Cooperative optimal scheduling strategy of source and storage in microgrid based on soft actor-critic

In recent years, the proportion of renewable energy and energy storage in microgrid is increasing, which brings new challenges to its optimal scheduling. Aiming at the difficulty in solving the cooperative optimal scheduling problem of source and storage in microgrid due to the non-convex nonlinear constraints, the deep reinforcement learning algorithm is used to construct the data-based strategy function, and the optimal strategy is found out through continuous interactive learning with the environment, so that avoiding the direct solution of the original non-convex nonlinear problem. Considering the strategy function may not meet the security constraints in the training process, furthermore, a learning method of cooperative optimal scheduling secure strategy of source and storage in microgrid based on partial model information is proposed, and the optimal strategy meeting the network security constraints is obtained. In addition, aiming at the problem of long time-consuming due to the interaction between agents and environment in the training process for reinforcement learning, the neural network is used to model the environment, so as to improve the learning efficiency.Review and prospect of robust optimization and planning research on generation and transmission systemYUAN　Yang1, ZHANG　Heng1, CHENG　Haozhong1, LIU　Lu1, ZHANG　Xiaohu2, LI　Gang2, ZHANG　Jianping2(1. Key Laboratory Control of Power Transmission and Conversion, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China;2. East China Branch of State Grid Corporation of China, Shanghai 200120, China)With the uncertainty of power system increasing gradually, the application of robust optimization and planning research on generation and transmission system to resist the uncertainty of extremely scenes has become a significant research method. Firstly, the robust optimization is divided into classical robust optimization and distributionally robust optimization from the perspective of whether the probability distribution characteristics of uncertain factors are considered, the mathematical models and uncertain set characteristics of these two kinds of robust optimization are sorted out. Secondly, the existing classical robust optimization and distributionally robust optimization research on generation and transmission system are divided into three aspects:considering the uncertainty of node injection power, considering the uncertainty of power capacity growth and cost, and considering the uncertainty of transmission network state, and the research framework and limitations of robust optimization planning research on generation and transmission system are refined. Finally, the problems worthy of further study in robust optimization planning on power generation and transmission system are prospected, which provides ideas and directions for the robust optimization planning follow-up research on power generation and transmission system. © 2022, Electric Power Automation Equipment Press. All right reserved.