Non-uniform pricing and thermal orders for the day-ahead Market

Transcription

1 Non-uniform pricing and thermal orders for the day-ahead Market PCR Stakeholder forum Brussels, January 11, 2016

2 Pricing: the current design Which requirements are concerned? Blocks, smart orders (and potentially also the price intuitiveness requirement) The current design imposes strict linear pricing, hence solutions cannot have paradoxically accepted orders. Consequently: Loss of welfare Blocks can be accepted paradoxically in welfare maximizing solutions Fairness issue Rejecting deep inthe-money orders Performance and transparency issue We sometimes find suboptimal solutions which may contain wrong PRBs (time restriction) We propose and illustrate a new approach to mitigate these issues. It is Based on Mathieu Van Vyve s article: Linear prices for non-convex electricity markets: models and algorithms. 2

3 Let us start with an example with blocks Price Quantity 3

4 Consider first that blocks are step orders Market price = 22 EUR/MWh Welfare = 70*40-10*15-60*22 = 1330 EUR Price Quantity This solution is not feasible, but this is the highest welfare we can achieve 4

5 Optimal solution, output by Euphemia if block 1 can be rejected (cf. next slide) Reject block 1, accept block 2 Welfare = 70*40-70*22 = 1260 EUR Price Quantity But block 1 was deep in the money! 5

6 Solution output by Euphemia if the delta P rule prevents rejecting block 1 Reject block 2, accept block 1 Welfare = 250 EUR Price Highly suboptimal! Quantity 6

7 A new proposal Accept block 1 and block 2. Welfare = 70*40+10*20-10*15-70*22 = 1310 EUR Price Quantity But there does not exist a strict linear price 7

8 A new proposal: two-step approach 1. Solve the primal problem to optimality (!) 2. Compute a price and some uplifts to compensate accepted out-of-the-money orders: Minimize maximum uplift Reach financial balance Bound the uplift min λ,u λ s.t. u i λ 8i X Q i x? i u i = 0 i Uplift of order i Market price Q i x? i u i Q i x? i p P i Q i x? i 8i where x? i is the value of x i in the optimal solution of the primal. On our examp min λ,u λ s.t. u i λ 8i Income-Welfare 10u u u u 4 =0 8

9 70u 2 70 p u 3 70 p u 4 10 p Applied to our example min λ,u λ s.t. u i λ 8i 10u u u u 4 =0 u 1 p 15 u 2 p 22 u 3 u 4 40 p 20 p Solution: u1 = u2 = u3 = EUR/MWh u4 = EUR/MWh p = EUR/MWh of this problem is u 1 = u 3 = 1.75EUR/ M W h, u 2 = 2EUR/ M W h. This ill be remunerat ed at = 18.25EUR/ M W h and order 3 will pay / M W hinterpretation: so that block 2 can be accepted and remunerat ed at 22EUR/ M W h. lus of 32.5EUR, order 3 makes a surplus of EUR, and the overall t 1310E UR. T he price of Order 20E UR/ 1 is Mremunerated W can be published. at 22In EUR/MWh addit ion, t he e published. Order 2 is remunerated at 22 EUR/MWh 2 Order 3 pays EUR/MWh Order 4 pays 20 EUR/MWh 9

10 Properties of the new proposal More transparent: we can prove optimality more easily More welfare than with the current pricing rule All orders contribute to the uplift until they are at-the-money No missing money issue Greatly simplifies the algorithm design We can use all the power of modern solvers (cf. next slide) Hence opportunity for more elaborate order types, e.g. thermal orders (cf. next section) 10

11 For the first step, the time to optimality is currently on average of 44 seconds Seconds data with flow based in CWE (times are stacked) Time2Integer roottime What makes it so fast? CPLEX MIP preprocessing CPLEX heuristics Multithreading (not used in this experiment) Feb 21-Feb 8-Mar 23-Mar 7-Apr 22-Apr 7-May 22-May 6-Jun 21-Jun 6-Jul 21-Jul 11

12 A new Thermal order Model a thermal unit Minimum stable generation (similar to minimum acceptance ratio) Load gradient (similar to complex orders) Start up profile and cost (similar to MIC fixed term) Minimum running time when started, minimum down time Shut down profile (similar to scheduled stop) Must run conditions (capacity not available to the market) Flexible in time (similar to exclusive groups) Could thus generalize (curtailable) block orders, exclusive groups, and complex orders Would be much easier to specify for market participants 12

13 Graphical definition Power Profile can be shifted in time Minimum up time Stable generation range Ramp up limit Ramp down limit Start up profile Shut down profile Time periods 13

14 Pricing 14

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