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

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Computational Intelligence (IJCCI 2015)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 669))

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Abstract

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.

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Notes

  1. 1.

    Peak-to-average ratio is calculated by the maximum load demand for a period of time over the average load demand, so a lower PAR is normally preferred due to e.g. maintenance costs [16].

  2. 2.

    In this work, we use the terms “substation transformer” and “PEV’s batteries” to differentiate between the two sources of energy.

  3. 3.

    Source: IEEE Xplore database searching for “Demand-side Management”. Last accessed date: 22/01/2015.

  4. 4.

    Details on how these figures were produced can be found in [22].

  5. 5.

    30 independent runs * 5 variants of the mutation operator.

  6. 6.

    30 independent runs, 4 different scenarios (i.e., 40 and 80 household units, trying to maximise: (a) energy consumption from PEVs, and (b) energy consumption from PEVs while also considering reducing highest load peaks; for each of the set of household units used in this work).

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Acknowledgements

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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Authors and Affiliations

  1. School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland

    Edgar Galván-López

  2. TAO Project, INRIA Saclay & LRI - Univ. Paris-Sud and CNRS, Orsay, France

    Marc Schoenauer

  3. Department of Informatics, University of Piraeus, Piraeus, Greece

    Constantinos Patsakis

  4. Doctorado en Ciencias de la Ingeniería, Instituto Tecnológico de Tijuana, Tijuana, Mexico

    Leonardo Trujillo

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  1. Edgar Galván-López
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  2. Marc Schoenauer
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  3. Constantinos Patsakis
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  4. Leonardo Trujillo
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Corresponding author

Correspondence to Edgar Galván-López .

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Editors and Affiliations

  1. Computer Architecture and Computer Technology, Universidad de Granada, Granada, Spain

    Juan Julián Merelo

  2. aSEEB-ISR-IST, Technical University of Lisbon (IST), Lisbon, Portugal

    Agostinho Rosa

  3. Facultad de Informática, University of Murcia, Murcia, Spain

    José M. Cadenas

  4. Departamento de Engenharia Informatica, University of Coimbra, Coimbra, Portugal

    António Dourado Correia

  5. Images, Signals and Intelligence Systems Laboratory, University PARIS-EST Creteil (UPEC), Créteil, France

    Kurosh Madani

  6. Campus de Gambelas, Universidade do Algarve, Faro, Portugal

    António Ruano

  7. Escola Superior de Tecnologia de Setúbal, Polytechnic Institute of Setúbal/INSTICC, Setúbal, Portugal

    Joaquim Filipe

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Galván-López, E., Schoenauer, M., Patsakis, C., Trujillo, L. (2017). Demand-Side Management: Optimising Through Differential Evolution Plug-in Electric Vehicles to Partially Fulfil Load Demand. In: Merelo, J.J., et al. Computational Intelligence. IJCCI 2015. Studies in Computational Intelligence, vol 669. Springer, Cham. https://doi.org/10.1007/978-3-319-48506-5_9

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  • DOI: https://doi.org/10.1007/978-3-319-48506-5_9

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