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Production Cost Model
Data Work Group
(PDWG) In-Person
Meeting - Day 1
August 14, 2019
Jamie Austin–PDWG Chair
Kevin Harris–PDWG Vice Chair
Tyler Butikofer–WECC Liaison
▪ Day 1
• 2028 ADS PCM Phase 2 V2.0
◦ Testing of CCCTs Disaggregation
◦ Modify default Summer Time Simulation Settings
• PCM 2030 2-Yr Work Plan
• Process Diagram
• Other Topics warrant further conversation
• Dispatch Cost for wind, solar and Hydro
• What is the minimum up & down time and what should it be?
▪ Day 2
• ADS Data Development and Validation Manual (DDVM)
• Development of the 2030 ADS PCM
◦ Managing Electric Vehicle Load, Building Electrification
◦ Coincident Energy Year
◦ Modify the definition of “Regions/Areas
• Heat Rate Curves Update
◦ Managing Station Service
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Overview
▪ DS turned down approving date changes for summer de-rates, at the May, 2019 meeting. Proposed was to use “June through September” to “April through October”. It is intended to address previous concerns with the following approach:
▪ Working with PMWG and ABB, it was agreed that a more robust solution can be achieved through software modification by applying de-rates at the area level. The broader approach suggested in point 1 attempts to cover all geographic diversities and results in misconstruing representations in some areas.
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Default simulation Summer Time
Schedule Potential Solutions
▪ Build the 2030 ADS PCM dataset, starting
with the 2028 ADS PCM Phase 2 – V2.0
▪ Transmission Topology – work with SDWG to
compile a 2030 HS power flow case starting
with and export hour from the 2028 ADS
PCM Phase 2 – V2.0
▪ For resource definition, use resources
submitted to WECC L&R, in March 2020
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PCM 2030 2-Yr Work Plan
▪ Review “Draft” Work Plan
▪ Agree on Scope of Work for the 2030 Dataset? e.g.:
• Do we need to recalculate the Heat Rates?
• Should we change the Coincident Energy Shapes Year?
▪ Hold a series of informational meetings that would lead to prioritizing the updates
▪ Brainstorm assumptions, key processes and potential benefits and risk
▪ Timeline
• ADSTF is working on a process diagram, charting what would be required to build a new PCM dataset?
• Have PDWG validate and sign off on the ADSTF recommendation.
• Coordinate with WECC staff on their availability to support what needs to be done?
▪ Who is going to do what?
• What to be expected from PDWG stakeholders?
• What to be expected from WECC staff?
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2030 ADS PCM “Draft” Work Plan
▪ Dispatch Cost for wind, solar and Hydro
▪ What is the minimum up & down time and
what should it be?
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Other Topics warrant further
conversation
Production Cost Model
Data Work Group
(PDWG) In-Person
Meeting - Day 2
August 15, 2019
Jamie Austin–PDWG Chair
Kevin Harris–PDWG Vice Chair
Tyler Butikofer–WECC Liaison
Demo:
Steven Wallace
Tyler Butikofer
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ADS Data Development and
Validation Manual (DDVM)
▪ Demonstration by Tyler Butikofer and Steven
Wallace
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ADS Data Development and Validation
Manual (DDVM)
Development of the 2030 ADS PCM
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Managing Electric Vehicle Load &
Building Electrification
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California 10 Million EV
Scenario Inputs
PCM Inputs for Preliminary Study
of Decarbonization
Richard Jensen
August 15, 2019
California Energy Commission
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What this is / is not…
• This is…
• The first cut at conducting
electrification scenarios
• 2030 only (for EV scenario)
and “simplistic” load build
• Built to meet 60% RPS req for
CA
• This is not…
• The final set of input assumptions and results
• An attempt to consider different or multiple EV charging profiles
• A WECC wide focused study
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• Spreadsheet tool developed by (retired) CEC staff
• Distributes user-defined number of EVs across CA
utilities based on 2015 DMV registration data
• Calculates annual energy demand by utility
• Different EV technologies considered within the tool
Load for 10mm EVs
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Load Shape used in PLEXOS
• 2018 CEDU hourly forecast – posted on CEC website
• Contains the hourly forecast for ~ 3.5mm EV’s – mid case
• Energy for 10mm EVs distributed across same shape
• Mean value of shape is .454
• EV load modeled on the demand side
0.000
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1.000
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Chronological Load EV Shape
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Typical Week Load Shape
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Load Comparison
• CA 2030 Mid Case (280 TWh) load vs 10mm EV (306 TWh)
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10,000
20,000
30,000
40,000
50,000
60,000
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77
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53
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63
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73
92
84
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94
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04
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14
85
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08
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45
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77
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00
76
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86
46
96
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06
93
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16
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39
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47
85
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08
68
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78
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Me
gaw
atts
Base Case Load (MW) 10M EV Load (MW)
• 60% of 26 TWh = 15.6 TWh
• Met with roughly 75% solar and 25% wind
• Capacity amount 7,900 MW total – all placed in CA
• CF may be a bit low for some technologies
• Results still under evaluation
• Offshore wind under consideration for next round of simulations
Preliminary RPS Build to meet CA requirement
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▪ Coincident energy year is the base year for hourly profiles: load, wind, hydro (monthly), solar, pumping load, …
• Currently using 2009 w/exception: 2008 monthly hydro for 15 projects on the Columbia river for Jun-Aug
▪ It’s desired to preserve coincident, weather related behavior, between load and the various supply shapes
▪ NREL has new hourly wind and solar data for 2014
• Previously 2007-2013
▪ It’s difficult to get agreement as to what is the best year to use given:
• What year is best represents normal for all: areas load, hydro, wind, solar, pumping load …
◦ Geographic diversity yields normal in one area but abnormal in others
▪ Load Shapes - If we switch to a more recent base year how do we manage actual BTM PV generation, included in the hourly load shape?
