20160120Referenced Compliance Filing.pdf

ROCKY MOUNTAIN
PO'IIER
A TXVISION OF MOFrcORP
RECIll,/i: il
2016 JAH 20 fil{ 11,25
ItlAl-lC i-'i- '.ii-'L
UT ILITIES CCIMI'4 ISSIOi{
1407 W. North Temple, Suite 310
Salt Lake City, Utah 84116
January 20,2016
VA OVERNIGHT DELIVERY
Idaho Public Utilities Commission
472West Washington
Boise,ID 83702
Attn: Jean Jewell
Commission Secretary
Re: PacifiCorp Case No. PAC-E-07-07 Compliance Filing
Dear Ms. Jewell:
Rocky Mountain Power, a division of PacifiCorp ("the Company"), in compliance with Order
No. 30497 in Case No. PAC-E-07-07 hereby submits a copy of the 2014 Wind Integration Study
("WIS") prepared as part of the Company's 2015 Integrated Resource Plan ("[U"';.
In the stipulation approved by Order No. 30497, the parties agreed that "Rocky Mountain Power
shall hereafter file notice with the Commission of any changes to its wind integration charge as
reflected in subsequent changes to its IRP." On March 3l,2Ol5 PacifiCorp filed its 2015 IRPI
with the Commission, and on October 9,2015 the Commission acknowledged2 the Company's
IRP. Volume II, Appendix H of the 2015 IRP contains the2014 Wind lntegration Study.
The 2014 Wind Integration Study calculates a wind integration rate of $3.06 per MWh. Table
H.3 - Wind Integration Costs from Volume II, Appendix H of the 2015 IRP is provided in
support of these wind integration costs.
Table IL3- Wind Integration Cost, $/MWh
The stipulation approved in Order No. 30497 instituted a wind integration adjustnent to
published avoided cost rates for wind qualifying facilities ("QFs"). Wind integration costs were
further updated in Case No. PAC-E-09-07. The Company proposes that the wind integration
I Case No. PAC-E-15-04.
'Order No. 33396.
Idaho Public Utilities Commission
January 20,2016
Page2
adjusfinent to published avoided cost rates for wind QFs be updated to reflect the costs in the
2014 Wind Integration Study.
Please direct any informal inquiries to Ted Weston, Idaho Regulatory Affairs Manager, at (801)
220-2963.
Sincerely,
\n44^y f *,rrtau,ul ctu)
Jeffrey K. Larsen
Vice President, Regulation
PACIFICoRP_2015 IRP APPENDX H_ WTND INTEGRATIoN
ApppNDIx H _ WINp INTpCRATION Sruoy
This wind integration study (WIS) estimates the operating reserves required to both maintain
PacifiCorp's system reliability and comply with North American Electric Reliability Corporation
(NERC) reliability standards. The Company must provide sufficient operating reserves to meet
NERC's balancing authority area control error limit (BAL-001-2) at all times, incremental to
contingency reserves, which the Company maintains to comply with NERC standard BAL-002-
WECC-2.22'23 Apart from disturbance events that are addressed through contingency reserves,
these incremental operating reserves are necessary to maintain area control errol* (ACE), due to
sources outside direct operator control including intra-hour changes in load demand and wind
generation, within required parameters. The WIS estimates the operating reserve volume
required to manage load and wind generation variation in PacifiCorp's Balancing Authority
Areas (BAAs) and estimates the incremental cost of these operating reserves.
The operating reserves contemplated within this WIS represent regulating margin, which is
comprised of ramp reserve, extracted directly from operational data, and regulation reserve,
which is estimated based on operational data. The WIS calculates regulating margin demand
over two common operational timeframes: l0-minute intervals, called regulating; and one-hour-
intervals, called following. The regulating margin requirements are calculated from operational
data recorded during PacifiCorp's operations from January 2012 through December 2013 (Study
Term). The regulating margin requirements for load variation, and separately for load variation
combined with wind variation, are then applied in the Planning and Risk (PaR) production cost
model to determine the cost of the additional reserve requirements. These costs are attributed to
the integration of wind generation resources in the 2015 Integrated Resource Plan (tRP).
Estimated regulating margin reserve volumes in this study were calculated using the same
methodology applied in the Company's 2012 WtSz5, with data updated for the current Study
Term. The regulating margin reserve volumes in this study account for estimated benefits from
PacifiCorp's participation in the energy imbalance market (EIM) with the California Independent
System Operator (CAISO). The Company expects that with its participation in the EIM future
wind integration study updates will benefit as PacifiCorp gains access to additional and more
specifi c operating data.
22 NERC Standard BAL-001-2: http://www.nerc.com/files/BAl-001-2.pdf
23 NERC Standard BAL-002-WECC-2 (http://www.nerc.com/files/BAl-002-WECC-2.ed0, which became effective
October 1,2074, replaced NERC Standard BAL-STD-0O2. which was in effect at the time of this study.
24 "AteaControl Error" is defined in the NERC glossary here: htto://www.nerc.com/oalstand/glossarv of
terms/slossary_olterms.pdf
2s 2Ol2 WIS report is provided as Appendix H in Volume II of the Company's 2013 IRP report:
htto://www.pacificorp.com/content/dam/nacificorp/doc/Enere), Sources/Intesrated_Resource Plar/20l3IRP/Pacifi
Corp-20l3IRP Vol2-AppendicesJ-30- l3.pdf
97
PecnrCoru-2015 IRP APPENDX H- WTND INTEGRATIoN
Technical Review Committee
As was done for its 2012 WIS, the Company engaged a Technical Review Committee (TRC) to
review the study results from the 2014 WIS. The Company thanks each of the TRC members,
identified below, for their participation and professional feedback. The members of the TRC are:
Andrea Coon - Director, Western Renewable Energy Generation Information System
(WREGIS) for the Western Electricity Coordinating Council (WECC)
Matt Hunsaker - Manager, Renewable Integration for the Western Electricity
Coordinating Council (WECC)
Michael Milligan - Lead research for the Transmission and Grid Integration Team at the
National Renewable Energy Laboratory NREL)
J. Charles Smith - Executive Director, Utility Variable-Generation Integration Group
(uvrG)
Robert Zavadil - Executive Vice President of Power Systems Consulting, EnerNex
In its technical review of the Comp-any's 2012 WIS, the TRC made recommendations for
consideration in future WIS updates.'o The following table summarizes TRC recommendations
from the 2012 WIS and how these recommendations were addressed in the 2014 WIS.
26 TRC's full report is provided at:
http://www.nacificorp.com/content/dan/pacificom/doclEnersy Sources/lnteerated-Resource Plar/Wind-Integratio
n/20 I 2WlS/Pacificom-2O I 2WIS-TRC-Technical-Memo-5- I 0- l3.pdf
Table H.l - 2012 WIS TRC Recommendations
The Company modeled reseryes on an hourly basis in
PaR. A sensitivity was performed to model reseryes on
basis as in the 2012 WIS.
Reserve requirements should be modeled on an hourly
basis in the production cost model, rather than on a
In discussing this recommendation with the TRC, it was
clarified that the intent was a request to better explain
how the exceedance level ties to operations. PacifiCorp
has included discussion in this 2014 WIS on its selection
of a99.7%o exceedance level when calculating regulation
reserve needs, and further clarifies that the WIS results
informs the amount of regulation reseryes planned for
Either the 99.1o/o exceedance level should be studied
parametrically in future work, or a better method to link
the exceedance level, which drives the reserve
requirements in the WIS, to actual reliability
requirements should be developed.
Future work should treat the categories "regulating,"
"following," and "ramping" differently by using the
capabilities already in PaR and comparing these results
to those usins of the
A sensitivity study was performed demonstrating the
impact of separating the reserves into different
categories.
PacifiCorp appreciates the TRC comment; however,
PacifiCorp continued to rely on spreadsheet-based
calculations when calculating regulation reserves for its
2014 WIS. This allows stakeholders, who may not have
access to specific statistics packages, to review work
ins PacifiCorD's 2014 WIS.
Given the vast amount of data used, a simpler and more
transparent analysis could be performed using a flexible
statistics package rather than spreadsheets.
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PACIFICoRP _ 2OI5 IRP AppBrrrorx H - WIND INTEGRATIoN
Because changes in forecasted natural gas and electricity
prices were a major reason behind the large change in
integration costs from the 2010 WIS, sensitivity studies
around nafural gas and power prices, and around carbon
tax assumptions, would be interesting and provide some
useful results.
Changes in wind integration costs continue to align with
movements in forward market prices for both natural gas
and electricity. PacifiCorp describes how market prices
have changed in relation to wind integration costs as
updated in the 2014 WIS. With the U.S. Environmental
Protection Agency's draft rule under gl I l(d) of the
Clean Air Act, CO2 tax assumptions are no longer
s official forward price curves.
Although the study of separate east and west BAAs is
useful, the WIS should be expanded to consider the
benefits of PacifiCorp's system as a whole, as some
reseryes are transferrable between the BAAs. It would
be reasonable to conclude that EIM would decrease
reserve requirements and intesration costs.
PacifiCorp has incorporated estimated regulation reserye
benefits associated with its participation in EIM in the
2014 WIS. With its involvement in EIM, future wind
studies will benefit as PacifiCorp gains access to better
operating data.
Executive Summary
The 2014 WIS estimates the regulating margin requirement from historical load and wind
generation production data using the same methodology that was developed in the 2012 WIS.
The regulating margin is required to manage variations to area control error due to load and wind
variations within PacifiCorp's BAAs. The WIS estimates the regulating margin requirement
based on load combined with wind variation and separately estimates the regulating margin
requirement based solely on load variation. The difference between these two calculations, with
and without the estimated regulating margin required to manage wind variability and uncertainty,
provides the amount of incremental regulating margin required to maintain system reliability due
to the presence of wind generation in PacifiCorp's BAAs. The resulting regulating margin
requirement was evaluated deterministically in the PaR model, a production cost model used in
the Company's Integrated Resource Plan (IRP) to simulate dispatch of PacifiCorp's system. The
incremental cost of the regulating margin required to manage wind resource variability and
uncertainty is reported on a dollar per megawatt-hour ($/lrrtWtr; of wind generation basis.27
When compared to the result in the 2012 WIS, which relied upon 201I data, the 201,4 WIS uses
2013 data and shows that total regulating margin increased by approximately 27 megawatts
(MW) in 2012 and 47 MW in 2013. These increases in the total reserve requirement reflect
different levels of volatility in actual load and wind generation. This volatility in turn impacts the
operational forecasts and the deviations between the actual and operational forecast reserve
requirements, which ultimately drives the amount of regulating margin needed. Table H.2
depicts the combined PacifiCorp BAA annual average regulating margin calculated in the 2014
WIS, and separates the regulating margin due to load from the regulating margin due to wind.
The total regulating margin increased from 579 MW in the 2012 WIS to 626 MW in the 2014
WIS.
2'The PaR model can be run with stochastic variables in Monte Carlo simulation mode or in deterministic mode
whereby variables such as natural gas and power prices do not reflect random draws from probability distributions.
