Multi-objective quantum-behaved particle swarm optimization for economic environmental hydrothermal energy system scheduling

With increasing attention paid to energy and environment in recent years, the hydrothermal scheduling considering economic and environmental objectives is becoming one of the most important optimization problems in power system. With two competing objectives and a set of operation constraints, the e...

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Vydáno v:Energy (Oxford) Ročník 131; s. 165 - 178
Hlavní autoři: Feng, Zhong-kai, Niu, Wen-jing, Cheng, Chun-tian
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Elsevier Ltd 15.07.2017
Elsevier BV
Témata:
algorithms
Chaotic mutation
Constraint handling method
Economics
Energy
Environmental objective
Hydroelectric plants
Hydroelectric power
Hydrothermal scheduling
Multi-objective optimization
Multiple objective analysis
Mutation
Optimization
Particle swarm optimization
Quantum physics
Quantum-behaved particle swarm optimization
Scheduling
system optimization
water power
Constraint handling method
Multi-objective optimization
Hydrothermal scheduling
Chaotic mutation
Quantum-behaved particle swarm optimization
ISSN:0360-5442, 1873-6785
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Shrnutí:With increasing attention paid to energy and environment in recent years, the hydrothermal scheduling considering economic and environmental objectives is becoming one of the most important optimization problems in power system. With two competing objectives and a set of operation constraints, the economic environmental hydrothermal scheduling problem is classified as a typical multi-objective nonlinear constrained optimization problem. Thus, in order to efficiently resolve this problem, the multi-objective quantum-behaved particle swarm optimization (MOQPSO) is presented in this paper. In MOQPSO, the elite archive set is adopted to conserve Pareto optimal solutions and provide multiple evolutionary directions for individuals, while the neighborhood searching and chaotic mutation strategies are used to enhance the search capability and diversity of population. Furthermore, a novel constraint handling method is designed to adjust the constraint violation of hydro and thermal plants, respectively. In order to verify its effectiveness, the MOQPSO is applied to a classical hydrothermal system with four hydropower plants and three thermal plants. The simulations show that the proposed method has competitive performance compared with several traditional methods. •Multi-objective quantum-behaved particle swarm optimization is proposed.•Mutation and neighborhood search enhance the population diversity.•Constraints handling method improves the feasibility of individuals.•MOQPSO obtains better solutions with less fuel cost and emissions.
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ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2017.05.013