W E S T E R N E L E C T R I C I T Y C O O R D I N A T I N G C OU N C I L
High and Low Load SensitivitiesTEPPC 2024 CC
Data Work GroupJanuary 27, 2015
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Overview• Background Information on Running Sensitivity
Analysis• Existing Assumptions for High & Low Loads
– 10 Year Loads– 20 Year Loads
• Assumptions for Load Sensitivities– Discuss assumptions for high and low load conditions used by the California
Energy Commission– Discuss the impact of high efficiency programs on the load forecast
• Assumptions for high loads– Loads used by the ISO for Reliability Analysis– Loads used by BPA for Reliability Analysis
• Where do we go from here?
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TEPPC\TAS Meeting 021611
• Running Sensitivities with Every Study Cycle
• DWG participants see value in running sensitivities (High and Low) with every study cycle (e.g., Gas Prices, Hydro and Loads)
• A regular schedule will give DWG and others sufficient time to prepare for getting the data and assumptions delivered on time.
• The question about submitting an associated study request to TEPPC been settled by Brad. The request is internal to the TEPPC process and should be settled as such.
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• Already planning to run low load and high DSM sensitivitiesoNo consideration been given to run high load
sensitivity.
• The LRS loads are one in two year risk exposure, whereas, transmission planning is typically performed with one in five and one in ten.
• TEPPC planning is transmission planning.
TEPPC\TAS 013012DWG report on Load Sensitivities:
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Existing Assumptions for High & Low Dan Beckstead, WECC
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Assumptions for High & Low Load Conditions Angela Tangahetti, CEC
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Introduction
Topics to Cover:
• Developing CEC IEPR “Common Cases”• Overview of Common Case Methodology• Common Case Input Assumptions
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Purpose of IEPR “Common Cases”
• Energy sectors serving California are complex, interdependent systems lead to three common cases that “easily” translate across sector
• Lack of common case assumptions led to sectors “fractured” analytical approaches
• Stronger analytical basis for policy discussions
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Common Cases Require Common Definitions
• Defining cases key to coordination• “High” & “Low” not specific enough • Three worldviews chosen to model
• Reference/Mid Case or Business as Usual• High Energy Consumption• Low Energy Consumption
Graphical Representation of Iterative Modeling Process
WECC Electricity Dispatch Model North American
Gas Model
Updated Economic/
Demographic Assumptions
CA Transportation
Demand Models
CA Electricity Demand Models
WECC Electricity Dispatch Model
Common Case Input Assumptions
• Gross Domestic Product Growth • CPI Inflation• Gross State Product• Population Growth• Energy Efficiency Improvements• Demand Response• Carbon Prices • Weather (HDD/CDD)
Trade-Offs in High and Low Energy Consumption Cases
• High and Low Consumption Scenario for one sector comes at expense of other sectors
• Some trade-offs necessary in defining high and low cases
• Chosen approach was “Major Driver” testIf input value was major driver in one model
but not others, value set by model where major driver
Resolution of Conflicting Variables
Variable• Electricity Price• NG Price• Crude Oil Price• EV Penetration• Coal Price• NGV Penetration
Controlling Model• Electricity• Natural Gas• Transportation• Transportation• Electricity• Transportation
Understanding Case Development
• Reference/Mid Case reasonably expected trajectory given best available input
• High and Low Cases Energy Consumption cases are reasonable range
• High and Low Cases Energy Consumption are NOT most extreme possible
High and Low Common Case AssumptionsRelative to Mid/Reference Case
Input Category High Demand Low Demand
Natural Gas Electricity Transportation Natural Gas Electricity Transportation
GDP High High High Low Low Low
Wholesale Electricity Price Low Low High High High Low
Retail Electricity Rates Low Low High High High Low
NG Supply Cost Low Low High High High Low
Crude Oil Price High No Effect Low Low No Effect High
Pop/Demographics High High High Low Low Low
Renewables (Gen) Low High High High Low Low
Energy Efficiency Low Low No Effect (High) High High No Effect (High)
Demand Response Low Low No Effect (High) High High No Effect (High)
CHP Low Low No Effect (High) High High No Effect (High)
Carbon Price Low Low Low High High High
Weather (HDD/CDD) High High No Effect Low Low No Effect
Low Carbon Fuel Standard High High Low Low Low High
Electric Vehicle Penetration High High Low Low Low High
Ren. Fuel Vehicles No Effect No Effect Low No Effect No Effect High
CAFÉ Standards No Effect No Effect Low No Effect No Effect High
Coal Price Low Low No Effect (Low) High High No Effect (High)
Notes For Chart on Previous Slide
• Terminology - "High" and "Low" case metrics is demand/consumption
• High - Represents a value above those identified in the Mid Case • Low - Represents a value below those identified in the Mid Case • Highlighted cells represent values which have a conflict between
what settings the modeling teams would each use• Parentheticals represent theoretical setting if the model allowed it.
