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Multiple energy carrier optimisation with intelligent agents

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  • Additional Information
    • Publication Information:
      Elsevier
    • Publication Date:
      2016
    • Collection:
      University of Sussex: Sussex Research Online
    • Abstract:
      Multiple energy carrier systems stem from the need to evolve traditional electricity, gas and other energy systems to more efficient, integrated energy systems. An approach is presented, for controlling multiple energy carriers, including electricity (AC or DC), heat, natural gas and hydrogen, with the objective to minimise the overall cost and/or emissions, while adhering to technical and commercial constraints, such as network limits and market contracts. The technique of multi-agent systems (MAS) was used. The benefits of this approach are discussed and include a reduction of more than 50% in the balancing costs of a potential deviation. An implementation of this methodology is also presented. In order to validate the operation of the developed system, a number of experiments were performed using both software and hardware. The results validated the efficient operation of the developed system, proving its ability to optimise the operation of multiple energy carrier inputs within the context of an energy hub, using a hierarchical multi-agent system control structure.
    • File Description:
      application/pdf
    • Relation:
      http://sro.sussex.ac.uk/id/eprint/57818/2/Multiple%20energy%20carrier%20optimisation%20with%20intelligent%20agents%20R1_not-marked.pdf; Skarvelis-Kazakos, Spyros, Papadopoulos, Panagiotis, Grau Unda, Iñaki, Gorman, Terry, Belaidi, Abdelhafid and Zigan, Stefan (2016) Multiple energy carrier optimisation with intelligent agents. Applied Energy, 167. pp. 323-335. ISSN 0306-2619
    • Accession Number:
      10.1016/j.apenergy.2015.10.130
    • Online Access:
      https://doi.org/10.1016/j.apenergy.2015.10.130
      http://sro.sussex.ac.uk/id/eprint/57818/
      http://sro.sussex.ac.uk/id/eprint/57818/2/Multiple%20energy%20carrier%20optimisation%20with%20intelligent%20agents%20R1_not-marked.pdf
    • Rights:
      cc_by_nc_nd
    • Accession Number:
      edsbas.C0268E72