A Q-learning based optimization method of energy management for peak load control of residential areas with CCHP systems

•An energy optimization management model for adaptive control of cooling, heating and electric peak load in coastal residential areas is presented.•The charging power of electric vehicle and the controllable power of air conditioner, water heater and refrigerator are coordinated for peak load control.•The coupling relationship and storage coordination of cooling, heating and electric energy is used to enhance the optimal control of peak load.•The deep Q-learning algorithm is used to solve the energy management optimization problem of coastal residential area for peak load control with good results. Aiming at the coastal residential area, considering the natural characteristics of wind and photovoltaic energy, the supply characteristics of gas and electric energy, the energy consumption law of cold, heat and electricity and the complementary and coupling relationship of various energy sources and load, this paper studies the formation conditions and various scenarios of peak load in the residential area with CCHP systems. Using the regulation characteristics of controllable loads such as electric vehicle, air conditioner, water heater and refrigerator, considering the coordination of cooling, heating and electric energy storage, the interchangeability and controllability of air conditioner based on electric/gas energy, and the transferability, interchangeability and controllability of water heater based on electric/gas/solar/heat energy, A collaborative energy management model for coastal resident area with electricity, gas, wind and solar energy is presented for peak load adaptive control of cooling, heating and electric energy. The Q-learning algorithm is used to solve the energy management optimization problem of peak load control in coastal residential areas, and good results are achieved, such as effectively reducing load fluctuations, improving the economy of the system, and increasing the self-sufficiency of the system.