Can modern multi-objective evolutionary algorithms discover high-dimensional financial risk portfolio tradeoffs for snow-dominated water-energy systems?
•Benchmarking multi-objective financial risk portfolios for snow-driven hydropower.•Self-adaptive search can more effectively capture complex financial risk tradeoffs.•Decomposition and reference point algorithms deteriorate and misrepresent tradeoffs. Hydropower generation in the Hetch Hetchy Power...
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| Veröffentlicht in: | Advances in water resources Jg. 145; S. 103718 |
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01.11.2020
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| ISSN: | 0309-1708, 1872-9657 |
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| Abstract | •Benchmarking multi-objective financial risk portfolios for snow-driven hydropower.•Self-adaptive search can more effectively capture complex financial risk tradeoffs.•Decomposition and reference point algorithms deteriorate and misrepresent tradeoffs.
Hydropower generation in the Hetch Hetchy Power System is strongly tied to snowmelt dynamics in the central Sierra Nevada and consequently is particularly financially vulnerable to changes in snowpack availability and timing. This study explores the Hetchy Hetchy Power System as a representative example from the broader class of financial risk management problems that hold promise in helping utilities such as SFPUC to understand the tradeoffs across portfolios of risk mitigation instruments given uncertainties in snowmelt dynamics. An evolutionary multi-objective direct policy search (EMODPS) framework is implemented to identify time adaptive stochastic rules that map utility state information and exogenous inputs to optimal annual financial decisions. The resulting financial risk mitigation portfolio planning problem is mathematically difficult due to its high dimensionality and mixture of nonlinear, nonconvex, and discrete objectives. These features add to the difficulty of the problem by yielding a Pareto front of solutions that has a highly disjoint and complex geometry. In this study, we contribute a diagnostic assessment of state-of-the-art multi-objective evolutionary algorithms’ (MOEAs') abilities to support a DPS framework for managing financial risk. We perform comprehensive diagnostics on five algorithms: the Borg multi-objective evolutionary algorithm, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Non-dominated Sorting Genetic Algorithm III (NSGA-III), Reference Vector Guided Evolutionary Algorithm (RVEA), and the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D). The MOEAs are evaluated to characterize their controllability (ease-of-use), reliability (probability of success), efficiency (minimizing model evaluations), and effectiveness (high quality tradeoff representations). Our results show that newer decomposition, reference point, and reference vector algorithms are highly sensitive to their parameterizations (difficult to use), suffer from search deterioration (losing solutions), and have a strong likelihood of misrepresenting key tradeoffs. The results emphasize the importance of using MOEAs with archiving and adaptive search capabilities in order to solve complex financial risk portfolio problems in snow-dependent water-energy systems. |
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| AbstractList | •Benchmarking multi-objective financial risk portfolios for snow-driven hydropower.•Self-adaptive search can more effectively capture complex financial risk tradeoffs.•Decomposition and reference point algorithms deteriorate and misrepresent tradeoffs.
