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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Published in:	Energy (Oxford) Vol. 131; pp. 165 - 178
Main Authors:	Feng, Zhong-kai, Niu, Wen-jing, Cheng, Chun-tian
Format:	Journal Article
Language:	English
Published:	Oxford Elsevier Ltd 15.07.2017 Elsevier BV
Subjects:	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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Abstract	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.
AbstractList	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. 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.
Author	Cheng, Chun-tian Feng, Zhong-kai Niu, Wen-jing
Author_xml	– sequence: 1 givenname: Zhong-kai surname: Feng fullname: Feng, Zhong-kai email: myfellow@163.com organization: School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China – sequence: 2 givenname: Wen-jing surname: Niu fullname: Niu, Wen-jing organization: Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian 116024, China – sequence: 3 givenname: Chun-tian surname: Cheng fullname: Cheng, Chun-tian organization: Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
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Keywords	Constraint handling method Multi-objective optimization Hydrothermal scheduling Chaotic mutation Quantum-behaved particle swarm optimization
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Snippet	With increasing attention paid to energy and environment in recent years, the hydrothermal scheduling considering economic and environmental objectives is...
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SubjectTerms	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
Title	Multi-objective quantum-behaved particle swarm optimization for economic environmental hydrothermal energy system scheduling
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