基于离散概率潮流的大风电接入后的电网规划

Power Network Expansion Planning Integrated with Wind Farms Based on Discrete Probabilistic Load Flow

利用离散概率模型描述风功率及负荷不确定性导致的网络潮流不确定性,利用Monte Carlo模拟技术分析并网风功率的概率分布特性,并利用正态分布描述负荷的不确定性。以一定的精度将风功率及负荷的概率密度函数离散化,并在此基础上结合线性直流潮流模型推导支路潮流的概率密度函数。规划模型以线路投资费用最少为优化目标,利用机会约束处理规划方案在正常运行及N-1情况下的过负荷。利用基本遗传算法(genetic algorithm,GA)对规划模型进行了求解。基于改进Garver 6节点系统的仿真试验证明所提算法及模型的有效性。

The discrete probabilistic formulation is utilized to describe the uncertain load flow resulted from uncertain nature of wind power generation and load. Monte Carlo simulation technique is adopted to simulate probabilistic distribution character of the integrated wind power, and normal distribution is utilized to describe the uncertainty of load. The uncertain wind power and load are converted into discrete representations with certain precision, and the probabilistic distributions of line flow are subsequently calculated based on the linear direct current power flow model. The minimized objective function of the planning model is the investment costs for the network expansion, and the chance constrained methodology is utilized to cope with the constraints on line load in the scenarios of normal and N-1 conditions. The basic Genetic Algorithm is employed to resolve the planning model. Finally, the simulation results on the modified Garver six-bus test system demonstrates the feasibility of the proposed methodology.