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Coincident Energy Year
▪ Recommend using time-coincident wind & solar data with loads
▪ Hydro
• Should allow for dispatch to respond to variable generation (wind and solar)
• Much more work needs to be done to determine cost-effective approaches within hydro constraints and reliability
▪ Underlying correlation between wind and load is dynamic/complex, but using time-sync’d data implicitly covers this
▪ Actual (or modeled) hourly for at least one year
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Modeling Variable Generation
▪ Pros
• Much work been spent on developing and validating the Wind hourly shapes
• Much work been spent on developing and validating the Solar hourly shapes
▪ Pending Work
• Need to revisit & validate Load shapes, making sure EE, AAEE, PV, AAPV and EV are disaggregated and represented independently.
• Need to update Hydro profiles
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Reasons to Reuse 2009 Shapes in the
2030 ADS PCM Case
▪ Step 1) Developing hourly load shapes
Notes:1. Historically we have assumed EE shape is same as base load shape therefore EE
forecast is subtracted from base forecast
2. BTM PV is modeled as a supply in GridView, therefore ensure base forecast includes BTM PV impact
3. If EV charging has a unique hourly shape, therefore ensure base forecast excludes EV charging and grow shape independently
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Creating Hourly Load Shapes (1) - Kevin
Harris
Summary of Growing Hourly Load ShapesHourly Monthly Adj Base Grow Hourly
Shape Forecast Forecast* Shape
Base Load Yes Yes Base
Energy Efficiency1 Yes If needed
BTM PV2
Yes If needed
EV Charging3 Yes Yes If needed EV
Future Items
Building Electrification Yes Yes If needed Build E
Time-of-Use Rates Yes Yes If needed TOU
Climate Change Yes Yes If needed Climate
▪ Step 2) Sum hourly shape
• Base load shape
• + EV Charging
• - Irrigation load shapes (if modeled as supply)
▪ Future Items
• + Building Electrification
• + Time-of-Use
• + Climate Change
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Creating Hourly Load Shapes (2)
▪ Jointly with CEC & PDWG agreed to use hourly CEMS data from years 2010 through 2014 to calculate the I\O curve for IEPR 2016 and the 2026 ADS PCM dataset; the same data is currently used in the 2028 ADS PCM dataset.• Plant operation changes over time, especially in
California, influenced by less and less dependency on thermal generation. Is the CEC ready to recalculate the I\Curves, using more recent CEMS data?
• Potential fixes that should be considered with the update:
◦ The method we use to account for Station Service.
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Heat Rate Curves Update – Jamie Austin
▪ Currently, manual intervention is necessary to reconcile differences with “Round-Trip” import-export
• The goal is to have consistent value for Pmax in both PF and PCM:
◦ PF Pmax = gross unit rating; models SS as stand alone
◦ PCM Pmax = gross unit rating net SS; SS is netted from Pmax
• Inconsistency in modeling SS leads to different values for Pmax
◦ The goal is to have a means to manage the differences between PF Pmax and PCM Max Rating
◦ Note: Pmax minus station service is not equal to reported max rating
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Station Service Modeling Approach
▪ Suggested approach• On PF import, SS load is mapped to generator on same
bus into new table
• Table can be edited by user to make appropriate correction, and map unmapped SS load
• SS load would be excluded from area load in PCM simulation
• On export if a generator is committed, mapped SS load is turned on, if unit is off, SS load is turned off
▪ Add capability in GridView to manage SS load differences, helps the Round-Trip process to use the same Pmax
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Modeling of Station Service Load – Kevin
Harris
▪ Modify the definition of “Region/Areas” to
lineup with the Western Planning Regions
Footprint.
▪ Why this is needed?
• What is the objective of the study?
◦ Load area, region other aggregation tools in PCM?
◦ If overlapping regions, any control for calculation?
◦ How does the CAISO & CEC split CAISO and non-
CAISO areas in their respective modeling?
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Modify the definition of “Regions and Areas”
Jamie Austin, Kevin Harris, Yi Zhang, & Angela Tanghetti
▪ Current set up lines up with BAAs, with
some exceptions (PACE & PACW).
▪ BAA level aggregation not all encompassing
(e.g., covers for Reserves, Wheeling, but not
for FERC 1000, Operation Planning, etc.)
▪ How we get GV to support different
aggregations? On both Supply & Load sides
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Potential Solutions to Reporting and
Aggregation – Jin Zhu, ABB
Contact:
Jamie Austin
Kevin Harris
Tyler Butikofer
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