Open AccessLyapunov‐based hybrid model predictive control for energy management of microgrids
Author(s) -
Olama Alireza,
Mendes Paulo R.C.,
Camacho Eduardo F.
Publication year - 2018
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.5852
Subject(s) - dispatchable generation , microgrid , renewable energy , controller (irrigation) , energy management , model predictive control , control theory (sociology) , lyapunov function , computer science , energy storage , energy management system , wind power , lyapunov stability , control engineering , photovoltaic system , engineering , power (physics) , distributed generation , energy (signal processing) , control (management) , mathematics , statistics , physics , nonlinear system , quantum mechanics , artificial intelligence , agronomy , electrical engineering , biology
This study presents an advanced control structure aimed at the optimal economic energy management of a renewable energy‐based microgrid. This control scheme is applied to energy optimisation in a microgrid with non‐dispatchable renewable sources, such as photovoltaic and wind power generation, as well as dispatchable sources, as distributed generators, hybrid storage systems compound by battery bank, supercapacitors, hydrogen storage unit, and one electric vehicle charging station. The proposed controller consists of a Lyapunov‐based hybrid model predictive control based on mixed logical dynamical (MLD) framework. The main contribution of the proposed technique is the assurance of the closed‐loop stability and recursive feasibility, by a novel approach focused on MLD models, using ellipsoidal terminal constraints and the Lyapunov decreasing condition. Finally, simulation tests under different operational conditions are performed and the attained results have shown the safe and reliable operation of the proposed control algorithm compared to existing and well‐known energy management techniques.