1. Western Interconnection Flexibility Assessment
Update to TEPPC
May 7, 2015
Arne Olson, Partner

2. About the Study
WECC and WIEB are interested in understanding power system reliability and flexibility needs under higher renewable penetration
There have been many stakeholder requests for studies to help understand the operational issues associated with higher renewables
WECC is interested in understanding and gaining experience with new reliability planning tools
WECC and WIEB engaged E3 and NREL to study operational needs
Funding for E3 work from WECC and WIEB (through ARRA)
Funding for NREL work from DOE

3. “Institutional” solutions
Study Goals
Assess the ability of the fleet of resources in the Western Interconnection to accommodate high renewable penetration while maintaining reliable operations
Quantify the size, magnitude and duration of operating challenges resulting from high renewable penetration
Investigate potential flexibility solutions, including:
Renewable dispatch as an operational strategy
Regional coordination
Flexible supply and demand-side resources
Energy storage
Transmission
Learn about how to do flexibility modeling and planning
“Institutional” solutions
“Physical” solutions

4. Project Team Partnership between WECC, WIEB, NREL and E3
WECC and WIEB provide general project oversight role
E3 directs technical work
NREL provides data, HPC resources, technical support, and insights from previous experience E3
Arne Olson, Partner
Nick Schlag, Project Manager
Dr. Elaine Hart
Ryan Jones
Ana Mileva
NREL
Bri-Mathias Hodge, Project Manager
Carlo Brancucci Martinez-Anido
Greg Brinkman
WECC
Dan Beckstead
Vijay Satyal
Donald Davies
Branden Suddeth
WIEB
Tom Carr
Maury Galbraith

5. Project Elements Renewable energy capacity value analysis
Effective load-carrying capability (ELCC) method using E3’s Renewable Energy Capacity Planning Model (RECAP)
Power system flexibility analysis
Regional model using E3’s Renewable Energy Flexibility (REFLEX) model for PLEXOS run on NREL’s High Performance Computing environment
Stakeholder participation opportunities
Technical review committee
Executive advisory group

6. Cases Studied 2024 Common Case 2024 High Renewables Case
Few reliability or flexibility issues anticipated
Primary purpose of case is calibration
2024 High Renewables Case
Renewable penetration that is high enough to show interesting operational challenges
Composition of case now being determined in consultation with technical review committee

7. Regional Focus Study focuses on five regions
Explicitly control interactions between regions through modeling of interties
Most regions share characteristics appropriate for a resource planning study:
Similar weather and load patterns across the region
Limited internal transmission constraints
Some degree of regional coordination already
Limited reliance on other regions

8. RECAP MODEL RESULTS

9. E3 Renewable Energy Capacity Planning Model (RECAP)
Flexibility Assessment utilizes RECAP, E3’s non-proprietary model for evaluating power system reliability and resource capacity value under high renewable penetration
Initially developed to support CAISO renewable integration modeling
Used by a number of utilities and state commissions
Will be transferred to WECC as part of study process

10. Calculating LOLP
LOLP is determined by comparing the distributions of potential load and resource states and calculating the probably that load exceeds generation
LOLP comes from the chance that net load exceeds net thermal generation
Net thermal generation distribution
Gross load distribution
Gross load
Net thermal generation
LOLP

11. Adding Renewables
After adding renewables to the system, net loads are reduced—distribution shifts to left
LOLP decreases in every hour (nearly)
Net thermal generation distribution
Gross load
Net load distribution with renewables
Gross load distribution
Thermal generation
Renewable
net load
Reduction in LOLP with increase in renewables

12. Additional load to return to original system LOLE
Calculating ELCC
Since LOLE has decreased with the addition of renewables, adding load will return the system to the original LOLE
The amount of load that can be added to the system is the effective load carrying capability (ELCC)
Original system LOLE
Additional load to return to original system LOLE
= ELCC
LOLE after renewables

13. Portfolio vs. Marginal ELCC Values
The cumulative portfolio capacity value is used for resource adequacy planning
Due to the complementarity of different resources the portfolio value will be higher than the sum of each individual resource measured alone
May need to attribute the capacity value of the portfolio to individual resources
There are many options, but no standard or rigorous way to do this
Individual Solar Capacity Value
Individual Wind Capacity Value
Combined Capacity Value
The marginal capacity value, given the existing portfolio, is used in procurement
Provides a measure of the value of the next resource to be procured
This value will change over time with the mix of system needs & resources

