Data Driven Decentralized Control of Inverter Based Renewable Energy Sources Using Safe Guaranteed Multi-Agent Deep Reinforcement Learning
Mengfan Zhang, Guodong Guo, Sindri Magnússon, Robert C. N. Pilawa-Podgurski, Qianwen Xu
Abstract
The wide integration of inverter based renewable energy sources (RESs) in modern grids may cause severe voltage violation issues due to high stochastic fluctuations of RESs. Existing centralized approaches can achieve optimal results for voltage regulation, but they have high communication burdens; existing decentralized methods only require local information, but they cannot achieve optimal results. Deep reinforcement learning (DRL) based methods are effective to deal with uncertainties, but it is difficult to guarantee secure constraints in existing DRL training. To address the above challenges, this paper proposes a projection embedded multi-agent DRL algorithm to achieve decentralized optimal control of distribution grids with guaranteed 100% safety. The safety of the DRL training is guaranteed via an embedded safe policy projection, which could smoothly and effectively restrict the DRL agent action space, and avoid any violation of physical constraints in distribution grid operations. The multi-agent implementation of the proposed algorithm enables the optimal solution achieved in a decentralized manner that does not require real-time communication for practical deployment. The proposed method is tested in modified IEEE 33-bus distribution and compared with existing methods; the results validate the effectiveness of the proposed method in achieving decentralized optimal control with guaranteed 100% safety and without the requirement of real-time communications.