基于改进径向移动算法的含风电场电力系统优化调度

为应对风电接入对电力系统稳定运行带来的影响,考虑风电高估低估成本、阀点效应、旋转备用约束和网络损耗等常需因素,建立计及风电不确定性的通用经济调度模型。为求解此模型,提出一种改进的径向移动算法(IRMO),该算法针对基本径向移动算法易陷入局部最优解的不足,一方面结合遗传算法中种群变异的思想,在迭代过程中随机对一部分粒子进行突变,改善种群多样性,使算法能够跳出局部最优;另一方面引入凹抛物线式的惯性权值非线性递减策略,以进一步增强算法中后期的搜索精度,更易找到全局最优解。最后对含风电场的电力系统进行算例分析和算法对比,验证模型的合理性以及IRMO的优越性。

POWER SYSTEM OPTIMAL DISPATCH CONSIDERING WIND FARMS BASED ON IMPROVED RADIAL MOVEMENT OPTIMIZATION

To cope with the impact of wind power on the stable operation of power system,a universal economic dispatch model considering wind power uncertainty is established,which takes into account the overestimation cost and underestimation cost of wind power,valve point effect,spinning reserve constraint and network loss.To solve this model,an improved radial movement optimization(IRMO)is proposed.In order to deal with the problem that the basic radial movement optimization is easy to fall into local optimal solution,on the one hand,combined with the idea of population variation in genetic algorithm,the algorithm randomly mutates some particles in the iterative process to improve the population diversity,so that the algorithm can jump out of the local optimal solution.On the other hand,the concave parabola nonlinear decreasing strategy of inertia weight is utilized to further enhance the search accuracy in the middle and final stage of the optimization and make it easier to find the global optimal solution.Finally,to verify the rationality of the model and the superiority of IRMO,example analysis and algorithm comparison of power system containing wind farm are performed.