| 引用本文: | 林毓军,苗世洪,杨炜晨,尹斌鑫,涂青宇,叶畅.面向多重不确定性环境的虚拟电厂日前优化调度策略[J].电力自动化设备,2021,41(12): |
| LIN Yujun,MIAO Shihong,YANG Weichen,YIN Binxin,TU Qingyu,YE Chang.Day-ahead optimal scheduling strategy of virtual power plant for environment with multiple uncertainties[J].Electric Power Automation Equipment,2021,41(12): |
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| 摘要: |
| 对虚拟电厂中多类型不确定性源的精确建模，有助于提升虚拟电厂调度策略的有效性。在详细分析不确定性源的不确定性特性的基础上，采用场景规划法和自适应鲁棒优化法对电价、风电出力和需求响应的不确定性进行建模。结合工程博弈思想，将不确定性源理性化为博弈主体，构建不确定性源和虚拟电厂运营商二者零和博弈模型，并采用粒子群优化算法求得博弈的均衡解。基于某地区的实际数据进行算例仿真分析，结果表明所提模型能够有效提升虚拟电厂调度结果的经济性与安全性。 |
| 关键词: 虚拟电厂 多重不确定性 自适应鲁棒-随机优化 工程博弈 日前调度 |
| DOI：10.16081/j.epae.202107027 |
| 分类号:TM73 |
| 基金项目:国家电网有限公司科技项目(52153220000U) |
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| Day-ahead optimal scheduling strategy of virtual power plant for environment with multiple uncertainties |
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LIN Yujun1, MIAO Shihong1, YANG Weichen1, YIN Binxin1, TU Qingyu1, YE Chang2
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1.Hubei Electric Power Security and High Efficiency Key Laboratory, State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;2.Electric Power Research Institute of State Grid Hubei Electric Power Co.,Ltd.,Wuhan 430077, China
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| Abstract: |
| Accurate modeling of multi-type uncertainty sources in virtual power plant is helpful for improving the effectiveness of scheduling strategy of virtual power plant. On the basis of detailed analysis of the uncertainty characteristics of uncertainty sources, the scenario planning method and self-adaptive robust optimization method are used for modeling the uncertainties of electricity price, wind power output and demand response. Combined with the engineering game theory, the uncertainty sources are rationalized into game agents, a zero-sum game model between the uncertainty source and virtual power plant operator is constructed, and the particle swarm optimization algorithm is used to obtain the equilibrium solution of the game. Example simulation analysis based on the practical data of a region is carried out, and results show that the proposed model can effectively improve the economy and safety of the scheduling results of virtual power plant. |
| Key words: virtual power plant multiple uncertainties self-adaptive robust-stochastic optimization engineering game day-ahead scheduling |
