With the extensive application of renewable energy as power sources and the stepwise maturity of synergetic techniques, multi-energy system（MES） is drawing increasing attention,turning into the main carrier of energy on the way to achieving carbon neutrality. However, the economic operation of MES is confronting challenges due to the complexity of energy production, energy storage and energy consumption in the system. To tackle the problem, an economic optimization model of an MES including new energy power plants, a battery energy storage system（BESS） and a combined cooling heating and power（CCHP） system is formulated, taking minimizing the penalty of abandoning wind and solar power, power discharge loss of BESS, fuel costs of gas turbines, carbon emission penalty and other costs as the objective function. The optimization model is solved with the charging and discharging characteristics of BESS, the output characteristics of photovoltaic units and wind turbines and the balance between cooling, heating and electricity load as constraints. Afterward, a novel adaptive learning rate firefly algorithm（ALRFA） is proposed to prevent the problem solving from falling into local optimum and slow convergence by taking adaptive learning rate. Taking the cooling, heating and power loads of an industrial park as study case,the effectiveness and feasibility of the proposed model and algorithm is verified.
