appendix a: probabilistic load resource balance …portfolio deficits. the data outputs of the...

Snohomish County PUD – 2017 Integrated Resource Plan Appendix A| A-1

APPENDIX A: PROBABILISTIC LOAD

RESOURCE BALANCE MODEL

PUBLIC UTILITY DISTRICT #1 OF SNOHOMISH COUNTY

Prepared by

Generation, Power, Rates, and Transmission Management Division


PROBABILISTIC LOAD RESOURCE BALANCE

MODEL

Appendix A provides additional detail on the modeling assumptions PUD Staff used to measure

and evaluate future portfolio needs in the 2017 IRP analysis. The Appendix is organized into

three sections as follows:

1. Load Resource Balance (LRB) Model Methodology

2. Planning Standards

3. Resource Need by Scenario

1. LRB Methodology

The 2017 IRP used a probabilistic portfolio modeling approach to analyze the range of load

forecasts and the PUD’s existing and committed resources for five different scenarios, across the

20 year study period (2018-2037). This probabilistic approach considered the entire range of

possible combinations of load and output from the PUD’s existing and committed resources, and

simulated them together in a model that identified the scale, timing, and likelihood of potential

portfolio deficits. The data outputs of the Probabilistic Load Resource Balance model were then

used as inputs to the Portfolio Optimizer Model (described in Appendix B). The Portfolio

Optimizer Model identified the lowest reasonable cost portfolio additions that could address

portfolio needs. A flow diagram representative of the 2017 IRP modeling is shown in Figure A1.

Figure A1


How the Probabilistic LRB Model Works

The PUD’s Probabilistic LRB model created

simulations using a Monte Carlo framework, where

random draws of possible loads and resource output

combinations produced a potential load-resource

balance. In this way staff could identify the amount

of PUD surplus or deficit in meeting its customers’ needs over the study period. The simulation

in the 2017 IRP was repeated 5,000 times with different random draws that produced different

potential outcomes. This became the sample set from which statistical inferences were made

about the range and likelihood of particular occurrences.

For a simplified example of this process, imagine rolling two dice, one die represents customer

demand (load), and one die represents total available resource (supply). Each dice has six sides

with an equal chance of occurring, and a simulation of each individual dice rolled 5,000 times

would yield a chart as in Figure A2 below. The chart in Figure A2 shows that each value on the

“Supply-Side Dice” has an equal ~17% chance of occurring.

Figure A2

To build upon this simplified example, suppose each iteration of the Monte Carlo simulation

now includes the random roll of the Supply Dice and the Demand Dice, and records the Load

0%2%4%6%8%

10%12%14%16%18%

0 1 2 3 4 5 6 7

Freq

uen

cy o

f O

ccu

ren

ce

Dice Values

Supply-Side Dice: 5,000 Iterations

What is a Probabilistic Model?

A probabilistic model is a statistical

model that simulates the possible

outcomes based on historical data for the

purpose of forecasting the probability of

an event occurring in the future.


Resource Balance outcome (Supply minus Demand) of that iteration. In this case, there are

eleven possible Load Resource Balance outcomes, ranging from -5 (the result of a “1” Supply

Dice roll and a “6” Demand Dice Roll) to 5 (the result of a “6” Supply Dice Roll and a “1”

Demand Dice roll). All possible outcomes are shown in Table 1. The outcomes of the simulation

no longer have an equal chance of occurring as shown by the graph of their frequency of

occurrence (on y-axis) in Figure A3. Instead, the outcomes follow a triangle-shaped distribution

with a peak at zero, the most likely individual Load Resource Balance outcome in the 5,000

iteration simulation.

Table 1

Possible LRB Outcomes from Supply Roll – Demand Roll

Supply 1 Supply 2 Supply 3 Supply 4 Supply 5 Supply 6

Demand 1 0 1 2 3 4 5

Demand 2 -1 0 1 2 3 4

Demand 3 -2 -1 0 1 2 3

Demand 4 -3 -2 -1 0 1 2

Demand 5 -4 -3 -2 -1 0 3

Demand 6 -5 -4 -3 -2 -1 0

Figure A3

-2.00 5.0020.0% 0.0%80.0%

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

-6 -4 -2 0 2 4 6

Freq

uen

cy

Value of Supply Dice minus Resource Dice

"Dice Roll" Load Resource Balance: 5,000 Iterations


The data in this Figure A3 also allows for an estimation of a range of occurrences using the

statistical sample. For example, there is a roughly 20% chance that the Load Resource Balance

has a less than -2 balance, and no chance the balance will be greater than 5. This type of

information is helpful for planners who seek an estimate of the likelihood of potential deficits of

different magnitudes, and is one of the reasons this methodology is used in this IRP.

