基于NSGA-Ⅱ算法的多目标水火电站群优化调度模型研究

Study on optimal scheduling model of NSGA-Ⅱalogorithm for system of hydro and thermal power plants

含有梯级水电厂的水火电力系统优化调度必须考虑各水电厂之间的水力耦合、上下游水电厂之间水流到达时间的延迟和可能弃水等因素。在考虑环境保护和节约能源以及水电厂运行特点的基础上,提出了一种以火电厂总运行费用、污染气体排放量、水电厂弃水量为优化目标的水火电站群多目标优化调度模型。快速分类非支配遗传算法(Non-dominated Sorting Genetic Algorithm Ⅱ,NSGA-Ⅱ)是一种新型的多目标遗传算法,文中首次将其应用于水火电站群的优化调度。计算表明,该模型有利于节能减排和环境保护,提高了水力资源的利用程度,提升了电力系统的综合运行效益,为水火电力系统短期优化调度提供了新的研究思路。

Hydraulic coupling between hydropower plants is crucial to optimal scheduling of a power system containing cascade hydropower plants and thermal planets, such as time delay of flow and waste water releasing. Aiming at environment protection and energy saving, this paper presents a new multi-objective genetic model adopting fast non-dominated sorting genetic algorithm （NSGA-Ⅱ） to optimize the total cost of thermal plants, the total of contaminative gas emission, and the total of spilling water of hydro plants. Simulation results show that the comprehensive benefit of a power system can be enhanced by optimization using the proposed model through increasing the hydropower benefits and reducing the thermal plant costs. This is the first time application of multi-objective algorithm to optimal scheduling, which provides a novel thought for short-term scheduling of a hydrothermal power system.