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A Self-Adaptive Collaborative Differential Evolution Algorithm for Solving Energy Resource Management Problems in Smart Grids
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DOI：10.1109/TEVC.2023.3312769.png)
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Abstract
En 中文
Handling energy resource management energy resources management (ERM) in today's energy systems is complex and challenging due to uncertainties arising from the high penetration of distributed energy resources. Such penetration introduces various uncertain factors, such as renewable energy, energy storage, and electric vehicles, making it difficult for traditional mathematical methods to find effective solutions. However, evolutionary algorithms (EAs) have shown good performance in solving this problem. Therefore, in this article, an self-adaptive collaborative differential evolution algorithm (SADEA) is proposed to solve the ERM problem under uncertainty. In SADEA, a three-stage adaptive collaboration strategy, includes boundary randomization stage, knowledge-assisted collaboration stage, and range restructuration stage, is used to generate collaborative solutions. The collaborative solutions generated in the above stages will jointly participate in the perturbation of differential evolution (DE) strategies to explore promising solutions. In addition, different DE strategies are selected according to count values and random factors. At the end of the algorithm, boundary control, elite selection and retention are used to ensure the legitimacy and robustness of solutions. The proposed SADEA is compared to several state-of-the-art algorithms on a real-world distribution network (DN) located in Salamanca, Spain. The results show that SADEA is superior to its competitors in terms of the objective function (OF), ranking index, and convergence. In summary, the proposed algorithm is effective to handle the ERM problem under uncertainty.
Keywords:
Differential evolution (DE)
energy resource management
optimization
smart grid (SG)
uncertainty scheduling
Differential evolution (DE)
energy resource management
optimization
smart grid (SG)
uncertainty scheduling
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12
Papers:
1.8K
Citations:
2.4W
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