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1.华北电力大学电气与电子工程学院,北京市 102206
2.国网经济技术研究院有限公司,北京市 102209
Received:25 September 2023,
Revised:2024-01-12,
Published Online:20 June 2024,
Published:10 August 2024
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LU Shuaichao,SUN Yingyun,ZHAO Pengfei,et al.Multi-stage Transmission-Storage Cooperative Distributionally Robust Planning for New Power System[J].Automation of Electric Power Systems,2024,48(15):15-24.
路帅超,孙英云,赵鹏飞等.新型电力系统多阶段输-储协同分布鲁棒规划[J].电力系统自动化,2024,48(15):15-24. DOI: 10.7500/AEPS20230925002.
LU Shuaichao,SUN Yingyun,ZHAO Pengfei,et al.Multi-stage Transmission-Storage Cooperative Distributionally Robust Planning for New Power System[J].Automation of Electric Power Systems,2024,48(15):15-24. DOI: 10.7500/AEPS20230925002.
在电力系统中长期规划中,可再生能源发展受到政策变迁、关键技术发展和气候变化等因素的影响,具有不确定性。这一不确定性将对短期规划决策产生不可忽视的影响。针对以上问题,在可再生能源发展场景树之间嵌套距离的基础上引入鲁棒因子,构建概率分布模糊集。随后,综合考察整个规划周期规划方案的经济性与鲁棒性,建立了考虑可再生能源出力不确定性与发展不确定性的电力系统多阶段输-储协同分布鲁棒规划模型,并采用列与约束生成算法对模型进行求解。最后,采用Garver-6节点系统和HRP-38标准测试系统进行算例分析,得到多阶段规划方案。通过调节鲁棒因子可以平衡模型的经济性与鲁棒性,证明了所提方法的有效性。
In the medium- and long-term planning of the power system
the development of renewable energy is affected by factors such as policy changes
key technology development and climate changes
and has uncertainties. The uncertainty will have a significant impact on short-term planning decisions. To solve the above problems
a probability distribution ambiguity set is constructed by introducing a robustness factor based on nested distance between renewable energy development scenario trees. Subsequently
considering the economy and robustness of the planning scheme in the whole planning cycle
a multi-stage transmission-storage cooperative distributionally robust planning model for the power system is established considering the output and development uncertainties of the renewable energy
and the column-and-constraint generation (C&CG) algorithm is used to solve the model. Finally
the Garver-6-bus system and HRP-38 standard test system are used for case analysis
and the multi-stage planning schemes for these systems are obtained. The economy and robustness of the model can be balanced flexibly by adjusting the robustness factor
which proves the effectiveness of the proposed method.
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