200 MW of installed capacity) of the hourly system load. At the extremewind generation can be as high as 25% (for 300 MW) to almost 80% (for 1200 MW) ofthe system load.
The variability of wind generation from hour-to-hour for each scenario is illustrated inFigure 15. For all scenarios most hourly changes are confined to a relatively small rangearound zero. This is a consequence of both the size and the geographic diversity of the
scenarios, which dramatically "smooth" wind production over time frames within thehour, and also reduce the variability over hourly and longer time frames.
For comparison, hourly changes in Idaho Power load for calendar year 2000 are shown
in Figure 16. While the distribution of the hourly load changes spreads over a wider
range than the 1 200 MW wind generation scenario, it must be noted that the loadchanges are quite predictable. For example , there is almost no chance that the morningload will do anything but increase; likewise in the evening, a drop in load is almostperfectly predictable. Wind generation is not as predictable (at least given the current
state of wind generation forecasting technology), so while the spread of hourly changes
may be smaller than what is already managed by Idaho Power operators, theuncertainty may impose additional operational costs.
100
-.J
;::...
:r:
1000 2000 3000 4000 5000 6000 7000 8000 9000
Number of Hours
300 MW
600 MW
900 MW
1200 MW
Figure 14. Wind generation as a fraction of hourly load for calendar year 2000
Page 35
.....
..c
..t
-----
-200 -150 -100
~~~----
100 150 200
Hourly Change (MW)
..II- 300
..II- 600
..II- 900
..II- 1200
Figure 15. Distribution of hourly production changes - calendar year 2000
..c
..t
-200 -150 -100
a.CL(l.CLQ.~
=", ~ "'-- ~
100 150 200
Hourly Change (MW)
Figure 16, Distribution of hourly changes in Idaho Power load from calendar year 2000
Page
Section 6
IMPACTS OF WIND GENERATION WITHIN THE HOUR
The main objective of this study is to detennine how the real-time operation of IdahoPowers Hells Canyon Complex will be impacted by the addition of significant windgeneration. Critical to the discussion of these impacts is the assumption that the real-
time energy market is not available during the course of a given hour to reconcileloadlresource imbalances occurring during that hour. In fact, market activity for agiven hour typically ceases as much as 50 minutes prior to the start of the hour.Therefore, because of the absence of a within-the-hour energy market, Idaho Powergenerating resources must have the flexibility during the course of an hour to manageboth:
variability in load and wind, and
differences between forecast and actual load and wind.
Maintaining this flexibility is essential in assuring system reliability and compliancewith NERC performance standards (CPSI and CPS2). Idaho Power presently maintainsthis operational flexibility to respond to unexpected and/or variable load conditions.
The objective of this section is to describe the analytical process followed to estimate theadditional flexibility needed to integrate wind generation without experiencing a decline
in system reliability and regulatory compliance. The analytical process consists of twoprimary parts. In the first, high-resolution load and wind data were analyzed toestimate the additional reserve needed to manage fast fluctuations in wind generation.
In the second part, la-minute load and wind data were analyzed to calculate the
reserve component needed to accommodate wind on the la-minute time step. The tworeserve components were then combined using a root-sum-square operation , yielding asingle additional reserve requirement for each wind penetration level. An alternative
method for computing the additional reserve requirement is described in Appendix
The results of the altemative method were not used as inputs to the Vista DSSmodeling. However, they do match well (and consequently support) the results
presented in this section.
ANALYSIS OF HIGH-RESOLUTION LOAD AND WIND DATA
As a first step, patterns in the high-resolution data for load alone were evaluated. Thiscomponent of reserves must be on AGC to compensate for the fastest fluctuations in theBalancing Authority Area demand. The analytical approach defines this as an energy-
neutral service over even a very short-term; it is simply a bi-directional capacity rangeover which the system must move to compensate for random variations in Balancing
Authority Area load.
The amount of this service required by the Balancing Authority is detennined by
extracting a "regulating characteristic" from high-resolution load data. This isaccomplished by subtracting the actual load from an underlying trend, usually
Page
constructed from a rolling average window on the actual load data. For the study, IdahoPower provided high resolution load data with 30-second resolution (shown in Figure17).
3000
2500
100 120 140 160 180
-.J
Hour
Sample
-----
Sample 2
- Sample 3
- - - Sample 4
Figure 17. High-resolution (3D-sec.) load data samples used for regulation analysis
A trend value was computed with a 20 minute rolling average window. A snapshot ofthe trend and actual load data for one of the samples is shown in Figure 18.
1600
Trend
---h Actual Load
1400
, ~
1.5 '10 1.55 '10 1.6'10 1.6510 4 1.7 '10
Figure 18. Extracting the regulation characteristic
The difference between the actual load and the trend, as shown in Figure 19, can beprocessed to determine the statistical characteristics (Figure 20). Because of theselection of the rolling average window, the average value is very near zero. In terms ofregulation capacity to compensate for the random fluctuations, the standard deviationis the more useful statistic. By carrying capacity equivalent to a multiple of the
Page 38
standard deviation, the number of all deviations in the sample for which enough
adjustment is available can be computed.
100
112 126 140 154 168
Hour
Figure 19, "Regulation characteristic" of Idaho Power load
2000
-+-
::J 1000
ft~~ft
-20
Figure 20. Distribution of Idaho Power load variations from 20-minute rolling average
From previous studies and background information provided by Oak Ridge NationalLaboratory, the regulation requirement to manage the fast fluctuations in Balancing
Authority demand is somewhere between 3 and 5 times the standard deviation(a) of the
load regulation characteristic. Using the larger multiplier (Sa), the regulationrequirement for the fast fluctuations in Idaho Power s load as described in this sample
data is about 29 MW.
Wind generation also exhibits variations on this time scale. Since these variations result
from completely separate and independent processes (meteorology and terrain vs.individual customer actions), it is safe to conclude that they are not correlated with
those of the load. Given this, the standard deviation of the load net wind can be
computed from the following equation:
Page 39
Cl'load neCwind := Cl'load 2 + Cl'wind 2
The resolution of the WindLogics data is not high enough to estimate the standard
deviation of wind generation fluctuations in this time frame. However, measurementprograms carried out by NREL, along with high-resolution archive data from utilities
with operating wind plants show that an estimate of 1.7 MW for a 100 MW wind plant isreasonable. Using this figure, the high-resolution reserve requirement for the IdahoPower Balancing Authority with various amounts of wind generation can be estimated.Results are shown in Table 7.
Table 7.Estimated wind generation impacts on high-resolution reserve
Increase Due to
Bi-directional Reserve WindCase (MW) (MW)
Load only
300 MW Wind
600 MW Wind
900 MW Wind
1200 MW Wind
28.
32.
35.
38,
41.5 12.
ANALYSIS OF IO-MINUTE LOAD AND WIND DATA
The first step in estimating the amount of additional reserve requirement on the 10-
minute time step is to analyze the data for load alone for the purpose of arriving at abase-level estimate of the current requirement. The objective of this step is to estimatehow much flexibility is required to cover an acceptable percentage of the deviations of
the 10-minute load data from the expected hourly average value. Operations personnelhave indicated that Idaho Power currently meets CPS2 compliance at approximately the
98% level (NERC required CPS2 compliance is 90%). To maintain this level ofcompliance, this analysis assumed that the system should carry enough flexibility to
cover 98% of the 10-minute load deviations from expected (or forecast) load to withinIdaho Power s LIO band. Data for calendar year 2005 were used for this phase of the
analysis.
Historical records of next-hour load forecast were not available for the analysis.
Therefore , it was necessary to reconstruct the record for next-hour load forecast for2005 using observed 10-minute load data. Using the actual data, the expected hourlyaverage load for a given hour was calculated after the fact by averaging the six 10-
minute system load readings observed within that hour, and then adding a randomerror pf + / - 2%. For example, an hour with six 10-minute load readings averages 2 000MW and the randomly selected error level is -, then the reconstruct of the next-hourload forecast is considered to equal 1 960 MW (2 000 MW + (-2%)x(2 000 MW)). Whenconverted to absolute value, the randomly selected error component over the long-termaverages 1%. An example next-hour forecast is illustrated in Figure 20.
Page 40
2555.
2550.NEXT -HOUR FORECASTlEXPECTED AVERAGE LOAD
FOR 10:00-11:00 -- AVERAGE OF OBSERVED 10-MINUTE
LOAD READINGS PLUS ERROR COMPONENT254S.
2540.
2535.
:; 2530.
i 252S.
'Iii
I ERROR I
OBS AVERAGE LOAD
2520.
251S.
2510.
25OS.
ABS VALUE DEVIATION OF OBSERVED 10-MIMJTE
SYSTEM LOAD FROM FORECAST
NEXT -HOURLOAD = ABS VALUE (2504.PitN 2535.MW) = 31.0 MW
2500.
08-Jul-OS 08-JuI..IJS 08-Jul-O5 08-Ju~OS 08-Jul,OS 08-Jul..IJS 08-Ju~05 08-Jul-OS 08-JuI..IJS 08....-..1-05 08-..U-OS 08-Jul..IJS O8-JuI..IJS10,00:00 10,05:00 10:10:00 10:15:00 10:20:00 10:25:00 10:30:00 10:35:00 10:40:00 10:45,00 10,50:00 10:S5,OO 11:00:00
Figure 21. Observed Idaho Power system load, July 8,2005 - 10:00-11 :00
Each of the 10-minute load readings observed for 2005 is then compared to its
associated expected value. The result is a time series comprised of 52 560 deviations(from the associated expected hourly average load), which were then converted to
absolute value. To meet the desired level of compliance (98%), the deviations wereanalyzed to determine the level of bi-directional reserve necessary to cover all but 2%
(approximately 1 000) of the deviations to within Idaho Power s LlO level of 38.52 MW(Table 2 in Section 2). Based on the 2005 load data alone, 38.9 MW ofreserve providesthis level of compliance. It should be noted that this base-case reserve level is basedsolely on analysis of Idaho Power load data, and does not include additional reserveprovided on a contract basis to external customers,
To include wind , the analysis was extended to consider the deviations of the
synchronous 10-minute readings for system load minus wind from the hour-ahead
expected hourly average system load minus wind. For this case, the hour-aheadexpectation is composed of the expected hourly average load (as discussed in the
preceding two paragraphs) minus the expected hourly average wind , where the expected
wind for a given hour is based on the observed wind at 5 minutes before the previoushour (e,g. the average wind generation during the 8:00 to 9:00 hour is based on
observed wind at 6:55). Based on the 2005 load and wind data, the levels ofbi-directional reserve given in the following table can be expected to allow the desired
compliance criterion (98%) to be met.
