基于粒子群与蜂群结合的算法求解含风电场电力系统经济调度问题

提出一种新的混合智能算法解决含阀点效应和系统约束的含风电场的电力系统经济调度问题，将蜂群中的觅食行为与聚群行为引入改进的粒子群，提出改进粒子群一蜂群混合智能算法．在算法上进行优化，大大地提高搜索的能力，从而使结果更优．通过引入交叉策略，对那些速度保持不变的点，重新赋值．以一定的比例选拔最优点，其中选拔出的最优点，不止一个．同时精英策略的采用，有利于加强全局寻优，跳出局部最优，从而使算法得到很大的改善．最后对一个10机系统的算例进行求解，通过与改进的粒子群算法、蜂群算法进行比较，验证了改进的粒子群一蜂群混合智能优化算法在解决含风申．场的申力系统终济调度问题中的有效性与优撼性．

Dynamic Economic Dispatching Considering Wind Power Penetration Based on Improved Particle Swarm Optimization-Artificial Bee Colony Algorithm

A new hybrid intelligence algorithm is used to solve dynamic economic dispatching problem in wind power integrated system for generating units with valve-point effect and system-related constrains. It will in- troduce foraging behavior and cluster behavior into improved particle swarm to optimize the algorithm; so the search capability is greatly improved, so as to make the results more optimal and better. By introducing cross strategy, for those who remain the same speed point give new assignment. What＇s more, it selects the most more than once optimum point with certain proportion; we use elite strategy to strengthen the global optimi- zation and jump out of local optimum; so the algorithm is greatly improved. At last, a case study is conducted based on a wind power system of 10 units. The effectiveness and feasibility of the proposed algorithm are demonstrated by comparing its performance with improved particle swarm optimization and artificial bee colo- ny algorithm.