The 2017 IRP measures the entire range of possible load-resource balance outcomes for various

time horizons, such as Annual Average Energy for each year 2018-2037, and December Heavy-

Load Hours (HLH) from 2018-2037. The Planning Standards used by the IRP require the PUD to

meet its Load Resource Balance in 95% of Monthly HLH and Peak Week periods, so the Load

Resource Balance value at P5, or the lowest 5% of occurrences, provides the information needed

for subsequent portfolio optimization tests1. These values determine the scale of portfolio needs

in different time periods, by measuring the need at specific likelihoods that the Probabilistic

Load Resource Balance provides information on.

Time Horizons.

In the probabilistic model used in this IRP, the modelling for supply and demand is more

complex than the dice example. One of the sources of complexity is the need to model multiple

time periods within and across the 20-year planning period simultaneously as part of each Monte

Carlo iteration. In the dice example, there was only one time horizon, and the data presented only

the likelihood of different occurrences on the next set of dice rolls. In the IRP model, estimates

of the range of potential supply and demand side values are made in overlapping time periods,

with a structure that makes the values consistent across the time periods. The time horizons

measured in the IRP model are shown in Figure A4.

1 Planning Standards are discussed in detail in Section 5-8: Planning Standards in the 2017 IRP document


Figure A4

The time periods were simulated simultaneously, and the model for each time period corresponds

with the others, such that the model produces a detailed look at the potential range of resource

output (generation) across all time periods, bound by the relationships between the time periods.

In addition, because the time periods are simultaneously simulated in the same Monte Carlo

iteration, the results are part of the same sample set, and the statistics that describe portfolio

attributes across different time periods avoid a sample bias relative to each other.

Model Inputs.

The probability distribution for load and each resource asset was derived from actual historical

data, simulated production, and forecast models, based upon the best information available.

Figures A5 & A6 describe the components that produced the probabilistic estimates of load and

resources for each time horizon.


Figure A5


Figure A6

Model Outputs.

The effect of the model’s structure can be seen most clearly in the outputs of the Monte Carlo

simulation. Figure A7 illustrates the results from a 2,500 iteration Monte Carlo Simulation of the

2018 load on an annual average basis under Business-As-Usual Case Load Conditions. The P50


value in the graph is 779aMW, and represents the value where 50% of occurrences are below the

value, and 50% are above. This is the load value that would be expected, and, if the load forecast

was a point estimate, would be the value that would represent 2018 load. The range of possible

2018 load values is driven by the probabilistic variables that effect annual load as described in

Figure A5. In this case, the range and likelihood of annual load is principally driven by the range

and likelihood of weather volatility relative to historical average weather and industrial loads.

Warmer weather reduces load, while cooler weather increases load on an annual average basis

for the PUD. Industrial loads are variable over time but the variability is not significantly caused

by weather. The bell graph of likely loads does not look like the dice roll graph from Figure A2,

rather, the specific drivers of the PUD load create a complex probability distribution shape over

the 2,500 iterations of the simulation, and provides rich information on the range and likelihood

of potential loads.

Figure A7

While Figure A7 is specific to annual aMW in 2018, all time periods within the 20 year plan are

simultaneously simulated. As a result, it is possible to see the potential range of annual loads by

year for each year of the planning period. Figure A8 provides a summary of the range of possible

749.0 779.35.0% 50.0%45.0%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

700

720

740

760

780

800

820

840

860

Freq

uen

cy in

2,5

00

iter

atio

ns

aMW

2018 Load Distribution (aMW)


annual average loads under the Business As Usual load conditions from 2018 to 2038. The very

top of the range of the green band represents the 95th percentile of the load occurrences, or, the

point at which 95% of annual load estimates in a 2,500 iteration simulation are below. In the case

of 2021, for example, that point is roughly 850 aMW. The very bottom of the range of the green

band represents the 5th percentile, or the point at which only 5% of occurrences in the 2,500

iteration simulation are below. In the year 2029 for example, there is only a 5% chance that load

would be below 850aMW, based on the 2,500 iteration simulation.

Figure A8

Similar to the Monte Carlo simulation of loads, the probability distribution of generating

resources is a complex shape driven by the variability of its component parts, described in Figure

A6. Contrary to the load model where the input variables produce an estimate for a single load

per time period, per iteration, the resource model estimates multiple resources (wind and hydro

for example) in each time period and adds them together to provide an estimate of portfolio

resource production for each iteration. It is possible in this model, as it is in reality, to have

strong hydro production, but weak wind production in a given time period, and vice versa. There

are also correlations between generating resources that inform how generation is likely to occur

for projects with similar characteristics, for example Washington State gorge-area wind projects,

700

750

800

850

900

950

1000

1050

2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038

aMW

Load Distribution: 2018 to 2038 (aMW)

5% - 95%

+/- 1 Std. Dev.