Page 4
Table 8,
Bi-directional ReserveCase (MW)
Bi-directional reserve to cover 98% of O-minute deviations to within LIO
Load only
300 MW Wind
600 MW Wind
900 MW Wind
1200 MW Wind
38.
64.
96.
141.1
201.0
COMBINING RESERVE COMPONENTS
A root-sum-square operation is used to combine the two bi-directional reservecomponents presented in this section. If the reserve to cover the fast fluctuations is
designated as RSRVFF, and that to cover the 10-minute deviations as RSRVIO, then thetotal reserve (RSRVTOT) is calculated as:
RSRVTOT = (RSRVFi + RSRVIO
)'!,
The total reserve requirements for each case are given in Table 9 and shown graphicallyin Figure 22.
Table 9,Total bi-directional reserve
Incremental % of Installed Wind
Total System Bi- Reserve Due to Genel'ationWind Penetration directional Reserve Wind Capacity
0 MW (Load Only)
300 MW
600 MW
900 MW
1200 MW
48.3 MW
72.5 MW
102.6 MW
146.3 MW
205.3 MW
24.2 MW
54.3 MW
98.0 MW
157.1 MW
00%
10%
10.90%
13.10%
A comparison of the ratio of the total reserve requirement to the penetration level may
explain (at least in part) the somewhat surprising result (given later in the report) inwhich the integration cost at the 300 MW penetration level equals or even exceeds thatat higher penetration levels. These ratios are as follows:
300 MW of nameplate wind 7 0.
600 MW of nameplate wind 7 0.
900 MW of nameplate wind 70.
200 MW of nameplate wind 7 0.17.
Page 42
350.
FAST FlUCTUATlOIiS
BIDIRECTIONAL
RESERVE (MW)
26.
32.
35.
36.
41.
300.
lOIlD ALONE
300 MW WIND
600 MW WIND
900 MW WIND
1200 MWWIIID
......,---_.._~..,--,.._--_...._.. "'~-"'-""-~--'---~-"--
_mm_____...._._---_..
_-_..-
INCREASE DUE
TO WIND (MW)
BIDIRECTIONAL RESERVE BY WIND
PENETRATION
10.
12.
BASED ON ANALYSIS OF 10.MINUTE lOAD
AND WIND DATA FOR CY 2005
10-MIIUTE DEVIATlOIiS50.BIDIRECTIONAL INCREASE DUE
RESERVE I,MW) TO WIND (MW)
36.
64.
962
141.
201.
TOTAL UPiDUNN RESERVE
COMBII~ED BY
ROOT-SUI'!I-SQUARE
OPER,I\TION
~ 200.
('J
t 150.
, ;: : : : : '- -,::: :::::::: .:: :::::::.:::,:::::.:::::::~::::::::::::-
FAST.A: - . - - " - . . . . . . FLUCTUATIONS~
" : ". - . . . . . . - . . - +. . . . . . . - . . - . . -+ . . . . . - . . . . . . - + . . . . . . . . .
NOTE: FAST FLUCTUATIONS RESERVE VALUES BASED ON ANALYSIS
OF 3D-SECOND LOAD DATA AND REPORTED VALUES FOR WIND
COr.BIIED THRU ROOT-SUM-SQUARE OP
BIDIRECTIONAL INCREASE DUE
RESERVE I,MW) TO WIND (MW)
46.
72.5
102.
146.
205.
LOIID ALONE
300 MW WIND
600 MW WIND
900 MW WIND
1200 MWWIND
100.
LOIID ALONE
300 MW WIND
600 MW WIND
900 MW WIND
1200 MWWIND
26.
57.
1022
162.
24.
54.
96.
157.
50..
300 600
wind penetration level (MW nameplate)
900 1200
Figure 22. Required bi-directional reserve versus wind penetration level- based on 2005 IdahoPowers system load and wind data
These results suggest that the system reserve requirement at the 300 MW penetrationlevel relative to the wind energy produced is substantially higher than that for thehigher levels of wind penetration. Therefore , even if the total integration costs are lowestfor the 300 MW level, they may approach those of the higher penetration levels when
expressed as a cost per MWh of wind energy delivered.
The reserve requirements are considered by Idaho Power to be met through the
provision of regulating reserve. Idaho Power views regulating reserve as spinning reserve
that is immediately responsive to automatic generation control (AGe) to providesufficient regulating reserve to allow compliance with NERC's Control PerformanceStandards (CPS1 & CPS2). This reserve constitutes generating capacity which can beimmediately loaded or unloaded as necessary to adjust for deviations in load and wind
from their expected or forecast levels. The regulating reserve was applied at constantlevels depending on the level of wind penetration (e.g. 102.6 MW of reserves responsiveto AGC for all hours for 600 MW simulations). Section 7 describes how the regulatingreserve requirements derived in this section are used in the hourly dispatch
simulations.
Page 43
Page
Section 7
HOURLY DISPATCH SIMULATIONS
The primary objective of this section is to present the results of the hourly dispatch
simulations. This discussion includes an overview of system operations concernsrelated to wind, a description of the modeling software (Vista DSS), and someexplanation of the model design, including how reserves are specified in the model. This
section also includes a review of the study design and presents a short discussion on
some limitations of the study.
OPERATIONAL CONSIDERATIONS FOR IDAHO POWER S SYSTEM
The normal operation of Idaho Power s system routinely accommodates fluctuations of
more than 200 MW per hour. These large fluctuations typically occur during the daily
ramp up or ramp down of the overall system load, but do not routinely occur at or nearthe system peak or low of the day. Within the bands of normal operation, the system isflexible to respond to load variability.
Wind integration is most problematic during light load and heavy load conditions when
the hydro system is at its least flexible state of either being fully committed or idle:
During Light Load Conditions - Problems may occur when the hydro units arereduced to near minimum generation levels and an unexpected increase in wind
generation occurs. The options of reducing generation under these conditions
are limited to backing down base load thermal generation or spilling water.
During Light Load Hours the Hells Canyon Complex releases are typically
reduced to or near their minimum allowable levels.
During Heavy Load Conditions - Problems may occur when the hydro units are
operating near maximum generation levels and a reduction in expected wind
generation could cause a degradation in CPS2 compliance. In the worst caseload may need to be shed to maintain system integrity.
During hydro spill conditions, unexpected wind energy would force Idaho Power toreduce hydro generation. This will result in additional spill, such that the wind energycosts" the opportunity to generate with the spilled water resulting in a zero net value
for the wind energy. Spill conditions are not uncommon for Idaho Power during the
spring.
Short-Term Wind Generation Forecast Error
As part of normal operations, Idaho Power regularly forecasts next hour load. The
forecast for a given hour is delivered to the operations group 75 minutes before the start
of that hour. Experience has shown an approximate error rate of 1 % over the course ofthe hour with the trend error being less. The load forecast is a dynamic target withrepeating daily and seasonal patterns or trends. Trend error is the observation that the
error at the beginning and end of the hour is typically higher than during the middle of
the hour. For example, during the morning ramp the forecast would overstate load at
Page
the beginning of the hour and understate at the end of the hour. The operation group
experience in understanding seasonal load shapes and the last hour load forecast errormake many of these errors predictable and part of business as usual.
The wind forecast error assumptions in the modeling are contrasted to the current
normal load forecast as follows:
1. The next hour wind forecast is assumed to be delivered to operations at 65
minutes before the hour, which is 10 minutes after the next hour load
forecast alone under current actual operations. This allows the operations
group time to assess, plan and execute transactions to meet next hour needs
in a manner consistent with current trading practice.
2. Historical records of daily and seasonal wind trend data are not available. In
the future , a daily or seasonal trend might be discovered to enhance the next
hour forecast for operational decisions. However, Idaho Power does notexpect this to be the case.
THE VISTA DECISION SUPPORT SYSTEM
The Vista DSS is a hydro optimization program developed by Synexus Global Inc. thatsimulates the operating characteristics of Idaho Power s system. The model has detailedgenerating unit definitions, a simplified bus level transmission architecture and hourly
inputs for streamflows, loads, electricity prices, reserve requirements and energy
contracts. The output of a Vista DSS simulation is an hourly generation schedule that
is considered to be optimized given the set of inputs (streamflows , loads, electricityprices, etc.), while observing hydraulic , transmission, and regulatory constraintsentered for the system.
With respect to Idaho Power s system Vista DSS has been set-up to model hydropoweroperations at the Hells Canyon Complex, other Idaho Power hydro generation facilities
are modeled as external energy sources. The purchased power from cogeneration
facilities are input as external energy sources. Thermal generation for each of the three
coal plants is modeled as an hourly energy purchase contract which can take on any
quantity between zero and project capacity (depending on maintenance), and having a
purchase price equal to the plant dispatch cost.
For the wind integration study, the hourly wind time series were input as zero-cost
purchase contracts which could take on only the value equal to the hourly wind
production. Heavy load power purchased during the summer under a long-termcontract from PPL Montana is also entered as a zero-cost purchase contract taking ononly the value as agreed upon in the existing contract.