14. Reliability Metrics LOLE
The RECAP model calculates conventional power system reliability metrics:
Loss of Load Probability (LOLP)
Loss of Load Expectation (LOLE)
Loss of Load Frequency (LOLF)
Expected Unserved Energy (EUE)
RECAP also calculates effective capacity of renewables, demand response, and other dispatch-limited resources:
Effective Load Carrying Capability (ELCC)
Marginal ELCC
Cumulative ELCC

15. Common Case Renewable Mix
Renewable penetration in the Common Case is approximately 20% of load (U.S. portion); wind and solar serve approximately 13% of load:
E3 and NREL have developed production profiles to reflect the operational characteristics of these resources

16. Target Planning Reserve Margins
RECAP estimates reserve margins needed to achieve a target reliability threshold
LOLF = 1 event in 10 years
Target PRM needed to meet standard varies by region
Common Case above Target PRM for all regions
Type
Target PRM
Common Case PRM
Basin
14%
17%
California
13%
25%
Northwest
15%
32%
Rockies
19%
Southwest

17. Marginal ELCC Curves by Technology and Region
Marginal ELCC = capacity contribution of next increment of capacity of a given type
Curves are illustrative – they assume a single technology
Southwest
California

18. Marginal ELCC Curves by Technology and Region (Cont.)
Basin
Rockies
Northwest

19. Observations on ELCC Values
Marginal ELCC of solar PV at low penetrations is 50-60% of nameplate capacity (except in NW)
Aligns well with commonly used heuristics
At low penetrations, marginal ELCC values for wind range from 15-30% of nameplate capacity
Slightly higher than common heuristics
ELCC values exhibit significant diminishing returns to scale, particularly solar PV which shifts net load peak into the evening
As penetration increases, heuristics become increasingly inaccurate

20. Effect of Diversity on ELCC Values
For a diverse portfolio, ELCC of combined portfolio is higher than individual ELCC values
At 20% of load: W = 3041, S = 6172, W+S = 12,861

21. Ongoing uses for RECAP by WECC
The need for ELCC in WECC’s planning studies will increase as the penetration of variable generation increases
As part of the flexibility assessment project, the RECAP model will be transferred to WECC staff to help support modeling efforts
TEPPC Common Case development
Summer load assessments
Section 111(d) impacts
As an open-source tool, RECAP can also be shared with or modified by stakeholders

22. REFLEX MODEL STATUS

23. E3’s Renewable Energy Flexibility (REFLEX) Model
REFLEX answers critical questions about flexibility need through stochastic production simulation
Captures wide distribution of operating conditions through Monte Carlo draws of operating days
Illuminates the significance of the operational challenges by calculating the likelihood, magnitude, duration & cost of flexibility violations
Assesses the benefits and costs of investment to avoid flexibility violations
Implemented as an add-on to Plexos for Power Systems

24. WECC Flexibility Assessment – Distinguishing Characteristics
The use of REFLEX for PLEXOS in this study is different from conventional production cost modeling of the WECC in several important respects:
Economic tradeoff between upward (loss of load) and downward (curtailment) flexibility violations
Endogenous determination of load following reserves as function of expected within-hour flexibility deficiencies
Stochastic sampling of load, wind, solar, and hydro conditions
Sub-regional study footprints with specified boundary conditions
Import/export limitations and maximum ramp rates

25. Renewable Dispatch is Used to Solve Upward Ramping Shortages
Model needs robust information on cost of upward vs. downward shortages
Cost of unserved energy due to ramping shortfall: very high ($5,000-50,000/MWh)
Cost of renewable dispatch: replace the lost production ($50-$150/MWh)
Limited Ramping Capability
Unserved Energy
Renewable Curtailment
Strategy to Minimize Downward Violations
Strategy to Minimize Upward Violations

26. Stochastic Sampling From a Range of Conditions
In order to ensure robust sampling results, RECAP and REFLEX sample from a broad range of load, wind, & solar conditions
Historical data matched up based on month of year, day type (i.e. load level)
Range of available data