Mean


which are likely to experience periods of high or low production together in a given time period.

The cumulative dynamics of each of the resources independently and interdependently create the

complex probability distribution shown in Figure A9, which represents the range and likelihood

of PUD existing resource production in 2018 on an annual average basis in a 2,500 iteration

Monte Carlo simulation.

Figure A9

Just as it was possible to display the range and variability of potential loads on an annual basis

across the 20-year planning period, it is also possible to chart the expected range of resource

production. The resulting chart in Figure A10 shows the volatility in resource production on an

annual basis as well as shifts in expected portfolio production as a result of expiring power

contracts, like the four wind project expiries depicted by dotted black lines on the chart.

Comparing the resource graph in Figure A10 to the load graph Figure A8 it is clear that the range

of potential resource production outcomes is much wider than the range of potential load

outcomes on an annual basis, under Base Case conditions.

892.6 1,000.95.0% 50.0%45.0%

0.000

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

0.010

850

900

950

1000

1050

1100

1150

Fre

qu

en

cy in

2,5

00

ite

rati

on

s

aMW

2018 Total Resource Distribution (aMW)


Figure A10

Along with the estimates for the range of loads and resources, the load resource balance of the

energy portfolio is also calculated, providing the range and likelihood of potential resource

adequacy across the planning period. Figure A11 displays the Load Resource Balance on an

annual basis from a Monte Carlo simulation run 2,500 times under Business As Usual Case

conditions. Any values below zero on the chart represent outcomes where the PUD’s existing

resources were not adequate to meet customer needs on an annual basis before new conservation

or additional resources. In the chart, the first such occurrence takes place in 2027, but the

likelihood and magnitude of the potential occurrence is low, with a 5% chance of a deficit at a

~25aMW magnitude or greater. As time progresses on the chart however, the magnitude and

likelihood of a deficit increases, such that by the year 2036, there is an expected deficit (the

Mean) of about 5aMW, and there is a 5% chance the deficit could be greater than 100aMW.

800

850

900

950

1000

1050

1100

11502018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

aMW

Total Resources 2018 to 2037

5% - 95%

+/- 1 Std. Dev.

Mean


Figure A11

The probabilistic model provides detailed information on the magnitude and likelihood of load

resource balance events across all of the time periods studied within the 20 year planning period

(listed in Table 1). The resulting information provides a rich data environment to help inform the

PUD about the size, timing, and likelihood of portfolio needs. For example, Figure A12 presents

the range and likelihood of all Load Resource Balance outcomes by 10% increment of likely

occurrence for each month in the year 2027. The shape of the potential deficits provide

information on when in the year the PUD is likely to be most deficit, by how much, and with

what certainty. The information is useful in considering what resources or programs could be

most beneficial to help the PUD ensure adequate resources. For example, the graph shows a near

certainty additional resources are needed in December, and a near certainty that resources are not

needed in May and June.

-150

-100

-50

0

50

100

150

200

250

300

350

aMW

Annual Avg LRB with no New Conservation (in aMW)

5% - 95%

+/- 1 Std. Dev.

Mean


Figure A12

2. Planning Standards

The probabilistic modeling approach of the PUD’s range of future load growth and output of

existing and committed resources provided the platform upon which to establish new planning

standards for the 2017 IRP analysis. These planning standards were established to ensure

adequate resources would be available on an annual, monthly and weekly basis to meet customer

demand across the IRP study period. An additional planning standard to test compliance with the

annual renewables compliance targets prescribed under the EIA (I-937) was also included in this

framework.

The Planning Standards used the outputs of the Probabilistic Load Resource Balance Model at

specific likelihoods to measure the risk of being inadequate in different time horizons, and the

amount of new resources that would be required to address the risk. The four planning standards

established in the 2017 IRP analysis to provide for an objective comparison of the impacts of

various scenario assumptions on future resource need are:

-300

-200

-100

0

100

200

300

400

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

2027 HLH Load Resource Balance by Monthly and Likelihood

P10 P20 P30 P40 P50 P60 P70 P80 P90


1. The Annual Energy Planning Standard measures the ability of the PUD to meet average

annual energy demand across the entire year. The PUD is deemed to have an energy need if

expected average annual load exceeds expected average annual resource production.

2. The Monthly On Peak Planning Standard measures the ability of the PUD to meet

monthly on peak demand, 19 out of 20 times, with its existing and committed resources. The

Monthly On Peak standard limits the quantity of on peak energy or capacity purchased from

the short-term wholesale market to satisfy any portfolio deficits, to no more than 100 aMW

in a given month.

