粒子群优化
数学优化
需求响应
差异进化
模拟退火
电力系统
可靠性(半导体)
计算机科学
模糊逻辑
帕累托原理
功率(物理)
数学
工程类
电
物理
人工智能
电气工程
量子力学
作者
Hanbiao Yang,Xinyu Zhang,Y. P. Chu,Yinghao Ma,Dabo Zhang,Josep M. Guerrero
标识
DOI:10.1016/j.ijepes.2023.109202
摘要
The reliability of power system will vary with different demand response strategies, and it is significant to make a decision on demand response for satisfying differential reliability demand of power system. First, a peak-valley period partition model with an objective of maximizing silhouette coefficient was established based on the fuzzy clustering and step-wise iteration technology. Second, this paper proposed a multi-objective time of use electricity price optimization model considering the interests of both supply and demand sides, as well as reliability demand, and a multi-objective simulated annealing particle swarm optimization algorithm was developed to obtain solution. Third, the Pareto front curve of the dual-objective function was fitted by using a third-order Hermite interpolation algorithm, and the time of use prices corresponding to the reliability demand was obtained by using the back propagation neural network algorithm. Finally, the proposed model and algorithm were verified by the RBTS system, and the results indicate good rationality and effectiveness of the proposed method.
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