27. Capturing Transmission in Flexibility Assessment
Multiple options for representing interregional power flows have been tested
Original
project plan
Model Complexity 1 2 3
Single-Zone Models
Each region modeled independently with no internal transmission
Imports and exports captured through supply curves
Offers simplest modeling framework, but difficult to represent interregional power exchange
Zonal Model
Loads and resources grouped together by region
Regions linked together by transport model
Provides macro level view of interregional power exchange, but ignores individual line and path flow limits
Nodal Model
All nodes in WECC (25,000) represented
Dispatch solution is constrained by DC OPF and enforced line limits
Provides greatest fidelity of transmission system, but requires significant development and is computationally intensive
Final project plan

28. Zonal Topology
Zonal topology chosen based on aggregations of interregional WECC paths
All Paths
P14: ID to NW
P18: NT - ID
P24: PG&E - Sierra
P28: Intermtn - Mona
P29: Intermtn – Gonder
P30: TOT 1A
P31: TOT 2A
P35: TOT 2C
P38: TOT 4B
P46: WOR
P65: COI
P66: PDCI
P76: Alturas Project
P78: TOT 2B1
P79: TOT 2B2
P80: MT SE
Northwest
BS to NW
P14
P18
P76
P80
Basin
Rocky Mountain
BS to RM
P30
P38
NW to CA
P65
P66
CA to BS
P24
P28
P29
BS to SW
P35
P78
P79
California
RM to SW
P31
Southwest
SW to CA
P46

29. Regional Ramping Constraints
Production simulation models tend to overstate ramps on interties compared to historical levels
Constrained case limits intertie ramps based on historical levels
Example: Historical vs. Modeled Flows over Path 46
Upward Ramps
Duration (hrs)
Downward Ramps
Source:

30. Strawman High Renewables Case
High renewables case should have enough wind and solar generation to illuminate significant flexibility constraints
Test simulations of this “Strawman” case, while preliminary, provide some useful insights into challenges at higher penetrations to be shared today

31. Strawman Limited-Draw Results: California
April Day
Curtailment: ~6% RG
Overgeneration occurs regularly and periodically, especially in the spring months
Overgeneration is solar-driven and occurs in the middle of the day
Hydro, pumped storage, and imports help to meet nighttime load
Curtailment due to solar oversupply
Strawman High Renewables
All dispatchable plants reduce output to minimum levels

32. Strawman Limited-Draw Results: Northwest
April Day
Curtailment: ~3% of RG
Overgeneration conditions occur during high hydro and/or high wind conditions
Curtailment may be concentrated during nighttime or could persist through day if wind output remains high
More day-to-day variability in conditions within seasons compared to regions with high solar penetration
Curtailment due to simultaneous high wind & hydro conditions
Strawman High Renewables

33. Strawman Limited-Draw Results: Northwest
April Day #2
During lower hydro conditions, high wind may not result in curtailment
Curtailment: ~3% of RG
Overgeneration conditions occur during high hydro and/or high wind conditions
Curtailment may be concentrated during nighttime or could persist through day if wind output remains high
More day-to-day variability in conditions within seasons compared to regions with high solar penetration
Strawman High Renewables

34. Strawman Limited-Draw Results: Southwest
April Day
Curtailment: ~3% of RG
Coal plants are cycled down the middle of the day to accommodate solar, but curtailment still occurs
Steep morning down-ramp and evening up-ramp of coal, hydro, and gas are challenging operational conditions
Curtailment due to solar oversupply
Strawman High Renewables
Large coal ramps require further investigation

35. Strawman Limited-Draw Results: Rocky Mountains
April Day
Curtailment: <1% of RG
Frequent ramping of coal plants indicates system is stressed although large-scale curtailment is not observed in the region
Suggests possible trade-off between curtailment and coal cycling
Strawman High Renewables
Large coal ramps require further investigation

36. Strawman Limited-Draw Results: Basin
April Day
Curtailment: <1% of RG
Steep up- and down-ramps as coal is cycled down in the middle of the day to accommodate solar
Basin has the least amount of curtailment of all regions
Strawman High Renewables
Large coal ramps require further investigation

37. NEXT STEPS

38. General Project Progress
Develop RECAP input data
Assess reliability of the 2024 Common Case
Develop REFLEX input data
Implement 2024 Common Case in REFLEX for PLEXOS
Develop assumptions for High Renewables Case
Conduct flexibility assessment of 2024 Common Case
Assess reliability of High Renewables Case
Conduct flexibility assessment of High Renewables Case
Investigate flexibility solutions for High Renewables Case

39. Updated Timeline
Updated timeline based on progress made to date in Common Case modeling

40. Thank You!
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