Energy Storage Scheduling Optimization Strategy Based on Deep Reinforcement Learning

Renewable energy growth will be a top priority for China's future energy development. However, while vigorously developing renewable energy, the problem of curtailment of wind and solar power has also arisen. In order to make full use of renewable energy, this paper constructs an energy storage scheduling model based on deep intensive chemical Xi. Since most of the index parameters in the actual complex scenario are continuous variables, in order to better simulate the real situation, this paper proposes a hybrid energy storage model based on the blocked energy in the continuous action space, and further incorporates solar energy on the basis of considering wind energy. At the same time, the model also introduces an alkaline water electrolyzer to convert electrical energy into hydrogen for storage, which enriches the destination and storage form of electric energy, and also makes the profit mode of electric field more diversified. It is worth mentioning that the model also takes into account the maintenance costs and related financial costs of the equipment when calculating the benefits and costs, so that the model is closer to the real production and life. By comparing the similarities and differences between the two in the training process and test results, the feasibility of energy storage scheduling in the face of complex scenarios is verified.