Tim Magee CEATI Fall 2016: Optimizing Hydropower Operations Mathematical Modelling Tools November 4, 2016

Transcription

1 RiverWare Optimization Tim Magee CEATI Fall 2016: Optimizing Hydropower Operations Mathematical Modelling Tools November 4,

2 Common Applications of RiverWare Long term planning of river/reservoir system for operating policies, yield analysis, climate change, flood risk and physical modifications of system Policy evaluation compare several policies with stochastic inputs and riskbased outputs (especially NEPA processes) with respect to critical decision variables Determine operating plan for today, this season or this year including hydropower optimization Forecast operations over next few months or seasons and develop a plan for allocations, curtailments, exchanges, purchases, etc Negotiation of international or state boundary agreements for water operations Keep track of water allocation and water rights (official accounting) 2

3 Water Surface Elevation (feet) RiverWare s Inputs and Outputs Hydrology Forecast OR Historic Record OR Stochastic Ensemble OR Models interaction of Hydrologic response of River /Reservoir system (includes Hydropower) OUT Values of Decision Variables, Performance Indicators Schedule for Operations Water accounting data Statistics Reports Rainfall Runoff Model Demands for Water and Energy Operating Objectives and Constraints (policies) IN With Multi-objective operating policies Post-Processing Export data to DSS, HDB, any DB Export directly to Excel, Tableau, Lake Mead Elevation netcdf, GPAT Interim Surplus Criteria Alternatives Statistical Analysis Policy Analysis Tradeoff Analysis Baseline Conditions Basin States Alternative Flood Control Alternative Six States Alternative California Alternative 90th Percentile 50th Percentile 10th Percentile 1000 Shortage Protection Alternative Year

4 Non-Power Soft Constraints River systems are operated for many purposes more important than hydropower 4

5 Hydropower s Degrees of Freedom Varies Water is very valuable in some systems (e.g. arid regions) Determines the daily reservoir releases Power optimization becomes timing within each day Often Water rights and accounting - follow the rules Often - Planning models with monthly or yearly time steps Power has more value in other systems DOF after water delivery DOF after other uses: flood control, navigation, water quality, recreation, etc. Maximize hydropower objective(s) with remaining DOF Actual DOF can be small when outside of normal conditions No Spill exception

6 Soft Constraints Constraints that are satisfied if conditions permit Still good to model constraints that are never violated Extreme conditions or data errors could lead to violation Can stop if any of these are violated Other soft constraints may lead to violations Uncomparable constraints don t allow tradeoff e.g. constraints with different units Modelling: prioritize or use extreme differences in weights Comparable constraints allow them to trade off e.g. same constraint for many time periods or locations e.g. balanced drafting of reservoirs Scale appropriately, many possible penalty functions

7 Extreme Weights Advantage Conventional single objective function optimization Disadvantages Numeric stability can be a challenge roundoff error Not obvious how to weight to get the correct solution Not easy to explain/defend the solution to stakeholders

8 Priorities If-then Rules or System Optimization Solve for one priority at a time Optimization: effectively freeze objective function value Lower priorities are limited by higher priorities Advantages / Disadvantages Priorities are never right on the first try Can be fixed methodically Can discuss with stakeholders Still not easy to explain the solution Can list higher priorities that prevented lower priority System solution is often the harder thing to explain Disadvantage build a priority solution mechanism

9 Objectives to Minimize Comparable Soft Constraint Violations Minimize weighted violations Balanced? Minimize weighted violations p e.g. p = 2 Convex nonlinear optimization Piecewise linear approximation possible Why is p usually 2? Minimize largest [scaled] violation i.e. p = Traditionally misrepresented with a Minimax objective Why? Repeated Minimax i.e. p Minimize largest violation, then second largest violation, then third largest violation, Effectively Freeze after each iteration Hybrid Objectives

10 RiverWare s Solvers 1. Simulation Models physical processes for a variety of input/output combinations (upstream/downstream; forward/backward in time) 2. Rulebased Simulation Simulation driven by user-specified operating rules (policy) expressed through an interpreted language. Solves each timestep completely then moves to next. 3. Optimization Preemptive linear goal programming solution. Same language as rules. Can share functions with rules. 4. Water Accounting (with or without rules) Models ownership, water type and water rights; can be coupled with rules 10

11 RiverWare Optimization Components Preprocessing Simulation, Rules, Seeds Calculations that affect the optimization formulation Optimization Formulation User created policy: soft constraints w/ objective type (automatically created objective from type), objective functions, user variables, and hard constraints Physical constraints based on physical method selection and links Automatic context-dependent linearization Includes piecewise linearization of convex constraints and objectives LP Solutions (CPLEX), freezing for priorities by RiverWare Post-optimization cleanup with Rules

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14 Alternative Hydropower Objectives Meet system load Minimize peak load Maximize generation on heavy load hours (and minimize generation on light load hours) System Lambda Costs (e.g. for each hour) Block Costs (i.e. market depth or replacement value of power) Maximize Energy in Storage Add your own!

15 Ancillary Services Frequency Regulation: Reg Up & Reg Down Contingency Reserves Spinning Non-Spinning Load Following Market structure Minimum requirements Block value for sales beyond requirements 15

16 BPA Reserves Model deployment of up and down reserves Single reservoir Single time step Physical model duplication Policy constraints Some apply to deployment Some don t Roughly triples model size

17 Alternative Optima Remaining DOF Desirable to reduce alternative optima, often after real priorities Smoothing Consistency with prior run Very low priority objectives Sometimes, performance is an issue

18 End Conditions Prevent draining reservoirs Constraints or objective function includes storage value Water disappearing in lagged reaches Constraints Model specific Tuning

19 Solution Path Information 19

20 Performance Improvements Use RBS to generate seed solution Skip over satisfied soft constraints Change Repeated Minimax to Summation with Reward Table Sacrifice solution quality some for speed Parallel barrier algorithm with crossover Instead of single-threaded primal simplex 4-7 threads

21 RiverWare and Model Coupling Automated data interfaces to databases, files, and spreadsheets Execute in batch mode via scripting Expanding integration with other tools Deltares FEWS (with and without RiverWare GUI) Corps Water Management System (CWMS) Planned: National Weather Service Community Hydrologic Prediction Systems (CHPS) 21

22 Hundreds of users including Water management agencies Reclamation, Corps of Engineers, States, Cities, Water Districts Other Federal Agencies and Tribes BIA, USGS, National Park Service, Intern tl Boundary Water Commission, FERC Power Utilities TVA, Lower Colorado River Authority, Southwest Power, Bonneville Power, East Bay Municipal Utility District, Idaho Power Consultants Universities, Research Labs and Institutes NGOs International Governments

23 Current RiverWare Optimization Tennessee Valley Authority 6 Hour Preschedule Bonneville Power Administration Big 10 Real Time Big 10 Short Term Pend Oreille Kootenay Mid-Columbia Real Time, hourly time step USACE Wiesner River, monthly time step

24 Support: