Research on a new dynamic model of power system investment decision based on differential evolution algorithm

W. Wen, Z. Wang, Z. Gu, Xiaoxia Xing
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引用次数: 1

Abstract

Under the environment of the new round of power system reform, the accounting mode of power grid is changed from price difference to cost plus income, and the competition on the power selling side is released, which greatly affects the income of regional power grids, thus greatly reducing the profit margin of power grid enterprises and greatly restricting the investment capacity. The new power reform also proposes to strengthen the overall planning of power. The investment decision-making problem of new power system is a high-dimensional, nonconvex and multi-constrained optimization problem, and the integration of wind farms further increases the difficulty of the problem. In order to optimize the problem better and enhance the convergence performance of the algorithm, based on DE(Differential Evolution) algorithm, some improvement measures such as shared fitness and adaptive adjustment of control parameters are introduced. The research results show that the improved algorithm proposed in this paper improves the convergence speed of DE algorithm, shortens the operation time to a certain extent, and obtains better optimization results, which verifies the effectiveness of the method.
基于差分进化算法的电力系统投资决策动态模型研究
在新一轮电力体制改革的环境下,电网的核算模式由价差变为成本加收益,售电端的竞争得到释放,极大地影响了区域电网的收入,从而大大降低了电网企业的利润空间,极大地制约了投资能力。新一届电力改革也提出要加强电力统筹。新电力系统投资决策问题是一个高维、非凸、多约束的优化问题,风电场的整合进一步增加了问题的难度。为了更好地优化问题,提高算法的收敛性能,在差分进化算法的基础上,引入了共享适应度和控制参数自适应调整等改进措施。研究结果表明,本文提出的改进算法提高了DE算法的收敛速度,在一定程度上缩短了运算时间,并获得了更好的优化结果,验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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