考虑需求响应的水火电优化调度改进型花朵授粉算法

针对花朵授粉算法(FPA)寻优能力的不足,提出3种策略对其进行改进.双向学习策略能够加强FPA的局部搜索能力;仿嗅觉搜索策略不仅能增加种群的多样性,还能提升算法的全局寻优能力;动态转换概率策略能够有效地平衡全局搜索与局部搜索之间的切换.基于上述策略,提出一种具有更强搜索能力的改进型花朵授粉算法(IFPA),并在此基础上提出一种新的水火电优化调度模型.该模型在考虑火电站煤耗成本最小和供电公司利润最大的同时,还考虑采用一定的补偿策略使得消费者降低电能的需求.最后,利用IFPA解决考虑需求响应的水火电优化调度.仿真结果表明,改进的算法具有收敛速度快、精度高等优点,考虑了需求响应的水火电优化调度模型可降低消费者对电能的需求,进而降低火电站的煤耗成本.

An improved flower pollination algorithm for hydrothermal scheduling incorporating demand response

In order to enhance the searching ability of the flower pollination algorithm (FPA), this paper presents an improved flower pollination algorithm (IFPA) with three strategies, i.e., a double-direction learning strategy to advance the local searching ability, an imitative osphresis search strategy to strengthen the diversity of population and global searching ability, and a dynamic switching probability strategy to balance the switch between global and local searching. On this basis, a hydrothermal scheduling model with demand respond is proposed, which minimizes the fuel cost, maximizes the benefit of power supply company and reduces the electricity demand by providing an appropriate compensation to customers. Finally, the presented model is solved by using the IFPA. The simulation results show that the IFPA has outstanding performance, such as fast convergence speed and high accuracy, and the proposed model incorporating demand response can reduce the demand for electrical supply of customers and fuel cost of thermal power plants.