Hybrid Deep Reinforcement Learning and Model Predictive Control for Microgrids

Abstract

The current microgrids are experiencing growing difficulties in voltage stability and operational capacity, particularly with constant power loads (CPLs), leading to negative impedance behavior and probability of voltage collapse. Although there are notable improvements in both conventional and smart control strategies, there is still a research gap, namely creating a framework of control that facilitates stability precision and quick adaptation to operation alterations without augmenting the computational load and the operational risks. The current work was based on the creation of a mathematical model based on state equations to model the dynamics of a system, as well as the application of a numerical simulation environment to test the performance of three control modes: MPC, RL, and an environment of a hybrid (hereafter referred to as Hybrid MPC -RL) framework. The approach involved the creation of an MPC algorithm where prediction horizons are brief, and the incorporation of a DRL algorithm into the structure of a hybrid regime that can switch or combine the two strategies based on operating conditions. The findings indicated that on average the DC bus voltage was about 46.7 V compared to the reference of 48 V with a standard deviation of 2.98 V and the current was between 4.09 and −3.97 A with an average of about zero. The data on working indicated that the system used MPC 99.7% of the time and 0.3% in the hybrid mode, without any use of RL alone. This proves the reliability and consistency of MPC, the adaptability of DRL, and the effective combination of these two approaches. The scientific feature of this paper is the addition of a hybrid framework that combines MPC and DRL in an adaptive safety mechanism that leads to high stability in CPL conditions and improves the efficiency of operation over the traditional control.