Challenges for Balancing Area Coordination Considering High Wind Penetration

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1 Challenges for Balancing Area Coordination Considering High Wind Penetration Robin Broder Hytowitz & Dr. Ben Hobbs, Johns Hopkins University Dr. Ozge Ozdemir, Energy Research Centre of the Netherlands (ECN) TAIC/FERC Workshop, November

2 Outline Motivation Background Initial Results Future Work

Motivation Renewables add variability to system operations Balancing area (BA) consolidation has been proposed Wind, load diversity reduce impact of variability Better resource use Reserve Unit

3 Motivation Renewables add variability to system operations Balancing area (BA) consolidation has been proposed Wind, load diversity reduce impact of variability Better resource use Reserve Unit scheduling Decrease peak generation & ramping needs M. Milligan, B. Kirby, and S. Beuning, Combining Balancing Areas Variability : Impacts on Wind Integration in the Western Interconnection, in WINDPOWER Conference and Exhibition, 2010.

Motivation Tradeoffs System size (more nodes & variables) makes solving to optimality more difficult Increasing complexity in systems operations (ex: stochastic UC) Most dramatic results are seen in

4 Motivation Tradeoffs System size (more nodes & variables) makes solving to optimality more difficult Increasing complexity in systems operations (ex: stochastic UC) Most dramatic results are seen in studies with large consolidation Does not address uncertainty What s more valuable? Challenge of merging different policies, rules, and regulations B. A. Corcoran, N. Jenkins, and M. Z. Jacobson, Effects of aggregating electric load in the United States, Energy Policy, vol. 46, pp , Jul

5 Questions Assuming full integration is not an option: How can we incent efficient coordination & trade? What coordination comes closest to integration?

6 Outline Motivation Background Initial Results Future Work

7 General Market Timeline Load & renewable forecasts, generation bids, fixed schedules, updates HA FMM Resource schedules (commitment, energy, ancillary services), prices, reliability checks = Day-Ahead, HA = Hour-Ahead, FMM = Fifteen Minute Market, = Real-Time

8 Hurdle Rates Definition Within models, a transaction cost that represents barrier to trade between BAs Economic friction [1] Parameterized to simulate actual amount of trade Not actual price Easy to implement in model objective: HR(Trade A B +Trade B A )

9 General US Markets BA 1 BA 2

10 EU Markets (non-market splitting), West Coast BA 1 BA 2 Bridge Out

11 EU Market-Splitting/Coupling BA 1 BA 2 Bridge Out

12 CAISO PacifiCorp Nevada Energy Imbalance Market (EIM) BA 1 BA 2

13 CAISO Energy Imbalance Market Details BA 1 BA 2 HA FMM HA FMM

14 Allocation of BA Interties Pseudo-tie line Dynamic scheduling Generation bids Self-schedule Market BA (ex: CAISO) Bilateral contracts (Loop flow) etc.

15 General US Markets BA 1 BA 2

16 Actual Markets Self-schedule Dynamic-schedule Generation bids Pseudo-tie line Bilateral contracts BA 1 BA 2 HA FMM HA FMM

17 Outline Motivation Background Initial Results Future Work

18 Scenarios Real-Time No Coordination Hurdle Rate Full Integration No Coordination Day- Ahead Hurdle Rate Old WECC, EU non-market splitting Regional authorities CAISO Full Integration EU market splitting/coupling (Nordpool, APX) Consolidation Single O

19 Scenarios No Coordination Hurdle Rate Day-Ahead Integration No Coordination Hour-Ahead Hurdle Rate Integration

20 Model Types of models Day-ahead scheduling: unit commitment Commits generation units for the next day MILP, binary decisions represent commitment Real-time model Optimizes (least cost) power flow Both models: Subject to transmission & generation constraints (KCL, KVL, capacity) Options to curtail wind and shed load

21 Model: Integrated Market Day-Ahead min t g c g P g,t + c g SU v g,t + c g NL u g,t Subject to: Line limits, transmission constraints (BΘ), capacity limits, ramp rates, minimum up & down times, spin & non-spin reserve requirements Real-Time min t g c g P g,t Subject to: Line limits, transmission constraints (BΘ), capacity limits, ramp rates, spin reserve requirements

