基于改进粒子群算法的梯级水电站优化调度

Optimization Scheduling of Cascade Hydropower Stations Based on Improved Particle Swarm Algorithm

梯级水电站的优化调度是一个具有复杂约束条件的大型动态非线性优化问题,运用标准线性粒子群算法求解有易陷入局部最优的特点。针对这个问题对惯性权重的更新策略进行改进,减弱了典型线性寻优的局限性,得到更优的优化结果。以恩施芭蕉河梯级水电站丰水期为例,建立以周期发电量最大为目标函数的梯级水电站的短期优化调度模型,运用粒子群优化算法对其进行求解,得到了较好的梯级水电站优化调度结果。

Optimal scheduling of cascade hydropower stations is a large complex constraints dynamic nonlinear optimization problem which is very complex to deal with.Besides,the standard linear particle swarm algorithm is easy to fall into local optimum characteristics.To solve this problem,the paper improves the update strategy of the inertia weight so as to weaken the limitations of typical linear optimization and get better optimization results.Taking the rainy period of the Enshi Bajiaohe cascade hydropower stations as an example,this paper establishes the short-term optimization scheduling model taking the maximum of cycle generating capacity as objective function,uses the improved particle swarm optimization algorithm to solve the problem,and obtains a better schedule.