3. The Peak Week Planning Standard measures the ability of the PUD to meet reliably meet

its highest on-peak demand during the most deficit week of the month, 19 out of 20 times,

with its existing and committed resources. The highest on peak demand has historically

occurred most often during December. The Peak Week standard limits the quantity of on

peak energy or capacity purchased from the short-term wholesale market to satisfy any

portfolio deficits to no more than 200 aMW.

4. The Regulatory Compliance Standard measures the portfolio’s compliance with the

provisions for determining cost effective conservation and annual renewables target set forth

under the Washington state Energy Independence Act (I-937). Several other regulatory

requirements including overgeneration events and consideration of renewable and

nonrenewable resources are addressed through this standard:2

Table 2 below shows how each Planning Standard was incorporated into the Portfolio Optimizer

as a parameter or constraint:

2 RCW 19.285 details conservation and renewables’ compliance requirements and RCW 19.280.030 addresses

developing a resource plan and considering overgeneration events.


Table 2

Planning Standard Probabilistic Model Data

Inputs

How the Planning Standard is

Used

Annual Energy Annual LRB

@P50 ≥ 0aMW after any needed

portfolio additions. No forecast

market purchases allowed to

meet standard

Annual LRB@P50 of the

existing portfolio before new

additions for all years 2018-

2037

Rule will mark as “invalid” any

portfolios with Annual LRB’s

less than 0 aMW in any year,

after portfolio additions.

Monthly On-Peak Monthly HLH LRB@P5 ≥

0aMW after any needed

portfolio additions,

Forecast Market Purchases to

Address Monthly HLH LRB

Deficit ≤100aMW

Monthly HLH LRB@P5 of the

existing portfolio before new

additions for 4 indicator months

per year (Dec, March, April,

August) for all years (2018-

2037)


portfolios with Monthly HLH

LRB’s less than -100 aMW in

any month and any year, after

portfolio additions (forecast


cover LRB deficits between

0aMW to -100aMW).

Monthly Peak Week HLH

Monthly Peak Week LRB@P5≥

0aMW after any needed

portfolio additions,

Forecast Market Purchases to

Address Monthly Peak Week

HLH LRB Deficit ≤200aMW

Monthly Peak Week LRB@P5

of the existing portfolio before

new additions for the peak week

period of 4 indicator months per

year (Dec, March, April,

August) for all years (2018-

2037)


portfolios with Monthly Peak

Week HLH LRB’s less than -200

aMW in any month and any year,

after portfolio additions, (forecast


cover LRB deficits between

0aMW to -200aMW).

Regulatory Compliance

“Valid” portfolio’s will meet

RPS compliance obligations

under the Target methodology

by some combination of

procuring renewable resources

with REC generating attributes

or purchasing unbundled RECs,

to augment RECs produced by

the existing portfolio.

Existing annual portfolio

production @P50 is imported

from the Probabilistic LRB

model for eligible resources.

Annual Load @P50 is also

imported from the LRB model

to set the annual RPS target in

MWh for each year 2018-2037.

Rule will not mark as “invalid”

any portfolios with insufficient

RECs (either through portfolio

resources or unbundled REC

purchases) to meet the RPS target

in any given year 2018-2037.

Table 3 shows the Load Resource Balance Positions at P5 for each of the four major Load

Growth Trajectories in the 2017 IRP for December On-Peak Hours (HLH) as an example of the

outputs of the Probabilistic Load Resource Balance Model used in further analysis. Because the

Monthly HLH Planning Standard requires that the Portfolio be in balance with no more than 100

aMW of forecast short-term market purchases, any values in the table below -100 aMW

represent time periods and magnitudes (below -100 aMW) that the Portfolio Optimizer

(described in Appendix B) must address with demand-side or supply-side resources to build a

portfolio that meets PUD planning standards. For example, in Table 3, the 2018 Load Resource


Balance has a deficit of 158aMW in the December On-Peak period at P5. This means to comply

with the PUD’s planning standards, at least 58aMW of resources that would be available during

the December on-peak period in 2018 must be added in order to make the portfolio comply with

the planning standards.

Table 3

December On-Peak Load Resource Balance with Existing/Committed Resources at P5

before New Resource/Conservation Additions (in On Peak aMW)

2018 2020 2022 2024 2026 2028 2030 2032 2034 2036

Climate Change -158 -166 -168 -183 -200 -222 -245 -272 -286 -303

Low Growth -118 -135 -132 -154 -174 -188 -220 -223 -225 -226

Business-As-Usual -111 -131 -153 -196 -228 -259 -300 -328 -355 -384

High Growth -111 -160 -192 -257 -298 -362 -426 -511 -588 -706

In prior IRP documents, the PUD used Planning Standards for two time horizons (Annual and

Winter) using two metrics (Critical and Blend). The resulting information gave the PUD an

indication of the scale of a potential load resource balance deficit in two circumstances (Critical

and Blend) and two time horizons (Annual and Winter), but did not provide any information on

the likelihood of occurrence or the appropriateness of the assumed load resource balance

conditions.

