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Sustainable resource allocation for power generation: The role of big data in enabling interindustry architectural innovation

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  • Chalvatzis, Konstantinos J.
  • Malekpoor, Hanif
  • Mishra, Nishikant
  • Lettice, Fiona
  • Choudhary, Sonal

Abstract

Economic, social and environmental requirements make planning for a sustainable electricity generation mix a demanding endeavour. Technological innovation offers a range of renewable generation and energy management options which require fine tuning and accurate control to be successful, which calls for the use of large-scale, detailed datasets. In this paper, we focus on the UK and use Multi-Criteria Decision Making (MCDM) to evaluate electricity generation options against technical, environmental and social criteria. Data incompleteness and redundancy, usual in large-scale datasets, as well as expert opinion ambiguity are dealt with using a comprehensive grey TOPSIS model. We used evaluation scores to develop a multi-objective optimization model to maximize the technical, environmental and social utility of the electricity generation mix and to enable a larger role for innovative technologies. Demand uncertainty was handled with an interval range and we developed our problem with multi-objective grey linear programming (MOGLP). Solving the mathematical model provided us with the electricity generation mix for every 5 min of the period under study. Our results indicate that nuclear and renewable energy options, specifically wind, solar, and hydro, but not biomass energy, perform better against all criteria indicating that interindustry architectural innovation in the power generation mix is key to sustainable UK electricity production and supply.

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  • Chalvatzis, Konstantinos J. & Malekpoor, Hanif & Mishra, Nishikant & Lettice, Fiona & Choudhary, Sonal, 2019. "Sustainable resource allocation for power generation: The role of big data in enabling interindustry architectural innovation," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 381-393.
    • Handle: RePEc:eee:tefoso:v:144:y:2019:i:c:p:381-393
    DOI: 10.1016/j.techfore.2018.04.031
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