The wind simulations also could access a spot purchase and spot sales market from theNorthwest on Idaho Power s western border, and a Southwest market on Idaho Powereastern border. The energy prices used for these contracts are based on historical
monthly prices for the three study years as recorded by Idaho Power for the Mid-Columbia (Mid-C) and Palo Verde (PV) markets. Prices for 1998 and 2000 were adjustedupward based on inflation rates as reported by InflationData.com(www.inflationdata.com). To reflect a transmission wheeling charge , hourly purchaseprices were adjusted upward by $5.00 for Mid-C and $8.00 for PV. Likewise, prices forthe sales contract were adjusted downward by $5.00 for Mid-C and $8.00 for PV to
account for transmission wheeling charges.
Page
Vista DSS's AGC Control reserve type was used to impose the regulation reserve
requirement (as presented in Section 6) onto operations at the Brownlee and Oxbow
projects in the Hells Canyon Complex (units at Hells Canyon Dam are not available for
AGe). Hourly contingency reserve requirements equal to approximately 5% of hydro and
wind generation and 7% of thermal generation were also included using the non-AGC
spinning and operating reserve types in Vista DSS. In accordance with WECCstandards, one-half of the contingency reserve requirement was specified as spinning
reserve using the Vista DSS designation labeled Non-AGC Spinning in the followingfigure. Assignment of the other half to the Operating Reserve type in Vista DSS allows itto be held as either non-spinning or spinning. The following schematic illustrates the
Vista DSS reserve categories used.
Not available
within 10 Not Available
min.
Available
within 10 Non.spinnlllg
min.
Operating non-AGCReserve
Spinning
AGC Control Regulating reserve
(on Automatic Generation Control)
GENERATION
Source: Synexus Global (modified from slide presentation - http://www.nwd-wc.usace .army .mil/PB/W orkshop/NiagaraO5/presentations/ 1 -7 -BG.pdf)
REVIEW OF STUDY DESIGN
This study analyzes the effects of wind generation variability and uncertainty on thesystem reserve requirements and ultimately the costs incurred to ensure that BalancingAuthority compliance, as measured by NERC perfonnance criteria CPS2, is notdegraded as a result of adding wind generation to the system. The impact of theincreased reserve requirement due to wind generation s variability and uncertainty is
quantified using the Vista DSS. The quantification is based on the difference between
the variable wind case and a flat wind case where the same amount of wind energy isdelivered as a flat 24-hour block. The following graph illustrates the actual hourly wind
generation case and the equivalent flat wind case for the 600 MW wind penetration level
for June 3-, 2000.
The variation in regulating reserve requirement between the flat wind case and variable
wind is provided via operational changes to the Hells Canyon Complex as simulated and
optimized by the Vista DSS model.
Page 47
Actual Wind
\ Energy Delivery
) I \\ r I
jl ~ rI \
('\
\ I
\ ! \
\ I \ I \ J
~ v-A.-
I / \
I \
I \
\ (
I ~
Ideal" Wind
Energy Delivery
Day
Figure 23. Illustration of variable and flat wind energy deliveries
STUDY LIMITATIONS
As with most studies of this type, there are some compromises that must be made
because of data availability or capabilities of the tools being used that result
limitations in the study. The most significant limitations are discussed below.
Actual versus Vista DSS reserve carrying capability of HCC
Under current Idaho Power operational practices , the actual reserve carrying capacity of
the Hells Canyon Complex will be less than the Vista DSS program assumptions. Thisdifference can be attributed to the model loading the units in a perfectly optimized
fashion and with the advantage of perfect foreknowledge of electricity prices, systemloads, reservoir inflow, and outages. Additionally, there are a number of operational
issues (such as needing to operate the power plants for voltage support, unit availabilityor de-rating due to maintenance, etc.) that prevent Idaho Power from ever achieving the
Vista DSS modeled "optimal" use of the hydro resources. These issues reduce the actualcapacity available from the hydro plants for reserve purposes and are not captured in
Vista DSS. Vista DSS overestimation of the available reserves is likely to have the
following impacts:
1. The actual AGC constraint violations would likely be higher at all penetration
levels than those identified in this report.
2. The actual opportunity costs of integrating wind at all penetration levels will
likely be higher than identified in this report.
Page 48
Transmission Costs for Integration Wind Generation
Except for the wheeling costs associated with market purchases and sales othertransmission related costs such as interconnection costs, transmission systembackbone upgrades and impacts to system voltage and stability are beyond the scope of
this study.
RESULTS
The Vista DSS program computes an hourly generation schedule given a set of inputs
(reservoir inflows, hourly loads, hourly market prices, hourly wind generation) and a setof operating constraints on the system. The operating constraints include hydraulic
restrictions (e.g. project outflow ramp rates), transmission tie-line limits, and requiredreserve levels. Aside from the differing time series for wind generation, the flat wind andvariable wind scenarios differ only with respect to their required reserve levels. All otherinputs and constraints are identical between matched pairs of simulations.
Furthermore, the Vista DSS simulations were constrained to pass identical streamflow
volumes and pool elevation targets for all simulations. Average annual flows for thestudy years are given in Table 10. The difference between Brownlee inflow and totalcomplex outflow reflects inflow local to the Hells Canyon Complex.
Table 10.Operation of Hells Canyon Complex for study years
Hells Canyon
Brownlee Inflow Complex OutflowYear Hydro Condition (avg. cfs) (avg. cfs)
1998
2000
2005
High
Medium
Low
25,167
1 6,436
12,326
25,772
16,966
12,798
The effect of the differing reserve requirements between the cases is illustrated in Figure, which depicts hourly Hells Canyon Complex generation for the 900 MW wind
penetration level as computed by Vista DSS for a two-week period in August 2000. The
blue dashed line, which is generation under the variable wind case , illustrates how theincreased reserve requirement forces generation levels higher during light load (off-peak)hours and lower during heavy load (on-peak) hours. This is a financially unfavorableoperation in that it generally results in increased sales during lower-priced off-peakhours and increased purchases during more costly on-peak hours.
Page 49
1110 J\ Ii r\
\ )
\/1 (I r II r'
I I II I ~ j I / I 11
1 I
r; ~ rIII I ( 1 I\I' II! J \
i \r I II III II II I I J I I I II I) I I I \ III I (I , I I I 1 ) I I I I I 11
1 \ I 1 (I I )1 \
I 111 Iv I) I I ): I I \ r \ I III I I \
lr (1 It ~l
) I~If
\/
II I j 1
lJ I\f
"- "-.; "
i luJ LJ 1 I I f'-
.....,
\J \,.v ~
Figure 24. Computed Hells Canyon Complex hourly generation August 6-19, 2000 - 900 MW windpenetration level
The cost differentials between the flat wind and variable wind cases as computed by
Vista DSS are generally the product of differing market transactions. The increasedreserve requirement in the variable wind case forces a different pattern of market
activity onto the generation scheduling. Therefore, the calculated integration costs arestrongly related to market energy prices. As stated earlier in this section, the market
energy prices used in the Vista DSS modeling are based on historical Mid-C prices, with1998 and 2000 prices adjusted for inflation to the 2005 level. There is considerablevariability within these price curves, both between years and seasonally within years.This is illustrated in comparing the average annual prices used in the analysis for the
three study years:
1998 (high-case hydrology) ~ $28/MWh average annual Mid-C price
2000 (medium-case hydrology) ~ $132/MWh average annual Mid-C price
2005 (low-case hydrology) ~ $58/MWh average annual Mid-C price.
Because of the high-degree of variability in energy prices between the study years, theresults were evaluated in terms of percentage of market energy price as well as a cost in
$ / MWh of wind generation. The $ / MWh costs are given in Table 11. Costs expressed as
a percentage of the average market price are given in Table 12. Results are shown
graphically in Figure 25 and Figure 26.
Table 11. Wind integration costs ($/MWh of delivered wind)
Wind
Penetration CY1998 CY2000 CY2005
300 MW
600 MW
900 MW
1200 MW
$3.
$4.73
$6.
$6.
$21.89
$30.
$39.
$39.40
$10.
$9.
$10.
$8.
Page
Table 12.Wind integration costs as a percentage of annual market energy price
WindPenetration CY1998 CY2000 CY2005
300 MW
600 MW
900 MW
1200 MW
11.
17.
21.
25.
16.
22.
29.
29.
18.4%
16.
18.
14.
$40.
--
2000 Historic Prices
$35.--1998 Historic Prices
--
2005 Historic Prices
$30.
$25.
$20.
$15.
$10.
$5.
$0.
300 MW 600 MW 900 MW 1200 MW
Figure 25. Absolute wind generation integration costs ($/MWh of delivered wind) as a function ofpenetration level for three historical years with varying water conditions
Page
28.---2000 Historic Prices
-+-1998 Historic Prices
--2005 Historic Prices
23.
18.
13.
300 MW 600 MW 900 MW 1200 MW
Figure 26. Wind generation integration cost as a percentage of the average market energy pricefor three historical years with varying water conditions
To help evaluate the impact of the electricity price volatility observed in 2000 and thesensitivity of the costs of wind integration the 1998 and 2000 hydro and wind
conditions were modeled with 2005 prices. The results of the study year 1998, 2000and 2005 with the 2005 price assumptions are given in Table 13.
Table 13.Summary of Vista DSS results with AGC constraint violation data
Vista Results Using Historical
Mid-C prices as Benchmark
Study
Year
Penetration
Level (MW)
Cost
per
MWh
Wind
Annual Vista Vista
Mid-Cost as AGC Res AGC Res
Avg Constraint Constraint
Energy Energy Violations Violations
Price Price Flat Case Var Case
1998 300 $3.$27.11.
1998 600 $4.$27.17.