For purposes of the WIS, the intention is not to evaluate stochastic portfolio risk, but to estimate production cost
impacts of incremental operating reserves required to manage wind generation on the system based on current
projections of future market prices for power and natural gas.
99
PACIFICoRP_2OI5IRP AppeNpx H _ WTND INTEGRATIoN
Table H.2 - Average Annual Regulating Margin Reserves, 2011- 2013 (MW)
20tt
(2012 WrS)
Load-Only Regulating Margin 147 247 394
Incremental Wind Regulating Margin 54 131 185
Total Rezulatins Margm 202 378 579
Wind Capaciw 589 1,536 2,126
2012
Load-Onlv Regulating Margin 141 259 400
lncremental Wind Reeulating Marsin 77 129 206
Total Resulatine Maxein 217 388 606
Wind Capacity 785 1,759 2,543
2013
(2014 wrs)
Load-Onlv Reeulatine Marein 166 275 441
Incremental Wind Regulating Margin 55 130 186
Total Resulatins Marsin 222 40s 626
Wind CanaciW 785 1,759 2,543
Table H.3 lists the cost to integrate wind generation in PacifiCorp's BAAs. The cost to integrate
wind includes the cost of the incremental regulating margin reseryes to manage intra-hour
variances (as outlined above) and the cost associated with day-ahead forecastvariances, the latter
of which affects how dispatchable resources are committed to operate, and subsequently, affect
daily system balancing. Each of these component costs were calculated using the PaR model. A
series of PaR simulations were completed to isolate each wind integration cost component by
using a "with and withouf' approach. For instance, PaR was first used to calculate system costs
solely with the regulating margin requirement due to load variations, and then again with the
increased regulating margin requirements due to load combined with wind generation. The
change in system costs between the two PaR simulations results in the wind integation cost.
Table H.3 - Wind Integration Cost, $/lVIWh
The 2014 WIS results are applied in the 2015 IRP portfolio development process as part of the
costs of wind generation resources. In the portfolio development process using the System
Optimizer (SO) model, the wind integration cost on a dollar per megawatt-hour basis is included
as a cost to the variable operation and maintenance cost of each wind resource. Once candidate
resource portfolios are developed using the SO model, the PaR model is used to evaluate the risk
profiles of the portfolios in meeting load obligations, including incremental operating reserve
needs. Therefore, when performing IRP risk analysis using PaR, specific operating reserve
requirements consistent with this wind study are used.
100
PlcnrConp-2015IRP APPENDX H - WTND INTEGRATION
The calculation of regulating margin reserve requirement was based on actual historical load and
wind production data over the Study Term from January 2012 through December 2013. Table
H.4 outlines the load and wind generation lO-minute interval data used during the Study Term.
Table H.4 - Historical Wind Production and Load Data Inventory
Chewon Wind 16.5 t/U2012 t2t3t/2013 East
Combine Hills 41.0 uy20t2 t2l3t/2013 West
Dunlap I Wind 111.0 t/u2012 t2t3v20t3 East
Five Pine and North Point 119.7 12/U2012 t2/3U2013 East
Foot Creek Generation 85.1 l1/20 2 l2/3t/2013 East
Glenrock III Wind 39.0 lt/20 2 t2t3y20t3 East
Glenrock Wind 99.0 Ut/20 2 2t3t/2013 East
Goodnoe Hills Wind 94.0 t/t/20 2 2/31/2013 West
Hieh Plains Wind 99.0 Ut/20 2 2131/2013 East
Leanins Juniper I 100.5 yU20 )2/3y2013 West
Marenso I 140.4 Uu20 2 2t3,/2013 West
Marenso II 70.2 t/t/20 2 2t3y2013 West
McFadden Ridse Wind 28.5 lt/20 2 213,/2013 East
Mountain Wind I OF 60.9 t/t/20 2 2/3U2013 East
Mountain Wind 2 OF 79.8 Ut/20 2 2t3t/2013 East
Power Countv North and Power County South 45.0 r/y20 2 2/3U2013 East
Oreeon Wind Farm OF 64.6 yt/20 2 2t3U2013 West
Rock River I 49.0 t/t/20 2 2/3t/2013 East
Rollins Hills Wind 99.0 lt/20 2 2l3t/2013 East
Seven Mile Wind 99.0 t/t/20 )2t3U2013 East
Seven Mile II Wind 19.5 Ut/20 ,)2t3t/2013 East
Soanish Fork Wind 2 OF 18.9 t/U20 2 213u2013 East
Stateline Contacted Generation 175.0 lt/20 2 2/3U2013 West
Three Buttes Wind 99.0 t/U20 2 2t3U2013 East
Top of the World Wind 200.2 Uu20 2 t2/3U2013 East
Wolverine Creek 64.5 t/t/20 2 t2t3U20t3 East
Lone Hollow Wind UU20 2 t213U2013 East
Campbell Wind Ut/20 2 tzl3U20t3 West
Horse Butte 6lt9l20L2 t2t3U20t3 East
Jollv Hills I UU2012 t2/31/2013 East
Jollv Hills 2 Uu20t2 t2/31/2013 East
PACW Load nla t/t/2012 t2t3t/2013 West
PACE Load n/a Ut/2012 tzl3U20t3 East
101
PACIFICoRP-20I5 IRP APPENDIX H _ WIND INTEGRATIoN
Historical Load Data
Historical load data for the PacifiCorp east (PACE) and PacifiCorp west (PACW) BAAs were
collected for the Study Term from the PacifiCorp PI system.28 The raw load data were reviewed
for anomalies prior to further use. Data anomalies can include:
o Incorrect or reversal of sign (recorded data switching from positive to negative);o Significant and unexplainable changes in load from one lO-minute interval to the next;o Excessive load values.
After reviewing 210,528 lO-minute load data points in the 2014 WIS, 1,011 l0-minute data
points, roughly 0.5% of the data, were identified as irregular. Since reserve demand is created by
unexpected changes from one time interval to the next, the corrections made to those data points
were intended to mitigate the impacts of irregular data on the calculation of the reserve
requirements and costs in this study.
Of the l,0ll load data points requiring adjustment, 984 exhibited unduly long periods of
unchanged or "stuck" values. The data points were compared to the values from the Company's
official hourly data. If the six l0-minute PI values over a given hour averaged to a different value
than the official hourly record, they were replaced with six l0-minute instances of the hourly
value. For example, if PACW's measured load was 3,000 MW for three days, while the
Company's official hourly record showed different hourly values for the same period, the six l0-
minute "stuck" data points for an hour were replaced with six instances of the value from the
official record for the hour. Though the granularity of the lO-minute readings was lost, the hour-
to-hour load variability over the three days in this example would be captured by this method. In
total, the load data requiring replacement for stuck values represented only 0.47Yo of the load
data used in the current study.
The remaining2T of data points requiring adjustment were due to questionable load values, three
of which were significantly higher than the load values in the adjacent time intervals, and 24 of
which were significantly lower. While not necessarily higher or lower by an egregious amount in
each instance, these specific irregular data collectively averaged a difference of several hundred
megawatts from their replacement values. Table H.5 depicts a sample of the values that varied
significantly, as compared to the data points immediately prior to and after those lO-minute
intervals. The replacement values, calculated by interpolating the prior value and the successive
lO-minute period to form a straight line, are also shown in the table.
" The PI system collects load and generation data and is supplied to PacifiCorp by OSISoft. The Company Web site
is htto ://www.osisoft .com/software-support/what-is-pi/what is PI .aspx.
102
PACIFICoRP_2015 IRP APPENDIX H _ WIND INTEGRATIoN
Table H.5 - Examples of Load Data Anomalies and their Interpolated Solutions
ll5l20l212:20 5.80s 5.805 nla
ll5l20l212z30 5.211 5.793 12:20 + l/5 of (13:10 minus 12:20)
ll5l20l2l2z40 5.074 5.781 12:20 + 215 of (13:10 minus 12:20)
1151201212250 5.063 5.769 12:20 + 3/5 of (13:10 minus l2:20)
ll5l20l213:00 5.465 5,756 12:20 + 4/5 of (13:10 minus 12:20\
ll5l20l213:10 5.744 5.744 nla
5/6/2013 8:50 5.651 5.651 nla
51612013 9t00 4,583 5.694 Averase of 8:50 and 9:10
51612013 9:10 5.737 5.737 nla
Historical Wind Generation Data
Over the Study Term, l0-minute interval wind generation data were available for the wind
projects as summarizedin Table H.4. The wind output data were collected from the PI system.
In 2011 the installed wind capacity in the PacifiCorp system was 589 MW in the west BAA and
1,536 MW in the east BAA. For 2012 and20l3, these capacities inueased to 785 MW and 1,759
MW in the west and east BAAs, respectively. The increases were the result of 195 MW of
existing wind projects transferring from Bonneville Power Administration (BPA) to PacifiCorp's
west BAA, and222 MW of new third parfy wind projects coming on-line during 2012 in the east
BAA.
Figure H.l shows PacifiCorp owned and contracted wind generation plants located in
PacifiCorp's east and west BAAs. The third-party wind plants located within PacifiCorp's BAAs
which the Company does not purchase generation from or own are not depicted in this figure.
103
PACIFICORP-20I5 IRP APPENDIX H_ WIND INTEGRATION
Figure H.l - Representative Map, PacifiCorp Wind Generating Stations Used in this Study
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The wind data collected from the PI system is grouped into a series of sampling points, or nodes,
which represent generation from one or more wind plants. In consideration of occasional
irregularities in the system collecting the data, the raw wind data was reviewed for
reasonableness considering the following criteria:
. Incorrect or reversal of sign (recorded data switching from positive to negative);o Output greater than expected wind generation capacity being collected at a given node;o Wind generation appearing constant over a period of days or weeks at a given node.
Some of the PI system data exhibited large negative generation output readings in excess of the
amount that could be attributed to station service. These meter readings often reflected positive
generation and a reversed polarity on the meter rather than negative generation. In total, only 38
of 3,822,048 l0-minute PI readings, representing 0.001% of the wind data used in this WIS,
required substituting a positive value for a negative generation value.
Some of the PI system data exhibited large positive generation output readings in excess of plant
capacity. In these instances, the erroneous data were replaced with a linear interpolation between
the value immediately before the start of the excessively large data point and the value
immediately after the end of the excessively large data point. In total, only 49 l0-minute PI
readings, representing0.002% of the wind data used in this WIS, required substituting a linear
interpolation for an excessively large generation value.
Similar to the load data, the PI system wind data also exhibited patterns of unduly long periods
of unchanged or "stuck" values for a given node. To address these anomalies, the l0-minute PI
values were compared to the values from the Company's official hourly data, and if the six l0-
minute PI values over a given hour averaged to a different value than the official hourly record,
caLtT0illta
104
PACIFICoRP_20I5 IRP APPENDIX H _ WTND INTEGRATION
they were replaced with six l0-minute instances of the hourly value. For example, if a node's
measured wind generation output was 50 MW for three weeks, while the official record showed
different hourly values for the same time period, the six lO-minute oostuck" data points for an
hour were replaced with six instances of the value from the official record for the hour. Though
the granularity of the l0-minute readings was lost, the hour-to-hour wind variability over the
three weeks in this example would be captured by this method. In total, the wind generation data
requiring replacement for stuck values represented only 0.2Yo of the wind data used in the WIS.
