Title: A genetic algorithm for real-time demand side management in smart-microgrids

Authors: Salvatore Venticinque; Massimiliano Diodati

Addresses: Department of Engineering, University of Campania 'Luigi Vanvitelli', via Roma 29, 81031 Aversa, Caserta, Italy ' Micron Semiconductor Italia S.r.l., Viale Remo De Feo 1, 80022 Arzano, Naples, Italy

Abstract: One of the main drawbacks in the management of renewable resources, including wind and solar energies, is the issue related to the uncertainty in their behaviour. Demand side management (DSM) shifts loads of a household from times characterised by a surplus in consumption to times with photovoltaic production surplus. In this paper we propose the utilisation of a genetic algorithm to find the schedule of energy loads that best matches the energy produced by photovoltaic panels. We aim at optimising self-consumption, but satisfying real-time constraints, which allow for addressing unforeseen changes of the planned schedule or unpredictable variations of renewable energy production. We designed specialised genetic operators to accelerate, already in the first iterations, the convergence to a local minima of the solution space, and evaluated how such improvements affect the optimality of results.

Keywords: smart microgrid; optimisation; genetic algorithm; demand side management; DSM.

DOI: 10.1504/IJCSE.2022.120792

International Journal of Computational Science and Engineering, 2022 Vol.25 No.1, pp.91 - 104

Received: 06 Dec 2020
Accepted: 29 Apr 2021
Published online: 08 Feb 2022 *

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