A Multiobjective Optimal Operation of a Stand-Alone Microgrid Using SAPSO Algorithm
Microgrid is an effective way to utilize renewable energy resources, especially for satisfying the electricity requirements in remote islands. The operation optimization of an island microgrid is critical to ensure the effective performance of the whole microgrid system, and it is usually a multicon...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/6042105 |
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| author | Guoping Zhang Weijun Wang Jie Du Hua Liu |
| author_facet | Guoping Zhang Weijun Wang Jie Du Hua Liu |
| author_sort | Guoping Zhang |
| collection | DOAJ |
| description | Microgrid is an effective way to utilize renewable energy resources, especially for satisfying the electricity requirements in remote islands. The operation optimization of an island microgrid is critical to ensure the effective performance of the whole microgrid system, and it is usually a multiconstrained and multiobjective optimization problem. The main contribution of this study is an operation optimization method for the stand-alone microgrid system in a remote island, which includes wind, PV, battery, and diesel generator. In this paper, a novel operation optimization model for stand-alone microgrid is proposed, in which the battery system is considered separately; the multiobjective day-ahead optimization model considering economic cost, battery depreciation cost, and environmental protection cost is established. In the optimization, the output power of diesel generator and energy storage system are chosen as the decision variables. For this purpose, an efficient search algorithm combining the particle swarm optimization (PSO) algorithm and the simulated annealing (SA) algorithm is developed. The hybrid algorithm is applied to search for the Pareto solution set of the optimization problem. The search results are compared with those from traditional PSO algorithm. Also, a grey target decision-making theory based on the entropy weight method is proposed to identify the best trade-off scheduling scheme among all the solutions, and the results are compared with those from two other commonly used subjective and objective methods. The results show that the proposed optimization method can be applied to the day-ahead operation optimization of the microgrid system and help the user obtain the best compromise operation scheme for stand-alone microgrid. |
| format | Article |
| id | doaj-art-279fe191edd64e53bf70233bfb18e846 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-279fe191edd64e53bf70233bfb18e8462025-02-03T05:51:13ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552020-01-01202010.1155/2020/60421056042105A Multiobjective Optimal Operation of a Stand-Alone Microgrid Using SAPSO AlgorithmGuoping Zhang0Weijun Wang1Jie Du2Hua Liu3Department of Military Installations, Army Logistics University of PLA, Chongqing 401331, ChinaDepartment of Military Installations, Army Logistics University of PLA, Chongqing 401331, ChinaElectric Power Research Institute, Chongqing Electric Power Company, Chongqing 401123, ChinaDepartment of Military Installations, Army Logistics University of PLA, Chongqing 401331, ChinaMicrogrid is an effective way to utilize renewable energy resources, especially for satisfying the electricity requirements in remote islands. The operation optimization of an island microgrid is critical to ensure the effective performance of the whole microgrid system, and it is usually a multiconstrained and multiobjective optimization problem. The main contribution of this study is an operation optimization method for the stand-alone microgrid system in a remote island, which includes wind, PV, battery, and diesel generator. In this paper, a novel operation optimization model for stand-alone microgrid is proposed, in which the battery system is considered separately; the multiobjective day-ahead optimization model considering economic cost, battery depreciation cost, and environmental protection cost is established. In the optimization, the output power of diesel generator and energy storage system are chosen as the decision variables. For this purpose, an efficient search algorithm combining the particle swarm optimization (PSO) algorithm and the simulated annealing (SA) algorithm is developed. The hybrid algorithm is applied to search for the Pareto solution set of the optimization problem. The search results are compared with those from traditional PSO algorithm. Also, a grey target decision-making theory based on the entropy weight method is proposed to identify the best trade-off scheduling scheme among all the solutions, and the results are compared with those from two other commonly used subjective and objective methods. The results show that the proposed optimization method can be applied to the day-ahead operation optimization of the microgrid system and help the user obtain the best compromise operation scheme for stand-alone microgrid.http://dx.doi.org/10.1155/2020/6042105 |
| spellingShingle | Guoping Zhang Weijun Wang Jie Du Hua Liu A Multiobjective Optimal Operation of a Stand-Alone Microgrid Using SAPSO Algorithm Journal of Electrical and Computer Engineering |
| title | A Multiobjective Optimal Operation of a Stand-Alone Microgrid Using SAPSO Algorithm |
| title_full | A Multiobjective Optimal Operation of a Stand-Alone Microgrid Using SAPSO Algorithm |
| title_fullStr | A Multiobjective Optimal Operation of a Stand-Alone Microgrid Using SAPSO Algorithm |
| title_full_unstemmed | A Multiobjective Optimal Operation of a Stand-Alone Microgrid Using SAPSO Algorithm |
| title_short | A Multiobjective Optimal Operation of a Stand-Alone Microgrid Using SAPSO Algorithm |
| title_sort | multiobjective optimal operation of a stand alone microgrid using sapso algorithm |
| url | http://dx.doi.org/10.1155/2020/6042105 |
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