Method Overview
This section presents the approach used to establish regulating margin reserve requirements and
the method for calculating the associated wind integration costs. l0-minute interval load and
wind data were used to estimate the amount of regulating margin reserves, both up and down, in
order to manage variation in load and wind generation within PacifiCorp's BAAs.
Operating Reserves
NERC regional reliability standard BAL-002-WECC-2 requires each BAA to carry sufficient
operating reserve at all times." Operating reserve consists of contingency reserve and regulating
margin. These reserve requirements necessitate committing generation resources that are
sufficient to meet not only system load but also reserve requirements. Each of these types of
operating reserve is further defined below.
Contingency reserve is capacity that the Company holds in reserve that can be used to respond to
contingency events on the power system, such as an unexpected outage of a generator or a
transmission line. Contingency reserve may not be applied to manage other system fluctuations
such as changes in load or wind generation output. Therefore, this study focuses on the operating
reserve component to manage load and wind generation variations which is incremental to
contingency reserve, which is referred to as regulating margin.
Regulating margin is the additional capacity that the Company holds in reserve to ensure it has
adequate reserve at all times to meet the NERC Control Performance Criteria in BAL-001-2,
which requires a EAA to carry regulating reserves incremental to contingency reserves to
maintain reliability.30 However, these additional regulating reserves are not defined by a simple
formula, but rather are the amount of reserves required by each BAA to meet the control
performance standards. NERC standard BAL-001-2, called the Balancing Authority Area
Control Error Limit (BAAL), allows a greater ACE during periods when the ACE is helping
frequency. However, the Company cannot plan on knowing when the ACE will help or
exaierbate frequency so the Lro is used for the bandwidth in both directions of the ACE. 31'32
Thus the Company determines, based on the unique level of wind and load variation in its
" NERC Standard BAL-002-WECC-2: htto://www.nerc.com/files/BAl-002-WECC-2.ndf
3o NERC Standard BAL-00 I -2:http://www.nerc.com/fi les/BAL-OO I -2.pdf3r The L1s represents a bandwidth of acceptable deviation prescribed by WECC between the net scheduled
interchange and the net actual electrical interchange on the Company's BAAs. Subtracting the L1e credits customers
with the natural buffering effect it entails.
32 The L1e of PacifiCorp's balancing authority areas are 33.4lMW for the West and 47.88 MW for the East. For
more information, please refer to:
htto://www.wecc.bizlcommittees/StandingCommittees/OC/OPS/PWG/Shared%o20Documents/Annual%o20Freouenc
v%o20BiasYo20Setlinssl20l2o/o20CPS2%o20Botndso/o20Reoort:/o20Final.pdf
105
PecmrConp-2015 IRP APPENDIX H - WIND INTEGRATION
system, and the prevailing operating conditions, the unique level of incremental operating
reserve it must carry. This reserve, or regulating margin, must respond to follow load and wind
changes throughout the delivery hour. For this WIS, the Company further segregates regulating
margin into two components: ramp reserve and regulation reserve.
Ramp Reserve: Both load and wind change from minute-to-minute, hour-to-hour,
continuously at all times. This variability requires ready capacity to follow changes in load
and wind continuously, through short deviations, at all times. Treating this variability as
though it is perfectly known (as though the operator would know exactly what the net
balancing area load would be a minute from now, l0-minutes from now, and an hour from
now) and allowing just enough generation flexibility on hand to manage it defines the ramp
reserve requirement of the system.
Regulation Reserve: Changes in load or wind generation which are not considered
contingency events, but require resources be set aside to meet the needs created when load or
wind generation change unexpectedly. The Company has defined two types of regulation
reserve - regulating and following reserves. Regulating reserve are those covering short term
variations (moment to moment using automatic generation control) in system load and wind.
Following reserves cover uncertainty across an hour when forecast changes unexpectedly.
To summarize, regulating margin represents operating reserves the Company holds over and
above the mandated contingency reserve requirement to maintain moment-to-moment system
balance between load and generation. The regulating margin is the sum of two parts: ramp
reserve and regulation reserve. The ramp reserve represents an amount of flexibility required to
follow the change in actual net system load (load minus wind generation output) from hour to
hour. The regulation reserve represents flexibility maintained to manage intra-hour and hourly
forecast errors about the net system load, and consists of four components: load and wind
following and load and wind regulating.
Determination of Amount and Costs of Regulating Margin Requirements
Regulating margin requirements are calculated for each of the Company's BAAs from
production data via a five step process, each described in more detail later in this section. The
five steps include:
1. Calculation of the ramp reserve from the historical data (with and without wind
generation).
2. Creation of hypothetical forecasts of following and regulating needs from historical load
and wind production data.
3. Recording differences, or deviations, between actual wind generation and load values in
each lO-minute interval of the study term and the expected generation and load.
4. Group these deviations into bins that can be analyzed for the reserve requirement per
forecast value of wind and load, respectively, such that a specified percentage (or
tolerance level) of these deviations would be covered by some level of operating reserves.
5. The reserve requirements noted for the various wind and load forecast values are then
applied back to the operational data enabling an average reserve requirement to be
calculated for any chosen time interval within the Study Term.
Once the amount of regulating margin is estimated, the cost of holding the specified reserves on
PacifiCorp's system is estimated using the PaR model. In addition to using PaR for evaluating
106
PACIFICORP_2015 IRP APPENDX H _ WTND INTEGRATION
operating reserve cost, the PaR model is also used to estimate the costs associated with daily
system balancing activities. These system balancing costs result from the unpredictable nature of
load and wind generation on a day-ahead basis and can be characterized as system costs borne
from committing generation resources against a forecast of load and wind generation and then
dispatching generation resources under actual load and wind conditions as they occur in real
time.
Regulating Margin Requirements
Consistent with the methodology developed in the Company's 2012 WIS, and the discussion
above, regulating margin requirements were derived from actual data on a l0-minute interval
basis for both wind generation and load. The ramp reserve represents the minimal amount of
flexible system capacity required to follow net load requirements without any error or deviation
and with perfect foresight for following changes in load and wind generation from hour to hour.
These amounts are as follows:
o If system is ramping down: [(Net Area Load Hour H - Net Area Load Hour (H+l))/2]. If system is ramping up: [(Net Area Load Hour (H+l) - Net Area Load Hour H)/2]
That is, the ramp reserve is half the absolute value of the difference between the net balancing
area load at the top of one hour minus the net balancing load at the top of the prior hour.
The ramp reserve for load and wind is calculated using the net load (load minus wind generation
output) at the top of each hour. The ramp reserve required for wind is the difference between that
for load and that for load and wind.
As ramp reserves represent the system flexibility required to follow the system's requirements
without any uncertainty or error, the regulation reserve is necessary to cover uncertainty ever-
present in power system operations. Very short-term fluctuations in weather, load patterns, wind
generation output and other system conditions cause short term forecasts to change at all times.
Therefore, system operators rely on regulation reserve to allow for the unpredictable changes
between the time the schedule is made for the next hour and the arrival of the next hour, or the
ability to follow net load. Also, these very same sources of instability are present throughout
each hour, requiring flexibility to regulate the generation output to the myriad of ups and downs
of customer demand, fluctuations in wind generation, and other system disturbances. To assess
the regulation reserye requirements for PacifiCorp's BAAs, the Company compared operational
data to hypothetical forecasts as described below.
Hypothetical Operational Forecasts
Regulation reserve consists of two components: (1) regulating, which is developed using the l0-
minute interval data, and (2) following, which is calculated using the same data but estimated on
an hourly basis. Load data and wind generation data were applied to estimate reserve
requirements for each month in the Study Term. The regulating calculation compares observed
l0-minute interval load and wind generation to a l0-minute interval forecast, and following
compares observed hourly averages to an average hourly forecast. Therefore, the regulation
reserve requirements are composed of four component requirements, which, in turn, depend on
differences between actual and expected needs. The four component requirements include: load
following, wind following, load regulating, and wind regulating. The determination of these
107
P.q.cInrCoRP - 201 5 IRP APPENDIX H - WTND INTEGRATION
reserve requirements began with the development of the expected following and regulating needs
(hypothetical forecasts) of the four components, each discussed in turn below.
Hypothetical Load Following Operational Forecast
PacifiCorp maintains system balance by optimizing its operations to an hour-ahead load forecast
every hour with changes in generation and market activity. This planning interval represents
hourly changes in generation that are assessed roughly 20 minutes into each hour to meet a
bottom-of-the-hour (i.e., 30 minutes after the hour) scheduling deadline. Taking into account the
conditions of the present and the expected load and wind generation, PacifiCorp must schedule
generation to meet demand with an expectation of how much higher or lower load may be. These
activities are carried out by the group referred to as the real-time desk.
PacifiCorp's real-time desk updates the load forecast for the upcoming hour 40 minutes prior to
the start of that hour. This forecast is created by comparing the load in the current hour to the
load of a prior similar-load-shaped day. The hour-to-hour change in load from the similar day
and hours (the load difference or oodelta") is applied to the load for the current hour, and the sum
is used as the forecast for the upcoming hour. For example, on a given Sunday, the PacifiCorp
real-time desk operator may forecast hour-to-hour changes in load by referencing the hour-to-
hour changes from the prior Sunday, which would be a similar-load-shaped day. If at I l:20 am,
the hour-to-hour load changebetween ll:00 a.m. and 12:00 p.m. of the prior Sunday was five
percent, the operator will use a five percent change from the current hour to be the upcoming
hour's load following forecast.
For the calculation in this WIS, the hour-ahead load forecast used for calculating load following
was modeled using the approximation described above with a shaping factor calculated using the
day from one week prior, and applying a prior Sunday to shape any NERC holiday schedules.
The differences observed between the actual hourly load and the load following forecasts
comprised the load following deviations.
Figure H.2 shows an illustrative example of a load following deviation in August 2013 using
operational data from PACE. In this illustration, the delta between hours ll:00 a.m. and 12:00
p.m. from the prior week is applied to the actual load at I l:00 a.m. on the "current day" to
produce the hypothetical forecast of the load for the 12:00 p.m. ("upcoming") hour. That is,
using the actual load at I l:00 a.m. (beginning of the purple line), the load forecast for the 12:00
p.m. hour is calculated by following the dashed red line that is parallel to the green line from the
prior week. The forecasted load for the upcoming hour is the point on the blue line at 12:00 p.m.
Since the actual load for the l2:00 p.m. hour (the point on the purple line at l2:00 p.m.) is higher
than the forecast, the deviation (indicated by the black arrow) is calculated as the difference
between the forecasted and the actual load for 12:00 p.m. This deviation is used to calculate the
load following component reserve requirement for 12:00 p.m.
