Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Multiobjective Operation Optimization of a Cascaded Hydropower System
In order to satisfy the practical requirement of the power grid in China, this paper presents a multiobjective operation model for a cascaded hydropower system simultaneously considering the maximization of gross generation and firm output, as well as various complex constraints. Then, a multiobjective particle swarm optimization (MOPSO) is presented here to solve this problem. This algorithm combines the merits of chaos theory, genetic operators, adaptive adjustment strategy, and the heuristic constraint-handling method to help the population explore the search space efficiently: the logistic map is introduced to generate the initial population distributed uniformly in problem space; both the inertia weight and learning coefficients are dynamically changed as iteration goes on; an archive set is used to conserve the nondominated solutions found during evolution; the personal and global best position for each particle are determined by crowding distance and a feasibility-based dominance relationship; the classical mutation and crossover operators are introduced to enhance the population diversity; a novel constraint-handling method is proposed to address the complicated constraints. To testify to its effectiveness, the MOPSO is applied to the Wu River cascade hydropower system in southwest China. The results in different cases indicate that MOPSO is able to provide better solutions than the NSGA-II method, providing an effective technique for the operation of a hydropower system.
Multiobjective Operation Optimization of a Cascaded Hydropower System
In order to satisfy the practical requirement of the power grid in China, this paper presents a multiobjective operation model for a cascaded hydropower system simultaneously considering the maximization of gross generation and firm output, as well as various complex constraints. Then, a multiobjective particle swarm optimization (MOPSO) is presented here to solve this problem. This algorithm combines the merits of chaos theory, genetic operators, adaptive adjustment strategy, and the heuristic constraint-handling method to help the population explore the search space efficiently: the logistic map is introduced to generate the initial population distributed uniformly in problem space; both the inertia weight and learning coefficients are dynamically changed as iteration goes on; an archive set is used to conserve the nondominated solutions found during evolution; the personal and global best position for each particle are determined by crowding distance and a feasibility-based dominance relationship; the classical mutation and crossover operators are introduced to enhance the population diversity; a novel constraint-handling method is proposed to address the complicated constraints. To testify to its effectiveness, the MOPSO is applied to the Wu River cascade hydropower system in southwest China. The results in different cases indicate that MOPSO is able to provide better solutions than the NSGA-II method, providing an effective technique for the operation of a hydropower system.
Multiobjective Operation Optimization of a Cascaded Hydropower System
Feng, Zhong-Kai (Autor:in) / Niu, Wen-Jing (Autor:in) / Zhou, Jian-Zhong (Autor:in) / Cheng, Chun-Tian (Autor:in)
17.07.2017
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Multiobjective Operation Optimization of a Cascaded Hydropower System
| Online Contents | 2017
Multiobjective Operation Optimization of a Cascaded Hydropower System
| British Library Online Contents | 2017
| DOAJ | 2020