Oper Res Int J https://doi.org/10.1007/s12351-018-0409-y ORIGINAL PAPER Hedging uncertainty in energy efficiency strategies: a minimax regret analysis 1 1 1 Georgios P. Trachanas · Aikaterini Forouli · Nikolaos Gkonis · Haris Doukas Received: 20 October 2017 / Revised: 21 May 2018 / Accepted: 23 May 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract A global consensus is growing around the fact that energy efficiency is an effective way to meet the new climate goals. Energy efficiency, forming a hid - den giant solution, has been proven more impactful than any other greenhouse gas emissions plan. However, all the energy related processes and the associated factors are fraught with multiple forms of uncertainties and complexities. Hedging against uncertainty, in the present paper we use minimax regret analysis to identify robust strategies towards energy efficiency. Expressing uncertainty through discrete sce - narios, we apply robust optimization to meet the optimal mix of energy efficiency measures, performing well, independently of any scenario’s realization, taking into account the employment factor. In particular, we apply the maximin, as well as the minimax regret criterion, to solve the linear stochastic mathematical program. More- over, a numerical computation on the improvement of the energy efficiency in differ - ent sectors
Operational Research – Springer Journals
Published: May 31, 2018
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