Combined heat and power economic emission dispatch using improved bare-bone multi-objective particle swarm optimization
An improved bare-bone multi-objective particle swarm optimization (IBBMOPSO) is proposed to solve the combined heat and power economic emission dispatch problems. To conquer the population diversity deficiency and premature convergence of bare-bone particle swarm optimization, IBBMOPSO integrates fo...
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| Vydáno v: | Energy (Oxford) Ročník 244; s. 123108 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Oxford
Elsevier Ltd
01.04.2022
Elsevier BV |
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| ISSN: | 0360-5442, 1873-6785 |
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| Abstract | An improved bare-bone multi-objective particle swarm optimization (IBBMOPSO) is proposed to solve the combined heat and power economic emission dispatch problems. To conquer the population diversity deficiency and premature convergence of bare-bone particle swarm optimization, IBBMOPSO integrates four improved strategies, that is, (i) a non-linear adaptive particle updating strategy is presented to automatically tune the weights of the personal best position (pbest) and the global best position (gbest), and to shrink the standard deviation for generating new particles; (ii) an improved strategy by comparing the sparsity of the pbest and the target particle instead of the domination is proposed to update the pbest; (iii) an improved strategy by selecting a random Pareto optimal solution from a newly filtered subset of the external archive is designed to determine the gbest for each target particle; and (iv) a modified strategy by combining the slope and the crowding distance is presented to determine the Pareto optimal frontier. IBBMOPSO is firstly validated by nine multi-objective benchmark test functions. Then, it is then applied to three test systems and the simulation results demonstrate that IBBMOPSO can achieve higher-quality dispatching schemes with lower generating fuel cost and less pollutant gas emission compared with other algorithms.
•An improved bare-bone multi-objective particle swarm optimization algorithm is proposed.•A nonlinear adaptive particle updating strategy based on exponential function is proposed.•Improved strategies to update the pbest and gbest are proposed.•The slope method and crowding distance method are combined to determine the POF.•Benchmark test functions and three CHPEED problems are used to verify the performance. |
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| AbstractList | An improved bare-bone multi-objective particle swarm optimization (IBBMOPSO) is proposed to solve the combined heat and power economic emission dispatch problems. To conquer the population diversity deficiency and premature convergence of bare-bone particle swarm optimization, IBBMOPSO integrates four improved strategies, that is, (i) a non-linear adaptive particle updating strategy is presented to automatically tune the weights of the personal best position (pbest) and the global best position (gbest), and to shrink the standard deviation for generating new particles; (ii) an improved strategy by comparing the sparsity of the pbest and the target particle instead of the domination is proposed to update the pbest; (iii) an improved strategy by selecting a random Pareto optimal solution from a newly filtered subset of the external archive is designed to determine the gbest for each target particle; and (iv) a modified strategy by combining the slope and the crowding distance is presented to determine the Pareto optimal frontier. IBBMOPSO is firstly validated by nine multi-objective benchmark test functions. Then, it is then applied to three test systems and the simulation results demonstrate that IBBMOPSO can achieve higher-quality dispatching schemes with lower generating fuel cost and less pollutant gas emission compared with other algorithms. An improved bare-bone multi-objective particle swarm optimization (IBBMOPSO) is proposed to solve the combined heat and power economic emission dispatch problems. To conquer the population diversity deficiency and premature convergence of bare-bone particle swarm optimization, IBBMOPSO integrates four improved strategies, that is, (i) a non-linear adaptive particle updating strategy is presented to automatically tune the weights of the personal best position (pbest) and the global best position (gbest), and to shrink the standard deviation for generating new particles; (ii) an improved strategy by comparing the sparsity of the pbest and the target particle instead of the domination is proposed to update the pbest; (iii) an improved strategy by selecting a random Pareto optimal solution from a newly filtered subset of the external archive is designed to determine the gbest for each target particle; and (iv) a modified strategy by combining the slope and the crowding distance is presented to determine the Pareto optimal frontier. IBBMOPSO is firstly validated by nine multi-objective benchmark test functions. Then, it is then applied to three test systems and the simulation results demonstrate that IBBMOPSO can achieve higher-quality dispatching schemes with lower generating fuel cost and less pollutant gas emission compared with other algorithms. •An improved bare-bone multi-objective particle swarm optimization algorithm is proposed.•A nonlinear adaptive particle updating strategy based on exponential function is proposed.•Improved strategies to update the pbest and gbest are proposed.•The slope method and crowding distance method are combined to determine the POF.•Benchmark test functions and three CHPEED problems are used to verify the performance. |
| ArticleNumber | 123108 |
| Author | Xiong, Guojiang Shuai, Maohang Hu, Xiao |
| Author_xml | – sequence: 1 givenname: Guojiang orcidid: 0000-0002-8913-7315 surname: Xiong fullname: Xiong, Guojiang email: gjxiongee@foxmail.com – sequence: 2 givenname: Maohang surname: Shuai fullname: Shuai, Maohang – sequence: 3 givenname: Xiao orcidid: 0000-0001-5251-8651 surname: Hu fullname: Hu, Xiao |
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| Keywords | Combined heat and power system Economic environment dispatch Multi-objective optimization Particle swarm optimization |
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| SubjectTerms | Algorithms Cogeneration Combined heat and power system Economic environment dispatch Economics Emissions energy energy costs heat Multi-objective optimization Multiple objective analysis Optimization Pareto optimum Particle swarm optimization Pollutants Power dispatch standard deviation |
| Title | Combined heat and power economic emission dispatch using improved bare-bone multi-objective particle swarm optimization |
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