Actual values are outside parenthesis and represent limitations of model complexity or setting.
• Major/Minor drivers - Not all models are strongly sensitive to all of these factors. The goal is to identify which models see these inputs as "Major" drivers and use those models to determine which of these values should be used for the common cases.
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The Impact of High Efficiency Programs on the Load Forecast
Galen Barbose, LBNL
2022 High DSM Case
• Assume “all cost-effective EE potential” is achieved throughout the West– Intended as a boundary case– Agnostic about what types of policies are used to get there
(codes, standards, customer-funded programs, etc.)• Rely on recent existing EE potential studies to estimate
potential for individual utilities/regions – Extrapolate to regions for which recent potential studies are
unavailable– Lots of complexities (e.g., differences in study vintage/scope,
baseline definition, etc.) and approximations
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2022 High DSM Forecast Adjust Only for Incremental EE Beyond What Is Contained in Common Case
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2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
En
erg
y o
r P
ea
k D
em
an
d
WECC Common Case
All Cost Effective EE
High DSM/DG Case
Balancing Authority Load Forecast Submitted to WECC
Embedded Savings from EE Policies & Programs (2011-2021)
Expected EE Savings from
Current Policies & Programs
(2011-2021)
2022 WECC-Wide EE ImpactsCommon Case vs. High DSM Case
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GWh MW
Cumulative Savings (2011-2021)Common Case 112,466 24,874High DSM Case 203,525 43,592
Cumulative Savings (% of 2021 Load)Common Case 10.2% 11.5%High DSM Case 18.4% 20.1%
CAGR (2010-2021)Common Case 1.4% 1.3%High DSM Case 0.5% 0.3%
• Common Case EE savings represent roughly 10% of total WECC-wide load• High DSM Case represents roughly an additional 8% reduction in WECC-
wide annual energy and 9% reduction in non-coincident peak
2022 High DSM Impacts: Reduction from Common Case by BA
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-25%
-20%
-15%
-10%
-5%
0%
CISO IID
LADW
PSM
UD
TID
AESO
BCTC
AVA
BPA
CHPD
DOPD
GCP
DIP
CN
WM
TPA
CW PGE
PSE
SCL
TPW
RW
AUW
APS
CFE
EPE
NEV
PPA
CEPN
MPS
CO SPP
SRP
TEP
WAC
MW
ALC
WEC
C To
tal
Annual Energy (GWh) Non-Coinc. Peak (MW)
California Canada Northwest Southwest
Redu
ction
in 2
021
Load
from
Com
mon
Cas
e
2032 High DSM Case
• Partnered with Itron to apply their Statistically Adjusted End-Use (SAE) load forecasting framework– Econometric model with stock efficiency and saturations specified for
30 separate end-uses– Produces monthly energy and peak demand, disaggregated by end-use
• Key Efficiency Assumption for High DSM Forecast: Average stock efficiency for each end-use reaches level equivalent to the most efficient models commercially available today
• Requires a structured sequence of load forecasts• Calibrated to the official WECC Reference Case
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2032 High DSM ImpactsReduction from Reference Case
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• Roughly 20% reduction in WECC-wide annual energy in 2032, relative to reference case
• Variation across states reflects differences in end-use mix, reference case efficiency level, weather, etc.
