Based on conventional particle swarm optimization(PSO),this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW)strategy,referred to as the ARIW-PSO algori...Based on conventional particle swarm optimization(PSO),this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW)strategy,referred to as the ARIW-PSO algorithm,to build a multi-objective optimization model for reservoir operation.Using the triangular probability density function,the inertia weight is randomly generated,and the probability density function is automatically adjusted to make the inertia weight generally greater in the initial stage of evolution,which is suitable for global searches.In the evolution process,the inertia weight gradually decreases,which is beneficial to local searches.The performance of the ARIWPSO algorithm was investigated with some classical test functions,and the results were compared with those of the genetic algorithm(GA),the conventional PSO,and other improved PSO methods.Then,the ARIW-PSO algorithm was applied to multi-objective optimal dispatch of the Panjiakou Reservoir and multi-objective flood control operation of a reservoir group on the Luanhe River in China,including the Panjiakou Reservoir,Daheiting Reservoir,and Taolinkou Reservoir.The validity of the multi-objective optimization model for multi-reservoir systems based on the ARIW-PSO algorithm was verified.展开更多
In regulated rivers,shaping seasonal flows to recover species at risk depends on understanding when to expect conflicts with competing water users and when their interests are aligned.Multi-objective optimization can ...In regulated rivers,shaping seasonal flows to recover species at risk depends on understanding when to expect conflicts with competing water users and when their interests are aligned.Multi-objective optimization can be used to reveal such conflicts and commonalities.When species are involved,multi-objective optimization is challenged by the need to simulate complex species responses to flow regimes.Previously,we addressed that challenge by developing a simplified salmon model(Quantus)that defines cohorts of salmon by the river section and time in which they were spawned.Salmon in these space-time cohorts are tracked from the time redds(nests)are constructed until the cohort exits the tributary en route to the ocean.In this study,we modeled seasonal patterns in energy value and developed a Pareto-optimal frontier of seasonal flow patterns to maximize in-river salmon survival and hydropower value.Candidate flow regimes were characterized by two pulse flows varying in magnitude,timing,and duration and constrained by a total annual flow near the historical median.Our analysis revealed times when economic and salmon objectives were aligned and times when they differed.Pulse flows that favored higher energy value were timed to meet demand during extreme temperatures.Both salmon and hydropower objectives produced optimal flow regimes with pulse flows in early summer,but only solutions favoring hydropower value included high flows in mid-winter.Solutions favoring higher age-0 salmon survival provided an extended pulse flow in late winter/early spring,which suggests that access to productive floodplain habitat allowed faster growth and earlier out-migration and reduced the need for higher temperature-moderating flows later in spring.Minimum flows were also higher among solutions favoring salmon over energy.The tools used to produce these results can help to design simplified seasonal flow regimes by revealing compromise solutions that satisfy both fish and energy producers and highlighting when potential conflicts are likely.展开更多
基金supported by the Foundation of the Scientific and Technological Innovation Team of Colleges and Universities in Henan Province(Grant No.181RTSTHN009)the Foundation of the Key Laboratory of Water Environment Simulation and Treatment in Henan Province(Grant No.2017016).
文摘Based on conventional particle swarm optimization(PSO),this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW)strategy,referred to as the ARIW-PSO algorithm,to build a multi-objective optimization model for reservoir operation.Using the triangular probability density function,the inertia weight is randomly generated,and the probability density function is automatically adjusted to make the inertia weight generally greater in the initial stage of evolution,which is suitable for global searches.In the evolution process,the inertia weight gradually decreases,which is beneficial to local searches.The performance of the ARIWPSO algorithm was investigated with some classical test functions,and the results were compared with those of the genetic algorithm(GA),the conventional PSO,and other improved PSO methods.Then,the ARIW-PSO algorithm was applied to multi-objective optimal dispatch of the Panjiakou Reservoir and multi-objective flood control operation of a reservoir group on the Luanhe River in China,including the Panjiakou Reservoir,Daheiting Reservoir,and Taolinkou Reservoir.The validity of the multi-objective optimization model for multi-reservoir systems based on the ARIW-PSO algorithm was verified.
基金This research,conducted by Oak Ridge National Laboratory(ORNL),was supported by the US Department of Energy's(DOE)Energy Efficiency and Renewable Energy Office,Wind and Water Power Technologies ProgramORNL is managed by UT-Battelle,LLC under Contract No.DEAC05-00OR22725 with the DOE+1 种基金The publisher,by accepting the article for publication,acknowledges that the U.S.Government retains a nonexclusive,paid-up,irrevocable,world-wide license to publish or reproduce the published form of this manuscript,or allow others to do so,for U.S.Government purposesThe DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan(http://energy.gov/downloads/doe-public-access-plan).
文摘In regulated rivers,shaping seasonal flows to recover species at risk depends on understanding when to expect conflicts with competing water users and when their interests are aligned.Multi-objective optimization can be used to reveal such conflicts and commonalities.When species are involved,multi-objective optimization is challenged by the need to simulate complex species responses to flow regimes.Previously,we addressed that challenge by developing a simplified salmon model(Quantus)that defines cohorts of salmon by the river section and time in which they were spawned.Salmon in these space-time cohorts are tracked from the time redds(nests)are constructed until the cohort exits the tributary en route to the ocean.In this study,we modeled seasonal patterns in energy value and developed a Pareto-optimal frontier of seasonal flow patterns to maximize in-river salmon survival and hydropower value.Candidate flow regimes were characterized by two pulse flows varying in magnitude,timing,and duration and constrained by a total annual flow near the historical median.Our analysis revealed times when economic and salmon objectives were aligned and times when they differed.Pulse flows that favored higher energy value were timed to meet demand during extreme temperatures.Both salmon and hydropower objectives produced optimal flow regimes with pulse flows in early summer,but only solutions favoring hydropower value included high flows in mid-winter.Solutions favoring higher age-0 salmon survival provided an extended pulse flow in late winter/early spring,which suggests that access to productive floodplain habitat allowed faster growth and earlier out-migration and reduced the need for higher temperature-moderating flows later in spring.Minimum flows were also higher among solutions favoring salmon over energy.The tools used to produce these results can help to design simplified seasonal flow regimes by revealing compromise solutions that satisfy both fish and energy producers and highlighting when potential conflicts are likely.
