Optimal dispatch of PV inverters in unbalanced distribution systems using Reinforcement Learning

In this paper, a Reinforcement Learning (RL)-based approach to optimally dispatch PV inverters in unbalanced distribution systems is presented. The proposed approach exploits a decentralized architecture in which PV inverters are operated by agents that perform all computational processes locally; while communicating with a central agent to guarantee voltage magnitude regulation within the distribution system. The dispatch problem of PV inverters is modeled as a Markov Decision Process (MDP), enabling the use of RL algorithms. A rolling horizon strategy is used to avoid the computational burden usually associated with continuous state and action spaces, coupled with a computationally efficient learning algorithm to model the action-value function for each PV inverter. The effectiveness of the proposed decentralized RL approach is compared with the optimal solution provided by a centralized nonlinear programming (NLP) formulation. Results showed that within several executions, the proposed approach converges either to the optimal solution or to solutions with a PV curtailment excess of less than 2.5% while still enforcing voltage magnitude regulation.

Automated summary

This paper introduces a decentralized approach based on Reinforcement Learning to optimally dispatch PV inverters in unbalanced distribution systems. By modeling the dispatch problem as a Markov Decision Process, using a rolling horizon strategy and efficient learning algorithm, effective voltage regulation of the PV system is achieved.