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1.武汉大学电气与自动化学院,湖北省武汉市 430072
2.交直流智能配电网湖北省工程中心(武汉大学),湖北省武汉市 430072
3.中国电力科学研究院有限公司,北京市 100192
Received:16 April 2023,
Revised:2023-06-01,
Published Online:31 July 2023,
Published:25 September 2023
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王强强,姚良忠,盛万兴等.基于机会约束规划的配电网分布式光伏承载力提升方法[J].电力系统自动化,2023,47(18):132-141.
WANG Qiangqiang,YAO Liangzhong,SHENG Wanxing,et al.Enhancement Method for Distributed Photovoltaic Hosting Capacity of Distribution Network Based on Chance-constrained Programming[J].Automation of Electric Power Systems,2023,47(18):132-141.
王强强,姚良忠,盛万兴等.基于机会约束规划的配电网分布式光伏承载力提升方法[J].电力系统自动化,2023,47(18):132-141. DOI: 10.7500/AEPS20230416003.
WANG Qiangqiang,YAO Liangzhong,SHENG Wanxing,et al.Enhancement Method for Distributed Photovoltaic Hosting Capacity of Distribution Network Based on Chance-constrained Programming[J].Automation of Electric Power Systems,2023,47(18):132-141. DOI: 10.7500/AEPS20230416003.
分布式光伏密集接入配电网是未来配电网的一种结构形态,配电网分布式光伏承载力提升是未来配电网规划与运行的一个关键技术问题。以配电网分布式光伏承载力提升为目标,从源侧分布式光伏逆变器无功控制和网侧最小附加无功补偿以及网络重构协同优化角度,并综合考虑源荷双侧不确定性影响,提出一种基于机会约束规划的配电网分布式光伏承载力提升方法。建立了以分布式光伏接入容量最大以及投资运行总成本最小为目标的多目标机会约束规划模型,并提出采用基于拉丁超立方采样蒙特卡洛模拟(LHS-MC)嵌入改进初始种群生成策略的协同进化算法对模型进行寻优求解,提高了搜索性能和求解效率。最后,通过仿真验证了所提方法的有效性。
The dense integration of distributed photovoltaic to distribution networks is one of the structural forms of future distribution networks
and the enhancement of distributed photovoltaic hosting capacity of distribution networks is one of the key technical issues for the planning and operation of future distribution networks. Aiming at enhancing the distributed photovoltaic hosting capacity of distribution networks
an enhancement method for the distributed photovoltaic hosting capacity of distribution networks based on the chance-constrained programming is proposed from the perspective of cooperative optimization among reactive power control of the distributed photovoltaic inverter on the source side
and the minimum additional reactive power compensation and network reconstruction on the grid side
as well as comprehensively considering the uncertainties of both the source side and the load side. A multi-objective chance-constrained programming model is established with the objective of maximizing the distributed photovoltaic hosting capacity and minimizing the total investment and operation costs. The co-evolution algorithm based on Latin hypercube sampling based Monte Carlo simulation (LHS-MC) embedded with an improved initial population generation strategy is used to search the optimal solution
in order to improve both the search performance and solution efficiency. Finally
the effectiveness of the proposed method is verified by simulations.
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