Optimal scheduling of virtual power plant based on energy storage and conditional value at risk

易文飞; 叶志刚

doi:10.3969/j.issn.1009-1831.2025.04.013

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Optimal scheduling of virtual power plant based on energy storage and conditional value at risk

更新时间：2026-03-05

- Optimal scheduling of virtual power plant based on energy storage and conditional value at risk
- Vol. 27, Issue 4, (2025)
- 作者机构：
  
  国网江苏省电力有限公司电力科学研究院,南京,211103
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2025.04.013
  CLC：
- Published：2025
- 稿件说明：
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易文飞, 叶志刚. Optimal scheduling of virtual power plant based on energy storage and conditional value at risk[J]. 2025, 27(4).
DOI：

易文飞, 叶志刚. Optimal scheduling of virtual power plant based on energy storage and conditional value at risk[J]. 2025, 27(4). DOI： 10.3969/j.issn.1009-1831.2025.04.013.

摘要

针对虚拟电厂（virtual power plant

VPP）中热电联产在“以热定电”运行方式下出现的弃风现象和低温环境下储能系统效率低的问题

提出一种考虑精细储能和条件风险价值的VPP优化调度方法。首先

根据低温环境下储能装置的特性建立精细储能模型；其次

在源侧采用条件风险价值理论量化分析风电不确定性

在荷侧引入热电联合需求响应机制减小热、电负荷峰谷差

促进风电消纳；最后

以VPP优化调度总成本最小为目标

在Matlab平台上对算例进行求解。仿真结果表明

相较于传统模型

所提模型具有更高的经济性和可靠性

且能有效提高风电消纳水平。

Abstract

Considering the problems of wind curtailment and low efficiency of energy storage system in virtual power plant（VPP）under the operation mode of“thermal fixed power”

An optimal VPP scheduling method considering fine energy storage and conditional value-atrisk is proposed. Firstly

a fine energy storage model is established according to the characteristics of energy storage device in low temperature environment. Secondly

the conditional value at risk theory is used to quantify the uncertainty of wind power on the source side. The combined heat and power demand response mechanism is introduced in the load side to reduce the peak-valley difference of heat and electricity load

so as to promote the consumption of wind power. Finally

the aim is to minimize the total cost of VPP optimal scheduling. The examples are solved on the Matlab platform. The simulation results show that the proposed model is more economical and reliable than the traditional model. It can effectively improve the level of wind power consumption.

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