1998 900 $6.$27.21.102
1998 1200 $6.$27.25.319
2000 300 $21.89 $132.16.
2000 600 $30.$132.22.
2000 900 $39.$132.29.37320001200$39.40 $132.29.059
2005 300 $10.$58.18.4%
2005 600 $9.$58.16.
2005 900 $10.$58.18.792
2005 1200 $8.$58.14.132
Page
The aggregate wind generation and Hells Canyon Complex generation and the respectivevalues for year 2000 under each penetration level are shown in Table 14. The decline inHells Canyon Complex generation is largely the product of the increased system
flexibility required in the variable wind cases, and the resultant inefficiencies in unitloading and hydro utilization. It should be emphasized that the Total Value results
calculated in Vista DSS as the net of coal plant dispatch costs plus power purchases
minus power sales, and are meaningful mostly in their differences between cases
(variable wind case versus flat wind case).
Table 14.Integration cost summary for CY2000-( average market price = $132)
Integration
Cost (Flat $-HCC HCC Variablel $)Wind Gener-Gener-HCC Flat Wind Variable /Wind GenWindGener-ation-ation-Change Case Wind Case /MarketPene-ation Flat Wind VOl". Wind In Output Total Value Tolal Value PriceIration(%Mkt.(MW)(MWh)(MWh)(MWh)(MWh)
($)($)
Price)
300 702,529 337 282 304 673 32,609 344,812,327 329.432,321 16.
600 587,534 350,878 250,816 100,062 463,894,653 415,797,225 22.
900 288,832 336,122 172,858 163,264 550,289.178 460,878,210 29.
200 016,611 343.432 023,013 320.419 642,706,857 523,862,288 29.
Page 53
Page
Section 8
SUMMARY AND CONCLUSIONS
The opportunity costs and potential limits for integrating up to 1 200 MW of windgeneration into Idaho Power s Balancing Authority have been quantified through a
comprehensive, simulation-based analysis. The data and methods utilized here are
consistent with the developing state-of-the-art for assessing the impacts of windgeneration variability and uncertainty on power system operations.
The Vista DSS was used as a platform for simulating the operation of Idaho Power
system at an hourly granularity for the historical years 1998, 2000, and 2005. Theseyears cover a typical range of expected historical hydrologic conditions. Four levels of
wind generation - 300 MW, 600 MW, 900 MW , and 1 200 MW - were evaluated for each
of the selected years. Vista DSS scheduled and dispatched hydroelectric and other
generation and made purchases and sales to meet the net of Idaho Power s loadobligations and wind generation while maximizing the value of the water resource
within the hydro system constraints. The hydro resources operated by Idaho Power andneighboring entities provide sufficient flexibility so that wind generation forecast weredetennined to not be critical in pre-scheduling of system operations.
SUMMARY OF STUDY FINDINGS
The results of the study are given in Table 15.
Table 15.Vista results using historical mid-C prices
Vista Results Using Historical Mid-C Prices
study year penetration cost per MWh annual avg cost as % energylevel (MW) wind energy price price
1998 300 $3.$27.11.
1998 600 $4.$27.17.
1998 900 $6.$27.21.
1998 200 $6.$27.25.
2000 300 $21.$132.16.
2000 600 $30.$132.22.
2000 900 $39.$132.29.
2000 200 $39.40 $132.29.
2005 300 $10.$58.18.4%
2005 600 $9.$58.16.
2005 900 $10.$58.18.
2005 200 $8.$58.14.
Page 55
Incremental requirements due to wind generation were determined for three categoriesof reserves:
Regulating Reserve - the amount of capacity needed to balance the Balancing
Authority Area on a minute-by-minute basis. Wind generation adds only
modestly to this requirement - about 1 % of the installed wind generationcapacity
Load Following Capability - Idaho Power allocates hydro capacity each hour to
follow the trend of the Balancing Authority demand. For load alone, this trend isrelatively predictable due to the familiar diurnal patterns that correspond to day
type, season, etc. Behavior of the Balancing Authority demand becomes less
predictable over the short-term as the amount of wind generation capacityincreases, so additional capacity must be set aside for balancing supply and
demand within the hour.
Additional Operating Reserves - The rules for real-time energy transactions withother Balancing Authorities require schedules be set some time in advance of
the hour in which they are to occur. Forecasts of load and wind generation inthe hours ahead are the basis upon which Idaho Power bases decisions topurchase or sell energy. During the hour in which the transaction is to be
executed, Idaho Power must be able to cover deviations between the forecast and
actual Balancing Authority demand with its own generation resources. Wind
generation adds to this short-term uncertainty, thereby increasing operating
reserve requirements.
The incremental amount of system "flexibility" - the sum of the above reserve categories(all carried as regulating reserve) - was found to be 13.1 % or less of the installed windgeneration capacity for all scenarios. Modified operations necessary to integrate wind
may impact Idaho Power s ability to provide regulation service under contract with
NorthWestern Energy or possibilities for acting upon similar contract opportunities withother parties.
DISCUSSION
Previous studies of this type have found that "integration costs" for wind generation asdefined here can be sensitive to the assumptions made. In addition, there are alwaysuncertainties about the future , in terms of physical characteristics such a load levelsand resource capabilities or institutional constructs which ultimately dictate how thepower system must be operated. These uncertainties all have potential to influence the
economics associated with managing a variable and uncertain resource.
It is appropriate, therefore, to recount some of the assumptions made to guide the
analysis as well as other uncertainties that could affect integration cost. These include:
Relicensing of Hydro Power Projects - Idaho Power s hydroelectric facilitiesoperate under licenses issued by the FERC. The initial 50-year license for theHells Canyon Complex expired in 2005, prompting the Company to seek a
renewed license for the projects. The application to relicense the Complex was
filed by Idaho Power in July 2003. However, a new FERC license to operate theprojects has yet to be granted because of ongoing negotiations and study
requests. Because of the importance of the Hells Canyon Complex with respect
to generating capacity and reserves, the outcome of the FERC relicensing of thethree-dam complex is critical to Idaho Power s capability for integrating windresources. In its long-term resource planning and for this study, Idaho Power is
Page 56
assuming that no reduction of the available capacity or operational flexibility of
the Complex will result from the relicensing process. However, if the grantedlicense for the Complex stipulates a reduction in operational flexibility, then thefindings of the wind integration study will need to be reexamined and may even
be rendered obsolete if newly imposed operational restrictions are particularly
severe.
Effect of Load Growth. Peak load in Idaho Power s service area is growing twiceas fast as the annual energy requirement. Going forward this growth will lead to
higher ramp rate requirements in the summertime and less available hydrocapacity for managing wind. The cost of reserves would then likely increase
which could increase the integration cost for wind.
Market Prices in the Pacific Northwest. The cost for managing wind generation isrelated to the market prices for energy, as shown in the analysis, and especiallyby the analysis for calendar year 2000 with the very high market prices. It was
assumed for this study that the addition of wind generation in Idaho would notinfluence market prices, so that historical profiles could be used to represent
other companies in the region. As more wind is considered and eventually
developed in the Pacific Northwest, this assumption would not be correct. It isdifficult to even conjecture how significant wind throughout the region (and even
the interconnection) would influence energy prices, but it is almost a certaintythat there would be significant effects.
Market Structure and Operating Agreements in the Pacific Northwest. A majorfinding is this study was how the structure for in-the-day transactions withother utilities leads to an increased requirement for operating reserves with windgeneration. This is due both to the uncertainty associated with wind generation
combined with the "lead time" for hourly transactions and the fact that thistrading is conducted with flat hourly blocks. Other integration studies have
shown that sub-hourly markets and arrangements can help to spread the
variability of individual Balancing Authority demand out over a larger region
with the effect being a reduction in relative tenDS of the overall variability. Asmore utilities in the region ponder how the effects of wind generation can bemanaged, there is a possibility that operating agreements which seek to utilize
geographic diversity and large aggregates of demand could reduce integration
costs.
Improvements in Wind Generation Forecasting. The commercial business of windgeneration forecasting is currently in the formative stages. There is research
underway to assess how and what type of wind generation forecast information
can best be used by power system schedulers and operators to optimize the
deployment of reserves to cover anticipated changes in and variability of windgeneration. Over time, the result of this and related work should reduce
integration costs that stem from conservative operating practices and policies.
Transmission Limitations. In addition to constraining the hourly operation of
Idaho Power s system, transmission limitations can affect the provision anddelivery of ancillary services. Low-cost regulating resources in the region, forexample, may not be accessible because of transmission capacity limitations.
Nature of Wind Generation Development in Idaho. The wind generation scenariosconstructed for this study are well distributed across the wind resource areas in
the southern half of Idaho. Geographic diversity has a dramatic impact on the
aggregate variability in the operational time frames. If actual wind generation
Page 57
development in Idaho is more concentrated in just one or two of the most
favorable areas, the variability and uncertainty of the aggregate generation
would be higher than what was considered in the study, almost certainly
increasing integration costs.
Modeling of Reserve Requirements. In the hourly dispatch simulations, it wasassumed that the total operating reserves were the same for each hour of the
year. In the analysis of incremental reserve requirements for wind generation, itwas recognized that those reserves attributable to the variability and uncertainty
of wind generation would most likely be a quantity that varies based on thestatus of wind generation, and therefore more of an hourly profile than a single
number. By using an average , the reserve amounts used are high when wind
generation is low or zero , and probably short of what would be required during
periods of substantial wind generation. Because the cost function is non-linearit is not possible to estimate how integration costs are affected by this modelingassumption. Future analysis will likely treat the reserve profile as a variable
quantity, since that is the most likely implementation by Idaho Power s systemoperators.
CONCLUSIONS
The previous uncertainties aside , there are substantial conclusions that can be drawn
from the work reported here. These include:
Wind integration costs stem mostly from the additional operating reserves that
are necessary to cover the additional variability and short-term uncertainty ofthe Idaho Power Balancing Authority demand net wind.
