Scipediacontent: Scipediacontent moved page Draft Content 247538316 to Patsakis et al 2015a

2021-02-02T01:10:38Z

Scipediacontent moved page Draft Content 247538316 to Patsakis et al 2015a

Scipediacontent: Created page with " == Abstract == International audience; Evolutionary Algorithms (EAs), or Evolutionary Computation, are powerful algorithms that have been used in a range of challenging real..."

2021-02-02T01:10:34Z

Created page with " == Abstract == International audience; Evolutionary Algorithms (EAs), or Evolutionary Computation, are powerful algorithms that have been used in a range of challenging real..."

New page

== 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 http://dx.doi.org/10.5220/0005607401060115]

* [https://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://hal.inria.fr/hal-01254912],
: [https://dblp.uni-trier.de/db/conf/ijcci/ijcci2015ecta.html#LopezSP15 https://dblp.uni-trier.de/db/conf/ijcci/ijcci2015ecta.html#LopezSP15],
: [https://hal.inria.fr/hal-01254912/document https://hal.inria.fr/hal-01254912/document],
: [https://academic.microsoft.com/#/detail/2295646217 https://academic.microsoft.com/#/detail/2295646217]

* [https://hal.inria.fr/hal-01254912 https://hal.inria.fr/hal-01254912],
: [https://hal.inria.fr/hal-01254912/document https://hal.inria.fr/hal-01254912/document],
: [https://hal.inria.fr/hal-01254912/file/Galvan_ECTA_2015.pdf https://hal.inria.fr/hal-01254912/file/Galvan_ECTA_2015.pdf]

* [ ]

← Older revision	Revision as of 01:10, 2 February 2021
(No difference)

Patsakis et al 2015a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 247538316 to Patsakis et al 2015a

Scipediacontent: Created page with " == Abstract == International audience; Evolutionary Algorithms (EAs), or Evolutionary Computation, are powerful algorithms that have been used in a range of challenging real..."