Power grid collaborative optimal dispatch based on load aggregator model

覃 丹; 马立红; 汪希玥; 文承毅; 王玫尹; 陈积芳; 廖思阳; 柯德平

doi:10.3969/j.issn.1009-1831.2023.02.009

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Power grid collaborative optimal dispatch based on load aggregator model

更新时间：2026-03-05

- Power grid collaborative optimal dispatch based on load aggregator model
- Vol. 25, Issue 2, (2023)
- 作者机构：
  
  1. 海南电网有限责任公司,海口,570100
  2. 武汉大学电气与自动化学院,武汉,430072
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2023.02.009
  CLC：
- Published：2023
- 稿件说明：
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覃丹, 马立红, 汪希玥, et al. Power grid collaborative optimal dispatch based on load aggregator model[J]. 2023, 25(2).
DOI：

覃丹, 马立红, 汪希玥, et al. Power grid collaborative optimal dispatch based on load aggregator model[J]. 2023, 25(2). DOI： 10.3969/j.issn.1009-1831.2023.02.009.

摘要

在新能源在电网的渗透率增高以及柔性负荷作为调节手段出现的背景下，选取合适的调度模式，使得海量分布式的负荷资源参与电网调控，能够帮助实现削峰填谷、平滑负荷曲线等目标。基于负荷聚合商模式的协同控制结构，对包括电动汽车、空调负荷与储能设备的负荷资源进行聚合，建立了日前经济调度模型；在负荷聚合商的集群内部控制中，考虑设备的调度潜能，提出一种基于裕度指标的状态队列优先算法，实现部分负荷优先响应。最后，仿真算例得出了负荷聚合商参与电力系统的日前协同调度运行计划，验证了优化方法以及调控策略的可行性。

Abstract

Under the background of increasing penetration of new energy in the power grid and the emergence of flexible load as a means of regulation

an appropriate dispatching mode is selected to enable a large number of distributed load resources to participate in the power grid regulation. This mode can help achieve the goals of peak shaving and valley filling and smoothing the load curve. Based on the cooperative control structure of load aggregator model

the load resources including electric vehicle

air conditioning load and energy storage equipment are aggregated

and the day ahead economic scheduling model is established. In the cluster internal control of load aggregator

considering the scheduling potential of equipments

a state queue priority algorithm based on margin index is proposed to realize partial load priority response. Finally

the simulation example shows that the load aggregator participates in the day ahead collaborative dispatching operation plan of the power system

and verifies the feasibility of the optimization method and regulation strategy.

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