Photovoltaic and energy storage control of partially observable distribution network based on deep reinforcement learning

After a large number of distributed power sources are connected to the distribution network, the volatility and uncertainty brought by them may lead to the over-limit of the distribution network voltage and the increase of network losses; at the same time, the distribution network itself is also in a partially observable state. In view of these problems, photovoltaic and energy storage are selected as the control objects. In this paper, a photovoltaic energy storage linkage control technology based on deep reinforcement learning is designed, and an example is used to verify the feasibility and effectiveness of the method proposed in this paper.