An improved genetic algorithm for determining the optimal operation strategy of thermal energy storage tank in combined heat and power units

Abstract

The TES (thermal energy storage) tank has been widely used in CHP (combined heat and power) units to consume more renewable energy. In this paper, the hourly operation model has been established to solve the operation strategy problem of TES tank. The limitations of standard genetic algorithm used in the operation strategy of TES tank have been analyzed, then an improved genetic algorithm is proposed. At last, the improved genetic algorithm is used to optimize the operation strategy of TES tank by combining the characteristic day method. The results show that the improved genetic algorithm can enhance the local search ability and calculation speed obviously, and the calculation speed will be further improved after combining the characteristic day method. In order to obtain the maximum profit, the TES tank should store thermal energy at high minimum power load and release thermal energy at low minimum power load. In addition, the optimal capacity of TES tank in different months varies greatly during the period of thermal energy supply. The optimal capacity of TES tank should be the maximum value among all the months to ensure the thermal energy supply.