Simulating the electricity spot market from a Danish perspective

Transcription

1 Simulating the electricity spot market from a Danish perspective OptAli Industry Days, Copenhagen Mette Gamst and Thomas Sejr Jensen, Energinet.dk mga@energinet.dk and tsj@energinet.dk 1

2 About Energinet.dk Date - Doc. no. 3

3 Electricity grid Date - Doc. no. 5

4 The energy system is changing Europe is in the middle of a historic transition of the energy system - Denmark has a leading position By 2020, wind power must constitute 50% of the electricity consumption; in 2013 it constituted 32%. By 2050, Denmark must be 100% fossil-free. Date - Doc. no. 8

5 The electricity market Spot market Day ahead Elbas market Intra day Regulating Power market Intra hour Spot market: One electricity price per region Focus is on demand and supply in region Focus is not on network bottlenecks inside regions Dato - Dok.nr. 5

6 Spot market Price Amount Dato - Dok.nr. 6

7 Simulating the spot market 7

8 Unit commitment problem Demand vs. supply Objective: minimize costs Who produces what? And when? When are units on and off (commitment) What are the energy prices? Is all demand met? Dato - Dok.nr. 8

9 Sifre: Simulating Flexible and Renewable Energy Sources Challenge of the future energy system Fluctuating renewable energy sources Integration of different energies (heat, electricity, gas ) Flexible demand: electric vehicles, price dependent consumption Dato - Dok.nr. 9

10 Modeling the energy system, Sifre Areas Production units Storages Interconnection lines Area. Energy/fuel area: E.g. Electricity in DK1, or Coal Production unit. Energy converter. E.g. power plant Storage. Stores energy: E.g. heat accumulator or battery Interconnection line. Connects two areas of same energy type. Typically electricity, e.g. DK2 and Germany (Kontek) Dato - Dok.nr. 10

11 Storage Bio mass Heat DK1 Heat accumulator CHP Oil Production unit Electr. DK1 Heat pump Storage Coal Wind Electr. DE Dato - Dok.nr. 11

12 Other components and functionality Electric vehicles Flexible demand (load shift and price cut) Emission reduction Taxes and subsidies Outages and maintenance planning Production units details: efficiency, fuel mixes, cold and warm starts, etc. 12

13 Sifre Input Method Output Energy system Formulate as MILP Behavior of entire energy system Production units, Fuels, Demands, Interconnection lines, Simulation algorithm, Gurobi Energy prices, Production patterns, Demand behavior, Export, Import, Dato - Dok.nr. 13

14 Sifre Minimize: Subject to: t T ψ i t + i N ψ t i ψ t i + co i o t i + cu i u t i + c t ih f ih i,h A + chi t t p hi h,i A + cij t t x ij + ch t t z h (i,j) A ICL h H ρ r END s r t π l t r R t T i N l D(i) p t t t t (h,i) A hi i,h A f ih + j,i A ICL x ji i,j A ICL x (v ij ir t γ t ri v t ri ) o t t r R i i + u i = d t t i + l D(i) d l, i N, t T i,h A f ih t t k ih α hl i,h A f ih t z t t h + β hl t α k hl z t h + β ih h,i A t p hi hl t h,i A, h H, t T, l L(h) t p hi, h H, t T d l t f t t ih m ih f jh, t T, i, h A s r t = γ r t s r START + s r t = γ r t s r t 1 + i A j,h A i A (γ t ir v t ir (γ t ir v t ir v ri t ) v ri t ), ρ r 0 r R, t = 0, ρ r t r R, t T\{0} p t hi z t t t h p hi p t hizh, h H, i N: h, i A, t T χ m t i,j A m RAMP+ (h,i) N(k) x t ij x ij p t t hi β hik a t t k z h(k), k K, t T t 1 x t ij i,j A m RAMP t 1 x ij χ m t, m RAMP, t T{0} χ t m x t ij x START ij x t START ij x ij i,j ARAMP+ i,j ARAMP m m χ m t, m RAMP, t = 0 Dato - Dok.nr. 14

15 Challenges Date - Doc. no. 2

16 Challenges: Linear model Production units have several non-linear properties Start up cost: cost = a + b 1 e t T Efficiency: f p = a p + b p + c p 2 Cost Fuel Offline time Production 16

17 Price, or Marginal cost Challenges: Defining marginal costs Amount Dato - Dok.nr. 17

18 Challenges: Defining marginal costs In each area: Supply = Demand p t hi + x t ij + s t ri = d i + f t ih + x t ji + s t ir, i Areas The area price is the dual of the balance constraint The balance constraint only considers energy (MWh) The area price only consider costs attached to energy ( /MWh) The area price is the marginal costs of energy production Open question: Do we include operation and start up costs (per hour/start up)? 18

19 Challenges: Degeneracy In each area: Supply = Demand p t hi + x t ij + s t ri = d i + f t ih + x t ji + s t ir, i Areas Degeneracy: A basic variable takes on value 0 19

20 Challenges: Simulation time Typical usage of Sifre: Electricity and district heating in Denmark ~250 production units (with binary UC variables) ~80 areas ~35 storages 8760 time steps (one year) The Unit Commitment problem (UC) is NP-hard Time consuming to solve with Gurobi Current simulation time: ~50 minutes 20

21 Challenges: Simulation time Reduce running time: Simulating the spot market is an online problem. Simulate at most 10 days at a time Careful formulation: Avoid unnecessary fractionality Allow a small MIP Gap LP-relax small production units (user defined) Keep the model deterministic! (for now at least) Last resort: Stop Gurobi after x minutes 21

22 Challenges: Solution quality Issues: Linearization MIP gap Degeneracy Test on historical data for Denmark (DK1, DK2): Comparison of import/export and electricity prices Import and export results were very similar Avg. prices differed with <1DKK >95% of hourly prices differed with less than 0,5% 22

23 Much bigger challenges Scope User wishes and ideas change over time Scrum Usability User interface Data: availability and data vs. model Data and user robustness IT architecture Support Support Maintenance Project management and deliveries 23

24 Conclusion Sifre Simulating Flexible and Renewable Energy Sources New model to meet the demands of the green transition Very good performance with respect to quality Very reasonable running time 24

25 Read more at energinet.dk Date - Doc. no. 18