With the continuous upgrading of the energy structure, the new parks with new energy power generation will play an important role in the future new power system. Uncertainties such as the randomness of demand, intermittency of wind and solar output, and volatility of electricity prices in electricity market are coupled together, making it difficult to achieve the reasonable operation between wind and solar energy and battery energy storage system. Considering the limitations of traditional optimization methods, a deep reinforcement learning method based on the PPO algorithm is proposed to solve the problem of interactive operation of wind-solar-storage in parks under uncertain environments. Based on the theoretical framework of reinforcement learning, a Markov decision model with continuous state space and continuous action space and unknown transition probability is constructed for the interactive operation of the park. The new load control system controls the battery energy storage system and flexible resources in the microgrid of the park to realize the economic operation, fully considering battery degradation.