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.
A genetic algorithm for real-time demand side management in smart-microgrids
Venticinque S.
;
2022
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
