The uncertainty of electric vehicle charging demand and photovoltaic output leads to a certain degree of uncertainty in the revenue of photovoltaic energy storage charging stations. In order to quantify the impact of this uncertainty on the revenue of photovoltaic energy storage charging stations, namely the CVaR value of revenue, a multi-objective capacity optimization allocation model for of photovoltaic energy storage charging stations is established, including revenue and CVaR. The CVaR function in the model is the expected revenue of the investor quantified by conditional value at risk, and the expected revenue function is the sum of the revenue and its probability product under typical scenarios of charging demand and photovoltaic output, combined with augmentation ε- constraint method focuses on revenue as the main objective, with the expected revenue CVaR as the secondary objective as the constraint. The pareto frontier of expected revenue and expected revenue CVaR under different risk preferences are obtained by the solution model, as well as the corresponding allocation capacity, and the entropy weight TOPSIS method is used to screen out objective decision-making solutions. Compared with the linear weighting method in traditional risk management, the augumented ε-constraint method can obtain more distributed and boundary optimal frontiers after processing the objectives, which can map the actual pareto frontiers of multi-objective problems and provide more detailed investment schemes for dividing the expected revenue value and expected revenue CVaR.