Demand-Side Management: Optimising Through Differential Evolution Plug-in Electric Vehicles to Partially Fulfil Load Demand

  • Edgar Galván-López
  • Marc Schoenauer
  • Constantinos Patsakis
  • Leonardo Trujillo
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 669)


In this paper, we investigate the use of an stochastic optimisation bio-inspired algorithm, differential evolution, and proposed two fitness (cost) functions that can automatically create an intelligent scheduling for a demand-side management system so that it can use plug-in electric vehicles’s (PEVs) batteries to partially and temporarily fulfil electricity requirements from a set of household units. To do so, we proposed two fitness functions that aim: (a) to use the most amount of energy from the batteries of PEVs while still guaranteeing that they can complete a journey, and (b) to enrich the previous function to reduce peak loads.


Differential evolution Demand-side management systems Plug-in electric vehicles 



Edgar Galván López’s research is funded by an ELEVATE Fellowship, the Irish Research Council’s Career Development Fellowship co-funded by Marie Curie Actions. The first author would also like to thank the TAO group at INRIA Saclay & LRI - Univ. Paris-Sud and CNRS, Orsay, France for hosting him during the outgoing phase of the ELEVATE Fellowship. The authors would like to thank all the reviewers for their useful comments that helped us to significantly improve our work.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Edgar Galván-López
    • 1
  • Marc Schoenauer
    • 2
  • Constantinos Patsakis
    • 3
  • Leonardo Trujillo
    • 4
  1. 1.School of Computer Science and StatisticsTrinity College DublinDublinIreland
  2. 2.TAO Project, INRIA Saclay & LRI - Univ. Paris-Sud and CNRSOrsayFrance
  3. 3.Department of InformaticsUniversity of PiraeusPiraeusGreece
  4. 4.Doctorado en Ciencias de la IngenieríaInstituto Tecnológico de TijuanaTijuanaMexico

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