108
PecrnCoRp- 2015 IRP APPENDIX H _ WTND INTEGRATION
Figure H.2 - Illustrative Load Following Forecast and Deviation
7,10O
7,m0
6,9m
6,800
6,700
6,600
6,500
6,400
5,300
q20o
6,100
Upcoming hour I
12:m Pi,l 1r0 PM
-f>Shnil.rOryDella
-For€(est
1r:mAM
-Prior
week (Acruel)+Actu.l
Hypoth etical Wi nd Fo llow ing Operational Forecast
The short term hourly operational wind forecast is based on the concept of persistence - using
the instantaneous sample of the wind generation output at 20 minutes into the current hour as the
forecast for the upcoming hour, and balancing the system to that forecast.
For the calculation in this WIS, the hour-ahead wind generation forecast for the "upcoming"
hour used the 20th minute output from the "current" hour. For example, if the wind generation is
producing 300 MW at 9:20 p.m. in PACE, then it is assumed that 300 MW will be generated
between l0:00 p.m. and 11:00 p.m., that same day. The difference between the hourly average of
the six 10-minute wind generation readings and the wind generation forecast comprised the wind
following deviation for that hour.
Figure H.3 shows an illustrative example of a wind following deviation in July 2013 using
operational data from PACE. In this illustration, the wind generation output at 9:20 p.m. (within
the oocurrent" hour) is the hour-ahead forecast of the wind generation for the 10:00 p.m. hour (the
"upcoming" hour). That is, following persistence scheduling, the wind following need for the
10:00 p.m. hour is calculated by following the dashed red line starting from the actual wind
generation on the purple line at 9:20 p.m. for the entire 10:00 p.m. hour (blue line). Since the
average of the actual wind generation during the 10:00 p.m. hour (dotted green line) is higher
than the wind following forecast, the deviation (indicated by the black arrow) is calculated as the
109
PACIFICoRP _ 20I5 IRP APPENDIX H_ WTND INTEGRATION
difference between the wind following forecast and the actual wind generation for the l0:00 p.m.
hour. This deviation is used to calculate the wind following component reserve requirement for
l0:00 p.m.
Figure H.3 - Illustrative Wind Following Forecast and Deviation
E{I'
7m
6m
YD
lrmI
3{n
2@
1@
o
rO(D PM 1l:m PM
j
-Actu.l
- - Ar,rralcActu.l6enrl.lhn -->P.rblan(c(9.20pm1
-pS6Folltringforcertl
Upcoming houl
Hypothetical Load Regulating Operational Forecast
Separate from the variations in the hourly scheduled loads, the lO-minute load variability and
uncertainty was analyzed by comparing the l0-minute actual load values to a line of intended
schedule, represented by a line interpolated between the actual load at the top of the oocurrent"
hour and the hour-ahead forecasted load (the load following hypothetical forecast) at the bottom
of the "upcoming" hour. The method approximates the real time operations process for each hour
where, at the top of a given hour, the actual load is known, and a forecast for the next hour has
been made.
For the calculation in this WIS, a line joining the two points represented a ramp up or down
expected within the given hour. The actual l0-minute load values were compared to the portion
of this straight line from the "current" hour to produce a series of load regulating deviations at
each l0-minute interval within the "current" hour.
Figure H.4 shows an illustrative example of a load regulating deviation in November 2013 using
operational data in PACW. In this illustration, the line of intended schedule is drawn from the
actual load at 7:00 a.m. to the hour-ahead load forecast at 8:30 a.m. The portion of this line
within the 7:00 a.m. hour becomes the load regulating forecast for that hour. That is, using the
forecasted load for the 8:00 a.m. hour that was calculated for the load following hypothetical
forecast, the line of intended schedule is calculated by following the dashed red line from the
actual load at 7:00 a.m. (beginning of the purple line) to the point in the hour-ahead forecast
ll0
PecmrConp-20l5IRP Appgrvorx H _ WIND INTEGRATION
(green line) at 8:30 a.m. The six l0-minute deviations within the 7:00 a.m. hour (one of which is
indicated by the black arrow) are the differences between the actual l0-minute load readings
(purple line) and the line of intended schedule. These deviations are used to calculate the load
regulating component reserve requirement for the six l0-minute intervals within the 7:00 a.m.
hour.
Figure H.4 - Illustrative Load Regulating Forecast and Deviation
2,An
2,7tn
Tlx)AM 900AM
i
-Actual -Lo.dtollflln8
- +lntrric.ttiEPm.ml
-l,o.d
Rr&Llhg rorcc.il |
Hypoth etical Wind Regulating Operational Forecast
Similarly, the l0-minute wind generation variability and uncertainty was analyzed by comparing
the l0-minute actual wind generation values to a line of intended schedule, represented by a line
interpolated between the actual wind generation at the top of the "current" hour and the hour-
ahead forecasted wind generation (the wind following hypothetical forecast) at the bottom of the
"upcoming" hour.
For the calculation in this WIS, a line joining the two points represented a ramp up or down
expected within the given hour. The actual lO-minute wind generation values were compared to
the portion of this straight line from the "current" hour to produce a series of wind regulating
deviations at each 10-minute interval within the "current" hour.
Figure H.5 shows an illustrative example of a wind regulating deviation in July 2013 using
operational data in PACE. In this illustration, the line of intended schedule is drawn from the
actual wind generation at 2:00 p.m. to the hour-ahead wind forecast at 3:30 p.m. The portion of
this line within the 2:00 p.m. hour becomes the wind regulating forecast for that hour. That is,
using the forecasted wind generation for the 3:00 p.m. hour that was calculated for the wind
following hypothetical forecast, the line of intended schedule is calculated by following the
dashed red line from the actual wind generation at 2:00 p.m. (beginning of the purple line) to the
point in the hour-ahead forecast (green line) at 3:30 p.m. The six l0-minute deviations within the
ill
PACIFICoRP_2015 IRP APPENDIX H - WIND INIEGRATION
2:00 p.m. hour (one of which is indicated by the black arrow) are the differences between the
actual l0-minute wind generation readings (purple line) and the line of intended schedule (red
line). These deviations are used to calculate the wind regulating component reserve requirement
for the six l0-minute intervals within the 2:00 p.m. hour.
Figure H.5 - Illustrative Wind Regulating Forecast and Deviation
Upcoming lG.minute feriod J
+Artual
-
wird roilouing --+ ht"nd.d t"in. (2oo Fn)
-whd
i.Sut.ting Fof.(zst
Analysis of Deviations
The deviations are calculated for each l0-minute interval in the Study Term and for each of the
four components of regulation reserves (load following, wind following, load regulating, wind
regulating). Across any given hourly time interval, the six 1O-minute intervals within each hour
have a common following deviation, but different regulating deviations. For example,
considering load deviations only, if the load forecast for a given hour was 150 MW below the
actual load realized in that hour, then a load following deviation of -150 MW would be recorded
for all six of the l0-minute periods within that hour. However, as the load regulating forecast and
the actual load recorded in each 1O-minute interval vary, the deviations for load regulating vary.
The same holds true for wind following and wind regulating deviations, in that the following
deviation is recorded as equal for the hour, and the regulating deviation varies each lO-minute
interval.
Since the recorded deviations represent the amount of unpredictable variation on the electrical
system, the key question becomes how much regulation reserve to hold in order to cover the
deviations, thereby maintaining system reliability. The deviations are analyzed by separating the
deviations into bins by their characteristic forecasts for each month in the Study Term. The bins
are defined by every 5s percentile of recorded forecasts, creating 20 bins for the deviations in
each month for each component hypothetical operational forecast. In other words, each month of
the Study Term has 20 bins of load following deviations, 20 bins of load regulating deviations,
and the same for wind following and wind regulating.
tt2
PACIFICORP _ 2OI5 IRP APPENDX H - WTND INTEGRATIoN
As an example, Table H.6 depicts the calculation of percentiles (every five percent) among the
load regulating forecasts for June 2013 using PACE operational data. For the month, the load
ranged from 4,521 MW to 8,587 MW. A load regulating forecast for a load at 4,892 MW
represents the fifth percentile of the forecasts for that month. Any forecast below that value will
be in Bin 20, along with the respective deviations recorded for those time intervals. Any forecast
values between 4,892 MW and 5,005 MW will place the deviation for that particular forecast in
Bin 19.
Table H.6 - Percentiles Dividing the June 2013 East Load Regulating Forecasts into 20
Bins
MAX 8,587
I 0.95 7.869
2 0.90 7.475
3 0.8s 7.220
4 0.80 6.984
5 0.75 6.807
6 0.70 6.621
7 0.6s 6.482
I 0.60 6,383
9 0.55 6.285
t0 0.50 6.1 58
11 0.45 6.023
t2 0.40 s,850
13 0.3s 5.720
t4 0.30 5.568
15 0.25 s,404
t6 0.20 5.275
17 0.l s s.134
18 0.10 5,005
t9 0.05 4.892
20 MIN 4,s21
Table H.7 depicts an example of how the data are assigned into bins based on the level of
forecasted load, following the definition of the bins in Table H.6.
ll3
06/0 12013 6:00 4.755 88 20
0610 12013 6:10 4.706 -67 20
0610 /2013 6:20 4.746 -13 20
0610 12013 6:30 4,786 -36 20
0610 /2013 6:40 4.826 -26 20
06/0 12013 6:50 4,866 -46 20
0610 /2013 7:00 4.905 -46 l9
0610 /2013 7:10 4.984 4 l9
0610 12013 7:20 5.016 -8 l8
06/0 12013 7:30 s.048 -10 l8
06/0 /2013 7:40 5,081 t6 l8
0610 /2013 7:50 s.l l3 3l l8
06/0 /2013 8:00 5.r45 t2 t7
0610 12013 8:10 s.1 58 16 t7
06/0 12013 8:20 5.182 -22 t7
0610 12013 8:30 5.207 -6 t7
06/0 12013 8:40 5.23r 4 t7
06/0 12013 8:50 5.256 18 t7
06/0 12013 9:00 5.280 10 16
06/0 12013 9:10 s.278 -30 t6
06/0 12013 9:20 5.287 ll l6
06/0 12013 9:30 5-295 2 t6
0610 12013 9:40 5,303 25 r6
0610 12013 9:50 s.31 I -4 16
PACIFICoRP_2OI5IRP APPENDX H _ WTND INTEGRATION
Table H,7 - Recorded Interval Load Regulating Forecasts and their Respective Deviations
for June 2013 Operational Data from PACE
The binned approach prevents over-assignment of reserves in different system states, owing to
certain characteristics of load and wind generation. For example, when the balancing area load is
near the lowest value for any particular day, it is highly unlikely the load deviation will require
substantial down reserves to maintain balance because load will typically drop only so far.
