Latest revision as of 02:10, 2 February 2021

Abstract

International audience; Evolutionary Algorithms (EAs), or Evolutionary Computation, are powerful algorithms that have been used in a range of challenging real-world problems. In this paper, we are interested in their applicability on a dynamic and complex problem borrowed from Demand-Side Management (DSM) systems, which is a highly popular research area within smart grids. DSM systems aim to help both end-use consumer and utility companies to reduce, for instance, peak loads by means of programs normally implemented by utility companies. In this work, we propose a novel mechanism to design an autonomous intelligent DSM by using (EV) electric vehicles' batteries as mobile energy storage units to partially fulfill the energy demand of dozens of household units. This mechanism uses EAs to automatically search for optimal plans, representing the energy drawn from the EVs' batteries. To test our approach, we used a dynamic scenario where we simulated the consumption of 40 and 80 household units over a period of 30 working days. The results obtained by our proposed approach are highly encouraging: it is able to use the maximum allowed energy that can be taken from each EV for each of the simulated days. Additionally, it uses the most amount of energy whenever it is needed the most (i.e., high-peak periods) resulting into reduction of peak loads.

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.5220/0005607401060115

• https://doi.org/10.5220/0005607401060115 under the license cc-by-nc-nd

• https://hal.inria.fr/hal-01254912,

https://dblp.uni-trier.de/db/conf/ijcci/ijcci2015ecta.html#LopezSP15,

https://hal.inria.fr/hal-01254912/document,

https://academic.microsoft.com/#/detail/2295646217

• https://hal.inria.fr/hal-01254912,

https://hal.inria.fr/hal-01254912/document,

https://hal.inria.fr/hal-01254912/file/Galvan_ECTA_2015.pdf

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