In general , incremental operating reserves required for wind generation fall into
three major categories: 1) regulating reserve; 2) load following reserve; and 3)
additional reserves to cover the expected short-term wind generation forecasterror over the next hour. Due to the prevalence of hydroelectric generation in thesupply portfolios , the current practice in the Pacific Northwest essentially
combines the regulating and load following reserve since they are generally
provided by the same hydroelectric units.
Idaho Power s hydro capacity is valued at the market price. Therefore, additionalreserve capacity that must be allocated to manage wind generation cannot be
used to generate revenue.
Wind integration costs are sensitive to hydro conditions. Because of the
influence wholesale electricity market prices have on integration costs , thesensitivity is related in part to the correlation in the Northwest energy market
between hydro conditions and energy prices. The sensitivity is also related to the
additional bi-directional reserve requirement necessary to integrate wind. In low
water years, Idaho Power strives to shape its generation scheduling such thatlight load reservoir releases are at or near their minimum allowable levels. Thusduring these years, forcing the system to carry the down-direction regulatingreserve is particularly punitive with respect to optimal shaping of limited water
supply. Conversely, in high water years, Idaho Power s generation schedulingstrategy is designed to move water through the system such that spill volumesare minimized (or ideally eliminated). In this case, the additional up-directionregulating reserve has the effect of limiting releases during all hours, andparticularly during heavy load hours. As a consequence, the system is not able
Page 58
to pass as much water during a given period , which is likely to result in
increased spill andlor increased generations during light load hours. Effectsduring average water years can be expected to be a blend of those during the
extreme years, and consequently are likely less predictable. Operations during
these years may be impacted to the greatest degree in that both up- and down-
direction reserve requirements are problematic , possibly driving integrationcosts to higher levels than incurred during extreme years.
The results suggest that integration cost is a function of the disparity betweenheavy and light load pricing and hydro conditions.
Interest in and the development of bulk wind generation is growing across the West.
Idaho Power will soon begin to accumulate actual operating experience with windgeneration facilities that will be "noticeable" from the control room. As was the casewith much of the current operating practice; strategies for managing the system with
wind will be evolved over time.
The results of this study indicate that present operational practices would suffice formanaging significant wind generation in Idaho Power s service area. The study alsoshows that there is a level where current practices are strained. At higher wind
penetration levels, the present practice of managing the system with generating
resources from the Hells Canyon Complex led to a level of reserve violations that wouldnot be tolerable in actual operations. Thus, the level of comfort on the part of IdahoPower with the lower penetration scenarios is much higher than for those whereinstalled wind capacity exceeds 20% of system peak load. In surveying the significant
body of integration study work that has been conducted over the past five years , thiscomfort" level is actually quite consistent with what has been discovered elsewhere inNorth America. In most of these studies, the assumptions for conducting the analysis
have been based on existing philosophies for managing power systems. A hypothesis
that might be drawn from these results is that the higher penetration levels require afundamental re-evaluation of current practices for power system operation and control.
There is a significant possibility that wind generation will continue its current growth
trajectory, and that the higher penetration scenarios will become reality. Much has been
learned by Idaho Power in this initial effort regarding the mechanics and economics of
managing significant wind generation. It is also likely that this will not be the last time
Idaho Power performs such an assessment. The data, methods, and tools upon whichthe initial study results are based will be augmented and enhanced going forward, sothat future evaluations of wind generation impacts will be more sophisticated and build
on the knowledge base that exists as a consequence of this first effort. The operatingexperience gained from the wind generation to be developed over the coming years willconstitute a new and critical input, and will serve to significantly increase the
confidence in the results of future studies.
Page 59
Page 60
Section 9
REFERENCES
Utility Wind Interest Group (UWIG): "Characterizing the Impacts of Significant Wind
Generation Facilities on Bulk Power System Operations Planning" May, 2003
www.uwig.org
Hirst, E. and Kirby, B. "Separating and Measuring the Regulation and Load Following
Ancillary Services" November, 1998 (available at www.EHirst.com)
Hirst, E. and Kirby, B. "What is the Correct Time-Averaging Period for the Regulation
Ancillary Service?" April, 2000 (available at www.EHirst.com)
Piwko, R., et.al. "The Effects of Integrating Wind Power on Transmission System
Planning, Reliability, and Operations - Report on Phase 1: Preliminary OverallReliability Assessment" for the New York State Energy Research and DevelopmentAuthority (NYSERDA), published February, 2004 (available at
www.nyserda.org/ energyresources I wind.html)
NRELjCP-500-26722: "Short-tenn Power fluctuation of Wind Turbines: Analyzing data
from the German 250 MW Measurement Program from the Ancillary Services Viewpoint"
Parsons, B.P, et. al. "Grid Impacts of Wind Power; A Summary of Recent Studies in theUnited States" presented at the 2003 European Wind Energy Conference, MadridSpain, June 2003.
Milligan, M.R. "A Sliding Window Technique for Calculating System LOLP Contributionsof Wind Power Plants" presented at the 2001 AWEA Windpower Conference
Washington, DC, June 4-2001. NREL/CP-500-30363
Milligan, M., et. al. "An Enumerative Technique for Modeling Wind Power Variations
in Production Costing" presented at the International Conference on ProbabilisticMethods Applied to Power Systems, Vancouver , BC , Canada, September 21-, 1997.NREL I CP-440-22868
Milligan, M., et. al. "An Enumerated Probabilistic Simulation Technique and Case
Study: Integrating Wind Power into Utility Production Cost Models" presented at theIEEE Power Engineering Society Summer Meeting, Denver, CO, July 29 - August 11996. NRELjTP-440-21530
Milligan, M.
, "
Measuring Wind Plant Capacity Value" NREL White Paper
Milligan, M. "Windpower and System Operation in the Hourly Time Domain" presentedat the 2003 AWEA Windpower Conference, May 18-2003, Austin, TX. NREL/CP-500-33955
Hirst, Eric
, "
Interaction of Wind Farms with Bulk Power Operations and Markets
prepared for the Project for Sustainable FERC Energy Policy, September 2001
Page 6
Milligan, M.R. "A Chronological Reliability Model to Assess Operating Reserve Allocation
to Wind Power Plants" presented at the 2001 European Wind Energy Conference, July2001, Copenhagen, Denmark. NRELjCP-500-30490
Milligan, M.R. "A Chronological Reliability Model Incorporating Wind Forecasts to
Assess Wind Plant Reserve Allocation" presented at 2002 AWEA Wind power ConferenceJune 3-2002, Portland, OR. NREL/CP-500-32210
Karady, George G., et. al.
, "
Economic Impact Analysis of Load Forecasting , IEEETransactions on Power Systems, Volume 12, No., August, 1997. pp. 1388 - 1392.
L.L. Garver, Effective Load Carrying Capability of Generating Units IEEE Transactions
on Power Apparatus and Systems VOL PAS-, No 8 , pp 910-919 August, 1966
Page
ApPENDIX A
DEVELOPING WIND GENERATION PROFILES FROM
WIND SPEED DATA
The objective is to devise a method for calculating hourly wind generation from the
measured wind data. The turbine power curve from Figure 27 is used.
2(;-:03
e:o3
---....
1e:O
1 CO:'
Co.
3IJ
.\',:-:d ,,~d \mM
Figure 27. Turbine power curve used for calculating generation data from wind speedmeasurements
Measurement data from an operating wind plant with 20 of the turbines referenced
above, consisting of a single wind speed and plant power at ten minute intervals was
processed to create a "plant" power curve. This curve is shown in Figure 28.
Page 63
-40
3.5
+ +
-!H-
Wrd Spe.ed mf'~
Figure 28.Empirical "power curve " for wind plant from measured values
Figure 28 shows the results of applying the power curve from Figure 27(scaledappropriately) to lO-minute wind speed data, then aggregating the results to hourly
average values. The striking feature of this figure is the "fuzziness . If the wind speeddata were averaged to hourly values before applying the power curve, the characteristicwould match that shown in Figure 29. The difference, of course, is that themathematical operations are not the same because of the non-linear nature of the
turbine power curve.
+::1
:;!;J
300
'N'J'o.d 3p~d jmfsi
Figure 29. Wind plant "power curve" calculated from lO-minute wind speed values
A closer comparison of the calculated and measured wind generation reveals that the
simple transformation from wind speed to power using a single power curve and wind
speed value leads to a calculated value that is higher than the actual, and a tendency tosaturate" during periods of high wind, sometimes unlike the measured data. A
Page 64
computation of the energy delivered shows that the calculated value is about 25%
higher than what was actually measured.
2CCO 2OEO 21CO 21EO 2200 22EO 23f'...O 23.EO 240:.
Hour
Calculated Generation
Measured Generation
Wind Speed
.;0
-41"'...':0 -41"'...oEO 410:,4i:9:'42C()42EO 31"'...(;'4-41"'...0
HO'~Calculated Generation
rvteasured Generation
Wind Speed
Figure 30. Measured wind generation YS. that from simple calculation (wind speed and singleturbine power curve)
Figure 31 illustrates why this is the case. The "knee" of the calculated plant powercurve is much more pronounced, although the "fit" is reasonable at lower power levels.Therefore, shifting the plant power curve to the right to approximately account for the
diversity of wind speeds over the plant area would degrade the fit at lower wind speed
levels.
Page 65
3.0
0:)
0:)
c;)
- ",
"4-
+++
-11+
2;0 2.5
+ + + r''tea~ured
0:) 0:) 0:) Calculated
V'Jfr.d 3peeaimM
Figure 31. Measured and calculated plant power curves
A better fit between the calculated and measured plant power curves (as well as thetime series data) can be achieved by modifying the measured wind speed prior to
applying the power curve. The modification consists of applying an exponent slightly
less than one to the measured wind speed value. Figure 32 illustrates this for an
exponent of 0.95. Note that the effect on low values of wind speed is much smaller thanfor larger ones. Also, for values well above the rated turbine wind speed, themodification makes no difference in the power calculation.