The previous annual portfolio metric, assumed hydro resource production at critical water

conditions, non-hydro resources at normal production, and load at normal weather, on an annual

basis. A sample depiction of this metric is presented in Figure A13. In the chart shown, the area

between the one load outcome (black dotted line) and the one resource outcome (blue bars)

represents the potential need of the PUD. What is not known is whether this combination of load

and resources is likely to occur, and whether the resulting proposed portfolio additions increase

or decrease risk.


Figure A13

Under the new methodology the PUD has access to information on thousands of possible load

resource balance outcomes across many more time horizons (Annual, Monthly aMW, Monthly

HLH, Monthly LLH, Monthly HLH Peak Week) allowing the PUD to develop a better

understanding of potential portfolio needs and the most beneficial shapes and deliveries of

resources to meet those needs. For example, Figure A14 shows the Load Resource Balance

under the Climate Change Load and Resource trajectory before New Conservation at P5 during

the December HLH period, the August HLH period– both drawn from the same 2,500 iteration

simulation. From the chart, it is clear that while the December HLH period is the larger portfolio

deficit in the short term at below -150aMW in 2018, at the end of the study period the August

HLH period is the more deficit period, with a portfolio deficit of less than -300 aMW in 2037.

The Monthly HLH Planning Standard and the Probabilistic Load Resource Balance model allows

the PUD to measure and address both of these needs and compare them against each other,

whereas the previous metric only measured one specific outcome across two possible time

horizons (Annual and Winter periods).

-

200

400

600

800

1,000

aMW

Previous Annual Load Resource Balance Metric

New Cumulative Conservation Winter Capacity ProductLandfill Gas WindGeothermal Loads-No New Conservation


Figure A14

3. Resource Need by Scenario

The PUD measured its potential resource need during the study period using the Probabilistic

Load Resource Balance Model at 760 different points in time. These 760 points in time include

monthly average, on-peak period, and peak week period estimates for every month and every

year from 2018-2037, and an annual average measurement for each year. The model produces

estimates of load, resource generation, and the load resource balance that different possible

combinations would produce at different likelihoods at each of those 760 points in time.

Figure A15 displays all of the point-in-time measurements for the Climate Change Scenario

Load Resource Balance at a P5 (the value likely to be exceeded 19 out of 20 times). The Annual

LRB which is shown at a P50. The data is shown in a heat map format, whereby the most

resource deficit periods are red, and most resource surplus periods are green. In general, more

(350)

(300)

(250)

(200)

(150)

(100)

(50)

0

50

100

aMW

Impact of Climate Change on Net Position PUD Load Resource Balance @P5 by Season

December HLH LRB August HLH LRB


deficit periods are found later in the time series, during Peak Week and HLH periods, and in the

summer and winter. In general, more surplus periods are found in the beginning of the time

series, during the spring, and during the annual average time period.

In analysis of the PUD’s Load Resource Balance deficits and the subsequent optimal portfolios

that resolved these deficits, it was found that a portfolio that adequately addressed all Load

Resource Balance deficits in the following time periods also resulted in the satisfactory

fulfillment of all 760 time periods: Annual Average LRB @P50, the December HLH and

December Peak LRB @ P5, and the August HLH LRB @ P5. For this reason, the Load Resource

Balance needs of different scenarios are shown in these time periods.


Figure A15: Load Resource Balances at P5 (and P50 for Annual Average) for all time periods in Climate Change Scenario


2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037

Annual Average @ P50 177 177 176 176 174 175 153 147 138 118 102 79 68 60 49 46 38 29 19 14

January (100) (101) (84) (80) (75) (73) (88) (88) (96) (91) (108) (120) (147) (147) (146) (145) (150) (158) (162) (169)

January HLH (163) (168) (151) (150) (147) (147) (155) (160) (165) (166) (185) (193) (222) (221) (223) (227) (231) (235) (241) (248)

January Peak Week (246) (248) (237) (234) (230) (227) (240) (246) (250) (253) (268) (274) (298) (300) (307) (306) (312) (311) (324) (328)

February (53) (56) (91) (44) (47) (43) (99) (58) (62) (71) (153) (98) (123) (130) (185) (137) (142) (145) (209) (158)

February HLH (103) (107) (145) (97) (100) (97) (157) (115) (118) (127) (215) (156) (185) (194) (257) (203) (207) (212) (280) (226)

February Peak Week (186) (188) (228) (179) (182) (178) (243) (197) (199) (215) (296) (229) (256) (261) (337) (272) (278) (284) (358) (297)