Similarly, when the load is near the peak of the load values in a month, it is likely to go only a
liule higher, but could drop substantially at any time. Similarly for wind, when wind generation
output is at the peak value for a system, there will not be a deviation taking the wind value above
that peak. [n other words, the directional nature of reserve requirements can change greatly by
the state of the load or wind output. At high load or wind generation states, there is not likely to
be a significant need for reserves covering a surprise increase in those values. Similarly, at the
lowest states, there is not likely to be a need for the direction of reserves covering a significant
shortfall in load or wind generation.
Figure H.6 shows a distribution of deviations gathered in Bin 14 for forecast load levels between
5,569 MW and 5,720 MW in June 2013. All of the deviations fall between -170 MW and +370
MW. Such deviations would need to be met by resources on the system in order to maintain the
balance of load and resources. That is, when actual load is 170 MW lowerthan expected, there
needs to be additional resources that are capable of being dispatched down, and when actual load
is 370 MW higher than expected, there needs to be additional resources that are capable of being
dispatched up to cover the increases in load.
tt4
PACIFICoRP-2015 IRP APPENDIX H _ WTND INTEGRATION
Figure H.6 - Histogram of Deviations Occurring About a June 2013 PACE Load
Regulating Forecast between 5,568 MW and 5,720 MW (Bin 14)
r$ f to z^ f ,rs rs $ * N *.rs$o.ls$s$rSnlotro4ofF+o+orqo$o
Devletlon Slze,IlflfV
Up and down deviations must be met by operating reserves. To determine the amount of reserves
required for load or wind generation levels in a bin, a tolerance level is applied to exclude
deviation outliers. The bin tolerance level represents a percentage of component deviations
intended to be covered by the associated component reserve. In the absence of an industry
standard which articulates an acceptable level of tolerance, the Company must choose a
guideline that provides both cost-effective and adequate reserves. These two criteria work
against each other, whereby assigning an overly-stringent tolerance level will lead to
unreasonably high wind integration costs, while an overly-lax tolerance level incurs penalties for
violating compliance standards. Two relevant standards, CPSI and BAAL, address the reliability
of control area frequency and error. The compliance standard for CPSI (rolling l2-month
average of area frequency) is 100%, while the minimum compliance standard for BAAL is a 30-
minute response. Working within these bounds and considering the requirement to maintain
adequate, cost-effective reserves, the Company plans to a three-standard deviation (99.7 percent)
tolerance in the calculation of component reserves, which are subsequently used to inform the
need for regulating margin reserves in operations. In doing so, the Company strikes a balance
between planning for as much deviation as allowable while managing costs, uncertainty,
adequacy and reliability. Despite exclusion of extreme deviations with the use of the 99.7 percent
tolerance, the Company's system operators are expected to meet reserve requirements without
exception.
The binned approach is applied on a monthly basis, and results in the four component forecast
values (load following, wind following, load regulating, wind regulating) for each l0-minute
interval of the Study Period. The component forecasts and reserve requirements are then applied
20
l52
a
ta
o
L3
Iz
o lJ- .
S ..+ ,+
115
PACIFICoRP_20I5IRP APPENDIX H _ WTND INTEGRAToN
back to the operational data to develop summary level information for regulation reserve
requirements, using the back casting procedure described below.
Back Casting
Given the development of component reserve requirements that are dependent upon a given
system state, reserve requirements were assigned to each l0-minute interval in the Study Term
according to their respective hypothetical operational forecasts to simulate the component
reserves values as they would have happened in real-time operations. Doing so results in a total
reserve requirement for each interval informed by the data.
To perform the back casts, component reserye requirements calculated from the bin analysis
described above are first turned into reference tables. Table H.8 shows a sample (June 2013,
PACE) reference table for load and wind following reserves at varying levels of forecasted load
and wind generation, and Table H.9 shows a sample (June 2013, PACE) reference table for load
and wind regulating reserves at varying forecast levels.
Table H.8 - Sample Reference Table for East Load and Wind Following Component
Reserves (MW)
266 10000 283 358 5000 157
I 266 784r 283 3s8 1061 157
2 250 7528 192 348 940 213
5 200 7220 285 512 839 205
4 315 7005 294 298 755 290
5 262 6804 334 356 698 207
6 150 6626 321 198 627 231
7 280 6s06 260 239 571 375
8 l9l 6381 212 332 s02 308
9 147 6265 135 238 4?8 284
0 273 6168 99 195 395 374
I 237 6017 168 163 355 172
I 199 5859 338 166 302 241
J 279 5719 295 lls 262 264
4 124 5574 l5l tt4 226 203
5 87 5406 195 l0l t97 287
6 144 5264 171 84 163 326
7 179 5125 98 90 122 225
8 102 4991 86 44 78 242
9 87 4870 t)35 47 288
20 290 4505 63 41 -7 8l
290 0 63 4t -7 8l
lt6
PACIFICoRP_20I5IRP APPENDIX H _ WTND INTEGRATIoN
Table H.9 - Sample Reference Table for East Load and Wind Regulating Component
Reserves
Each of the relationships recorded in the table is then applied to hypothetical operational
forecasts. Building on the reference tables above, the hypothetical operational forecasts
described in the previously sections were used to calculate a reserve requirement for each
interval of historical operational data. This is clarified in the example outlined below.
Application to Component Reserves
For each time interval in the Study Term, component forecasts developed from the hypothetical
forecasts are used, in conjunction with Table H.8 and Table H.9, to derive a recommended
reserve requirement informed by the load and wind generation conditions. This process can be
explained with an example using the tables shown above and hypothetical operational forecasts
from June 2013 operational data for PACE. Table H.l0 illustrates the outcome of the process for
the load following and regulating components.
t77 10000 261 373 10000 173
1 177 7869 261 373 1070 173
2 254 7475 183 459 935 228
5 l6l 7220 189 297 827 203
4 255 6984 222 277 762 306
5 271 6807 271 393 695 277
6 327 6621 253 233 628 219
7 232 6482 213 305 562 372
8 182 6383 164 279 508 22s
9 179 6285 143 t7't MO 233
l0 210 6158 158 172 394 406
ll 258 6023 260 l3l 351 t4s
t2 225 5850 448 134 30s 168
13 237 5720 431 t44 264 224
t4 149 5568 3s3 tt2 229 158
l5 163 5404 231 85 196 279
16 153 5275 104 74 t62 494
l7 96 5134 125 76 116 240
l8 69 5005 llr 44 82 94
t9 51 4892 97 38 46 154
20 179 4521 87 2t -7 112
179 0 87 21 -7 t12
tt7
PacrrrConp - 2015 IRP AppeNorx H WIND INTEGRATToN
Table H.10 - Load Forecasts and Component Reserve Requirement Data for Hour-ending
ll:00 a.m. June 1,2013 in PACE
The load following forecast for this particular hour (hour ending I l:00 a.m.) is 5,344 MW,
which designates reserve requirements from Bin l6 as depicted (with shading for emphasis) in
Table H.8. Because the 5,344 MW load following forecast falls between 5,264 MW and 5,406
MW, the value from the higher bin, 144 MW, as opposed to 87 MW, is assigned for this period.
Note the same following forecast is applied to each interval in the hour for the purpose of
developing reserve requirements. The first l0 minutes of the hour exhibits a load regulating
forecast of 5,319 MW, which designates reserve requirements from Table H.9, Bin 16. Note that
the load regulating forecast changes every 10 minutes, and as a result, the load regulating
component reserve requirement can change very ten minutes as well-although, this is not
observed in the sample data shown above. A similar process is followed for wind reserves using
Table H.11.
Table H.11 - Interval Wind Forecasts and Component Reserve Requirement Data for
Hour-ending 11 a.m. June 1,2013 in PACE
The wind following forecast for this particular hour (hour ending I I :00 a.m.) is 207 MW, which
designates reserve requirements from Bin l5 under wind forecasts as depicted in Table H.8. Note
the following forecast is applied to each interval in the hour for developing reserve requirements.
Meanwhile, the regulating forecast changes every 10 minutes. The first l0 minutes of the hour
Erst
Actud Loed
(10-min
Avg)
MW
Actud Loed
(IIourly
Avg)
MW
Following
Forccrst
Loed
MW
Loed
following
Up
Rcscrvcs
Spccificd
by
Tolcmncc
Lcvcl
MW
Lord
Following
Down
Rcscrvcg
Spccified
by
Tolcrrnc
c Lcvcl
MW
Rcgulrting
Loed
Forccrst
MW
Lord
Rcgulatin
8up
Rescrves
Spccificd
by
Tolcrencc
Lcvcl
MW
Lord
Regulrtin
g Ilown
Rcservcs
Spocificd
by
Tolcrencc
Lcvcl
Mlf,,l
06/01/2013 l0:00 5.117 s.395 5^344 144 t7l 5.31 9 153 104
06/01/2013 l0:10 s.383 s 1qs J i44 144 t7t 5.350 153 104
06/01/2013 l0:20 s.3 86 5.3 95 5.344 144 t7t 5.363 r53 104
06/01/2013 l0:30 5.403 5.395 5.344 144 t7t 5.375 1s3 104
06/01/2013 10:40 ).4JJ 5.395 5.344 144 l7t 5.3 88 153 104
06/01/2013 l0:50 5.428 5.3 95 5.344 144 t7t 5 40t 153 104
E.st
Time
Actual
Wlnd (10-
nin Awol
Actud
Wind
(Hourly
Awol
FolloMng
Forccrst
lVind:
Wind
Follow Up
Rcscrvcs
Specified by
Tolerancc
Lcvel
Wind
Follow
Down
Rccarvcs
Spccilicd
by
Tolcrrncc
Lcvcl
EestWind
Rcgulating
tr'nrecrst:
rilind
Rcguleting
Up Reservcs
Specificd by
Tolcrencc
f rvrl.
Wind
Regulrtin
g Down
Rcscrves
Spccified
by
Tolerance
f .rwel.
06/01/2013 l0:00 190 217 207 l0l 287 219 85 279
06/01/2013 l0:10 208 211 207 101 287 r93 74 494
06/01/2013 l0:20 212 217 201 IOI 287 195 74 494
06/01/2013 10:30 23 I 21',7 207 l0l 287 198 85 279
06/01/2013 l0:40 234 2t'7 207 I01 287 200 85 279
06/01/2013 10:50 226 zt7 20'1 t0l 287 203 85 219
ll8
PecrrCoRp-2015IRP APPENDIX H _ WTND INTEGRATIoN
exhibits a wind regulating forecast of 219 MW, which designates reserve requirements from Bin
15 as depicted in Table H.9. Similar to load, the wind regulating forecast changes every l0
minutes, and as a result, the wind regulating component reserve requirement may do so as well.
In this particular case, the second interval's forecast (193 MW) shifts the wind regulating
component reserve requirement from Bin 15 into Bin 16, per Table H.9, and the component
reserve requirement changes accordingly.
