考虑源荷不确定性的分布式电源选址定容

大规模的分布式电源入网给配电网的规划带来诸多不确定性因素。为使配网规划结果更加合理,考虑待规划地区的风速、光照强度和负荷的不确定性,构建了以年综合费用最少为目标函数的分布式电源选址定容规划模型。首先,对风、光和负荷进行了概率建模;其次,采用拉丁超立方采样方法生成初始场景,并采用改进的同步回代缩减法对场景进行削减;最后,鉴于粒子群算法具有收敛速度慢和容易早熟的缺点,将自适应惯性权重和混沌优化算法融入粒子群算法中,进而提出了一种改进型粒子群算法,并且在IEEE 33节点的标准算例系统上进行了仿真,结果表明所建模型和所提算法的合理性与有效性。

Siting and Sizing of Distributed Generations Considering Uncertainties in Source and Load

The integration of large-scale distributed generations(DGs)into power grid brings many uncertain factors to the planning of distribution network.In order to make the result of distribution network planning more reasonable,the uncertainties in wind speed,light intensity and load in the planning area are considered,and a planning model for the optimal siting and sizing of DGs is constructed with the objective function of minimizing the annual comprehensive cost.First,the probabilistic modeling of wind turbines,photovoltaics and loads is conducted.Then,the Latin hypercube sampling method is used to generate the initial scene,which is reduced by the improved simultaneous backward reduction method.Finally,in view of the defects in the particle swarm optimization(PSO)algorithm such as slow convergence speed and prematurity,the adaptive inertia weight and chaos optimization algorithm are integrated into the PSO algorithm,and an improved PSO algorithm is proposed accordingly.Simulations are carried out on a standard example of an IEEE 33-bus system,and results show the rationality and effectiveness of the established model and the proposed algorithm.