An SDP/SDDP Model for Medium-Term Hydropower Scheduling Considering Energy and Reserve Capacity Markets

Transcription

1 An SDP/SDDP Model for Medium-Term Hydropower Scheduling Considering Energy and Reserve Capacity Markets Arild Helseth, Marte Fodstad, Birger Mo SINTEF Energy Research Trondheim, Norway Technology for a better society 1

2 Outline Background and motivation Methodology Case study Technology for a better society 2

3 The Nordic Power System General: Mixture of generation sources Liberalized market Interconnectors to central and eastern Europe Hydropower: Accounts for more than 50 % of the total capacity More than 1000 hydro reservoirs in the system Annual inflow ranging from TWh Trends: Stronger interconnections to other European markets Increasing share of renewables in Europe Hydropower has great potential for balancing "unpredictable" generation from solar and wind source: Technology for a better society 3

4 Medium-term Hydropower Scheduling Problem characteristics: Stochastic (inflow and energy price) Dynamic in time (hydro storage) Large-scale (state variables, stochastic variables, time stages) Producer: Production forecasts Targets for short-term scheduling T max E c x t= 1 + f ( V) T t t t Technology for a better society 4

5 Medium-term Hydropower Scheduling Energy only in the future? Multiple markets and prices More constraints Different decision stages Impact on water values? max E c x T T t t + f t t= 1 + ( V) Technology for a better society 5

6 Profit from capacity reserve markets? Technology for a better society 6

7 Market clearing sequences Technology for a better society 7

8 Outline Background and motivation Methodology Case study An SDP/SDDP model for Medium-Term Hydropower Scheduling Considering Energy and Reserve Capacity Markets Technology for a better society 8

9 Stochastic Dual Dynamic Programming (SDDP) Presented ~25 years ago Proven methodology; Applied in operational models in several countries Sampling-based variant of multi-stage Benders decomposition Allows a large number of state variables Well suited for medium-term hydropower scheduling problems Many decision stages Uncertainty in right-hand side (e.g. Inflow) Uncertainty in price is a challenge Technology for a better society 9

10 Combined SDP/SDDP A. Gjelsvik, M. M. Belsnes, and A. Haugstad, An algorithm for stochastic medium-term hydrothermal scheduling under spot price uncertainty, in Proc. 13th Power Syst. Comput. Conf., Trondheim, Norway, 1999,pp Technology for a better society 10

11 Combined SDP/SDDP Technology for a better society 11

12 Basic Decomposed Stage Problem Objective: max (profit from sales of energi + future expected profit) Constraints: Reservoir balances Energy balance Cuts Technology for a better society 12

13 Extended Decomposed Stage Problem Objective: max (profit from sales of energi + capacity(t+1)+ future expected profit) Constraints: Reservoir balances Energy balance Cuts Capacity balances Distribute capacity on stations that: Run below max Operate ("spin") c t + 1 ct 1 Coupling in time Technology for a better society 13

14 Extended Decomposed Stage Problem Objective: max (profit from sales of energi + capacity(t+1)+ future expected profit) Constraints: Reservoir balances Energy balance Cuts Capacity balances Distribute capacity on stations that: Run below max Operate α pt, + 1 µ 1 µ 2 c t + 1 Technology for a better society 14

15 Extended Decomposed Stage Problem Objective: max (profit from sales of energi + capacity(t+1)+ future expected profit) Constraints: Reservoir balances Energy balance Cuts Capacity balances Distribute capacity on stations that: Run below max Operate P max C up Prod C dn P min Technology for a better society 15

16 Outline Background and motivation Methodology Case study Technology for a better society 16

17 Case study Case setup: 7 hydropower reservoirs&stations 986 MW installed capacity Scheduling period of 104 weeks 21 time steps within each week Sales in spot and primary reserve market Located in Nord Pool price area NO2 Price scenarios from fundamental market model (EMPS) Capacity sales limited to 10 % of installed capacity Symmetric reserves Technology for a better society 17

18 Technology for a better society 18

19 Technology for a better society 19

20 Some data concerns Balancing markets in the Nordics are 'under construction'' Limited historical data Changing rules Low volumes Technology for a better society 20

21 Some modeling concerns Not unit commitment. Cannot strictly enforce Pmin in a linear model Concave production function. Stations are allowed to generate electricity at low rates at an artificially high efficiency for the purpose of delivering reserves Further/ongoing work: Benchmark against detailed SDP model PhD on nonconvexities in SDDP applied to this type of problem Technology for a better society 21

22 References A. Helseth, M. Fodstad and B. Mo, Optimal Medium-Term Hydropower Scheduling Considering Energy and Reserve Capacity Markets, IEEE Transactions on Sustainable Energy, vol. 7, no. 3, A. Helseth, B. Mo, M. Fodstad and M. N. Hjelmeland, Co-optimizing Sales of Energy and Capacity in a Hydropower Scheduling Model, in Proc. IEEE PowerTech, Eindhoven, The Netherlands, July Technology for a better society 22