March (32) (28) (20) (32) (24) (26) (34) (39) (44) (78) (84) (88) (109) (117) (128) (129) (138) (139) (145) (140)

March HLH (79) (79) (76) (81) (75) (77) (89) (95) (102) (135) (140) (145) (172) (181) (189) (189) (201) (201) (212) (208)

March Peak Week (137) (144) (136) (144) (139) (140) (156) (165) (169) (188) (209) (206) (240) (251) (261) (258) (270) (272) (279) (287)

April 39 37 43 40 42 46 24 24 19 (25) (16) (53) (54) (66) (59) (71) (80) (78) (78) (89)

April HLH (6) (8) (1) (7) (6) (4) (23) (26) (31) (80) (74) (112) (111) (123) (118) (131) (142) (139) (136) (151)

April Peak Week (69) (70) (64) (74) (75) (72) (86) (90) (94) (121) (133) (160) (165) (175) (177) (183) (199) (194) (189) (204)

May 157 158 166 164 153 155 135 129 123 93 86 49 43 35 34 26 16 5 1 (2)

May HLH 139 90 144 92 129 86 114 58 97 14 61 (28) 18 (43) 3 (56) (13) (73) (29) (87)

May Peak Week 95 45 97 37 83 40 64 8 49 (21) 9 (71) (34) (88) (47) (105) (63) (121) (75) (135)

June 126 128 120 111 108 102 80 61 42 18 2 (41) (45) (60) (79) (85) (101) (111) (114) (137)

June HLH 136 70 123 55 113 45 79 (2) 43 (45) (1) (106) (54) (131) (89) (154) (111) (181) (132) (210)

June LLH 248 188 246 179 233 169 207 125 173 88 140 36 90 20 70 (2) 49 (23) 29 (45)

June Peak Week 70 16 60 1 49 (14) 26 (52) (14) (98) (63) (149) (121) (186) (155) (207) (172) (228) (188) (263)

July 61 49 47 39 36 31 (3) (17) (31) (59) (69) (116) (135) (148) (156) (165) (179) (193) (215) (228)

July HLH (10) (21) (23) (33) (39) (44) (80) (91) (110) (137) (151) (201) (216) (231) (239) (252) (268) (283) (302) (317)

July Peak Week (47) (58) (65) (73) (80) (82) (119) (133) (148) (173) (192) (235) (256) (270) (279) (291) (307) (321) (341) (359)

August 24 20 23 13 (2) (7) (30) (37) (51) (79) (103) (148) (157) (161) (177) (198) (210) (225) (226) (241)

August HLH (26) (31) (34) (46) (58) (65) (88) (100) (116) (146) (167) (213) (224) (233) (252) (269) (283) (299) (305) (322)

August Peak Week (81) (89) (91) (104) (114) (125) (142) (153) (169) (196) (218) (256) (269) (280) (296) (317) (328) (343) (350) (371)

September 46 38 30 22 16 17 (1) (21) (32) (42) (66) (90) (104) (124) (135) (148) (154) (158) (182) (194)

September HLH (14) (26) (30) (40) (46) (46) (69) (86) (99) (109) (133) (165) (179) (197) (209) (222) (229) (239) (259) (273)

September Peak Week (83) (91) (97) (109) (115) (117) (136) (153) (167) (189) (197) (224) (238) (258) (271) (286) (291) (300) (321) (339)

October 31 29 38 42 37 36 15 5 1 (15) (24) (57) (64) (71) (66) (75) (85) (100) (108) (114)

October HLH (9) (11) (3) (4) (8) (13) (31) (41) (45) (66) (76) (108) (115) (123) (123) (131) (144) (156) (163) (171)

October Peak Week (100) (101) (95) (98) (102) (106) (123) (135) (138) (160) (166) (198) (206) (214) (214) (226) (241) (250) (258) (266)

November (25) (17) (5) (20) (17) (15) (27) (28) (34) (67) (74) (95) (96) (97) (113) (122) (125) (133) (125) (139)

November HLH (94) (87) (79) (91) (91) (88) (102) (107) (115) (144) (153) (171) (175) (180) (192) (204) (205) (215) (213) (227)

November Peak Week (204) (199) (200) (204) (207) (210) (215) (227) (236) (267) (268) (296) (301) (301) (315) (328) (333) (341) (341) (357)

December (82) (89) (89) (90) (90) (75) (98) (111) (118) (143) (135) (159) (160) (183) (187) (192) (193) (196) (216) (220)

December HLH (158) (167) (166) (164) (168) (160) (183) (192) (200) (225) (222) (246) (245) (265) (272) (275) (286) (289) (303) (309)

December Peak Week (246) (250) (247) (253) (249) (257) (272) (278) (292) (317) (323) (348) (357) (373) (376) (388) (388) (399) (411) (422)


Resource Need - Climate Change Scenario

The Climate Change Scenario shows capacity resource needs before conservation on an Annual

Average basis, the December On-Peak period, the December Peak Week period, and the August

On-Peak period. There is no measured annual energy need in the Climate Change scenario.

