Authors:
Edgar Galván-Lopez
1
;
Marc Schoenauer
2
and
Constantinos Patsakis
3
Affiliations:
1
Trinity College Dublin, Ireland
;
2
INRIA Saclay & LRI - Univ. Paris-Sud and CNRS, France
;
3
University of Piraeus, Greece
Keyword(s):
Demand-Side Management, Electric Vehicles, Evolutionary Algorithms, Differential Evolution.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Representation Techniques
;
Soft Computing
Abstract:
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.
(More)