2()
":1
:II
":1
" /, ., '- -, ," ., ., ., ,, ..--~:-.""...--
t."le,cz,-\,e,d 'NErd 3pe.e-dimf:;:~
Figure 32. Exponential modification of measured wind speed
Page 66
The comparison of measured and actual power curves using this modification is shown
in Figure 33. The calculated energy over the entire year for the calculated data differs by
less than 1 % from the measured data.
2:0
, +:;.+++
-!H-
2:;
+ + +
Measured
0 0 0 Calculated
""'me; Speed jm!5~
Figure 33. Measured and modified calculated plant power curves
The improvement is also evident in the time series data. Figure 34 shows Measured and
calculated hourly wind generation for some time periods from the sample data, with the
calculated value here based on a modified wind speed value. Note that whileimprovement is evident, the time periods selected for illustration are not the best ones
to show the difference. Figure 35 provides a comparison of the simplified versus
modified method.
Page 67
-40
2\."00 2050 2100 2150 22::0 2250 23CO 23.5C 24':.(:-
Calculated Generation
Mea;;ured Generation
Wind Speed
-40
4':.CQ 4':.cQO 41ce 4150 42C.:)42:50 4-~43~:'44':.C-
He.."
Calculated Generation
~...tea;;ured Generation
Wind Speed
Figure 34. Comparison of measured wind generation to that calculated with wind speedmodification
..()~:-
11C()112'0 1; ..0 11 ",0 1 j~,:-12":';:-
He","Simple
Modified
'Ieo-"ured
Wind Speed
Figure 35. Comparison of simple versus modified method for calculating wind generation fromwind speed data
Page 68
ApPENDIX B
ESTIMATING INCREMENTAL RESERVE REQUIREMENTS
USING LOAD AND WIND GENERATION DATA
LOAD FOLLOWING
This method for assessing needs for additional regulating capacity (over periods longer
than the minute or so covered by regulation capacity on AGC and extending through
the balance of the hour) takes a general view of the load following challenge.
The general procedure for load alone is as follows:
1. Using the ten-minute data, compute the hourly average value for load
2. Compute the difference between each ten minute value of load and the hourly
average. The difference is the load following requirement for load alone.
3. Devise an algorithm that could be implemented by operators to project the
maneuverability that is needed to follow the load movements. For load alonethis algorithm is based on the previous hour average value (which is known)and the forecast average value for the next hour (which is assumed to beperfectly forecasted).
4. The estimated load following capability is then the difference between the next
hour forecast average and the previous hour average.
5. The requirements are roughly symmetrical about the average value. In themorning, for example, the load at the beginning of the hour will be less than
the hourly average. If the unit base points are moved to the hourly average
there will be a need to back some flexible generation down , and then move it up
over the hour as the load increases.
6. This load following "rule" is tested with the ten-minute data. The number of
ten-minute load values outside of the up and down load following bands is
computed. For the rule above , the coefficient is adjusted until the number ofviolations" is about 1000 out of almost 50000 ten-minute samples. It was also
necessary to add in the REGUP jREGDN capacity since the ten minute valuesare snapshots, not averages over the interval.
Page
The "equation" for this baseline load following rule is
Where:
LO =
LHour =
RegO =
FO =
55
LOh1 :=2 LHourh1-LHourh1-
FOh1 := LOh1 + RegO
the up and down load following requirement based on the change in
average load over the hour
the average hourly load, with the subscript hl-1 corresponding to the
hour just concluding and hI the upcoming hour
the regulation capacity for load alone determined from analysis of
higher resolution data (28.6 MW)
The load following capacity that results in the desired performance of
1000 violations out of 50000 ten-minute intervals.
(Note that the squaringl square root procedure is only used to calculate the absolute
value)
Figure 36 illustrates how the rule works. The upper and lower load following "bandsbracket almost all of the ten-minute load values.
1869.
1674.
1479.
::?:
1284.
1089.
894.
2736.779 2742.335 2747.891 2753.447 2759.002 2764.558 2770.114
Hour
----
Hourly Average
Load
LFUP
LFDN
Figure 36. Illustration of load following rule for load alone.
From that baseline , a new rule is devised for wind generation. Going into the next hour
it is assumed that the average hourly load can be forecast perfectly. For wind
generation, the forecast is based on persistence, where the next hour average
Page 70
generation is assumed to be the same as in the hour just concluding. The load followingrule" must be modified to account for the additional variability that is introduced by
wind generation.
Another component of the reserve requirement for wind generation stems from the rules
for in-the-day transactions with the market. These transactions must be made
sometime prior to the hour in which the transaction is to be affected, with forecasts ofsystem demand one of the primary inputs to the decision-making process.
To meet the hourly transaction obligation, generation flexibility must be available to
meet the difference between the forecast demand and the actual demand during thehour. For example , if the system demand is larger than what was forecast in making
transactions for the hour, the difference must be made up with Idaho Power generating
resources. If the demand is smaller than anticipated, Idaho Power resources must bebacked down to accept the energy from the obligated transaction.
Wind generation adds uncertainty to this process, and increases the need for generationflexibility to cover deviations between actual system demand and short-term forecasts.With the wind generation data developed for this study, this effect on conventional
generation flexibility can be computed. Two assumptions are made to simplify the
computation:
The load can be perfectly forecast one-hour ahead, and
Forecasts of wind generation over the hour beginning one-hour from now arebased on persistence, i.e. the forecast for that hour is the same as the deliveryfor the current hour.
From these assumptions, the additional flexibility can be quantified as a function of the
one-hour persistence forecast error. What results , then, is an additional requirementrelated to short-term uncertainty, in addition to within-the-hour variability.
The standard deviation of one-hour production changes were calculated for ten levels of
production as shown in Figure 37. The results show that wind generation is most
variable over the one-hour time frame in the mid-range of the aggregate production
curve. This is not unexpected, since the slope of an individual wind turbine power curve
is steepest for wind speeds below rated capacity.
Page 71
:;:.::;
J::
..... -....- '
300 MW
----- 600
/ - "- ,
- 900
I, - - 1200
.........- ..-- ................................................---....- -.....-....................../ / / / ...."..,
m..m"
uum,
. "~----,-.....
Production Level (pu)
Figure 37. One-hour variability of wind production as a function of production level
Quadratic expressions were developed to approximate the standard deviation as afunction of production for each wind penetration levels. The approximate variabilityequations for the four wind generation scenarios are:
(x-150)f1 (x) := 28 -
1200
(x-300)f2 ( x) := 35
5000
(x - 550)f3 (x) := 50 -
9000
( x - 700 )f4(x):= 70-
11000
The load following rules for wind generation were constructed by adding the quadratic
expression for wind variability to the expression for load change. These new load
following rules for the four wind generation scenarios were "tested" in the same way asfor the load only rule. The calibration factor "D" was adjusted so that the number ofviolations for the wind scenarios was about the same as for load only.
The equations for the four wind generation scenarios are:
Page
Fl hl := FOhl + Dl.f1 (Wl HOUrhl-) + DReg1
F2hl := FOhl + D2.f2(W2HOUrhl-) + DReg2
F3hl := FOhl + D3.f3(W3HOurhl-) + DReg3
F4hl := FOhl + D4.f4(W4HOUrhl-) + DReg4
Where:
Dl := 0.775
D2 := 1.0
D3 := 1.5
D4 := 1.
and
DReg 1 := 3.7
DReg2 := 7.
DReg3 := 10.
DReg4 := 12.
Figure 38 and Figure 39 depict how this load following approach works. Each plot
shows the ten-minute value of the Balancing Authority demand, the hourly averagevalue, and the load following limits (up and down). Both plots are for the same hours in
the year, with the first for load only and the second for the 900 MW wind penetration
level.
The load following requirement varies by hour. Looking at the statistics over the year
however, is interesting. The average load following requirement for load alone is 57 MW.
Adding wind generation increases the average only modestly, from 59 MW in the lowest
penetration scenario , to almost 70 MW in the highest. Because the value used in anyhour is computed from information that would be available to the operator, such anapproach could actually be implemented in the control room. Additionally, the rules
have been "tested" with the actual data, and the algorithms adjusted so that the
performance matches the load-only case. No assumptions were necessary.
Page
1800
1400
1200
1000
800
1020 1028 1036 1044 1052 1060 1068
Hour
~"
Hourly Average
Load
LFUP
LFDN
Figure 38. Average net load, actual net load, and load following "bands" for three-day period
1800
1600
1400
1200
1000
800
1020 1028 1036 1044 1052 1060 1068
Hour
Hourly Average
Load net Wind
LFUP
LFDN
Figure 39. Average net load, actual net load, and load following "bands" for another three-dayperiod
The summary statistics for the required flexible generation profiles are summarized in
Table 16.
Page 74
Table 16.Statistics for load following and regulation profiles
Total System Up/Down RegUp/Reg DnWind Penetration Flexibility Component
(average over all hours)
0 MW (Load Only)75.6 MW 28.6 MW
300 MW 94.0 MW 32.3 MW
600 MW 111.2 MW 35.6 MW
900 MW 140.6 MW 38.6 MW
200 MW 175.6 MW 41.5 MW
Page
Page 76
ApPENDIX C
IDAHO WIND RESOURCE MAP
11~1140 112"11~
48" -
-----~
Wind Power Classilicalion
'Mnd Ae8tnne 'MncI PoWllf' WlI1d Speed"
~":
PooIl1I!aI ~tv..t&O1I1 ~~1I1 \\1100 Speed"mWm
,"pi!