Climate Change

- Annual

Average (P50)

2018 2020 2022 2024 2026 2028 2030 2032 2034 2036

Resources

940

968

977

976

974

954

930

931

932

933

Load (Adjusted)

763

792

802

823

836

852

863

882

894

914

Load Resource

Balance Deficit

Before New

Resources

177

176

174

153

138

102

68

49

38

19

0

200

400

600

800

1,000

1,200

aMW

Climate Change Load and Resources: Annual Energy before New Conservation

Existing Resources Load


Climate Change

- August HLH

@ P5

2018 2020 2022 2024 2026 2028 2030 2032 2034 2036

Resources

722

738

737

727

717

690

655

650

643

638

Load (Adjusted)

747

772

795

815

833

857

879

901

926

943

Load Resource

Balance Deficit

Before New

Resources

(26)

(34)

(58)

(88)

(116)

(167)

(224)

(252)

(283)

(305)

0

200

400

600

800

1000

1200

aMW

Climate Change Load and Resources in August HLH Before New Conservation

Existing resources Load


Climate

Change -

December

HLH @P5

2018 2020 2022 2024 2026 2028 2030 2032 2034 2036

Resources

1,107

1,157

1,174

1,199

1,219

1,232

1,246

1,280

1,298

1,322

Load

(Adjusted)

949

991

1,006

1,016

1,019

1,011

1,001

1,008

1,012

1,019

Load Resource

Balance Deficit

Before New

Resources

(158)

(166)

(168)

(183)

(200)

(222)

(245)

(272)

(286)

(303)

0

200

400

600

800

1,000

1,200

1,400

aMW

Climate Change Load and Resources: December HLH before New Conservation



Climate

Change -

December

Peak Week @

P5

2018 2020 2022 2024 2026 2028 2030 2032 2034 2036

Resources

960

1,001

1,012

1,028

1,032

1,021

1,003

1,011

1,016

1,020

Load

(Adjusted)

1,206

1,248

1,261

1,299

1,324

1,344

1,360

1,386

1,403

1,431

Load Resource

Balance Deficit

Before New

Resources

(246)

(247)

(249)

(272)

(292)

(323)

(357)

(376)

(388)

(411)

Business As Usual w/No Carbon, w/California Carbon - Resource Need

The Business As Usual Scenario for both carbon policy levels (with No Carbon, with California

Carbon in 2022) shows capacity resource needs before conservation in the December and August

-

200

400

600

800

1,000

1,200

1,400

1,600

aMW

Climate Change Load and Resources December Peak Week before New Conservation



On-Peak period, and the December Peak Week period. There is a limited annual energy need in

the Business as Usual scenario before conservation.

BAU - Annual

Average 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036

Resources 972 991 989 974 971 950 926 926 926 926

Load (Adjusted) 778 806 821 841 857 874 890 908 925 944

Load Resource

Balance Deficit

Before New

Resources

194 185 168 133 114 76 36 18 1 -18

-

200

400

600

800

1,000

1,200

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

20

32

20

33

20

34

20

35

20

36

20

37

aMW

BAU Load and Resources: Annual Energy before New Conservation



BAU - August

HLH @P5 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036

Resources 749 765 760 746 743 721 691 692 691 692

Load (Adjusted) 741 762 780 797 811 832 845 862 881 892

Load Resource

Balance Deficit

Before New

Resources

8 3 -20 -51 -68 -111 -154 -170 -190 -200

-

200

400

600

800

1,000

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

20

32

20

33

20

34

20

35

20

36

20

37

aMW

BAU Load and Resources: August HLH before New Conservation



BAU - December

HLH @P5 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036

Resources 989 1016 1012 999 995 982 967 969 967 968

Load (Adjusted) 1101 1147 1164 1195 1223 1241 1267 1297 1322 1352

Load Resource

Balance Deficit

Before New

Resources

-111 -131 -153 -196 -228 -259 -300 -328 -355 -384

-

200

400

600

800

1,000

1,200

1,400

1,600

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

20

32

20

33

20

34

20

35

20

36

20

37

aMW

BAU Load and Resources: December HLH before New Conservation



BAU - December

Peak Week @P5 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036

Resources 995 1026 1023 1010 1010 988 972 969 972 970

Load (Adjusted) 1204 1258 1280 1308 1348 1359 1399 1422 1457 1491

Load Resource

Balance Deficit

Before New

Resources

-208 -232 -258 -298 -337 -370 -427 -453 -485 -520

Low Growth Scenario - Resource Need The Low Growth Scenario shows limited capacity resource needs before conservation in the

December and August On-Peak period, and the December Peak Week period. There is no energy

need in the Low Growth scenario before conservation.

