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Strategies for Wind Power Trading in Sequential Short–Term Electricity Markets Franck Bourry and George Kariniotakis Center for Energy and Processes EWEC.

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Presentation on theme: "Strategies for Wind Power Trading in Sequential Short–Term Electricity Markets Franck Bourry and George Kariniotakis Center for Energy and Processes EWEC."— Presentation transcript:

1 Strategies for Wind Power Trading in Sequential Short–Term Electricity Markets Franck Bourry and George Kariniotakis Center for Energy and Processes EWEC 2009, Marseille

2 2 Wind Power in Europe Challenges: Economic and secure operation of a power system Competitiveness of wind energy in a liberalized electricity market Installed capacity EWEA’s 2007 scenario for wind energy installations up to 2030 Source: EWEA

3 3 Objective To develop a methodology for the optimal participation of independent wind power producers in day–ahead and intraday electricity markets; –Aim : reduction of imbalance penalties resulting from low predictability of wind generation

4 4 Hypothesis : Balance Responsible Wind Power Producer Selling bids : A wind power producer is assumed to be an energy producer participating in the electricity market with only selling bids. Balance responsible : –Energy imbalance is the difference between the contracted and the delivered energy; – Imbalance penalties may apply ═ ►reduction of the revenue.

5 5 Day–ahead and intraday market description Day – ahead market: Submission of bids the day before the energy delivery; Power exchange sessions; OMEL, SpainElbas NordPool, Nordic Countries 6 power exchange sessions with the gate closure time 2h15 before the start of the energy delivery period. Continuous trading till one hour before delivery. Examples : Intraday market: For enabling & encouraging self–balancing of market parties (adjustment market ); Different mechanisms such as power exchange sessions, power exchange continuous trade or Over The Counter (OTC);

6 6 Sequential bids in day–ahead and intraday markets: D D+1 Day-ahead market participation D+2 Intraday market participation Example of a combined participation in the Elspot and Elbas markets (NordPool). Bids in the Elbas market are proposed 6 hours before the delivery time.

7 Model of the participation in the day–ahead market Bid ( price-taker ) Market settlement  Contract Quantity: E b0 = P pred × ΔtQuantity: E c0 = E b0 Price : Π b0 = 0Price : Π c0 = Π c0 market Day–ahead bid [E b0,Π b0 ] : –Price taker hypothesis : price independent bid, at zero price; –Bid quantity based on the available wind power forecast. Market settlement : –Unique market price resulting from meeting aggregated purchase/sale curves (marginal pricing); –Accepted energy quantity dependent on the bid price; all price taker bids are accepted. 7

8 8 Model of the participation in the intraday market Intraday bid [E b1*,Π b1* ] : [E b1*,Π b1* ] are the results of a DECISION MAKING method which aims to reduce imbalance penalties. Market settlement ( for continuous trade market ) : –Trade occurs when the selling and buying bids match; –The contract price is the bid price (pay as bid pricing); –The contracted energy quantity depends on the buying bids of the other participants : α : proportion of traded energy over the bid quantity Bid Market settlement  Contract Quantity: E b1* Quantity: E c1 = α × E b1* Price : Π b1* Price : Π c1* = Π b1*

9 Modeling of α : E c1 = α × E b1* α = probability(Π b1* ≤ Π c1 ) = 1 – probability(Π c1 < Π b1* ) = 1 – F(Π b1* ) Model of the participation in the intraday market 9 Intraday market price distribution model: Triangular distribution model; Estimated from [min(Π c1 ), mean(Π c1 ), max(Π c1 )] F f Example: Elbas (NordPool ) 11/04/2004 at AM (prices in DKK/MWh) : α = 0.91 Π b1* =181.2 mean = Π b1

10 Imbalance penalty model (1/2) Contract Income E c0 × Π c0 E × Π c ====== Revenue Π c0 : Day–ahead contract price E c0 : Day–ahead contract energy Π c1 : Intraday contract price E c1 : Intraday contract energy Π + /- : Positive/Negative Imbalance price E : Delivered energy Regulation costs (E c0 –E) × Π + /- δ (E ) { (E - E c0 ) × (Π c0 – Π + ), E > E c0 (E - E c0 ) × (Π c0 – Π - ), E ≤ E c0 δ (E) = Day–ahead market participation E × Π c0 - = E c1 × (Π c0 – Π c1 ) + δ (E –E c1 ) Production IncomeImbalance Penalty Day–ahead and intraday market participation: 10 δ’ (E) = E c1 × (Π c0 – Π c1 ) + δ (E – E c1 )) Revenue { {

