Optimal scheduling of clean energy storage and charging integrated system by fusing DE algorithm and kernel search algorithm

In the context of rapid developments in artificial intelligence and the clean energy industry, the optimal scheduling of clean energy storage and charging systems has become increasingly prominent. This study proposes an optimal scheduling method that integrates Differential Evolution (DE) and Kerne...

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Vydáno v:	Energy Informatics Ročník 8; číslo 1; s. 33 - 21
Hlavní autoři:	Wang, Xinhua, Jia, Yujie, Su, Hao, Dang, Hua, Lu, Songfu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cham Springer International Publishing 01.12.2025 Springer Nature B.V SpringerOpen
Témata:	Algorithms Artificial intelligence Charging Clean energy Clean technology Climate action Climate change Computer Science Convergence Differential evolution algorithm Effectiveness Electric power grids Emissions Emissions control Energy industry Energy management Energy storage Energy utilization Evolutionary computation Greenhouse gases Information Systems and Communication Service Kernel search Operating costs Optimal scheduling Optimization Renewable energy Renewable resources Scheduling Search algorithms Storage and charging integration Sustainable development Storage and charging integration Differential evolution algorithm Kernel search Clean energy Optimal scheduling
ISSN:	2520-8942, 2520-8942
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Abstract	In the context of rapid developments in artificial intelligence and the clean energy industry, the optimal scheduling of clean energy storage and charging systems has become increasingly prominent. This study proposes an optimal scheduling method that integrates Differential Evolution (DE) and Kernel Search Optimization (KSO) algorithms. By incorporating DE’s mutation, crossover, and selection operations into the KSO framework, the method effectively avoids local optima while retaining KSO’s advantages in handling complex structures and large-scale data. Experimental results demonstrate that the convergence speed of the fusion algorithm is improved by 34.2%, 30.8%, 28.6%, and 23.4% over four other algorithms for hybrid functions, and by 56.7%, 52.9%, 25.3%, and 21.4% for combined functions. Additionally, the utilization of renewable energy increased from 40% to nearly 70% within 24 h. It can be seen that the convergence speed and renewable energy utilization of the fusion algorithm are significantly improved compared with the four baseline methods, highlighting its effectiveness in large-scale clean energy systems. This research provides an effective scheduling strategy for optimizing clean energy storage and charging systems. This study provides an effective scheduling strategy for optimizing clean energy storage and charging systems, and supports scalable and efficient energy management of urban and rural energy grids. The results show that the optimization of the integrated charging system can not only achieve optimal scheduling in a shorter time, but also reduce operating costs and resource waste, and effectively improve the overall operating efficiency of the energy system. Research to promote the efficient use of renewable energy will help reduce dependence on fossil fuels, thereby reducing greenhouse gas emissions and environmental pollution, which will have a positive impact on achieving the Sustainable Development goals and addressing climate change, and promote a win-win situation for the economy and the environment.
AbstractList	Abstract In the context of rapid developments in artificial intelligence and the clean energy industry, the optimal scheduling of clean energy storage and charging systems has become increasingly prominent. This study proposes an optimal scheduling method that integrates Differential Evolution (DE) and Kernel Search Optimization (KSO) algorithms. By incorporating DE’s mutation, crossover, and selection operations into the KSO framework, the method effectively avoids local optima while retaining KSO’s advantages in handling complex structures and large-scale data. Experimental results demonstrate that the convergence speed of the fusion algorithm is improved by 34.2%, 30.8%, 28.6%, and 23.4% over four other algorithms for hybrid functions, and by 56.7%, 52.9%, 25.3%, and 21.4% for combined functions. Additionally, the utilization of renewable energy increased from 40% to nearly 70% within 24 h. It can be seen that the convergence speed and renewable energy utilization of the fusion algorithm are significantly improved compared with the four baseline methods, highlighting its effectiveness in large-scale clean energy systems. This research provides an effective scheduling strategy for optimizing clean energy storage and charging systems. This study provides an effective scheduling strategy for optimizing clean energy storage and charging systems, and supports scalable and efficient energy management of urban and rural energy grids. The results show that the optimization of the integrated charging system can not only achieve optimal scheduling in a shorter time, but also reduce operating costs and resource waste, and effectively improve the overall operating efficiency of the energy system. Research to promote the efficient use of renewable