Model for Area Price Determination and Congestion Management in Joint Power Markets

Transcription

1 Gardermoen, Oct Model for Area Price Determination and Congestion Management in Joint Power Markets Market splitting Flow based market coupling Kjetil Uhlen, Leif Warland, Ove S. Grande 1

2 Outline of presentation Background and aims of the model Hourly day-ahead markets, focus on the Nordic system Different requirements and solutions for price calculations and congestion management Alternative model for area price determination Implementation of the demo model Case studies and sample results Concluding remarks 2

3 Market splitting Market coupling Basically the same thing Implicit auction Difference only in the way prices are calculated MS: First compute system price. Then, split into separate price areas if exchange limits are violated MC: First compute price vs. exchange in each area. Then, connect the areas and compute actual prices and exchanges. Best suited in systems where the areas are radially connected (refer to EXCHANGES not FLOWS ) Well suited for hierarchical structures MC can be used to harmonise two different markets (e.g. Nordel vs. Germany) 3

4 Advantages of market splitting/coupling The day-ahead prices in the total system are calculated simultaneously The price differences between the predefined areas will adjust the power exchange to the available transfer capability (ATC) and reflect the consequences of congested corridors Separate auctions for the network are not necessary 4

5 Limitations with market splitting One major weakness / limitation of the Nordic Area Price model: The model lacks representations of the physical lines / transmission corridors between the price areas may lead to sub-optimal utilization of the transfer capacity (in meshed networks) The real physical Flow as a consequence of different area prices is not calculated Major drawback in highly meshed networks 5

6 Flow of dollars vs. the flow of physics Market splitting (zonal pricing) 1 LMP (nodal pricing) 3 E 1 E 3 E 2 2 Compromise? E 4 4 E i = Net exchange from area i Good solution from the market point of view Too simple for congestion management Good solution for congestion management Complex solution and less acceptable from the market point of view 6

7 Proposed model Basic principle: Combination of market splitting and nodal pricing Each area is defined as one node Uses a network equivalent representing the transmission system between the areas Price calculation based on dc optimal power flow Criterion of optimization: Minimization of the socioeconomic congestion cost Improved utilisation of the available transfer capability and reduced market player risk 7

8 Alternative model for Area Price determination and congestion management E 1 E 3 E F 13 F23 F E 4 (E= Exchange) 4 F 34 F 24 (F = Flow) Present model New model 8

9 Area equivalent and cost function Ε p 0 p 0 E 0 E 9

10 Implementation A demo model of the proposed method is implemented in MATLAB Simple graphical interface to display results Use of the model: Input data are: aggregate supply curves (marginal cost of generation) for each area (Constant) load and exchange to external (not modelled) areas network impedances limits on transmission corridors The model computes a solution by minimizing the cost function without violating transfer limits In addition, the load flow equations must be fulfilled 10

11 Case studies Sample results to demonstrate the functionality of the model and the user interface. Examples are shown to highlight main features of the model: How one congested corridor result in different area prices How marginal generation changes in one area can affect congested corridors and prices in other areas. How control of HVDC links is included to maximize utilization of the transmission system. Disclaimer: The examples do not intend to represent a real system or a real operating situation. 11

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14 Market demand: Power flow identifies bottleneck on NO2 NO1 14

15 Run : Minimizes congestion costs while enforcing transfer limits 15

16 Trying to reduce loop flow NO1 SE1 DK1 16

17 Trying to increase export from Finland 17

18 Concluding remarks An alternative approach to area price determination in physical day-ahead (spot) markets has been described. The method combines the advantages of market splitting (area pricing) with power flow calculations. The criterion of optimization is minimization of the socioeconomic congestion cost. The proposed method is implemented and demonstrated in a demo model The proposed method/model corresponds to the FMC model proposed by EU 18

19 Extra slides.. 19

20 European Power Exchanges Market region in which prices correlate APX APX Transmission constraint SWEDEN FINLAND NORDPOOL NORDPOOL NORWAY UKEK UKPX USAPX POWERNEXT EPX/POWERNEXT POWERNEXT (2001?) IRELAND Britannica UK BELGIUM LUX. FRANCE DENMARK Northwest Europe Nordic/Baltic GERMANY POLAND Switz Mittel ESTONIA RUSSIA LATVIA LITHUANIA BELARUS UKRAINE CZECH REP. MOLDOVA SLOVAKIA AUSTRIA SLOVENIA HUNGARY ROMANIA CROATIA PPX PPX EEX LPX PHELIX SPX SPX SPAIN Iberia BOSNIA ITALY MONTENEGRO SERBIA BULGARIA MACEDONIA IPX (2001 IPX?) ALBANIA GREECE TURKEY 20