11 11 Imbalance penalty model (2/2) Imbalance price : Proposed model (valid in the NordPool system for example) δ E E C0 δ E System down– regulation System up– regulation Π + <Π c0 Π + =Π c0 Π - =Π c0 Π - >Π c0 E C0 E C1 Delivered energy δ'δ' E C1 x( Π C0 – Π C1 ) δ δ (E ) δ (E –E c1 ) δ’ (E) = E c1 × (Π c0 – Π c1 ) + δ (E – E c1 )) { Hedging cost

12 12 General Formulation of the Intraday Bidding Problem Model for participating in the intraday market for the hour h i :  Π c0 is the day–ahead market price for the hour h i ;  E  E hi is the estimated energy delivery for hour h i ;  δ Π + Π -  δ hi is the estimated imbalance penalty function for hour h i (Π + hi,Π - hi ); α α  E c1 = α hi × E b1*, where α hi is the estimated proportion of traded energy over the bid quantity for hour h i ; E b1, Π b1 αδEα [E b1*, Π b1* ] = argmin( (Π b1 – Π c0 ) × α hi × E b1 + δ hi (E hi – α hi ×E b1 ) ) hihi hihi hihi hihi E b1, Π b1 [E b1*, Π b1* ] = argmin( δ’ (Π b1, E b1 ) ) hihi hihi

13 Proposed intraday bidding approach α δE α δ’ (E b1, Π b1 ) = (Π b1 – Π c0 ) × α × E b1 + δ (E – α × E b1 ) E b1 Π b1 Π+Π+ E E – E c0 System down– regulation System up– regulation E b1 Π b1 Π c0 = Π – Π–Π– 0 3 α With α : Π+Π+ Π c0 Π–Π– 1 Π + = Π c0 δ’δ’ Proposed simplified approach : minimization of energy imbalance Π + Π + Π b1* = Π + + β × (Π c0 – Π + ), with – 0.2 ≤ β ≤ 1.2 ; { E E (E – E c0 ) if E ≥ E c0 ; 0 else. E b1* = E E c0 E b1*

14 14 Simulation methodology WP forecasts Day–ahead contract Imbalance penalties Day–ahead bid Intraday bid Intraday contract WP measures B. Market Settlement B. Bid Balance settlement Up/down regulation price Market price Up/down regulation price forecast M. S. Delivered energy

15 15 Case Study Test case based on real world data; 18 MW wind farm in Denmark; Wind power forecasts from a statistical model, based on power curve modeling; Participation in the Nord Pool day–ahead (Elspot) and intraday (Elbas) markets from January to March 2004 (3 months); Penalties associated to the balancing mechanism are applied.

16 16 Results Π b1* Π + Estimation of the regulation price Π + = constant Perfect knowledge of the regulation price Π + Π + Π b1* = Π + + β × (Π c0 – Π + ), with – 0.2 ≤ β ≤ 1.2 ;

17 Π + Π + Π b1* = Π + + β × (Π c0 – Π + ), with – 0.2 ≤ β ≤ 1.2 ; E c1 = α × E b1*, with α = 1 –F(Π b1* ). { E E (E – E c0 ) if E ≥ E c0 ; 0 else. E b1* = 17 Results Π b1* E c1 Π + Estimation of the regulation price Π + = constant Perfect knowledge of the regulation price no intraday

18 18 Results -18 % Π + Estimation of the regulation price Π + = constant Perfect knowledge of the regulation price

19 19 Conclusions A model for the settlement of continuous trading market is proposed. This model is based on the available data of market prices; The participation in an intraday market is formulated as a hedging method which aims to reduce the imbalance penalties. The present case study shows that the participation in the intraday market can reduce the imbalance penalties by up to 18 %.

20 Thank you for your attention 20 ARMINES participates in: This work was performed in the frame of the Anemos.Plus project (FP 6).


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