计及负荷不确定性的强化学习实时定价策略.

Facing the current situation of load uncertainty, new energy grid integration, and “dual carbon” target in the power system, the paper established a real-time pricing model considering load uncertainty and carbon trading in the context of the smart grid with full consideration of the welfare of both supply side and user side. Based on the advantages that reinforcement learning can handle variable complexity, non-convex, and nonlinear problems, this paper used the Q-learning algorithm in reinforcement learning to solve the model iteratively. Firstly, this paper transformed the real-time interaction process between the user and the power supplier into a Markov decision process corresponding to the reinforcement learning framework. Secondly, the process represented the information interaction between the user and the power supplier as the iterative exploration of the agent in a dynamic environment. Finally, this paper found the optimal value by the Q-learning algorithm in reinforcement learning, i. e., the maximal social welfare value. The simulation results show that the proposed real-time pricing strategy can effectively enhance social welfare and reduce total carbon emissions, which verifies the feasibility and effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]