The assignment of component reserves using component hypothetical operational forecasts as
described above is replicated for each l0-minute interval for the entire Study Term. The load
following reserves, wind following reserves, load regulating reserves, and wind regulating
reserves are then combined into following reserves and regulating reserves. Given that the four
component reserves are to cover different deviations between actual and forecast values, they are
not additive. In addition, as di-scussed in the Company's 2012 WIS report, the deviations of load
and wind are not correlated.33 Therefore, for each time interval, the wind and load reserve
requirements are combined using the root-sum-of-squares (RSS) calculation in each direction (up
and down). The combined results are then adjusted as the appropriate system L1e is subtracted
and the ramp added to obtain the final result:
Load Regulatingiz + Wind Regulatingiz + Load Followingt2 + Wind Fottowingiz - Ln * Ramp,
where i represents a l0-minute time interval. Assuming the ramp reserve for the
10:00 a.m. is 50 MW, and drawing from the first lO-minute interval in the example
H.l0 and Table H.l1.
Load Regulatingi: 153 MW
Wind Regulating; = 85 MW
Load Followingi: 144 MW
Wind Followingi: l0l MW
East System Lro:48 MW
East Ramp; = 50 MW,
The regulating margin for l0:00 a.m. is determined as:
@-4g+11=ZSLMW
In this manner, the component reserve requirements are used to calculate an overall reserve
requirement for each l0-minute interval of the Study Term. A similar calculation is also made
for the regulating margin pertaining only to the variability and uncertainty of load, while
assuming zero reserves for the wind components. The incremental reserves assigned to wind
generation are calculated as the difference between the total regulating margin requirement and
the load-only regulating margin requirement.
33 The discussion starts on page I I I of Appendix H in Volume II of the Company's 2012 IRP report:
htto://www.pacificorp.com/contenVdam/pacificorp/doc/Energ.v-SourcesAnteemted_Resource_Plar/20l3IRP/Pacifi
Cofe-20l3lRP_Vol2-Aependices 4-30- l3.pdf
east at
in Table
ll9
PACIFICoRP_2015 IRP APPENDX H - WIND INTEGRATION
Application of Regulating Margin Reserves in Operations
The methodology for estimating regulating margin requirements described above subsequently
informs the projected regulating margin needs in operations. PacifiCorp applies the data from the
reserve tables, as depicted in Table H.8 and Table H.9, to derive regulating margin requirements
within its energy trading system, which is used to manage PacifiCorp's electricity and natural
gas physical positions. As such, the regulating margin requirements derived as part of this wind
integration study are used when PacifiCorp schedules system resources to cost effectively and
reliably meet customer loads. In operations, scheduling system resources to meet regulating
margin requirements ensures that PacifiCorp can meet the BAAL reliability standard. This
standard is tied to real-time system frequency, and as this frequency fluctuates, real-time
operators use regulating margin reserves to maintain or correct frequency deviations within the
allowable 30-minute period, 100% of the time.
Determination of Wind Integration Costs
Wind integration costs reflect production costs associated with additional reserve requirements to
integrate wind in order to maintain reliability of the system, and additional costs incurred with
daily system balancing that is influenced by the unpredictable nature of wind generation on a
day-ahead basis. To characterize how wind generation affects regulating margin costs and
system balancing costs, PacifiCorp utilizes the Planning and Risk (PaR) model and applies the
regulating margin requirements calculated by the method detailed in the section above.
The PaR model simulates production costs of a system by committing and dispatching resources
to meet system load. For this study, PacifiCorp developed seven different PaR simulations.
These simulations isolate wind integration costs associated with regulating margin reserves and
system balancing practice. The former reflects wind integration costs that arise from short-term
variability (within the hour and hour ahead) in wind generation and the latter reflects integration
costs that arise from effors in forecasting wind generation on a day-ahead basis. The seven PaR
simulations used in the WIS are summarized in Table H.12.
120
PACIFICoRP _ 2015 IRP AppgNUX H_ WTND INTEGRATION
Table H.l2 - Wind Integration Cost Simulations in PaR
Reculith! Merllr Reserve Coit Rmr
I 2015 2015lnad
Forecast Eryected Profle tad Norp
2 2015 2015 toad
Forecast Expected Profile Load ad Wind None
?equlating Marpin Cost : System Cost from PaR Simulation 2 less System Cost from PaR Simulation l
lwtcmBelerclnq ColtRrB
J 2015 2013 Day-ahead
Forecast
2013 Day-ahead
Forecast Yes None Comnit urits based on day-ahead load
furecast, ad day-ahead wird frrecast
4 2015 2013 Actual 2013 Actual Yes For Load ard Wird Apply conrnimrm fiom Sirulation 3
5 20ts 2013 Acfial 2013 Day-ahead
Forecast Yes Norp Comnit units based on achal Load, ard
dav-ahead wird forecasr
6 2015 2013 Achnl 2013 Actual Yes ForWird AppV conrniErEnt from Simuhtbn 5
7 2015 2013 Actul 2013 Achnl Yes Norn Conmit units based on actual toad, and
ach:alwird furecast
,oad System Bahrcing Cos = System Cost from PaR Simulation 4, whbh tses the unit conmiErsfi from Simuhtion 3 based on day-ahead
furecast bad (ard day-alrcad wird) less System Cost fiom PaR Simuhtbn 6, which rses the mit conrniterEnt from Simulatbn 5
based on actr:al load (ard dav-a}ead wird)
ilind System Balarrcing Cost = System Cost fiom PaR Simulatbn 6, which uses the urit conrnitrBnt tom Simulation 5 based on day-aMead
wfud (ard actual load) less System Cost fiom PaR Simulatbn 7, whbh conrnits units based on actral wid (ard acugl load)
The first two simulations are used to determine operating reserve wind integration costs in
forward planning timeframes. The approach uses "P50", or expected, wind generation profiles
and forecasted loads that are applicable to 2015.34 Simulation I includes only the load regulating
margin reserves. Simulation 2 includes regulating margin reserves for both load and wind, while
keeping other inputs unchanged. The difference in production costs between the two simulations
determines the cost of additional reserves to integrate wind, or the intra-hour wind integration
cost. The remaining five simulations support the calculation of system balancing costs related to
committing resources based on day-ahead forecasted wind generation and load. These
simulations were run assuming operation in the 2015 calendar year, applying 2013 load and wind
data. This calculation method combines the benefits of using actual system data with current
forward price curves pertinent to calculating the costs for wind integration service on a forward
basis, as well as the current resource portfolio.3s PacifiCorp resources used in the simulations
are based upon the 2013 IRP Update resource portfolio.36
Determining system balancing costs requires a comparison between production costs with day-
ahead information as inputs and production costs with actual information as inputs. 2013 was
the most recent year with the availability of these two types of data. Day-ahead wind generation
forecasts for all owned and contracted wind resources were collected from the Company's wind
forecast service provider, DNV GL.37 For 2012 and2Ol3, DNV GL provided data sets for the
historical day-ahead wind forecasts. The day-ahead load forecast was provided by the
3a P50 signifies the probability exceedance level for the annual wind production forecast; at P50 generation is
expected to exceed the assumed generation levels half the time and to fall below the assumed generation levels half
the time.
35 The Study uses the December 3l,2}l3 official forward price curve (OFPC).
36 The 2013 Integrated Resource Update report, filed with the state utility commissions on March 31,2014 is
available for download from PacifiCorp's IRP Web page using the following hyperlink:
http ://www.pac ifi corp. com/es/irp.html
" This is the same service provider as used by the Company previously, Garrad Hassan. Ganad Hassan is now part
ofDNV GL.
t2t
PecrrConp-2015 IRP APPENDX H - WIND INTEGRATIoN
Company's load forecasting department. There are five PaR simulations to estimate daily system
balancing wind integration costs, labeled as Simulations 3 through 7. In this phase of the
analysis, PacifiCorp generation assets were committed consistent with a day-ahead forecast of
wind and load, but dispatched against actual wind and load. To simulate this operational
behavior, the five additional PaR simulations included the incremental reserves from Simulation
2 and the unit commitment states associated with simulating the portfolio with the day-ahead
forecasts.
Load system balancing costs capture the difference between committing resources based on a
day-ahead load forecast and committing resources based on actual load, while keeping inputs for
wind generation unchanged. Similarly, wind system balancing costs capture the difference
between committing resources based on day-ahead wind generation forecasts and committing
resources based on actual wind generation, while keeping inputs for load unchanged. Simulation
3 determines the resource commitment for load system balancing and Simulation 5 determines
the resource commitment for wind system balancing. The difference in production costs between
Simulations 4 and 6 is the load system balancing cost due to committing resources using
imperfect foresight on load. The difference in production cost between Simulations 6 and 7 is the
wind system balancing cost due to committing resources using imperfect foresight on wind
generation.
Table H.12 above is a revision from what was presented in the 2012 WIS. The revision was
made to remove the impact of volume changes between day-ahead forecasts and actuals on
production costs. Table H.13 lists the simulations performed in the 2012 WIS, which shows that
wind system balancing costs were determined based on the change in production costs between
Simulation 5 and Simulation 4. The wind system balancing costs are captured by committing
resources based on a day-ahead forecast of wind generation, while operating the resources based
on actual wind generation. However, between Simulation 4 and Simulation 5, the volume of
wind generation is different. As a result, the production cost of Simulation 5 is impacted by
changes in wind generation. Using the approach adopted in the 2014 WIS as discussed above
isolates system balancing integration costs to changes unit commitment.
122
PACIFICORP_2OI5IRP APPENDIX H _ WIND INTEGRATION
Table H.13 - Wind Integration Cost Simulations in PaR,2012 WIS
Reculatlnr Melpin Resene Cost Rsns
I 2015 20l5Inad
Forecast Eryected Profile No None
)2015 2015 Load
Forecast Erpected Profile Yes None
Renrlatins Marsin Cost = System Cost from PaR Simulation 2 less System Cost from PaR Simulation I
Svstsm Bellncirc Cost Rrtm
3 2015 2013 Day-alrcad
Forecast
2013 Day-ahead
Forecast Yes None
4 2015 2013 Acnnl 2013 Day-alrcad
Forecast Yes For l.oad
5 2015 2013 Actual 2013 Actual Yes For Load and Wind
Load Sptem Bahncing Cost: Sptem Cost fom PaR simuhtbn 4 (which wes the urit con:rnirrrent fom
Sinnrhtbn 3) less system cost from PaR sinurhtbn 3
Wind System Balancing Cost = System Cost fom PaR sinmhtbn 5 (whbh uses the urit cormnitrnerf from
Sinmhtbn 4) less svstem cost from PaR sinmhtbn 4
Also different from the 2012 WIS, the regulating margin reserves are input to the PaR model on
an hourly basis, after being reduced for the estimated benefits of participating in the EIM, as
discussed in more detail below. Table H.l4 shows the intra-hour and inter-hour wind integration
costs from the 2014 WIS.
In the.2015 IRP process, the System Optimizer (SO) model uses the 2014 WIS results to develop
a cost for wind generation services. Once candidate resource portfolios are developed using the
SO model, the PaR model is used to evaluate the risk profiles of the portfolios in meeting load
obligations, including incremental operating reserve needs. Therefore, when performing IRP risk
analysis using PaR, specific operating reserve requirements consistent with this wind study are
used.
