| 杨 轩,曹晓庆,程少靖,杨振华,吴 恒.计及多元不确定性的虚拟电厂竞价策略[J].电力需求侧管理,2025,27(6):112-117 |
| 计及多元不确定性的虚拟电厂竞价策略 |
| Bidding strategies of virtual power plants considering diverse uncertainties |
| 投稿时间：2025-06-30 修订日期：2025-09-08 |
| DOI：10. 3969 / j. issn. 1009-1831. 2025. 06. 017 |
| 中文关键词: 虚拟电厂 不确定性 竞价策略 古诺博弈 |
| 英文关键词: virtual power plant uncertainty bidding strategy Cournot game |
| 基金项目:国家电网公司科技项目（5400-202318574A-3-2-ZN） |
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| 摘要点击次数: 114 |
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| 中文摘要: |
| 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。 |
| 英文摘要: |
| 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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