A multi agent system reinforcement learning based optimal power flow for islanded microgrids
In this paper, a distributed intelligence algorithm is used to manage the optimal power flow problem in islanded microgrids. The methodology provides a suboptimal solution although the error is limited to a few percent as compared to a centralized approach. The solution algorithm is multi agent based. According to the method, couples of agents communicate with each other only if the buses where they are located are electrically connected. The overall prizing system required for learning uses a feedback from an approximated model of the network. Based on the latter, a distributed reiforcement learning algorithm is implemented to minimize the joule losses while meeting operational constraints. Simulation studies with a small microgrids show that the method is computationally efficient and capable of providing sub optimal solutions. Due to the limited computational complexity, the proposed method has great potential for online implementation.
2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC)