基于随机潮流的分布式电源多目标优化配置

风光等间歇性分布式电源(DG)出力具有随机性和波动性,在规划过程中采用确定性变量和约束处理间歇性DG出力与实际发电情况不符,难以得到真实的规划结果,同时也会造成不必要的资源浪费。对此,采用机会约束规划方法,建立综合投资效益、网损和电压偏差的DG多目标优化配置模型,采用基于半不变量法的随机潮流结果对相关机会约束进行概率校验,并将累积排序操作、精英保留操作和拥挤距离操作引入随机黑洞粒子群算法,提出一种改进多目标粒子群算法对模型求解。PGE-33节点配电系统仿真结果表明,DG合理接入与优化配置能有效提高电网运行经济性和供电可靠性,且从概率角度分析源荷侧不确定因素对电压水平的影响,评估规划方案与实际电网运行契合度,辅助规划人员进行科学决策。

Multi-objective Optimal Allocation of Distributed Generation Based on Stochastic Power Flow

Wind power and photovoltaic and other intermittent distributed generation output power has randomness and volatility. The deterministic variables and constraints for dealing with intermittent DG output does not match with the actual situation, which is difficult to get the real planning results and will cause unnecessary waste of resources. Multi-objective optimal allocation model is established with chance constrained programming method, which considers investment benefit, net loss and voltage deviation for this problem. The results of stochastic power flow are used to verify correlation chance constraint based on the semi-invariant method. An improved multi-objective particle swarm optimization algorithm is proposed to solve the model by introducing the cumulative ranking operation, elite reservation operation and crowding distance operation into the random black hole particle swarm optimization algorithm. PG&E-33 node distribution system simulation results show that DG reasonable access and optimal allocation can effectively improve the economy and reliability of power grid operation. The influence of uncertainty factors on the voltage level is analyzed from the point of view of probability to evaluate planning scheme and the actual power grid operation fit and assist planners in making scientific decisions.