21 Congestion Management in Continental Europe ETSO paper and EU statements conclude that: Market splitting (Area pricing) is a very interesting principle for congestion management, but it has severe requirements that have to be addressed before considering implementation outside Nordel: * In highly meshed networks will congested lines change with demand and generation * Neighbouring constraints are strongly interdependent (* Impact on bilateral trade) Optimal utilisation of a highly meshed network will imply coordinated load-flow calculations as an integral part of the final allocation procedure. 21

22 Proposed concept to integrate market information in Power system simulators Market inputs: Aggregate bid curves Load data: -Load forecast -outage schedules Technical data: -Network data -generator data MARKET model Prices Exchanges DISPATCH model P gen, P load Topology data SIMULATORS -Power flow -Dynamic simulator - User control 22

23 Application examples (Potential benefits of a market model) It can be used simply as an aid to establish study cases of interest. It becomes possible to study the impact of changing transfer limits on system security, stability, etc. and on market prices study the impact of the (supply and demand) bid curves on system operation (sensitivity analysis) e.g. study the impact of having more price-responsive demand. Educational purposes useful enhancement of operator training simulators 23

24 Further work and possibilities The Nordic Area price model is considered as a basis for the future model of the European spot market Advantages seen from ETSO and EU - The day ahead prices in the system are calculated simultaneously - Price differences between areas adjust the transfer to the transfer capacity - Separate auctions of transfer capacity will not be needed 24

25 Socio-economic congestion costs A (deficit) F max B (surplus) Price g Area A Price System Pric e Area B p A h j f i e b c d Consumer surplus Producer surplus p s p B b i c e d a F re d Flow Flow F re d Flow 25

26 Congestion management Principle of Market Splitting A (surplus) F max B (deficit) Price System Price Price Demand A Supply A Σ Supply Supply B p S p A p A F 1 F max p A-B Σ Demand p B p B F 1 F max Demand B MW MW MW 26

27 Congestion management Principle of Market Coupling A (surplus) F max B (deficit) Price Area Prices at F max Price Price vs. exchange A Price vs, exchange B p S F 1 p B p A F max -F 1 p B p S p A p A-B MW MW MW 27

28 Methodology and approach The proposed model meets the following requirements 1) Each node represents a predefined price area (reflecting network constraints) 2) The network equivalent represent the real power lines between the price areas 3) The Bid Curves are estimated based on real curves from Nord Pool 4) Optimisation criterion: Minimization of the Socioeconomic congestion cost 28

29 Optimisation criterion Reduce the total congestion costs in the Nordic system An optimal power flow problem pi Ci ps pi 0 pi ( Ei ) Ei + b E E si i0 psi C i = 1 2 p E s si p i0 E i0 ( E i E si ) 2 = 1 2 p i E i ( E i E si ) 2 pi0 Cost 1 p N i total = E i E si 2 i= 1 Ei ( ) 2 Ei0 Esi Ei 29

30 Supply and demand curves Data from Nordpool C:\Flex\SAPRI\ \ak t20.txt NOK/MWh MWh/h 30

31 Optimisation problem.. An optimal power flow problem with a quadratic object function and linear constraints: N 1 pi min ( E i E si) Ei 2 E P min gi i= 1 P gi max Sij S ij i P max gi 2 1 T T min Cost total = min G( x) = x Hx + F x x x 2 Ax b Aeqx beq = Lb x Ub max Sji S ji The HVDC links are represented in the optimization as control variables (load or generation) with zero cost 31

32 Methodology cont. Solving the load flow - The method used is Direct Current load flow - Relevant in cases where the only interest is in the active power flow and where a rough approximation is accepted The power E i injected into the bus for each area is E=P+Yδ, P is the flow in the HVDC links and represented as load/generation depending on the direction of the flow The flow F ij between area i and j is F ij =Y ij (δ i -δ j ) 32

33 Methodology cont. Estimating the impedances in the area model Y12 Y δ F12 Y13 0 Y 13 δ 2 F = 13 0 Y23 Y 23 3 δ F 23 F F Y Y 13 Y δ F 23 * 0 Y δ F * Y12 Y * δ1 F * Y = * δ Y F ^ 23 ^ Y12 Y δ 12 1 F ^ Y13 0 δ ^ Y F13 23 ^ F 23 33