Hedging uncertainty in energy efficiency strategies: a minimax regret analysis

Hedging uncertainty in energy efficiency strategies: a minimax regret analysis 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Operational Research Springer Journals

Hedging uncertainty in energy efficiency strategies: a minimax regret analysis

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Business and Management; Operations Research/Decision Theory; Operations Research, Management Science; Computational Intelligence
ISSN
1109-2858
eISSN
1866-1505
D.O.I.
10.1007/s12351-018-0409-y
Publisher site
See Article on Publisher Site

Abstract

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

Journal

Operational ResearchSpringer Journals

Published: May 31, 2018

References

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