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

Publication date: Available online 9 May 2018Source: Technological Forecasting and Social ChangeAuthor(s): Konstantinos J. Chalvatzis, Hanif Malekpoor, Nishikant Mishra, Fiona Lettice, Sonal ChoudharyAbstractEconomic, 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 energ...
Source: Technological Forecasting and Social Change - Category: Science Source Type: research