125-143-15,15.7-16.16.8-11.119-19,19.7-24,
M&/OIO8I 200 - :;00 5,6 - 6,Fai' 300-400 6-,./1-Good 400- 500 7.0- 7.ExCt!119I1o1 500 - 600 75 - 6.OWtal1dinQ 600- eoo 8.0- 8,SUpefll 800 - 1600 8,8 - 11.1
Wind "f)HWs -1I=ed an a 'MIb" k vII... tf 2.0
Transmil*on Une
Voltage (kV)
t\,/
IS/. l1S. 161
SaUll:O: POW1!1Anop.6I!OO2 PI;It\$,A OMIio. of II", MtGt;tw.Hift c""""'-
Indl8.C1Reservations
CQll\lr 0;1' Alene
Cyct;vQlley
m""",....11rn Kootenaj
151 Ne, Pwce
" 460
46"
The wind power lor this map was PtOducoo
by TrueWind Solutions using the Mesomapsystem and historical wea~ data, II hasbeen validated with available surface data by
the National Renewable En~1" Laboralaty .
and wind energy meteoroIogic81 conwltants.
-- 44.
440
42"
--,
" 42"
116"114.112"
25
---
7S 100 lAie!\
~)PIEL
S. Department of Energy
Natiooal Renewable Enllt'gy Laboratory
~---".
40
- --
4() eo 120 160 Kii)met"",
Page 77
Page
ApPENDIX D
DAY-AHEAD WIND GENERATION FORECAST ERROR
As part of the analysis of wind integration cost, the uncertainty of the wind generation
was considered. System operations must be structured to accommodate second-to-
second through day-ahead uncertainty. The day-ahead uncertainty or day-ahead
forecast error is discussed in this appendix.
EnerNex constructed a wind generation forecast based on the wind simulation data
from WindLogics. The forecast used the typical WECC day-ahead trading schedule ofone day ahead for Tuesday, Wednesday, Thursday and Friday and two days ahead for
Saturday and Sunday, and thee days ahead for Monday. A wind forecast was
constructed for each of the four penetration levels using the year 2000 wind data. Table
17 summarizes the monthly forecast eITor for each of the wind penetration levels.
Table 1 7.MWh (over-) or under- forecast for year 2000; HL-heavy load, LL;-light load300 MW 600 MW 900 MW 1200 MWMonth HL LL HL LL HL LL HL
999 410 050 588 (1.326)(464)(3,333)(1,302)
(5,875)555)(6,351)(3,977)(7.7 47)(4.722)(8,944)(5,074)
(2.401)(1.7 40)(3,906)(3.337)(9,090)(7,314)(14,942)(11,230)
055)(3,085)(14.340)(6.463)(24,096)(11.760)(33,827)(17,338)
(5,299)132)(1,385)1.425 (3,567)068 (7,242)041
(8,227)(1,964)(7,874)(2,900)(12 391)(5,602)(17,833)(8.067)
(16,823)(4,555)(28,012)(7,900)(41.400)(11,990)(53.487)(14 877)
(7,142)(3,326)(11.199)(5,340)(18.972)(9,003)(26,358)(12,248)
(12,116)(2,957)(22,285)(6,177)(35,388)(11,829)(49,388)(17,613)
(12,592)(4,878)(12 003)(5,096)(14 369)(6.467)(18,197)(8,374)
(4,613)(2,798)778 (351)270)587)(6,379)(3.177)
(10.440)(4.481)(5.297)(1.548)(3,918)(1.282)(1,935)(1.664)
Total (90,583)(34,061)(110,823)(41,075)174,533)(69,951)(241,867)(98,922)
As the above data shows, the forecast is biased to over forecast the available wind
generation. Although it is impossible to know exactly how this forecast would have
impacted the day-ahead market decisions , it is fair to assume that additional day-aheadsales would have taken place. These additional day-ahead sales would have then had to
be covered in the real-time market as the wind generation did not come in as forecast.
This additional exposure to real-time market purchases and sales exposes Idaho Powerto risk that potentially results in additional costs. One way to quantify this cost is to
determine the spread between the day-ahead and real-time prices and apply that
spread to a hypothesized day-ahead decision. That decision is reconciled in real-time to
balance the forecast eITor. An alternative assessment of this situation is to view the
Pacific Northwest electricity market as efficient with a sufficient number of participants
Page 79
liquidity, and storage capability that over a period of time the net effect of the day aheaderror would be zero.
Assuming the additional day-ahead exposure to market purchases and sales does
increase costs a method of quantifying the costs could utilize Idaho Power s day-aheadand real-time buy and sell transactions data for 2005. The monthly average of IdahoPowers purchase and sales prices for heavy and light load firm products aresummarized in the Table 18
Table 18. Monthly average of Idaho Powers' purchase and sales prices for heavy and light loadfirm products (day-ahead = DA, real-time = RT)HL LL HL
Month DA Buy RT buy DA Buy RT buy DA Sell RT sell DA Sell RT sellJon $51.86 $47.19 $41.47 $41.34 $ 56.05 $48.95 $ 42.06 $ 43.Feb $47.75 $47.60 $41.19 $44.28 $ 50.85 $52.21 $ 43.42 $ 43.Mar $ 51 .09 $ 48.98 $ 46.96 $ 45.55 $ 52.78 $ 46.42 $ 46.08 $ 40.
April $ 52.40 $ 54.02 $ 43.92 $ 48.46 $ 53.69 $ 49.03 $ 48.04 41.80May $ 30.75 $ 25.67 $ 30.00 $ 41.54 $ 40.18 $ 25.30 $ 25.84 30.42Jun $ 40.00 $ 33.80 $ 37.93 $ 31.91 $ 42.24 $ 34.42 $ 25.14 $ 30.July $ 57.21 $ 36.46 $ 35.25 $ 58.48 $ 61.56 $ 36.42 $ 34.79 42.76Aug $67.27 $61.67 $61.02 $63.03 $ 78.48 $52.68 $ 51.14 $ 46.Sep $79.75 $ 70.15 $ 66.89 $ 73.98 $ 94.57 $ 57.38 $ 71.20 $ 54.Oct $ 85.42 $ 83.06 $ 73.76 $ 90.00 $ 93.84 $ 82.54 $ 72.58 77 .Nov $72.49 $ 83.65 $ 62.75 $ 65.89 $ 72.53 $ 89.50 $ 61.07 61.42Dee $100.12 $121.65 $83.46 $94.84 $77.60 $83.69 $65.68 49.76
Firm product transactions did not occur in all months. In these months non-firm
product transaction prices were used.
The difference between the day ahead and real-time price for a 1 MWh transaction is
summarized below in Table 19 (parenthesis indicate a loss transaction).
Table 19.
DA Buy RT Sell HL DA Buy RT Sell LL DA Sell RT Buy HL DA Sell RT Buy LL
Difference between the day ahead and real-time for 1 MWh transaction
Jon (2.91)
Feb 4.46 (0.86)
Mar (4.67)(6.84)
April (3.37)(2.12)(0.33)(0.42)
May (5.45)0.42 14.(15.70)
Jun (5.58)(7.69)8.44 (6.77)
July (20.79)25.(23.69)
Aug (14.59)(14.71)16.(11.89)
Sep (22.37)(12.69)24.42 (2.78)
Oct (2.88)10.(17.42)
Nov 17.(1.33)(11.12)(4.82)
Dee (16.43)(33.70)(44.05)(29.16)
Page
The results of applying the previous transaction to the total forecast error for the 300
MW penetration level are shown in Table 20:
Table 20.
level
Applying the previous transaction to the total forecast error for the 300 MW penetration
DA Buy RT DA Buy RT DA Sell RT DA Sell RTMonth Sell HL Sell LL Buy HL Buy LL Total Market
(Ioss)goin
Jon 815.893.($4,922)
Feb 19,094 839 $20,933
Mar 122 1.496 $7,626
April 328 635 ($693)
May 76,892 896 $75,996
Jun 69.436 30,839 $38,597
July 422,262 30,837 $391.425
Aug 120 059 78.797 $41,263
Sep 295,867 35,157 $260.710
Oet 135.7 41 13,561 $122,180
Nay 51.291 48.7 43 ($100,034)
Dee 459,868 596 ($481.465)
($5,816)894 634 986 258.448 $371 616
Wind MWh 702 507
Value of Forecast Trade
error $0.
At the 300 MW wind penetration level with the 2005 trade data results in the wind day-
ahead forecast error producing a net trading profit of $371 616 or $0.53 for each MWhof wind produced. Although a savings occurred in this example , generally forecasterrors would be expected to increase costs. The wind forecast simulation which over
forecasts consistently biases the usefulness of the data in quantifying the forecast error
using the methodology described above.
Page 8 1
Page 82
ApPENDIX E
VISTA Bus CONFIGURATION
Ire.
BOARDMAN
wsr.
S\LES (!) :
PU~H (f)
IDI I ,MW : mUM : d, I : !I
- - --~-------
I:j;lr- m
HCP~! .
! IIUIIJNIfED
! I iOBP H ,t.III . II
BLP ~I w.JC8 j
: II
: U
Bus
Configuration
Page 83
ESI8 ~MTPf'&LPU~I-I\SE
~~"
...IIIW ~VALIIYi- PU~H (f)600- u-sms
UPS8
UPPHI SNAKE
EES
- - - - ----------- -
1 r -- - - - - - --
----------------
IIWIfIBIIIIW
C0G8
CO GE N
UIU,\IfEDI89
X MWWINDTII!ESERIESx=:m OOJoo~a1200
'*.
iif/r"
. "
c;.
Page
ApPENDIX F
WHOLESALE ELECTRICITY MARKET PRICE DATA
Mid-Columbia prices (inflation
adjusted to 2005 level)
LIGHT LOAD
,-, -- --.'"
$:19
$13.71)
$18.
