Many-Objective Scenario Optimization of Regional Water Resource Systems Under Uncertainty

Transcription

1 Many-Objective Scenario Optimization of Regional Water Resource Systems Under Uncertainty Ivana Huskova, Evgenii S. Matrosov, Julien J. Harou, Joseph R. Kasprzyk, Patrick M. Reed BHS 2014, Birmingham, UK 4 th September 2014

2 River Thames basin water resource system planning Demand management options Active Leakage Control Pipe repair campaign Enhanced Efficiency Improvements Installation of Smart Meters Seasonal Tariffs Which possible mix of supply and demand options (portfolio)? At what capacity?

3 Many-Objective Scenario Optimization Approach (1) Simulation optimization model IRAS-2010 Thames model (simulation) ε-nsgaii (MOEA) Objectives Capital cost Cost of implementing new supply and demand options ( B) Supply deficit Average annual experienced by London WRZ (%) Supply resilience Maximum duration of failure (weeks) Supply reliability Frequency of failures (%) Eco-deficit Difference between natural and simulated low flows (%) Energy cost Annual average operating cost ( M/a) Constraints Levels of Service (max. frequency of imposing demand restrictions) Mutual exclusivity of some supply options

4 Many-Objective Scenario Optimization Approach (2) Incorporating uncertainties Climate change: 11 Hydrology flows scenarios (using Future Flows 1 from NRFA) Socio-economic: 2 Demand projection and 2 Energy prices scenarios Institutional: 2 Sustainability reductions scenarios 88 possible combinations Statistical analysis of objectives Feasibility constraints (Levels of Service) seeking robust solutions 1 PRUDHOMME, C., HAXTON, T., CROOKS, S., JACKSON, C., BARKWITH, A., WILLIAMSON, J., KELVIN, J., MACKAY, J., WANG, L., YOUNG, A., AND WATTS, G Future Flows Hydrology: and ensemble of a daily river flow and monthly groundwater levels for use for climate change impact assessment across Great Britain. Earth System Science Data, 5,

5 Deterministic optimization results Searches by simulating over historical data ( ; no uncertainty) Same problem formulation

6 Single objective optimization Least Cost solution

7 Two objective optimization Least Cost solution Perfect reliability solution

8 Many objectives Many alternative solutions

9 Adding Eco-deficit (color)

10 Adding Supply deficit ( depth )

11 Adding Supply deficit ( depth )

12 Adding Resilience (orientation)

13 Adding Energy cost (size) What happens if we incorporate uncertainties into the optimization?

14 Higher variability in external conditions higher investments to satisfy Levels of Service

15 Zooming into the multiscenario search objective space

16 Deterministic and multi-scenario optimization results comparison How do the Pareto approximate portfolios differ? Why are they different?

17 Achieving perfect reliability requires higher capital investment under varied conditions

18 Portfolio exploration

19 Portfolio exploration All demand management schemes for London WRZ implemented in all portfolios

20 Performance comparison

21 How would these portfolios perform under higher variability of external conditions? HH

22 Only 4% of the deterministic solutions would make it into the multi-scenario objective space Almost 60% of the plans considered optimal under historic conditions would experience the worst service failure in more than 20% of future scenarios

23 Scheduling preliminary study Which supply and demand options, at what capacity and when? Same Case study Planning horizon Problem formulation (but total energy cost instead of average annual) Transient demand Discounting capital and operational costs Only hydrological uncertainty explored so far Scheduling of supply options only 5 year periods

24 Scheduling preliminary results

25 Proof of concept preliminary results

26 Scheduling preliminary results Proof of concept preliminary results

27 Scheduling preliminary results Proof of concept preliminary results

28 Scheduling preliminary results Proof of concept preliminary results

29 Scheduling preliminary results Proof of concept preliminary results

30 Discussion Findings Reservoir and all demand management schemes are likely no-regret options (provide benefits even in the absence of climate change) Focus on demand reduction rather than on supply increase Good performance can be achieved even with delaying large investments Benefits Suggests many alternative plans and identifies the trade-offs between them Identifies robust plans given many plausible futures Limitations Computational burden uncertainties and scheduling increase the complexity Results dependent on the choice of scenarios Scheduling of schemes investigated only with hydrological uncertainty and adaptive options not considered - Future work

31 Acknowledgements Evgenii S. Matrosov The University of Manchester Prof. Julien J. Harou The University of Manchester Assist. Prof. Joseph R. Kasprzyk University of Colorado Legion High Performance Computing Cluster (UCL) Iridis High Performance Computing Cluster (University of Southampton) AeroVis visualization software (Dr. Joshua Kollat) Project s Sponsors Prof. Patrick M. Reed Cornell University

32 Many-Objective Scenario Optimization of Regional Water Resource Systems Under Uncertainty Thank you Ivana Huskova