火电厂厂级负荷分配的多目标优化和决策研究

Study of Multi-objective Optimization and Multi-attribute Decision Making of Economic Load Dispatch Problem

火电厂的负荷优化分配系统通常是以机组煤耗特性为基础的,其经济分配对应于满足稳态工况下全厂发电成本最低的要求。对于自动发电控制方式下的厂级负荷运行分配还要满足调整时间的要求,以尽可能快的速度满足目标负荷的调整。考虑机组运行的经济性和快速性,将基于进化算法的多目标优化技术与多属性决策方法联合运用,针对火电厂厂级负荷优化分配的问题进行研究。对于多目标优化问题,采用改进的非支配解排序的多目标遗传算法,求出Pareto最优解,由Pareto最优解构成决策矩阵,使用客观赋权的信息熵方法对最优解的属性进行权值计算,然后用逼近理想解的排序方法进行多属性决策研究,对Pareto最优解给出排序。文中给出了10台机组负荷分配的优化设计算例。

The optimal load distribution system in thermal power plants is usually based on the unit coal consumption characteristics. However, these characteristic parameters are valid only on stable conditions. At the same time, the plant-level economic load dispatch problem should also satisfy the adjustment time required in automatic generation control （AGC） mode. A hybrid approach for multi-objective optimization study of plant-level load distribution was proposed. A non-dominated sorting genetic algorithm II （NSGA II） was employed to approximate the set of Pareto solution through an evolutionary optimization process. In the subsequent stage, a multi-attribute decision making （MADM） approach was adopted to rank these solutions from best to worst and to determine the best solution in a deterministic environment with a single decision maker. A example with 10-unit was conducted to illustrate the analysis process. Pareto frontiers were obtained and the ranking of Pareto solution was based on entropy weight and technique for order preference by similarity to ideal solution （TOPSIS） method.