Hydropower generation in the Hetch Hetchy Power System is strongly tied to snowmelt dynamics in the central Sierra Nevada and consequently is particularly financially vulnerable to changes in snowpack availability and timing. This study explores the Hetchy Hetchy Power System as a representative example from the broader class of financial risk management problems that hold promise in helping utilities such as SFPUC to understand the tradeoffs across portfolios of risk mitigation instruments given uncertainties in snowmelt dynamics. An evolutionary multi-objective direct policy search (EMODPS) framework is implemented to identify time adaptive stochastic rules that map utility state information and exogenous inputs to optimal annual financial decisions. The resulting financial risk mitigation portfolio planning problem is mathematically difficult due to its high dimensionality and mixture of nonlinear, nonconvex, and discrete objectives. These features add to the difficulty of the problem by yielding a Pareto front of solutions that has a highly disjoint and complex geometry. In this study, we contribute a diagnostic assessment of state-of-the-art multi-objective evolutionary algorithms’ (MOEAs') abilities to support a DPS framework for managing financial risk. We perform comprehensive diagnostics on five algorithms: the Borg multi-objective evolutionary algorithm, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Non-dominated Sorting Genetic Algorithm III (NSGA-III), Reference Vector Guided Evolutionary Algorithm (RVEA), and the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D). The MOEAs are evaluated to characterize their controllability (ease-of-use), reliability (probability of success), efficiency (minimizing model evaluations), and effectiveness (high quality tradeoff representations). Our results show that newer decomposition, reference point, and reference vector algorithms are highly sensitive to their parameterizations (difficult to use), suffer from search deterioration (losing solutions), and have a strong likelihood of misrepresenting key tradeoffs. The results emphasize the importance of using MOEAs with archiving and adaptive search capabilities in order to solve complex financial risk portfolio problems in snow-dependent water-energy systems. Hydropower generation in the Hetch Hetchy Power System is strongly tied to snowmelt dynamics in the central Sierra Nevada and consequently is particularly financially vulnerable to changes in snowpack availability and timing. This study explores the Hetchy Hetchy Power System as a representative example from the broader class of financial risk management problems that hold promise in helping utilities such as SFPUC to understand the tradeoffs across portfolios of risk mitigation instruments given uncertainties in snowmelt dynamics. An evolutionary multi-objective direct policy search (EMODPS) framework is implemented to identify time adaptive stochastic rules that map utility state information and exogenous inputs to optimal annual financial decisions. The resulting financial risk mitigation portfolio planning problem is mathematically difficult due to its high dimensionality and mixture of nonlinear, nonconvex, and discrete objectives. These features add to the difficulty of the problem by yielding a Pareto front of solutions that has a highly disjoint and complex geometry. In this study, we contribute a diagnostic assessment of state-of-the-art multi-objective evolutionary algorithms’ (MOEAs') abilities to support a DPS framework for managing financial risk. We perform comprehensive diagnostics on five algorithms: the Borg multi-objective evolutionary algorithm, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Non-dominated Sorting Genetic Algorithm III (NSGA-III), Reference Vector Guided Evolutionary Algorithm (RVEA), and the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D). The MOEAs are evaluated to characterize their controllability (ease-of-use), reliability (probability of success), efficiency (minimizing model evaluations), and effectiveness (high quality tradeoff representations). Our results show that newer decomposition, reference point, and reference vector algorithms are highly sensitive to their parameterizations (difficult to use), suffer from search deterioration (losing solutions), and have a strong likelihood of misrepresenting key tradeoffs. The results emphasize the importance of using MOEAs with archiving and adaptive search capabilities in order to solve complex financial risk portfolio problems in snow-dependent water-energy systems. |
| ArticleNumber | 103718 |
| Author | Reed, Patrick M. Characklis, Gregory W. Gupta, Rohini S. Hamilton, Andrew L. |
| Author_xml | – sequence: 1 givenname: Rohini S. surname: Gupta fullname: Gupta, Rohini S. email: rg727@cornell.edu organization: Department of Civil and Environmental Engineering, Cornell University, Ithaca, NY, USA – sequence: 2 givenname: Andrew L. surname: Hamilton fullname: Hamilton, Andrew L. organization: Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA – sequence: 3 givenname: Patrick M. surname: Reed fullname: Reed, Patrick M. organization: Department of Civil and Environmental Engineering, Cornell University, Ithaca, NY, USA – sequence: 4 givenname: Gregory W. surname: Characklis fullname: Characklis, Gregory W. organization: Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA |
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| Keywords | Many objective optimization Hydropower Financial risk management Evolutionary algorithms Direct policy search Algorithm benchmarking |
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| SubjectTerms | Algorithm benchmarking algorithms diagnostic techniques Direct policy search Evolutionary algorithms Financial risk management geometry Hydropower issues and policy Many objective optimization mountains risk risk reduction snowmelt snowpack water water power |
| Title | Can modern multi-objective evolutionary algorithms discover high-dimensional financial risk portfolio tradeoffs for snow-dominated water-energy systems? |
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