摘要
近年来,以风电、光伏为代表的分布式能源发展迅速,然而其出力的不确定性可能会导致出力严重偏离预测值,出现极端恶劣的场景,从而给配电网规划工作与可靠、稳定运行带来挑战。在上述背景下,文章以适应分布式能源以及负荷的不确定性为目标,以分布式电源的接入位置、安装数量以及新建线路为投资决策内容,提出了一种考虑极限场景的配电网鲁棒扩展规划方法。首先建立了配电网双层规划模型,通过大M法和二阶锥松弛将非线性模型转化为混合整数线性模型;其次,采用极限场景法处理随机变量,建立了基于极限场景法的配电网两阶段鲁棒规划模型;然后,采用了基于极限场景法的列和约束生成(column and constraint generation,C&CG)算法进行求解;最后,仿真算例表明,文章采用的鲁棒规划方法可以增强配电网在极端情况下的普遍适应能力,提高了配电网的可靠性和经济性。
In recent years,distributed energies represented by photovoltaic and wind power have developed rapidly.However,the uncertainty of their output will bring challenges to the distribution network planning and reliable and stable operation.Under the above background,this paper aims at adapting to the uncertainty of distributed energies and loads,taking the access location,installation quantity and new power lines of distributed power as investment decision content,and proposes a two-stage robust planning method for distribution networks.Firstly,a bi-level programming model of the distribution network is established,and the nonlinear model is transformed into a mixed integer linear model by the big Mapproach and the second-order cone relaxation.Secondly,the extreme scenario method is used to deal with random variables,and a two-stage robust planning method for the distribution network is proposed.Then,the column and constraint generation based on the extreme scenario method is used to solve the problem.Finally,a 23-node distribution network example is used to verify the rationality and effectiveness of the proposed model.
作者
黄家祺
张宇威
贺继锋
颜炯
朱旭
杨军
HUANG Jiaqi;ZHANG Yuwei;HE Jifeng;YAN Jiong;ZHU Xu;YANG Jun(Economic and Technology Research Institute of State Grid Hubei Electric Power Co.,Ltd.,Wuhan 430000,China;School of Electrical Engineering and Automation,Wuhan University,Wuhan 430072,China)
出处
《电力建设》
北大核心
2020年第7期67-74,共8页
Electric Power Construction
基金
国网湖北经研院2019年考虑用户侧多种资源接入的负荷集成形态及电网规划技术研究(外委部分)(SGHBJY00PSJS1900026)。
关键词
配电网规划
二阶段鲁棒规划
极限场景法
列和约束生成算法
distribution network planning
two-stage robust planning
extreme scenario
column and constraint generation(C&CG)