-

200

400

600

800

1,000

1,200

1,400

1,600

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

20

32

20

33

20

34

20

35

20

36

20

37

aMW

BAU Load and Resources: December Peak Week before New Conservation



Low - Annual

Average 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036

Resources 957 971 981 974 971 951 926 926 926 926

Load (Adjusted) 768 788 798 813 821 828 833 837 836 838

Load Resource

Balance Deficit

Before New

Resources

190 183 183 161 150 122 93 88 90 88

-

200

400

600

800

1,000

1,200

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

20

32

20

33

20

34

20

35

20

36

20

37

aMW

Low Load and Resources: Annual Energy before New Conservation



Low - August

HLH @P5 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036

Resources 736 749 756 746 743 721 691 692 691 692

Load (Adjusted) 731 746 759 770 776 787 790 792 796 791

Load Resource

Balance Deficit

Before New

Resources

5 2 -3 -24 -33 -66 -99 -100 -105 -99

-

200

400

600

800

1,000

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

20

32

20

33

20

34

20

35

20

36

20

37

aMW

Low Load and Resources: August HLH before New Conservation



Low - December

HLH @P5 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036

Resources 971 991 1003 999 996 982 967 968 967 969

Load (Adjusted) 1089 1126 1134 1153 1170 1170 1187 1192 1191 1195

Load Resource

Balance Deficit

Before New

Resources

-118 -135 -132 -154 -174 -188 -220 -223 -225 -226

-

200

400

600

800

1,000

1,200

1,400

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

20

32

20

33

20

34

20

35

20

36

20

37

aMW

Low Load and Resources: December HLH before New Conservation



Low - December

Peak Week @P5 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036

Resources 982 1026 1023 1010 1010 988 972 969 972 970

Load (Adjusted) 1196 1229 1240 1262 1278 1287 1308 1307 1307 1313

Load Resource

Balance Deficit

Before New

Resources

-214 -230 -227 -253 -275 -296 -336 -336 -338 -342

High Growth Scenario - Resource Need The High Growth Scenario shows significant capacity resource needs before conservation in the

December and August On-Peak period, and the December Peak Week period. There is a

significant annual energy need in the High Growth scenario before conservation.

-

200

400

600

800

1,000

1,200

1,400

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

20

32

20

33

20

34

20

35

20

36

20

37

aMW

Low Load and Resources: December Peak Week before New Conservation



High - Annual

Average 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036

Resources 987 992 988 974 971 950 926 926 926 926

Load (Adjusted) 788 826 846 880 907 943 977 1031 1086 1165

Load Resource

Balance Deficit

Before New

Resources

199 165 142 94 64 7 -51 -105 -159 -240

-

200

400

600

800

1,000

1,200

1,400

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

20

32

20

33

20

34

20

35

20

36

20

37

aMW

High Load and Resources: Annual Energy before New Conservation



High - August

HLH @P5 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036

Resources 761 765 762 746 743 722 691 692 691 692

Load (Adjusted) 750 781 808 835 860 899 927 980 1035 1104

Load Resource

Balance Deficit

Before New

Resources

11 -15 -46 -88 -117 -177 -236 -288 -344 -412

-

200

400

600

800

1,000

1,200

1,400

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

20

32

20

33

20

34

20

35

20

36

20

37

aMW

High Load and Resources: August HLH before New Conservation



High - December

HLH @P5 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036

Resources 1009 1017 1011 1000 996 982 967 968 967 968

Load (Adjusted) 1121 1177 1203 1256 1293 1344 1393 1479 1555 1675

Load Resource

Balance Deficit

Before New

Resources

-111 -160 -192 -257 -298 -362 -426 -511 -588 -706

-

500

1,000

1,500

2,000

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

20

32

20

33

20

34

20

35

20

36

20

37

aMW

High Load and Resources: December HLH before New Conservation



High - December

Peak Week @P5 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036

Resources 1018 1029 1016 1011 1005 994 969 971 970 974

Load (Adjusted) 1225 1291 1313 1373 1419 1480 1542 1635 1716 1863

Load Resource

Balance Deficit

Before New

Resources

-207 -263 -296 -363 -414 -487 -574 -664 -746 -890

-

500

1,000

1,500

2,000

2,500

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

20

32

20

33

20

34

20

35

20

36

20

37

aMW

High Load and Resources: December Peak Week before New Conservation


appendix a: probabilistic load resource balance …portfolio deficits. the data outputs of the...

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