Resilient Distributed MPC Algorithm for Microgrid Energy Management under Uncertainties

Abstract

This paper proposes a resilient distributed energy management algorithm able to cope with different types of faults in a DC microgrid system. A distributed optimization method allows to solve the energy management problem without sharing any private data with the network and reducing the computational cost for each agent, with respect to the centralised case. A distributed MPC scheme based on distributed optimization is used to cope with uncertainty that characterizes the microgrid operation. In order to be resilient to faults that limit the amount of power available to consumers, we propose to adaptively store an amount of power in the storage systems to support the loads. A soft constraint on the minimum energy stored in each battery is introduced for feasibility and to cope with persistent faults. The effectiveness of the method is proved by extensive simulation results considering faults on three types of components: renewable generator, distribution grid and communication network.

Learning-based MPC using Differentiable Optimisation Layers for Microgrid Energy Management

“…We consider a microgrid model similar to [8] composed of a load, a renewable generator, a connection to the utility grid and an energy storage system. The goal is to design an MPC algorithm ensuring to meet load demand while minimizing the energy cost for the microgrid.…”

“…In other words, the amount of power that is injected in the microgrid has to be equal to the amount of power that is drawn from the microgrid. In case of complex microgrid topologies power flow constraints can be enforced as in [8], however in this paper we only consider power balance constraints. Our goal is to fulfill the load demand minimising the total energy cost.…”

Model Predictive Control Strategies in Microgrids: A Concise Revisit

“…The optimization process is integrated into the feedback loop. The necessary control actions are computed by analyzing the disturbances and new grid states [66]. Standard MPC procedures include linear programming (LP), quadratic programming (QP), nonlinear programming (NLP), mixed integer linear programming (MILP), mixed integer quadratic programming (MIQP), and metaheuristics.…”