The Company performed several sensitivity scenarios to address recommendations from the
TRC in its review of PacifiCorp's20t2 WIS. Each is discussed in turn below.
Modeling Regulating Margin on a Monthly Basis
As shown in Table H.10 and Table H.11, the component reserves and the total reserves are
determined on a l0-minute interval basis. In the 2012 WIS, PacifiCorp calculated reserve
requirements on a monthly basis by averaging the data for all l0-minute intervals in a month and
Table II.14 - 2014 Wind Integration Costs, $Mwh
Total Wind In
123
PacHConp-2015 IRP APPENDIX H - WTND INTEGRATIoN
applying these monthly reserve requirements in PaR as a constant requirement in all hours during
a month. The TRC recommended that the reserve requirements could be modeled on an hourly
basis to reflect the timing differences of reserves. In calculating wind integration costs for the
2014 WIS, the PacifiCorp modeled hourly reserve requirements as recommended by the TRC.
Table H.15 compares wind integration costs from the2012 WIS with wind integration costs from
the 2014 WIS calculated using both monthly and hourly reserve requirements as inputs to the
PaR model.
Table H.15 - Comparison of Wind Integration Costs Calculated Using Monthly and
Hourly Reserve Requirements as Inputs to PaR, ($lMWh)
Compared to the 2012 WIS intra-hour reserve cost, the 2014 WIS intra-hour reserve cost is lower
when reserves are modeled on a monthly basis in PaR. This is primarily due to the addition of a
the Lake Side 2 combined-cycle plant, which can be used to cost effectively meet regulating
margin requirements. Without Lake Side 2, the intra-hour reserye costs for the 2014 WIS
Monthly Reserve sensitivity would increase from $1.66Artwh to $2.65/lr4Wh. As compared to
the 2012 WIS, which reported wind integration costs using monthly reserve data, the increase in
cost is primarily due to increases in the market price for electricity and natural gas. Table H.16
compares the natural gas and electricity price assumptions used in the 2012 WIS to those used in
the 2014 WIS.
Table H.16 - Average Natural Gas and Electricity Prices Used in the 2012 anil2014 Wind
Integration Studies
When modeling reserves on an hourly basis in PaR, the intra-hour reserve cost is higher than
when modeling reserves on a monthly basis. This is due to more reserves being shifted from
relatively lower-priced hours to relatively higher-priced hours. Figure H.7 shows the average
profiles of wind regulating margin reserves from 2013.
124
PACIFICoRP_20I5 IRP APPENDD( H - WTND INTEGRATION
Figure H.7 - Average Hourly Wind Reserves for 2013, MW
=E
160
140
120
100
80
60
40
20
0
7 8 9 101112t3L4L5L6L7 18192021222324
Hour
ft35l +West
Separating Regulating and Following Reserves
In its review of the 2012 WIS, the TRC recommended treating categories of reserves differently
by separating the component reserves of regulating, following and ramping. That is, instead of
modeling regulating margin as:
Load Regutatingiz + Wind Regulatingiz + Load Foltowingiz + Wtnd Followtngt' - Lro I Ramp,
The TRC recommendation requires calculating regulating reserves and following reserves using
two separate calculations:
Regutating Reserves = -Lls,and
Following Reserves = I Ramp.
Because regulating reserves are more restrictive than following reserves (fewer units can be used
to meet regulating reserve requirements), the L1e adjustment is applied to the regulating reserve
calculation. Ramp reserves can be met with similar types of resources as following reserves, and
therefore, are combined with following reserves.
The impact of separating the component reserves as outlined above is to increase the total
reserve requirement required on PacifiCorp's system. Table H.l7 shows the total reserve
requirement when the separately calculated regulating and following reserves are summed as
compared to the total reserves combined using one RSS equation. The total reserve requirement,
125
PecnrCopp - 2015 IRP APPENDD( H - WIND INTEGRATIoN
when calculated separately, is over 30o/o higher than the reserve requirement calculated from a
single RSS equation. This is a significant increase in the amount of regulation reserves that is
inconsistent with how the Company's resources are operated and dispatched. As a result,
PacifiCorp did not evaluate this sensitivity in PaR.
Table H.l1 - Total Load and Wind Monthly Reserves, Separating Regulating and
Following Reserves (M!Y)
Jan 238 400
Feb 212 363
Mar 219 357
Apr 240 422
May 192 400
Jun 183 462
Jul 219 427
Aug 220 428
Sep 210 392
Oct 153 335
Nov 30r 438
Dec 274 433
EIM is an energy balancing market that optimizes generator dispatch between PacifiCorp and the
CAISO every five minutes via the existing real-time dispatch market functionality. PacifiCorp
and the CAISO began a phased implementation of the EIM on October 1,2014, when EIM was
activated to allow the systems that will operate the market to interact under realistic conditions,
allowing PacifiCorp to submit load schedules and bid resources into the EIM and allowing the
CAISO to use its automated system to generate dispatch signals for resources on PacifiCorp's
control areas. The EIM is expected to be fully operational November 1,2014.
Once EIM becomes fully operational, PacifiCorp must provide sufficient flexible reserve
capacity to ensure it is not leaning on other participating balancing authorities in the EIM for
reserves. The intent of the EIM is that each participant in the market has sufficient capacity to
meet its needs absent the EIM, net of a CAISO calculated reserves diversity benefit. In this
manner, PacifiCorp must hold the same amount of regulating reserve under the EIM as it did
prior to the EIM, but for a calculated diversity benefit.'o Figure H.8 illustrates this process.
38 Under the EIM, base schedules are due 75 minutes prior to the hour of delivery. The base schedules can be
adjusted at 55 minutes and 40 minutes prior to the delivery hour in response to CAISO sufftciency tests. This is
consistent with pre-EIM scheduling practices, in which schedules are set 40 minutes prior to the delivery hour.
107 196 211 354 318 550
100 182 187 318 287 500
97 179 202 313 299 492
123 224 208 362 331 s86
84 205 180 348 264 553
70 240 179 393 249 633
88 80 206 391 294 572
90 88 206 388 296 576
100 71 188 36r 287 533
75 59 131 301 206 461
165 228 249 375 414 603
122 216 251 375 373 592
126
PecrrConp-2015 IRP APPENDIX H - WTND INTEGRATIoN
Figure H.E - Energy Imbalance Market
\Hcrr@np
Dctcrminc Rcgulating Maryin
Rcscrves .: :
Brscd on WlS ... -- .
Rcsults
t
\PrcrrEoRP
Rcalic "Divcrsity 8cnefft'
end RcflGGt Rcductlon
ln Rcaulatiry Margln Rcscrvcs
fr $ cotib.io tso
"DiveEity Bcnc{it"
The CAISO will calculate the diversity benefit by first calculating the reserve requirement for
each individual EIM participant and then by comparing the sum of those requirements to the
reserve requirement for the entire EIM area. The latter amount is expected to be less than the
sum due to the portfolio diversification effect of load and variable energy resource (wind and
solar) variations. The CAISO will then allocate the diversity benefit among all the EIM
participants. Finally, PacifiCorp will reduce its regulating reserve requirement by its allocation of
diversity benefit.
In its 2013 report, Energy and Environmental Economics (E3) estimated the following benefits
of the EIM system implementation:"
- PacifiCorp could see a 19 to 103 MW reduction in regulating reserves, depending on the
level of bi-directional transmission intertie made available to EIM;
- Interregional dispatch savings: Five-minute dispatch efficiency will reduce "transactional
friction" (e.9., transmission charges) and alleviate structural impediments currently
preventing trade between the two systems;
- Intraregional dispatch savings: PacifiCorp generators will dispatch more efficiently
through the CAISO's automated system (nodal dispatch software), including benefits
from more efficient transmission utilization;
- Reduced flexibility reserves by aggregating the two systems' load, wind, and solar
variability and forecast errors;
- Reduced renewable energy curtailment by allowing BAAs to export or reduce imports of
renewable generation when it would otherwise need to be curtailed.
Based on the E3 study, the relationship between the benefit in reducing regulating reserve
requirements and the transfer capability of the intertie is shown in Table H.18.
@ Colibrnio ISO
Dctcrmine
Flcxiblc Rampln3 Rcqulrcmcnts
fur PaclfiCorp and CAISO,
Separatcly and Combined
t
3e http://www.caiso.com/Documents/PacifiCorp-ISOEnersylmbalanceMarketBenefits.pdf
127
PACIFICoRP-2015 IRP APPENDIX H _ WIND INTEGRATIoN
Given that the transfer capacity in this WIS is assumed to be approximately 330 MW, through
owned and contracted rights, the reduction in regulating reserve is assumed to be approximately
65 MW. This benefit is applied to reduce the regulating margin on PacifiCorp's west BAA
because the current connection between PacifiCorp and CAISO is limited to the west only. Table
H.l9 summarizes the impact of estimated EIM regulating reserve benefits assuming monthly
application of reserves in PaR to be comparable to how the 2012 WIS wind integration costs
were calculated. The sensitivity shows that EIM regulating reserve benefits reduce wind
integration costs by approximately $0.2 I /IvIWh.
Table H.19 - Wind Integration Cost with and without EIM Benefit, $/MWh
The 2014 WIS determines the additional reserve requirement, which is incremental to the
mandated contingency reserve requirement, needed to maintain moment-to-moment system
balancing between load and generation while integrating wind resources into PacifiCorp's
system. The 2014 WIS also estimates the cost of holding these incremental reserves on its
system.
PacifiCorp implemented the same methodology developed in the 2012 WIS for calculating
regulating reserves for its 2014 WIS, and implemented recommendations from the TRC to
implement hourly reserve inputs when determining wind integration costs using PaR. Also
consistent with TRC recommendations, PacifiCorp further incorporated regulation reserve
benefits associated with EIM in its wind integration costs. Table H.20 compares the results of the
2014 WIS total reserves to those calculated in the 2012 WIS.
128
PacmrConp-2015 IRP AppsNolx H _ WIND INTEGRATION
Table H.20 - Regulating Margin Requirements Calculated for PacifiCorp's System (MW)
20tt
(2012 wrs)
Load-Onlv Rezulatine Reserves 99 176 il9 394
Incremental Wind Reserves 50 126 9 185
Total Reserves 149 302 128 579
2012
Load-Onlv Rezulatine Reserves 95 186 ll9 400
Incremental Wind Reserves 7l 123 lt 206
Total Resenes 166 309 130 606
2013
(2013 wrs)
Load-Onlv Reeulatine Reserves 119 203 ll9 441
Incremental Wind Reserves 51 123 t2 186
Total Reserves 169 326 131 626
The anticipated implementation of EIM with the CAISO is expected to reduce PacifiCorp's
reserve requirements due to the diversification of resource portfolios between the two entities.
PacifiCorp estimated the benefit of EIM regulating reserve benefits based on a study from E3.
