Research on multiobjective capacity configuration optimization of grid‐connected wind–solar–storage microgrid system based on improved BWO algorithm
Abstract How to effectively utilize renewable energy and improve the economic efficiency of microgrid system and its ability to consume renewable energy has become one of the main problems facing China at present. In response to this challenge, this paper establishes a multiobjective capacity optimi...
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Online Access: | http://dx.doi.org/10.1002/ese3.1727 https://onlinelibrary.wiley.com/doi/pdf/10.1002/ese3.1727 |
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crwiley:10.1002/ese3.1727 2024-09-15T17:59:00+00:00 Research on multiobjective capacity configuration optimization of grid‐connected wind–solar–storage microgrid system based on improved BWO algorithm Wang, Ziheng Wang, Tao Niu, Qunfeng Wu, Jianfeng Li, Mingwei Zhu, Shuaiqi 2024 http://dx.doi.org/10.1002/ese3.1727 https://onlinelibrary.wiley.com/doi/pdf/10.1002/ese3.1727 en eng Wiley http://creativecommons.org/licenses/by/4.0/ Energy Science & Engineering volume 12, issue 6, page 2365-2384 ISSN 2050-0505 2050-0505 journal-article 2024 crwiley https://doi.org/10.1002/ese3.1727 2024-07-18T04:23:15Z Abstract How to effectively utilize renewable energy and improve the economic efficiency of microgrid system and its ability to consume renewable energy has become one of the main problems facing China at present. In response to this challenge, this paper establishes a multiobjective capacity optimization model with the minimum levelized cost of energy, the maximum proportion of renewable energy consumption, and the minimum comprehensive system cost. Based on this model, a new improved beluga whale optimization algorithm is proposed to solve the multiobjective optimization problem in the capacity allocation process of wind–solar–storage microgrid system with the goal of ensuring that the microgrid can meet the maximum load demand at different moments throughout the year. In this paper, opposition‐based learning, artificial bee colony, dynamic opposite, and beluga whale optimization are combined to improve the population diversity and convergence accuracy, thereby enhancing the optimization performance of the algorithm. Finally, after finding the optimal Pareto front solution, the Technique for Order Preference by Similarity to an Ideal Solution is used to help decision‐makers select the optimal solution. Using real load data and meteorological data, the results of this paper show that the multiobjective capacity allocation optimization method of grid‐connected scenic storage microgrid system based on the improved beluga whale optimization algorithm can improve the economics of the wind–solar–storage microgrid system and promote the photovoltaic consumption simultaneously, providing a solution for the realization of low‐carbon power and regional economic development. The best‐found levelized cost of energy for the wind–solar–storage microgrid system is 0.192 yuan/kWh. Article in Journal/Newspaper Beluga Beluga whale Beluga* Wiley Online Library Energy Science & Engineering |
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Abstract How to effectively utilize renewable energy and improve the economic efficiency of microgrid system and its ability to consume renewable energy has become one of the main problems facing China at present. In response to this challenge, this paper establishes a multiobjective capacity optimization model with the minimum levelized cost of energy, the maximum proportion of renewable energy consumption, and the minimum comprehensive system cost. Based on this model, a new improved beluga whale optimization algorithm is proposed to solve the multiobjective optimization problem in the capacity allocation process of wind–solar–storage microgrid system with the goal of ensuring that the microgrid can meet the maximum load demand at different moments throughout the year. In this paper, opposition‐based learning, artificial bee colony, dynamic opposite, and beluga whale optimization are combined to improve the population diversity and convergence accuracy, thereby enhancing the optimization performance of the algorithm. Finally, after finding the optimal Pareto front solution, the Technique for Order Preference by Similarity to an Ideal Solution is used to help decision‐makers select the optimal solution. Using real load data and meteorological data, the results of this paper show that the multiobjective capacity allocation optimization method of grid‐connected scenic storage microgrid system based on the improved beluga whale optimization algorithm can improve the economics of the wind–solar–storage microgrid system and promote the photovoltaic consumption simultaneously, providing a solution for the realization of low‐carbon power and regional economic development. The best‐found levelized cost of energy for the wind–solar–storage microgrid system is 0.192 yuan/kWh. |
format |
Article in Journal/Newspaper |
author |
Wang, Ziheng Wang, Tao Niu, Qunfeng Wu, Jianfeng Li, Mingwei Zhu, Shuaiqi |
spellingShingle |
Wang, Ziheng Wang, Tao Niu, Qunfeng Wu, Jianfeng Li, Mingwei Zhu, Shuaiqi Research on multiobjective capacity configuration optimization of grid‐connected wind–solar–storage microgrid system based on improved BWO algorithm |
author_facet |
Wang, Ziheng Wang, Tao Niu, Qunfeng Wu, Jianfeng Li, Mingwei Zhu, Shuaiqi |
author_sort |
Wang, Ziheng |
title |
Research on multiobjective capacity configuration optimization of grid‐connected wind–solar–storage microgrid system based on improved BWO algorithm |
title_short |
Research on multiobjective capacity configuration optimization of grid‐connected wind–solar–storage microgrid system based on improved BWO algorithm |
title_full |
Research on multiobjective capacity configuration optimization of grid‐connected wind–solar–storage microgrid system based on improved BWO algorithm |
title_fullStr |
Research on multiobjective capacity configuration optimization of grid‐connected wind–solar–storage microgrid system based on improved BWO algorithm |
title_full_unstemmed |
Research on multiobjective capacity configuration optimization of grid‐connected wind–solar–storage microgrid system based on improved BWO algorithm |
title_sort |
research on multiobjective capacity configuration optimization of grid‐connected wind–solar–storage microgrid system based on improved bwo algorithm |
publisher |
Wiley |
publishDate |
2024 |
url |
http://dx.doi.org/10.1002/ese3.1727 https://onlinelibrary.wiley.com/doi/pdf/10.1002/ese3.1727 |
genre |
Beluga Beluga whale Beluga* |
genre_facet |
Beluga Beluga whale Beluga* |
op_source |
Energy Science & Engineering volume 12, issue 6, page 2365-2384 ISSN 2050-0505 2050-0505 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1002/ese3.1727 |
container_title |
Energy Science & Engineering |
_version_ |
1810435948827115520 |