基于改进灰狼算法的热电联供系统负荷优化分配策略研究

Research on Load Optimal Distribution Strategy of Combined Heat and Power System Based on Improved Gray Wolf Algorithm

为了研究热电联供系统中机组的热电负荷优化分配策略,针对传统灰狼优化算法中繁琐的更新机制导致的时效性差等问题,提出了一种改进的多目标灰狼优化(MOGGWO)算法,直接利用前三等级狼的位置和高斯采样完成进化过程,最后将该算法应用于某600 MW双机热电联供系统的多目标热电负荷优化分配中。结果表明:MOGGWO算法能极大缩短负荷分配的求解时间,且在多目标优化下提升系统经济性的同时其可再生能源消纳能力将会减弱,应根据现场实际情况权衡,进而选择热电负荷最优分配策略。

In order to study the optimal distribution strategies of thermoelectric load in combined heat and power(CHP)system and solve the time-delay problems caused by the tedious updating mechanism in the traditional gray wolf optimization algorithm,an improved multi-objective gray wolf algorithm(MOGGWO)was proposed.The evolution process was completed directly by using the position of the top three wolves and Gaussian sampling.The improved algorithm was applied to the multi-objective optimal distribution of thermoelectric load in the CHP system consisting of two 600 MW units.Results show that the improved multi-objective gray wolf algorithm can greatly shorten the solving time of load distribution,and while improving the system economy,its renewable energy absorption capacity will be weakened.Therefore,the optimal distribution strategy of thermoelectric load should be selected according to the actual situation on site.