Security Constrained Dispatch for Renewable Proliferated Distribution Network Based on Safe Reinforcement Learning

Abstract

As the terminal of electricity consumption, the distribution network is a vital field to lower the carbon emission of the power system. With the integration of distributed energy resources, the flexibility of the distribution network has been promoted significantly where dispatch actions can be employed to lower carbon emissions without compromising the accessibility of reliable electricity. This study proposes a security constrained dispatch policy based on safe reinforcement learning for the 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 the reinforcement learning agent, various scenarios are generated in the offline training stage, including randomness of renewables, scheduled maintenance, and different load profiles. A case study is performed on a modified version of the IEEE 33-bus system, and the numerical results verify the effectiveness of the proposed method in decarbonization.