$22;42
$1 0.03
~~.#~~
$30..
$30.
, $26.22
$28'27
$29:16
$18
$38.67
$59,
$74.
tl09.23
$1(JQ.
$~L1.o
~64.
. $505 iM
$4111
$43.
$46.
$46.01
$25.
$26:38
$38:58
$54.22
~7,35
$79.
$62.
$92.
Palo Verde prices (inflation adjusted
to 2005 level)
Page 85
LIGHT LOAD
".." ,." ,
$17,65
$14,1)8
. $15:74
$1r06
$a.S!!
:$8.
$21,24 ,
$27.76 .
, '
$2a:6S
. $18~58
, ~1f.
, $19.
.~,
O()
$21'.:36
$26.
~:~5:
$:J9:38
$52,46
/$1'30:.04
, "
$68:72.~7,
. $42.
~'~
$1.0.0.
~6~.o
. $37,25,
$37.68
$39.
.$29.
$31).3.0
$4.0,
$52:88 '
$63,
$7.o.
$54.
$78.
BEFORE THE IDAHO PUBLIC UTiliTIES COMMISSION
CASE NO. IPC-O7-
IDAHO POWER COMPANY
ATTACHMENT 2
IDAHO POWER COMPANY
AVOIDED COST RATES FOR NON-FUELED PROJECTS
SMALLER THAN TEN MEGAWATTS
December 1, 2004
Mills/Kwh
IPUC Order NO. 29646
;:':;;
~J~6~!i$~eij~~~;ji
;;!
:B;;
?j:
Contract Length ON-LINE YEAR Contract Non-Levelized(Years)2004 2005 2006 2007 2008 2009 Year Rates
49.50.51.52.53.55.2004 49.
49.50.52.53.54.55.2005 50.
50.51.52.53.55.56.2006 51.
50.52.53.54.42 55.56.2007 52.
51.52.53.54.56.57.2008 53.
51.53.54.55.56.58.2009 55.
52.53.54.56.57.58.2010 56.41
52.54.55.56.57.59.2011 57.
53.54.55.57.58.47 59.2012 59.
53.55.56.57.59.60.2013 60.40
54.55.56.58.59.60.2014 61.
54.56.57.58.60.61.2015 63.
55.56.57.59.60.61.2016 64.
55.57.58.59.61.62.2017 66.
56.57.48 58.60.61.62.2018 67.
56.57.59.60.62.63.2019 69.
57.58.59.61.62.63.2020 70.
57.58.60.61.62.64.40 2021 72.
57.59.60.61.63.40 64.2022 74.
58.59.60.62.40 63.65.2023 75.
2024 77.
2025 79.41
2026 81.
2027 83.
2028 85.
2029 87.
Wind InteQration Cost Adiustment 10.72 Mills/Kwh
IPUC Docket - IPC-07-
Idaho Power Company
Attachment #2
IDAHO POWER COMPANY
AVOIDED COST RATES FOR NON-FUELED PROJECTS
TEN AVERAGE MEGAWATTS OR SMALLER
Seasonal Factors
Season 1
Season 2
Season 3
73.50% (Applied to March , April and May)
120.00% (Applied to July, August, November and December)
100.00% (Applied to June, September, October January and February)
Non-Levelized Rate Structure
Year
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Season 1 pricing Season 2 pricing Season 3 pricing
Including Wind Including Wind Including Wind
Integration Cost Integration Cost Integration Cost
Non Levelized Adjustment of:Adjustment of:Adjustment of:
Price 10.72 Mills! Kwh 10.72 Mills! Kwh 10.72 Mills! Kwh
52.38.28.63.52.52.41.
53.39.28.64.53.53.43.
55.40.29.66.55.45 55.44.42
56.41 41.46 30.67.56.56.41 45.
57.42.42 31.69.58.57.46.
59.43.32.70.60.59.48.
60.40 44.40 33.72.61.60.49.
61.45.34.74.63.44 61.51.
63.46.35.75.65.63.52.
64.47.36.77.66.64.53.
66.48.37.79.40 68.66.55.
67.49.39.81.70.67.56.
69.50.40.83.72.69.58.
70.52.41.85.74.70.60.
72.49 53.42.86.76.72.49 61.
74.54.43.88.78.74.63.44
75.55.45.91.80.75.65.
77.57.46.93.82.43 77.66.
79.41 58.47.95.84.79.68.
81.59.49.97.86.81.70.
83.61.50.99.89.83.72.
85.62.51.102.91.85.74.
87.63.53.04.40 93.87.76.
IPUC Docket - IPC-07-
Idaho Power Company
Attachment #2
IDAHO POWER COMPANY
AVOIDED COST RATES FOR NON-FUELED PROJECTS
TEN AVERAGE MEGAWATTS OR SMALLER
Seasonal Factors
Season 1
Season 2
Season 3
73.50% (Applied to March , April and May)
120.00% (Applied to July, August, November and December)
100.00% (Applied to June , September, October January and February)
Levelized Rate Structure
Project Online in 2007
Contract
Length
(Years)
Levelized
Price
52.
53.
53.
54.
54.
55.
56.
56.
57.
57.
58.
58.
59.
59.
60.
60.
61.
61.
61.
62.40
Season 1 pricing
Including Wind
Integration Cost
Adjustment of:
10.72 Mills! Kwh
38.28.
39.28.43
39.28.
40.29.
40.41 29.
40.30.
41.30.
41.30.
42.31.
42.31.
42.32.
43.32.42
43.32.
43.33.
44.33.
44.56 33.
44.34.
45.34.
45.34.
45.35.
Season 2 pricing
Including Wind
Integration Cost
Adjustment of:
10.72 Mills! Kwh
63.52.
63.53.
64.53.
65.54.
65.55.
66.55.
67.56.
67.57.
68.57.
69.58.
69.59.
70.59.
71.60.
71.60.
72.61.
72.62.
73.62.
73.63.
74.63.
74.64.
52.
53.
53.
54.
54.
55.
56.
56.
57.
57.
58.
58.
59.
59.
60.
60.
61.
61.
61.
62.40
Season 3 pricing
Including Wind
Integration Cost
Adjustment of:
10.72 Mills! Kwh
41.
42.
43.
43.
44.
44.
45.
45.
46.44
46.
47.
47.
48.47
48.
49.44
49.
50.
50.
51.
51.
IPUC Docket - IPC-07-
Idaho Power Company
Attachment #2
IDAHO POWER COMPANY
AVOIDED COST RATES FOR NON-FUELED PROJECTS
TEN AVERAGE MEGAWATTS OR SMALLER
Seasonal Factors
Season 1
Season 2
Season 3
73.50% (Applied to March, April and May)
120.00% (Applied to July, August, November and December)
100.00% (Applied to June, September, October January and February)
Levelized Rate Structure
Project Online in 2008
Season 1 pricing Season 2 pricing Season 3 pricing
Including Wind Including Wind Including Wind
Contract Integration Cost Integration Cost Integration Cost
Length Levelized Adjustment of:Adjustment of:Adjustment of:
(Years)Price 10.72 Mills! Kwh 10.72 Mills! Kwh 10.72 Mills! Kwh
53.39.28.64.53.53.43.
54.40.29,65.40 54.54.43.
55.40.29.66.55.55.44.
55.40.30.66.56.55.44.
56.41.30.67.56.56.45.
56.41.31.68.57.46 56.46.
57.42.31.45 68.58.57.46.
57.42.31.69.58.57.47.
58.47 42.32.70.59.45 58.47.
59.43.32.70.60.59.48.
59.43.33.71.60.59.48.
60.44.33.42 72.61.60.49.
60.44.33.72.61.60.49.
61.44.34.73.62.61.50.
61.45.34.73.63.61.50.
62.45.34.74.43 63.62.51.
62.45.35.74.64.62.51.
62.46.35.75.64.62.52.
63.40 46.35.76.65.63.40 52.
63.46.36.76.65.63.53.
IPUC Docket - IPC-07-
Idaho Power Company
Attachment #2
IDAHO POWER COMPANY
AVOIDED COST RATES FOR NON-FUELED PROJECTS
TEN AVERAGE MEGAWATTS OR SMALLER
Seasonal Factors
Season 1
Season 2
Season 3
73.50% (Applied to March, April and May)
120.00% (Applied to July, August, November and December)
100.00% (Applied to June, September, October January and February)
Levelized Rate Structure
Project Online in 2009
Contract
Length
(Years)
Levelized
Price
55.
55.
56.
56.
57.
58.
58.
59.
59.
60.
60.
61.
61.
62.
62.
63.45
63.
64.40
64.
65.
Season 1 pricing
Including Wind
Integration Cost
Adjustment of:
10.72 Mills! Kwh
40.29.
40.30.
41.42 30.
41.31.
42.31.
42.32.
43.32.42
43.32.
43.33.
44.33.
44.34.
45.34.44
45.34.
45.35.
46.35.
46.35.
46.36.
47.36.
47.36.
48.37.
Season 2 pricing
Including Wind
Integration Cost
Adjustment of:
10.72 Mills! Kwh
66.55.45
66.56.
67.56.
68.57.
69.58.
69.59.
70.44 59.
71.60.
71.61.
72.44 61.
73.62.
73.63.
74.63.
74.64.
75.64.
76.65.
76.66.
77.66.
77.67.
78.67.
55.
55.
56.
56.
57.
58.
58.
59.
59.
60.
60.
61.44
61.
62.46
62.
63.
63.
64.40
64.
65.
Season 3 pricing
Including Wind
Integration Cost
Adjustment of:
10.72 Mills! Kwh
44.42
45.
45.
46.
46.
47.41
47.
48.
49.
49.
50.
50.
51.
51.
52.
52.
53.
53.
54.
54.
IPUG Docket - IPG-07-
Idaho Power Company
Attachment #2