基于改进灰狼算法的梯级电站中长期优化调度

Joint Medium and Long-term Optimal Scheduling of Cascade Hydropower Stations Based on Improved Gray Wolf Algorithm

为解决梯级电站中长期优化调度中非线性离散决策困难的问题,结合灰狼算法良好的局部寻优特性,建立了约束破坏度最小与发电量最大的双目标模型,前者作为种群迭代的全局目标,后者作为个体优化的局部目标,引入“当前搜索解的空间识别”机制,促进非可行解向可行解的过渡。通过闽江流域金溪梯级水库实例研究表明,相比于传统智能算法,改进灰狼算法在保障搜索全局性的基础上,可以提升目标的优化效果。

To solve the problem of nonlinear discrete decision-making in the medium and long-term optimal operation of cascade hydropower stations,a dual objective model with minimum constraint damage and maximum power generation is established combining the advanced performance of global search and local optimization of gray wolf algorithm.The objective of minimum constraint damage is served as the global objective of population and the objective of maximum power generation is served as the local objective of individual optimization in the proposed model.The mechanism of spatial recognition of current search solution is introduced into the model to promote the transition from infeasible solution to feasible solution.The case of Jinxi cascade reservoirs in Minjiang River Basin is studied,and the results show that,compared with the traditional intelligent algorithm,the improved gray wolf algorithm can effectively improve the effect of objective optimization without compromising the performance of overall searching.