A scenario-based capacity planning method for distributed photovoltaic power generation(PV)and power-to-gas(P2G)equipment in a multi-energy coupled system is proposed. Based on the significant temporality and seasonality of solar intensity and load

an idea is proposed which uses the reduced scenario results of the seasonal daily solar intensity and load as the input data for the optimal capacity mathematical optimization model. Firstly

the sum of infrastructure investment and operating costs of the multi-energy coupled system under multiple scenarios is regarded as the goal. Secondly

a mathematical optimization model with the objective function and constraints is put forward. The probability-weighted operational cost and benefit are calculated under multiple seasonal scenarios. Then

the bacterial forging algorithm is used to solve this mathematical optimization model. Finally

one benchmark multi-energy coupled system with the co-operation of distribution and gas network including distributed PV and P2G is simulated. The effectiveness of the proposed method is verified and the influence of various price parameters on the results is analyzed.