A genetic algorithm for real-time demand side management in smart-microgrids. (4th February 2022)

• Record Type: Journal Article
• Authors: Venticinque, Salvatore; Diodati, Massimiliano
• Is Part Of: International journal of computational science and engineering. Volume 25:Number 1(2022)
• Page Start: 91
• Page End: 104
• Publication Date: 2022-02-04
• Subjects: smart microgrid -- optimisation -- genetic algorithm -- demand side management -- DSM; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

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.