Day-ahead scheduling based on reinforcement learning with hybrid action space

Driven by the improvement of the smart grid, the active distribution network (ADN) has attracted much attention due to its characteristic of active management. By making full use of electricity price signals for optimal scheduling, the total cost of the ADN can be reduced. However, the optimal dayahead scheduling problem is challenging since the future electricity price is unknown. Moreover, in ADN, some schedulable variables are continuous while some schedulable variables are discrete, which increases the difficulty of determining the optimal scheduling scheme. In this paper, the day-ahead scheduling problem of the ADN is formulated as a Markov decision process (MDP) with continuous-discrete hybrid action space. Then, an algorithm based on multi-agent hybrid reinforcement learning (HRL) is proposed to obtain the optimal scheduling scheme. The proposed algorithm adopts the structure of centralized training and decentralized execution, and different methods are applied to determine the selection policy of continuous scheduling variables and discrete scheduling variables. The simulation experiment results demonstrate the effectiveness of the algorithm.