A Stochas*c Unit Commitment Model for Integra*ng Renewable Supply and Demand Response

Transcription

1 A Stochas*c Unit Commitment Model for Integra*ng Renewable Supply and Demand Response Anthony Papavasiliou, Shmuel Oren IEEE PES General Mee*ng 2012 July 25, 2012

2 Outline Introduc*on Flexible Demand Centralized Load Dispatch Demand Bids Coupling Results Conclusions and Perspec*ves 2

3 Load Flexibility 3 fundamental approaches to deal with renewable variability via demand response Centralized co- op*miza*on of dispatchable supply resources and flexible loads by system operator Price response: Renewable producers bid in centralized real- *me market Consumers can communicate with system through instantaneous response to price Coupling aggregated load with renewables Flexible loads communicate basic needs to renewable suppliers Flexible loads follow dynamic supply signal from renewable resources, system operator faces reduced variability Introduc*on - 3

4 Research Objec7ve Want to quan*fy Renewable energy u*liza*on Cost of unit commitment and economic dispatch Day- ahead genera*on capacity requirements Stochas*c unit commitment an appropriate model Quan*fies renewable energy u*liza*on (decision variable) Quan*fies opera*ng costs (objec*ve func*on) Endogenously determines reserves (as opposed to ad hoc rules) Introduc*on - 4

5 Two- Stage Stochas7c Unit Commitment In the first stage we commit slow generators (corresponds to day- ahead market): ugst = wgt, vgst = zgt, g Gs, s S, t T Uncertainty is revealed: net demand = firm demand + deferrable demand wind supply Fast generator commitment and produc*on schedules are second- stage decisions (corresponds to real- *me market): ugst, g G f, pgst, g G f Gs Objec*ve: π s (K gugst + Sg vgst + Cg pgst ) g G,s S,t T Introduc*on - 5

6 Integra7ng Demand Response in Stochas7c Unit Commitment 6

7 Centralized Load Dispatch Stochas*c unit commitment with demand sa*sfac*on constraint: pgst = R t T Assump*ons of centralized load control: Central co- op*miza*on of genera*on and demand (computa*onally prohibi*ve) Perfect monitoring and control of demand Centralized load control represents an idealiza*on that can be used for: Quan*fying the cost of decentralizing demand response Es*ma*ng the capacity savings of deferrable demand Flexible Demand - 7

8 Demand Bids Based on retail consumer model of Borenstein and Holland (2005), Joskow and Tirole (2005), Joskow and Tirole (2006) State con*ngent demand func*ons used in R economic dispatch Dt (λt, ω ) = at (ω ) α bλ (1 α )bλt Note that the demand func*on model has to: Be comparable to the deferrable demand model in terms of total demand R Be consistent with the observed inflexible demand in the system Flexible Demand - 8

9 Implementa7on of Coupling Match renewable power suppliers to aggrega*ons of flexible consumers Consumers specify deadlines for flexible consump*on tasks (EV charging, water pumping, refrigera*on, etc.) Aggregators serve deferrable loads primarily from renewable genera*on assets, possible resor*ng to real- *me market purchases Two types of real- *me market par*cipa*on constraints: Quan*ty (fuse size) Threshold price (callable forward) Flexible Demand - 9

10 Coupling Flexible Demand - 10

11 Dynamic Programming on Recombinant LaHces Flexible Demand - 11

12 Strike Price Flexible Demand - 11

13 Schema7c of WECC Results - 11

14 Model Summary 124 units (82 fast, 42 slow) MW power plant capacity 225 buses Two studies Deep wind integra*on (14% energy integra*on, 2020) Moderate wind integra*on (7% energy integra*on, 2012) Results - 13

15 Day Types 8 day types considered, one for each season, one for weekdays / weekends Day types weighted according to frequency of occurrence 4 x 10 2 SummerWD FallWD SummerWE Net Load (MW) FallWE WinterWD 0.5 SpringWD SpringWE WinterWE Hour 15 Results

16 Data Sources Wind power produc*on California ISO interconnec*on queue lists all loca*ons of planned wind power installa*ons NREL Western Wind and Solar Interconnec*on Study archives wind speed wind power for Western US (2006) Real- *me price: California ISO Oasis online database (2004) Results - 15

17 Calibra7on Remove systema*c effects: yt µ mt y = σˆ mt S t Transform data to obtain a Gaussian distribu*on: ytgs = N 1 (F (yts )) Es*mate the autoregressive parameters and covariance matrix using Yule- Walker equa*ons: φˆ j, Σ Results - 16

18 Data Fit Results - 17

19 Firm Demand Uncertainty Second order autoregressive model Data: California ISO 2004 Oasis database Single- area model without transmission constraints Results - 18

20 Study Cases Zero Wind capacity 0 (MW) Moderate 6,688 Deep 14,143 DR capacity (MW) 5,000 10,000 Daily wind 0 energy (MWh) 46,485 95,414 Daily DR 0 energy (MWh) 40,000 80,000 DR/firm energy (%) Results - 19

21 Opera7ng Costs and Lost Load Daily Cost ($) Daily Load Shed (MWh) No wind Centralized Moderate 9,021,031 8,677, Bids Moderate Coupled Moderate Centralized Deep Bids Deep +211, ,128 8,419, , Coupled Deep +705, Results - 20

22 Summary No wind Moderate Deep Capacity (MW) Daily Spillage (MWh) 26,123 26,254 26,789 N/A 0 2 Results - 21

23 Conclusions Capacity requirements: For the studied cases, increased load demand is almost fully absorbed by installed wind power capacity. Cost of anarchy: The cost of anarchy increases from 3.06% to 8.38% as the integra*on level increases. Demand bids: Price- responsive demand achieves beler cost performance than coupling (2.43%- 6.88% cost increase rela*ve to centralized dispatch) but performs poorly in terms of load sa*sfac*on. Wind spillage: Negligible spillage of wind power. Conclusions & Perspec*ves - 22

24 Perspec7ves Price- responsive smart charging Stochas*c dual dynamic programming algorithm with state- con*ngent real- *me bids Transmission constraints Paralleliza*on of the model in Lawrence Livermore Na*onal Laboratory high performance compu*ng cluster Conclusions & Perspec*ves - 23

25 References A. Papavasiliou, S. S. Oren, Large- Scale Integra*on of Deferrable Electricity and Renewable Energy Sources, submiled to IEEE Transac*ons on Power Systems. A. Papavasiliou, S. S. Oren, R. P. O Neill, Reserve Requirements for Wind Power Integra*on: A Stochas*c Programming Framework, IEEE Transac*ons on Power Systems. A. Papavasiliou, S. S. Oren, Mul*- Area Stochas*c Unit Commitment for High Wind Penetra*on in a Transmission Constrained Network, submiled to Opera*ons Research. 24

26 References (II) A. Papavasiliou, S. S. Oren, Integra*on of Contracted Renewable Energy and Spot Market Supply to Serve Flexible Loads, 18th World Congress of the Interna*onal Federa*on of Automa*c Control, Milano, Italy, August A. Papavasiliou, S. S. Oren, R. P. O Neill, Supplying Renewable Energy to Deferrable Loads: Algorithms and Economic Analysis, 2010 Power Energy and Society General Mee*ng, Minneapolis, Minnesota, July A. Papavasiliou, S. S. Oren, Coupling Wind Generators with Deferrable Loads, IEEE Energy 2030, Atlanta, Georgia, November

27 Thank you Ques*ons? Contact: hlp://www3.decf.berkeley.edu/~tonypap/ publica*ons.html 26