Bidding strategies of virtual power plants considering diverse uncertainties

杨 轩; 曹晓庆; 程少靖; 杨振华; 吴 恒

doi:10.3969/j.issn.1009-1831.2025.06.017

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Bidding strategies of virtual power plants considering diverse uncertainties

更新时间：2026-03-05

- Bidding strategies of virtual power plants considering diverse uncertainties
- Vol. 27, Issue 6, (2025)
- 作者机构：
  
  1. 武汉华源电力设计院有限公司,武汉,430000
  2. 江苏智臻能源科技有限公司,南京,210000
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2025.06.017
  CLC：
- Published：2025
- 稿件说明：
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杨轩, 曹晓庆, 程少靖, et al. Bidding strategies of virtual power plants considering diverse uncertainties[J]. 2025, 27(6).
DOI：

杨轩, 曹晓庆, 程少靖, et al. Bidding strategies of virtual power plants considering diverse uncertainties[J]. 2025, 27(6). DOI： 10.3969/j.issn.1009-1831.2025.06.017.

摘要

虚拟电厂（virtual power plant，VPP）作为聚合型的能源系统，面临风光不确定性和电动汽车（electric vehicle，EV）充电行为随机性影响自身经济性的难题。为此，提出一种计及多元不确定性的虚拟电厂竞价策略，以提升可再生能源消纳能力，同时提高收益水平。首先，采用粒子群（particle swarm optimization，PSO）—长短期记忆网络（long-short term memory，LSTM）算法，基于真实数据以及充电容量边界将风光出力和EV充电需求转换为确定性场景。然后，考虑可再生能源与电动汽车群体负荷的不确定性，基于出力偏差变量和古诺博弈建立虚拟电厂的竞价策略模型。最后，通过算例验证所提模型的有效性。

Abstract

Virtual power plant（VPP）

as an aggregated energy system

is faced with a difficult problem that the uncertain wind power and solar power and the random charging behavior of electric vehicles affect its economy. Therefore

a virtual power plant bidding strategy that takes into account diverse un-certainties is proposed

which can improve the absorption capacity of renewable energy and in-crease the income level. Firstly

the particle swarm optimization（PSO）-long term memory（LSTM）algorithm is used to convert the wind power and EV charging demand into deterministic scenarios based on the real data and the charging capacity boundaries. Then

considering the uncertainty of group loads of renewable energy and electric vehicles

a bidding strategy model of virtual power plant is established based on the output deviation variables and the Cournot game. Finally

a numerical example is given to verify the effective-ness of the proposed model.

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