Q学习差分进化算法求解热电动态经济排放调度OA

The dynamic economic emission scheduling of cogeneration takes into account both fuel cost and pol-lution gas emission,and the thermoelectricity output in the next period is affected by the thermoelectricity output in the current period,which is an important problem in power system operation in recent years.In this study,a new QLMODE(Q-Learning Multi-Objective Differential Evolution)algorithm is proposed to solve the CHPDEED(Combined Heat and Power Dynamic Economic Emission Dispatch)problem.In QLMODE,the Q-learning tech-nique is used to adjust the scale factor parameters of the algorithm,that is,in the iterative process,the action reward and punishment are determined by using the dominant relationship between the child solution and the parent solution,and the parameter values are adjusted by Q-learning to obtain the most suitable algorithm parameters for the environ-mental model.The proposed QLMODE is used to solve the CHPDEED with 11 units and 33 units.The simulation re-sults show that compared with four mature multi-objective optimization algorithms,the QLMODE algorithm has the least fuel cost and the least pollution gas emission,the convergence and diversity index of QLMODE algorithm is bet-ter than the other four algorithms,and QLMODE has a better Pareto optimal frontier on both sets of problems.