摘要
分布式可再生能源与负荷的相关性对配电网规划结果有着至关重要的影响。首先,以截断的R-Vine Copula模型为基础,建立多种负荷与分布式风、光发电的高维时序联合概率分布模型;然后,提出了一种两阶段随机配电网扩展规划模型和相对应的机会约束规划模型。在该两阶段模型中,第1阶段为配电网投资决策模型,以配电网总成本最低为目标确定合适的馈线、变电站和电容器的拓展和升级方案;第2阶段为多场景下的配电网运行模型,该阶段以网损和配电网维护成本最低为目标,以配电网安全运行为边界条件确定最优的运行方案。负荷和分布式电源的相关性和随机性由场景及其实现的概率引入模型中。考虑到基于场景的随机优化模型和机会约束模型的求解较为困难,提出了一种新的双线性Benders分解算法,能够有效地求解含有大量二阶锥潮流方程的随机优化模型。最后,以中国南通市某38节点配电网为例,验证了所提模型和算法。
The correlation between distributed renewable energy and load has a crucial impact on the distribution network planning.Firstly,based on the truncated R-Vine Copula model,the joint probability distribution model of multiple loads,distributed wind power and photovoltaic generation is established.Then,a two-stage stochastic expansion programming model of the distribution network and its corresponding chance-constrained programming model are proposed.In this two-stage model,the first stage is the investment decision model of the distribution network,in order to minimize the total cost of the distribution network to determine the appropriate expansion and upgrade scheme of feeders,substations and capacitors.The second stage is the operation model of the distribution network in multiple scenarios.The objective of this stage is to minimize the network loss and the maintenance cost of the distribution network,and the optimal operation scheme is determined with the safe operation of the distribution network as the boundary condition.The correlation and randomness of loads and distributed generators are introduced into the model by the scenario and its realized probability.Considering that the solution of the scenario-based stochastic optimization model and the chance-constrained models is difficult,a new bilinear Benders decomposition algorithm is proposed,which can effectively solve the stochastic optimization model containing a large number of second-order cone power flow equations.Finally,taking a 38-bus distribution network in Nantong of China as an example,the proposed model and algorithm are verified.
作者
吴涵
孙力文
项晟
袁越
WU Han;SUN Liwen;XIANG Sheng;YUAN Yue(Smart Grid Research Institute,Nanjing Institute of Technology,Nanjing 211167,China;College of Energy and Electrical Engineering,Hohai University,Nanjing 211100,China)
出处
《电力系统自动化》
EI
CSCD
北大核心
2022年第16期40-51,共12页
Automation of Electric Power Systems
基金
国家重点研发计划资助项目(2016YFB0900100)。