energy will help reduce dependence on fossil fuels, thereby reducing greenhouse gas emissions and environmental pollution, which will have a positive impact on achieving the Sustainable Development goals and addressing climate change, and promote a win-win situation for the economy and the environment. In the context of rapid developments in artificial intelligence and the clean energy industry, the optimal scheduling of clean energy storage and charging systems has become increasingly prominent. This study proposes an optimal scheduling method that integrates Differential Evolution (DE) and Kernel Search Optimization (KSO) algorithms. By incorporating DE’s mutation, crossover, and selection operations into the KSO framework, the method effectively avoids local optima while retaining KSO’s advantages in handling complex structures and large-scale data. Experimental results demonstrate that the convergence speed of the fusion algorithm is improved by 34.2%, 30.8%, 28.6%, and 23.4% over four other algorithms for hybrid functions, and by 56.7%, 52.9%, 25.3%, and 21.4% for combined functions. Additionally, the utilization of renewable energy increased from 40% to nearly 70% within 24 h. It can be seen that the convergence speed and renewable energy utilization of the fusion algorithm are significantly improved compared with the four baseline methods, highlighting its effectiveness in large-scale clean energy systems. This research provides an effective scheduling strategy for optimizing clean energy storage and charging systems. This study provides an effective scheduling strategy for optimizing clean energy storage and charging systems, and supports scalable and efficient energy management of urban and rural energy grids. The results show that the optimization of the integrated charging system can not only achieve optimal scheduling in a shorter time, but also reduce operating costs and resource waste, and effectively improve the overall operating efficiency of the energy system. Research to promote the efficient use of renewable energy will help reduce dependence on fossil fuels, thereby reducing greenhouse gas emissions and environmental pollution, which will have a positive impact on achieving the Sustainable Development goals and addressing climate change, and promote a win-win situation for the economy and the environment. In the context of rapid developments in artificial intelligence and the clean energy industry, the optimal scheduling of clean energy storage and charging systems has become increasingly prominent. This study proposes an optimal scheduling method that integrates Differential Evolution (DE) and Kernel Search Optimization (KSO) algorithms. By incorporating DE’s mutation, crossover, and selection operations into the KSO framework, the method effectively avoids local optima while retaining KSO’s advantages in handling complex structures and large-scale data. Experimental results demonstrate that the convergence speed of the fusion algorithm is improved by 34.2%, 30.8%, 28.6%, and 23.4% over four other algorithms for hybrid functions, and by 56.7%, 52.9%, 25.3%, and 21.4% for combined functions. Additionally, the utilization of renewable energy increased from 40% to nearly 70% within 24 h. It can be seen that the convergence speed and renewable energy utilization of the fusion algorithm are significantly improved compared with the four baseline methods, highlighting its effectiveness in large-scale clean energy systems. This research provides an effective scheduling strategy for optimizing clean energy storage and charging systems. This study provides an effective scheduling strategy for optimizing clean energy storage and charging systems, and supports scalable and efficient energy management of urban and rural energy grids. The results show that the optimization of the integrated charging system can not only achieve optimal scheduling in a shorter time, but also reduce operating costs and resource waste, and effectively improve the overall operating efficiency of the energy system. Research to promote the efficient use of renewable energy will help reduce dependence on fossil fuels, thereby reducing greenhouse gas emissions and environmental pollution, which will have a positive impact on achieving the Sustainable Development goals and addressing climate change, and promote a win-win situation for the economy and the environment.
ArticleNumber	33
Author	Lu, Songfu Wang, Xinhua Dang, Hua Jia, Yujie Su, Hao
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SubjectTerms	Algorithms Artificial intelligence Charging Clean energy Clean technology Climate action Climate change Computer Science Convergence Differential evolution algorithm Effectiveness Electric power grids Emissions Emissions control Energy industry Energy management Energy storage Energy utilization Evolutionary computation Greenhouse gases Information Systems and Communication Service Kernel search Operating costs Optimal scheduling Optimization Renewable energy Renewable resources Scheduling Search algorithms Storage and charging integration Sustainable development
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Title	Optimal scheduling of clean energy storage and charging integrated system by fusing DE algorithm and kernel search algorithm
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