1. Hydro Optimization Tom Halliburton

4. Variety Stochastic Deterministic Linear, Non-linear, dynamic programming Every system is different Wide variety of physical constraints Studied for many years - lots of legacy systems.

5. Time Scales Long term expansion planning Long / medium term operational planning Week / day ahead ahead planning Market clearing Short term operations planning Real time economic dispatch Real time unit loading

7. Large Lake Small headpond Power house Concrete or earth dam Tailwater Penstock Transmission system

8. Water Value Value of an extra increment of water If lake full, extra spilled  Value = 0 If empty, extra replaces combustion turbine or avoids blackout  high value Expected marginal value of water = E[marginal cost of thermal station displaced by generation from this water] Dual value of flow balance equation in LP Use water so that Marginal Value of water used this period = EMV of water in storage

9. Merit Order Dispatch Hours MW Must run Zero cost resources Base load plants Flexible plant Peakers

10. Long Term Planning 10 to 30 year horizon, 1 to 4 week time step Hydro, thermal, transmission system Transmission important especially with hydro Some aggregation of chains of stations Model large reservoirs only Stochastic load, inflows, thermal plant availability Load duration curve load representation

11. Long Term Planning Simulation of a specified set of conditions Optimization to get a reasonable hydro operating pattern Thermal dispatch models (eg Henwood) use rule based dispatch. Hydro operating patterns specified by user Stochastic inflows, energy limitation problematic Use of mean flows risky

12. Stochastic DP with Heuristic 30 year hydro-thermal planning with HVDC constraint in New Zealand Determine reservoir levels at which EMV = marginal cost of each thermal plant 60 simulations of detailed operation using historical inflows Major impact on electricity planning in NZ Used for long term planning, medium term operations

13. Reservoir Guidelines Time Lake Level $0/MWh $15/MWh $30/MWh $5/MWh $100/MWh

14. SDDP - by Mario Pereira Stochastic Dual Dynamic Programming’ 1 to 10 year horizon, weekly / monthly time steps Used in numerous countries Stochastic DP with a sampling strategy to enable multi reservoir optimization Hydro, thermal, with detailed transmission system, area interchange constraints Solves an LP for each one period sub problem

15. SDDP Simulate forward with 50 inflow sequences, using a future cost function – gives upper bound on objective function DP backward optimization considering only storage states that the simulation passed through - gives lower bound on objective Each optimization iteration adds hyper planes to the future cost function, improving the approximation

16. SDDP Subproblems Time State (storage) tt+1 At each state point Solve one LP for each inflow outcome

17. SDDP Future Cost Storage Level Future Cost One hyper plane per state point Slope = average dual of water balance Height = average cost to go from that state

18. Medium Term Planning 1 or 2 year horizon, weekly time steps Load duration curve Norwegian power pool model - successive approximations DP Hydro Quebec “Gesteau” - stochastic dynamic program Acres International, Charles Howard, PG&E … stochastic linear programming solved by CPLEX. SDDP – Central America, Colombia,……

19. Medium Term Planning Stochastic DP or Stochastic LP – gaining due to increased LP solver power Key output – water values from large lakes Maintenance planning Permitting studies Plant upgrade studies

20. Day or Week Ahead 24 to 168 hour horizon One hour, ½ hour time steps - chronological Deterministic Link to medium term model by water values Maybe with bid curve generation strategy LP, sometimes with successive linearizations, sometimes MIP Detailed model of waterways, lakes, hydro units

21. Day or Week Ahead Send output to market operator or real time control center Nasty features: –Overflow spill weirs –Rate of change of flow constraints –Non convex unit characteristics –Unit prohibited zones –Spinning reserve

22. Unit Modeling Water Flow MW Maximum efficiency Full load Rough running ranges

23. Market Clearing 24 hour horizon, 1 or ½ hour steps Bids and offers can be specific to each bus Optimize accounting for transmission system losses and constraints for optimal clearing price at each bus. CEGELEC ESCA (NZ, Australia) Simple price / quantity stack Cal PX Ignore coupling of time periods – problems for hydro operators

24. Hydro Economic Dispatch 30 to 120 minutes horizon, 10 minute steps Used in control center with SCADA Takes system status from SCADA (lake levels, flows, current set points) Time step short, run frequently, 10 minutes Given a load change, what should be done Answer needed quickly Feasibility essential, optimality desirable

25. Hydro Economic Dispatch Input water values, overall strategy from day ahead model Models whole system of stations, canals, lakes, gates, spillways Individual units, stop / start costs Environmental constraints, operating rules Issue new set points automatically, with operator review

26. Optimal Unit Loading Static optimization, solve on demand Objective: Minimize water use for given station output how many units should be on-line what load on each unit Run by operator or within a SCADA system Simple, quick, clearly defined payoff Every unit is a unique individual – even more so with age – cavitation repairs

27. Optimal Unit Loading Tailrace and headrace geometry, penstock losses, interaction between units. Calibrate unit performance using ultrasonic flow measurement, accurate MW meters Rough running zones Non symmetrical station layout – different tailwater levels, penstock losses.

29. Optimal Unit Loading One unit loads Two unit loadsThree unit loads Desired station load

30. Decision making Year ahead to set water values Week/day ahead using water values to generate market bids Market clearing model to determine day ahead results Day ahead model to plan implementation Real time instructions issued to control center by grid operator

31. Decision making Economic Dispatch determines allocation of grid operator requests Station receives set points Unit loading algorithm adjusts unit set points ED runs frequently AGC adjusts some unit set points to correct frequency or Area Control Error (Ace)