以600 MW双机热电联供系统为研究对象，引入基于灰狼捕食行为模拟的群智能优化算法，针对其繁琐更新机制导致热电负荷分配时效性差的问题，进一步提出改进的灰狼优化算法(GGWO)，利用前3等级狼的位置和高斯采样进行种群进化机制更新。通过EBSILON平台开展仿真试验，揭示600 MW双机热电联供系统的热电耦合特性和系统运行特性，并将改进的灰狼优化算法应用于该系统的热电负荷优化分配。结果表明：两台机组电负荷一定时，尽可能增大抽凝机组的抽汽供热量可减小系统总热耗量；通过智能热电负荷运行优化，可有效降低系统总热耗量，提高系统经济效益。

The combined heat and power(CHP) system consisting of 600 MW double units was taken as the research object. A swarm intelligence optimization algorithm based on Gray Wolf predation behavior simulation was introduced. Aiming at the problem of poor timeliness of thermoelectric load distribution caused by its tedious updating mechanism,an improved Gray Wolf optimization algorithm (GGWO) was further proposed to update the population evolutionary mechanism by using the positions of the top three wolves and Gaussian sampling. The simulation tests were carried out on EBSILON platform to reveal the thermoelectric coupling characteristics of the units and the operation characteristics of the system. And the improved Gray Wolf optimization algorithm was applied to the optimal distribution of thermoelectric load in the system. The results show that when the electric load of the double units is constant,the total heat consumption of the system can be reduced by increasing the extraction mass flow rate of the condensing unit with intermediate extraction as much as possible. In addition,through intelligent thermoelectric load operation optimization,the total heat consumption of the system can be effectively reduced,and the economic benefits of the system can be improved.

TK47

国家自然科学基金面上项目（51976031）；江苏省基础研究计划（自然科学基金）青年基金项目（BK20210240）