22 Model: Hurdle Rate Day-Ahead ( ) min c g P g,t + c SU g v g,t + c NL g u g,t + HR(S AB t + S BA t ) t g Subject to Real-Time S t AB S t BA = k IT line P k,t t Line limits, transmission constraints (BΘ), capacity limits, ramp rates, minimum up & down times, spin & non-spin reserve requirements min ( c g P ) g,t + HR(S AB t + S BA t ) t g

23 Model: No Coordination Day-Ahead min t g Subject to c g P g,t + c g SU v g,t + c g NL u g,t Line limits, transmission constraints (BΘ), capacity limits, ramp rates, minimum up & down times, separate spin & non-spin reserve requirements Real-Time min k IT t g line P k,t c g P g,t = 0 t

24 System Topography Reliability Test System Zone (99 generators, 24 hours 73 buses) Each BA is similar in size # generators Type of generation Wind capacity [3]

25 Results Real Time Costs Total Cost (Millions $)!! No Coordination! Hurdle Rate! Full Integration! No Coordination! ! Hurdle Rate! Full Integration!

26 Results Prices Average LMP ($/MWh)!! No Coordination! Hurdle Rate! Integrated Market! No Coordination! ! Hurdle Rate! Integrated Market!

27 Results - deterministic and stochastic wind scenarios 7% $3.40 Trade as a percent of demand 6% 5% 4% 3% 2% 1% $3.20 $3.00 $2.80 $2.60 $2.40 $2.20 Total Cost (Million $) 0% No Coordination Integrated Hurdle Rate- $3 Hurdle Rate- $6 Hurdle Rate- $10 $2.00 Detrm - Trade Stoch - Trade Detrm - Total Cost Stoch - Total Cost

28 Price results single wind forecast LMP ($/MWh) % 12% 16% 11% 11% 18% 16% 14% 12% 10% 8% 6% 4% 2% 0% Percent change between and Mean Mean Percent Change

29 Results All scenarios Net!Trade!as!a!%!of!Demand! 1.6! 1.4! 1.2! 1! 0.8! 0.6! 0.4! 0.2! Integration Day-Ahead Hurdle Rate Day-Ahead Integration HR NC No Coordination Day-Ahead 0! $2,000,000! $2,500,000! $3,000,000! $3,500,000! Total!Cost!($)!

30 Conclusions Integrated markets yield most gains from trade Barriers on interties impede efficient markets Going from no coordination to hurdle rates makes large difference Further lowering hurdle rates most beneficial in rather than Further work needed to determine the simplifying effect of hurdle rates relative to actual barriers Which barriers are most important to address? Who should be responsible for removing inefficiencies? When no coordination, there might not be enough generation on-line to meet demand Average prices more consistent vs when there is no coordination day-ahead

31 Outline Motivation Background Initial Results Future Work

32 Future Work Different size BAs, different generation mix Modeling specific barriers on the intertie line Self-scheduling Dynamic-scheduling COMPETES model ECN Look at European grid Additional balancing areas Greatest benefits for renewables

33 Thank you! Questions?

34 References [1] Joseph H. Eto, Douglas R. Hale and Bernard C. Lesieutre, Toward More Comprehensive Assessments of FERC Electricity Restructuring Policies: A Review of Recent Benefit-Cost Studies of Os The Electricity Journal [2] Frank Wolack, The Economics of Self Scheduling, Presentation available: Presentation-Economics-Self-Scheduling- MSCPresentation.pdf [3] Jesús María López-Lezama, Mauricio Granada-Echeverri, and Luis A. Gallego-Pareja, A combined pool/bilateral dispatch model for electricity markets with security constraints Ingeniería y Ciencia, June 2011.

Outline. Background & Motivation Reserve Modeling Framework. Results. Types of improvements COMPETES simulations

Outline. Background & Motivation Reserve Modeling Framework. Results. Types of improvements COMPETES simulations Outline Background & Motivation Reserve Modeling Framework Types of improvements COMPETES simulations Results Challenges Arising from Wind Quack! Source: Flexibility in 21st Century Power Systems, NREL