Droop-Free Optimal Feedback Control of Distributed Generators in Islanded DC Microgrids

Abstract

This article introduces a droop-free, approximate optimal feedback control strategy to optimally control distributed generators (DGs) in islanded dc microgrids (DCMGs). Each DG is modeled as a control affine dynamical system, and constrained input of each DG is designed to minimize the infinite horizon quadratic state cost and nonquadratic control effort. The approximate dynamic programming (ADP) method is employed to solve the infinite horizon optimal control problem by successive approximation of the value function via a linear in the parameter (LIP) neural network (NN). The NN weights are updated online by a reinforcement learning (RL)-based tuning algorithm, and the convergence of the unknown weights to a neighborhood of the optimal weights is guaranteed without the persistence of excitation (PE). Simulation and experimental results are presented to demonstrate the effectiveness and applicability of the proposed concept.