A matheuristic for a bi-objective demand-side optimization for cooperative smart homes
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ORIGINAL PAPER
A matheuristic for a bi-objective demand-side optimization for cooperative smart homes Zineb Garroussi1 · Rachid Ellaia1 · El-ghazali-Talbi2 · Jean-yves Lucas3 Received: 4 April 2019 / Accepted: 8 April 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract This paper proposes the demand-side management (C-DSM) of collaborative homes during one day. Some homes may have photovoltaic panels and batteries, other homes may have batteries, and the remaining homes are pure energy consumers. Homes are interconnected and connected to the main grid. The proposed C-DSM is formulated as a constrained bi-objective and mixed-integer linear optimization model with one objective related to the total net energy cost and the other related to discomfort caused by allowing flexibility of controllable loads within an acceptable comfort range. A matheuristic approach has been proposed to determine an efficient Pareto set for the problem, combining the non-dominated sorting genetic algorithm II (NSGAII) with an exact solver. In this approach, discrete decision variables are represented as partial chromosomes and an exact solver determines the continuous decision variables in an optimal way. A number of simulations are performed and compared with the weighted sum algorithm (WSA) under four cases for small to large number of homes. The results demonstrate the effectiveness of power cooperation among homes and show that our algorithm is able to obtain more Pareto solutions in a much shorter time that are far better than those obtained by the WSA. The proposed algorithm is suitable for large-sized C-DSM problem instances, promotes power cooperation between homes, reduces the dependency to the main grid and achieves individual fairness of energy net cost of each home without the need for installation of photovoltaic panels and batteries for all homes. Keywords Microgrids · Demand-side management of multiple homes · Multi-objective evolutionary algorithm · Mixed-integer linear programming · Matheuristic
1 Introduction
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Zineb Garroussi [email protected] Rachid Ellaia [email protected] El-ghazali-Talbi [email protected] Jean-yves Lucas [email protected]
1
LERMA Laboratory, Engineering for Smart & Sustainable Systems Research Center (E3S), Mohammadia School of Engineers, Mohammed V University of Rabat, Ibn Sina avenue, 765, Rabat, Morocco
2
Big Optimization and Ultra-Scale computing team (BONUS), Inria Lille - Nord Europe Research Centre, 59655 Villeneuve d’Ascq cedex, France
3
Électricité de France S.A, EDF R&D, 1, avenue du Général de Gaulle, 92140 Clamart, France
With the rapid development of information technologies and increasing awareness of environmental sustainability, interest in “microgrids” has been attracting attention over the past few years [12,32]. A microgrid (MG) is an extension of the smart grid designed to supply energy to small areas. Its main objective is to guarantee a local, economic and reliable source of energy for a local community [10]. A MG is
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