The assumed benefits reduce regulating reserves in PacifiCorp's west BAA by approximately 65
MW from the regulating reserves shown in the table above, which lowers wind integration costs
by approximately $0.2 I /IvIWh.
Two categories of wind integration costs are estimated using the Planning and Risk (PaR) model:
one for meeting intra-hour reserve requirements, and one for inter-hour system balancing. Table
H.2l compares 2014 wind integration costs, inclusive of estimated EIM benefits, to those
published in the 2012 WIS.
Table H.2l - 2014 WIS Wind Integration Costs as Compared,to2012 WIS, $/Mwh
The 2014 WIS results are applied to the 2015 IRP portfolio development process as a cost for
wind generation resources. Once candidate resource portfolios are developed using the SO
model, the PaR model is used to evaluate portfolio risks. After resource portfolios are developed
using the SO model, the PaR model is used to evaluate the risk profiles of the portfolios in
meeting load obligations, including incremental operating reserve needs. Therefore, when
performing IRP risk analysis using PaR, specific operating reserve requirements consistent with
the2014 WIS are used.
129
Date: December 22,20L4
To: PacifiCorp
From: 2014 Wind lntegration Study Technical Review Committee (TRC)
Subject: PacifiCorp 2014 Wind lntegration Study Technical Memo
Background
The purpose of the PacifiCorp 2072 wind integration study as identified by Pacificorp in the lntroduction
to the 2015 lRP, Appendix H - Draft Wind lntegration Study, is to estimate the operating reserves
required to both maintain PacifiCorp's system reliability and comply with North American
Electric Reliability Corporation (NERC) reliability standards. PacifiCorp must provide sufficient
operating reserves to meet NERC's balancing authority area control error limit (BAL-001-2) at
all times, incremental to contingency reserves, which PacifiCorp maintains to comply with
NERC standard BAL-002-WECC-2.I'2 Apart from disturbance events that are addressed through
contingency reserves, these incremental operating reserves are necessary to maintain area control
error3 IACE), due to sources outside direct operator control including intra-hour changes in load
demand and wind generation, within required parameters. The wind integration study estimates
the operating reserve volume required to manage load and wind generation variation in
PacifiCorp's Balancing Authority Areas (BAAs) and estimates the incremental cost of these
operating reserves.
PacifiCorp currently serves 1.8 million customers across 136,000 square miles in six western states.
According to a company fact sheet available at
http://www.pacificorp.com/content/dam/pacificorp/doc/About Us/Companv Overview/PC-FactSheet-
Final Web.pdf. PacifiCorp's generating plants have a net capacity of 10,595 MW, including about 1,900
t NERC Standard BAL-001-2: http://www.nerc.com/files/BAL-0O1-2.odf
' NERC Standard BAL-OO2-WECC-2 (http://www.nerc.com/files/BAL-002-WECC-2.pdf), which became effective
October t,20L4, replaced NERC Standard BAL-STD-O02, which was in effect at the time of this study.,.?reaControlErro/,isdefinedintheNERCglossaryhere:
terms/elossarv of terms.pdf
MW of owned and contracted wind capacity, which provides approximately 8o/o of PacifiCorp's annual
energy. PacifiCorp operates two BAAs in WECC, referenced as PACE (PacifiCorp East) and PACW
(PacifiCorp West). The BAAs are interconnected by a limited amount of transmission, and the two BAAs
are operated independently at the present time, so wind generation in each BAA is balanced
independently.4 PacifiCorp has experienced continued wind growth in each BAA, and has been
requested to update its wind integration study as part of its lRP. The total amount of wind capacity in
PacifiCorp's BAAs, which was included in the 2014 wind integration study, was 2,544 MW.
TRC Process
The Utility Variable-Generation lntegration Group (UVIG) has encouraged the formation of a Technical
Review Committee (TRC) to offer constructive input and feedback on wind integration studies
conducted by industry partners for over 10 years. The TRC is generally formed from a group of people
who have some knowledge and expertise in these types of studies, can bring insights gained in previous
work, have an interest in seeing the studies conducted using the best available data and methods, and
who will stay actively engaged throughout the process. Over time, the UVIG has developed a set of
principles which is used to guide the work of the TRC. A modified version of these principles was used in
the conduct of this study, and the same version was used for the conduct ofthe TRC process for the
2012 wind integration study. A copy is included as an attachment to this memo. The composition of the
TRC for the 2014 PacifiCorp study was as follows:
o Andrea Coon - Director, Western Renewable Energy Generation lnformation System
(WREGIS) for the Western Electricity Coordinating Council (WECC)
o Matt Hunsaker - Manager, Operations for the Western Electricity Coordinating Council
(wEcc)
o Michael Milligan - Principal Researcher for the Transmission and Grid lntegration Team at
the National Renewable Energy Laboratory (NREL)
o J. Charles Smith - Executive Director, Utility Variable-Generation lntegration Group (UVIG)
o Robert Zavadil - Executive Vice President of Power Systems Consulting, EnerNex
The TRC was provided with a study presentation in July of 20L4, and met by teleconference on 2
occasions during the course of the study, which was completed in November 2014. PacifiCorp provided
presentations on the status and results of the work on the teleconferences, with periodic updates
a PacifiCorp and the CAISO began operating an energy imbalance market (ElM) on Oct. L,2OL4, which will likely
make wind integration somewhat easier. With the ElM, there would seem to be more impetus for this policy to be
reviewed and potentially revised going fonvard. The TRC recommends that this topic be explored in future work.
during the course of the study, and engaged with the TRC in a robust discussion throughout the work.
The teleconferences were followed up with further clarifications and responses to requests for
additional information. While the conclusions appear justified by the results of the study, the TRC
review should not be interpreted as a substitute for the usual PUC review process.
lntroduction
The Company should be acknowledged for the diligent efforts it made in implementing the
recommendations by the TRC from the 2012 wind integration study in the 2014 study, as summarized in
Table H.1. For example, the company modeled the reserve requirements on an hourly basis in the
production cost model, rather than on a monthly average basis; the regulating margin reserve volumes
accounted for estimated benefits from PacifiCorp's participation in the energy imbalance market (ElM)
with the California lndependent System Operator (CAISO); and a discussion on the selection of a 99.7%
exceedance level when calculating regulation reserve needs was provided, including a description of
how the WIS results inform the amount of regulation reseryes planned for operations. Sensitivity
studies were performed, including the modeling of the regulating reserves on a monthly basis, and
demonstrating the impact of separating the reserves into different categories. The 2014 wind
integration study report thoroughly documents the company's analysis.
As pointed out in the report, there is a small but meaningful difference in the integration costs between
the 2012 study and the 2014 study. The 2012 value of S2.55/MWh of wind generation, using monthly
reserves in PaR, is slightly less than the 2014 value of 53.06/MWh, using hourly reserves in the Planning
and Risk (PaR) production cost model, with the major difference attributed to the modest increase in
the cost of electricity and natural gas. When modeling reserves on an hourly basis in PaR, the
intra-hour reserve cost is higher than when modeling reserves on a monthly basis. This is due to
more reserves being shifted from relatively lower-priced hours to relatively higher-priced hours.
Analytical Methodology
o The first paragraph on p. 24 of the revised Appendix H, entitled "Application of Regulating Margin
Reserves in Operations" is a criticalaspect of this study, albeit a little late to the interactions
between Pacificorp and the TRC. ln effect, it means that the results of this study are and have
been applied in operations, which is very unique in the universe of wind integration analysis since
nearly all other studies are forward looking and utilize synthesized data and other
assumptions. While this paragraph sufficiently addresses the points raised by the TRC in the late
summer of 20L4, it should receive more prominence in the report. A comparison of the
interaction between the 2012 study methodology and PacifiCorp operations with the 2014 study
methodology and Pacificorp operations should be included at the front of the document.
Assumptions
r The assumptions generally seem reasonable. PAC does a good job of laying out the process they use
for the modeling and analysis. They have also provided discussion of the previous suggestions (from
the 2012) study made by the TRC.
o The report addresses the issue of the 99.7o/o coverage of variability, and says that the operators are
expected to have sufficient reserves to cover all variability all of the time. lt would be interesting to
contrast the company's policy of ensuring 100% reserve compliance with actual system
performance. ln the November TRC call there was some helpful discussion on this issue. One item
discussed was that using99.7% provides some margin of error in case a lower value, such as 95%, is
used in the study but insufficient if the actual variability of wind/load were to increase. lt would be
nice to see this discussion reflected in the report, which would provide some additionaljustification
for the 99.7 percentile. The reason this point is raised is to magnify the point that PAC makes in the
report; that there is a tradeoff between economics and reliability. Holding the system to an
extremely high effective CPS performance will be somewhat costly, and it is not clear what impact
this is having on wind integration costs.
o The use of actual historical wind production data is excellent, and something that many studies are
unable to do. This means that the PAC study is somewhat unique and PAC is to be commended for
doing this work. At the same time, the report provides some illumination on the difficulties in using
actual data, because data recovery rates can compromise the time series. PAC has done a good job
in analyzing and correcting these inevitable data gaps, and this should not have a significant impact
on the study results.
Results
o Table H.15 documents a comparison of the monthly versus hourly reserve modeling, and shows
that a constant monthly reserve is less costly than reserves modeled on an hourly basis. The
explanation provided is useful, but may leave out some factors such as non-linearity in reserve
supply curve. ln addition, the shifting of reserves from lower price hours to higher price hours
only seems to apply to the East area, as the West area exhibits the opposite characteristic.
Discussion and Conclusions
o Table H.17 shows that the total reserves increase with consideration of regulation and following
separately. lt should be noted that while the arithmetic sum of the reserves does increase, it
would not necessarily lead to higher costs as some of the following reserve could be obtained
from non-spinning and quick-start resources which cost little to have on standby for such
purpose.
o Based on the information provided by PacifiCorp, the methodology used in the wind integration
study appears to be reasonable. Based on the draft study report, the findings and conclusions
appear sound. The findings appear to be useful to inform the lntegrated Resource Planning
process.
Recommendations for Future Work
Wind lntegration modeling presented is unique in how it is integrated with the operating process at
PacifiCorp. There are some sensitivity studies which could be done to shed additional light on the
results and provide some useful insights:
o Future work should explore balancing area cooperation between PACE and PACW under the
EIM framework.
o Regulating margin implies reserve capacity available on very short notice (ten minute or
less). The ramping and following reserve categories do not all require fast response. Future
sensitivity studies could be done to compare the results from PaR to use of the RSS formula.
o lt might be usefulto perform some additional sensitivities on natural gas price. For
example, integration costs would be expected to increase with gas prices, yet at higher gas
prices PAC would be getting a larger benefit from wind energy.
o A sensitivity analysis with carbon tax assumptions could also provide some useful insight
and results.
Concurrence provided by:
Andrea Coon - Director of WREGIS, WECC
Matt Hunsaker - Manager, Operations, WECC
Michael Milligan - Principal Researcher, Transmission and Grid lntegration Team, NREL
J. Charles Smith - Executive Director, UVIG
Robert Zavadil - Executive Vice President, EnerNex