-30.0%
-25.0%
-20.0%
-15.0%
-10.0%
-5.0%
0.0%
AESO
APS
AVA
BCTC
BPA
CFE
CHPD
DO
PDEP
EFA
R_EA
STG
CPD
IID LDW
PM
AGIC
_VLY
NEV
PN
WM
TPA
CE_I
DPA
CE_U
TPA
CE_W
YPA
CWPG
E_BA
YPG
E_VL
YPG
NPN
MPS
CPS
ESC
ESC
LSD
GE
SMU
DSP
PSR
PTE
PTI
DC
TPW
RTR
EAS_
VLY
WAC
MW
ALC
WAU
WW
ECC
High DSM Case Incremental Savings: Percentage Reduction from SAE Reference Case (2032)
Annual EnergyNon-Coincident Peak
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A Few Take-Aways and Questions to Consider
• Increasing or decreasing EE by 50% relative to what is embedded in Common Case forecast is equivalent to about a +/- 5% adjustment in total WECC-wide loads over 10 years
• Prior high DSM study cases suggest aggressive EE alone could reduce loads by almost 10% over 10 years, compared to Common Case– Is an aggregate 10% reduction for Low Load Sensitivity too modest, given
additional potential drivers for low load beyond EE?• EE impacts differ greatly by load zone (e.g., 2-20% of load)
– Is a one-size-fits-all 10% reduction for the Low Load Sensitivity too coarse; are region- or BA-specific adjustments warranted?
• EE impacts on peak demand are not proportional to energy impacts, but perhaps close enough for level of precision desired
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Loads Used by the CAISO for Reliability AnalysisIrina Green, CAISO
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Load Forecast for Annual Transmission Planning Process (TPP)
• CEC Load forecast is used as the starting point
– For 2014-2015 TPP, the mid-case California Energy Demand Forecast 2014-2024 released by California Energy Commission (CEC) dated January 2014 with the Mid-Case LSE and Balancing Authority Forecast spreadsheet updated as of February 8, 2014.
– Energy Efficiency Adjustments based on 2013 IERP final report from January 23, 2013
Using Mid Additional Achievable Energy Efficiency scenario
• http://www.energy.ca.gov/2013_energypolicy/documents/demand-forecast_CMF/Additional_Achievable_Energy_Efficiency/January_2014_files/
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Load Forecast (continued)
• Methodologies used by Participating Transmission Owners (PTOs) to create bus-level load forecast are documented in the Study Plan
• 1-in-10 year heat wave load projection for individual local area studies
• 1-in-5 year heat wave load projection for bulk system studies• PTOs subtract losses from the CEC forecast• Pumping loads are modeled as generators with negative output• CAISO studies summer peak, summer off-peak, minimum load
cases, winter peak cases for some areas, and other cases as needed (spring peak, summer partial peak)
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Study Areas
Northern Area: PG&E – bulk system and local areasSouthern area:SCE, SDG&E, VEA
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Load Forecast MethodologyPG&E
• PG&E creates bus-level load forecast (using CEC forecast as the starting point)– PG&E loads in the base case
• Determination of Division Loads based on historical data and projected load growth.
• Total PG&E load growth allocated to divisions and adjusted based on 1-in-10 or 1-in-5 temperatures
• Allocation of Division Load to Transmission Bus Level depending on types of loads: conforming, non-conforming, self generation and plant (not included in division loads)
• Municipal loads in the base case – municipal utilities provide their load forecast to PG&E
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Load Forecast MethodologySCE
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Load Forecast MethodologySDG&E
• Utilize CEC’s latest load forecast as the starting point
• SDGE’s methodology to create bus-level load forecast
– Actual peak loads on low side of each substation bank transformer from historical data
– Normalizing factors applied for achieving weather normalized peak
– Adverse temperature adjustment factor applied to get the adverse peak
– Adverse load and coincident load determined for each substation
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Load Forecast MethodologyValley Electric (VEA)
• Historical SCADA data and load plans• Adjusted to CEC forecast
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• Questions?• Comments?
• Irina Green [email protected] 916-608-1296
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Loads Used by the BPA for Reliability Analysis
Reed Davis, BPA
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BPA’s methodology to create BUS forecasts
• Historical data used to create monthly energy and peak forecasts
• Weather normalizing factors included as appropriate
• Forecasted with a 34 year average peak producing temperature. Considered a 1 in 2 peak. Temperatures are updated every 10 years.
• Forecasts reviewed with local utility staff to make sure it aligns with local construction plans.
• New large loads are reviewed and added directly to forecast if load has a greater than 70% chance of occurring.
• Permanent load transfers between BUS are included.
• Specific large conservation projects are included directly at the BUS level.
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BPA’s methodology for special BUS forecasts
• Using the base BUS forecast we – Adjust to 1 in 20 weather conditions
– Add economic uncertainty based on economic conditions surrounding the BUS and load delivery over the BUS based on load type: conforming, non-conforming.
– Adjust for changing conservation plans