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Title A Hierarchical MPC Framework to Mitigate Faults and Risks in Microgrids
Authors Zafra-Cabeza A. , VELARDE RUEDA, PABLO ANIBAL, Bordons C. , Ridao M.A.
External publication No
Means IFAC-PapersOnLine
Scope Conference Paper
Nature Científica
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204300129&doi=10.1016%2fj.ifacol.2024.08.355&partnerID=40&md5=f291f2eb5d3f415e7b880bf0eca97db7
Publication date 01/01/2024
Scopus Id 2-s2.0-85204300129
DOI 10.1016/j.ifacol.2024.08.355
Abstract This paper presents a hierarchical MPC-based control framework for a real microgrid including solar panels and batteries, that considers the uncertainty from the point of view of faults and risks (F&R) mitigation. While fault management is applied during plant operation, risk management considers external factors that can change microgrid planning in the medium-long term. Due to their different time-scales, a two-layer control scheme is proposed using Model Predictive Control (MPC) at both levels. At the bottom layer, the fault-tolerant predictive controller optimizes the operation by manipulating inputs to follow microgrid set-points. A reconfiguration strategy is implemented using structured residuals and stochastic thresholds. On the other hand, the upper layer develops an optimal mitigation strategy, also based on MPC, to reduce the effects of risks obtained from external information, i.e., unexpected changes in demands, maintenance costs, or deviations in generation. The decision variables of this layer are the selection of mitigation actions to be undertaken, which minimise a proposed multicriteria objective function. Different simulations have been carried out to show the efficacy of this methodology in a F&R scenario from a stochastic point of view. Copyright © 2024 The Authors.
Keywords Battery management systems; Hierarchical systems; Microgrids; Predictive control systems; Risk management; Stochastic control systems; Stochastic models; Control framework; Energy; Faults tolerant controls; Hierarchical control; Hierarchical model; Management systems; Microgrid; Model-predictive control; Risks management; Stochastics; Stochastic systems
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