Security Constrained Dispatch for Renewable Proliferated Distribution Network Based on Safe Reinforcement Learning - Cui, Han; Ye, Yujian; Tian, Qidong; Tang, Yi (Frontiers in Energy Research, 2022)
As the terminal of electricity consumption, distribution network is a vital field to lower the carbon emission of power system. With the integration of distributed energy resources, flexibility of distribution network has been promoted significantly where dispatch actions can be employed to lower carbon emission without compromising the accessibility of reliable electricity. This paper proposes a security constrained dispatch policy based on safe reinforcement learning for distribution network. The researched problem is set up as a Constrained Markov Decision Process, where continuous-discrete mixed action space and high dimensional state space are in place. In addition, security related rules are embedded into the problem formulation. To guarantee the generalization of RL agent, various scenarios are generated in the offline training stage, including randomness of renewables, scheduled maintenance and different load profiles. Case study is performed on a modified version of IEEE 33-bus system and the numerical results verify the effectiveness of proposed method in decarbonization.