Title: A hybrid backtracking search algorithm for energy management in a microgrid

Authors: Zineb Garroussi; Rachid Ellaia; El-Ghazali Talbi; Jean-Yves Lucas

Addresses: LERMA Laboratory, Engineering for Smart and Sustainable Systems Research Center (E3S), Mohammadia School of Engineers, Mohammed V University of Rabat, BP. 765, Ibn Sina av, Rabat, Morocco ' LERMA Laboratory, Engineering for Smart and Sustainable Systems Research Center (E3S), Mohammadia School of Engineers, Mohammed V University of Rabat, BP. 765, Ibn Sina av, Rabat, Morocco ' Big Optimization and Ultra-Scale Computing Team (BONUS), Inria Lille-Nord Europe Research Centre, 59655 – Villeneuve d'Ascq cedex, France ' Lab Paris-Saclay EDF R&D, Department OSIRIS, Électricité de France S.A., 7, Boulevard Gaspard Monge, 91120 Palaiseau, France

Abstract: Recently, due to the high penetration of distributed generators and storages, and the rapid growth of environmental concerns, energy optimisation plays a vital role in the operation of microgrids. In this paper, we propose a hybrid backtracking search matheuristic algorithm with a multiobjective indirect encoding (H-MOBSA) for the multiobjective mixed-integer nonlinear formulation of economic-emission dispatch in a grid-connected microgrid. In this approach, each partial discrete solution represented by the backtracking search algorithm is associated with a set of Pareto solutions of the related continuous subproblem where the fitness and diversity assignments are adapted to take into account all associated decoded solutions. The proposed algorithm aims to provide generation planning and the battery size that satisfy the trade-off between the total operating cost and pollutant emissions under equality and inequality constraints. To show the effectiveness of our approach, the proposed H-MOBSA is tested on typical MG and compared to other algorithms.

Keywords: backtracking search algorithm; battery storage sizing; distributed generation; matheuristics; microgrid.

DOI: 10.1504/IJMMNO.2021.114481

International Journal of Mathematical Modelling and Numerical Optimisation, 2021 Vol.11 No.2, pp.143 - 167

Received: 23 Oct 2019
Accepted: 20 May 2020

Published online: 10 Feb 2021 *

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