Abstract: Photovoltaic power generation is affected by meteorological factors such as sunshine intensity, and its output power has uncertainty, resulting in significant fluctuations in the load curve. Therefore, a new energy station group joint output scenario shared energy storage optimization method under large-scale photovoltaic access to the power grid is proposed. Based on the response characteristics of photovoltaics, the maximum output is calculated. Then, with the goal of maximizing annual revenue, a shared energy storage optimization configuration model is constructed by considering factors such as station operation and maintenance, construction costs, and penalty for abandoned light. A correction conversion coefficient is introduced into the model to adjust the construction cost of new energy stations. At the same time, considering the charging and discharging power limitations of shared energy storage, system power balance, station demand response, and state of charge management, constraints are set. To solve the model, an optimization configuration method based on improved genetic algorithm is proposed, which achieves shared energy storage optimization through an adaptive search mechanism. The experiment analyzed the impact of shared energy storage on user electricity costs and grid load fluctuations in different scenarios. The results indicate that optimized shared energy storage can reduce user electricity costs, improve grid stability, and smooth grid load fluctuations through peak shaving and valley filling mechanisms.