基于多层电价响应机制的主动配电网源—网—荷协调方法

XU Xilin

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基于多层电价响应机制的主动配电网源—网—荷协调方法

更新时间：2025-08-13

- 基于多层电价响应机制的主动配电网源—网—荷协调方法
- Automation of Electric Power Systems Vol. 42, Issue 5, Pages: 9-17(2018)
- 作者机构：
  
  电力传输与功率变换控制教育部重点实验室(上海交通大学),上海市,200240
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2018
- 稿件说明：
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XU Xilin. [J]. Automation of electric power systems, 2018, 42(5): 9-17.
DOI：

XU Xilin. [J]. Automation of electric power systems, 2018, 42(5): 9-17. DOI：

摘要

为应对可再生能源高渗透率配电网多层协调难题，提出了一种基于多智能体和多层电价响应机制的配电网模糊机会约束源—网—荷功率协调响应方法。针对分布式电源、主动负荷及微电网的运行特性，分析了可再生能源高渗透率配电网的源—网—荷协调难题，引入多智能体系统和多层电价响应机制构建协调优化框架。在此基础上，考虑可再生能源及主动负荷的不确定性因素和多智能体对风险的追求差异，利用模糊机会约束规划刻画主动配电网与源、荷及微电网的协调互动，分别构建配电网层、直接协调源荷层和间接协调微电网层的优化模型。随后，利用神经元网络和等价类转化处理随机规划模型中的模糊因素，并采用蝙蝠算法和黄金分割法进行求解。最终，通过算例仿真验证了该优化方法的有效性。

Abstract

In order to deal with the problem of multi-level coordinated operation of distribution network with high penetration of renewable energy

a fuzzy chance constrained source-grid-load coordinated optimal operation method is proposed based on the multi-agent system and multi-level electricity price response mechanism. According to the operating characteristics of distributed generators

active loads and microgrids

this paper analyzes the existing problems in the coordinated source-grid-load operation of distribution networks with high penetration of renewable energy. The coordinated optimization architecture is designed based on the multi-agent system and multi-level electricity price response mechanism. Considering the uncertain factors of renewable energy and active loads

and the risk difference of multi-agents

the coordinated interaction between the active distribution network and the source

load

and microgrid is described by the method of fuzzy chance constrained programming. The optimization models at the distribution network layer

directly coordinated source-load layer and indirectly coordinated microgrid layer are developed. The neuronal network and the equivalence class transformation are introduced to deal with the uncertain factors in the stochastic programming model. The bat algorithm and golden section method are used to solve the problem. Finally

the simulation results show the effectiveness of the proposed method.

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