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Hedging uncertainty in energy efficiency strategies: a minimax regret analysis

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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 hidden 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 scenarios, 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. Moreover, a numerical computation on the improvement of the energy efficiency in different sectors is presented.

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References

  • Aissi H, Bazgan C, Vanderpooten D (2009) Min–max and min–max regret versions of combinatorial optimization problems: a survey. Eur J Oper Res 197(2):427–438

    Article  Google Scholar 

  • Asif M, Muneer T (2007) Energy supply, its demand and security issues for developed and emerging economies. Renew Sustain Energy Rev 11(7):1388–1413

    Article  Google Scholar 

  • Aven T, Renn O (2009) On risk defined as an event where the outcome is uncertain. J Risk Res 12(1):1–11

    Article  Google Scholar 

  • Averbakh I (2000) Minmax regret solutions for minimax optimization problems with uncertainty. Oper Res Lett 27(2):57–65

    Article  Google Scholar 

  • Bean P, Hoppock D (2013) Least-risk planning for electric utilities, Nicholas Institute for Environmental Policy Solutions, Working paper NI WP 13-05

  • Bertsimas D, Sim M (2003) Robust discrete optimization and network flows. Math Program Ser B 98(1–3):49–71

    Article  Google Scholar 

  • Bertsimas D, Sim M (2004) The price of robustness. Oper Res 52(1):35–53

    Article  Google Scholar 

  • Chang NB, Davila E (2007) Minimax regret optimization analysis for a regional solid waste management system. Waste Manag 27(6):820–832

    Article  Google Scholar 

  • Chevé M, Congar R (2002) Managing environmental risks under scientific uncertainty and controversy. In: Proceedings of the international conference on risk and uncertainty in environmental and resource economics. Wageningen University, 5–7

  • Daskin M, Hesse SM, Revelle CS (1997) α-reliable p-minimax regret: a new model for strategic facility location modeling. Locat Sci 5(4):227–246

    Article  Google Scholar 

  • Dong C, Huang GH, Cai YP, Xu Y (2011) An interval-parameter minimax regret programming approach for power management systems planning under uncertainty. Appl Energy 88(8):2835–2845

    Article  Google Scholar 

  • U.S. Energy Information Administration (2017) International Energy Outlook 2017, Analysis & Projections

  • European Wind Energy Association (2009) Wind at work: wind energy and job creation in the EU. The European Wind Energy Association, Brussels, Belgium

    Google Scholar 

  • Gang W, Wang S, Yan C, Xiao F (2015) Robust optimal design of building cooling systems concerning uncertainties using mini-max regret theory. Sci Technol Built Environ 21(6):789–799

    Article  Google Scholar 

  • Hillebrand B, Buttermann HG, Behringer JM, Bleuel M (2006) The expansion of renewable energies and employment effects in Germany. Energy Policy 34(18):3484–3494

    Article  Google Scholar 

  • Inuiguchi M, Sakawa M (1995) Minimax regret solution to linear programming problems with an interval objective function. Eur J Oper Res 86(3):526–536

    Article  Google Scholar 

  • Jiang R, Wang J, Zhang M, Guan Y (2013) Two-stage minimax regret robust unit commitment. IEEE Trans Power Syst 28(3):2271–2282

    Article  Google Scholar 

  • Kalai R, Aloulou MA, Vallin P, Vanderpooten D (2005) Robust 1-median location problem on a tree. In: Proceedings of third edition of the operational research peripatetic postgraduate programme, Valencia, Spain, pp 201–212

  • Kazakci AO, Rozakis S, Vanderpooten D (2007) Energy supply in France: a min–max regret approach. J Oper Res Soc 58(11):1470–1479

    Article  Google Scholar 

  • Kouvelis P, Yu G (1997) Robust discrete optimization and its applications. Kluwer Academic Publishers, Dordrecht

    Book  Google Scholar 

  • Krähmer D, Stone R (2005) Dynamic regret theory, working paper, University College, London

  • Lehr U, Nitsch J, Kratzat M, Lutz Ch, Edler D (2008) Renewable energy and employment in Germany. Energy Policy 36(1):108–117

    Article  Google Scholar 

  • Li YP, Huang GH (2006) Minimax regret analysis for municipal solid waste management: an interval-stochastic programming approach. J Air Waste Manag Assoc 56(7):931–944

    Article  Google Scholar 

  • Li YP, Huang GH, Chen X (2011) An interval-valued minimax-regret analysis approach for the identification of optimal greenhouse-gas abatement strategies under uncertainty. Energy Policy 39(7):4313–4324

    Article  Google Scholar 

  • Li S, Coit DW, Felder F (2016) Stochastic optimization for electric power generation expansion planning with discrete climate change scenarios. Electr Power Syst Res 140:401–412

    Article  Google Scholar 

  • Loulou R, Kanudia A (1999) Minimax regret strategies for greenhouse gas abatement: methodology and application. Oper Res Lett 25(5):219–230

    Article  Google Scholar 

  • Mausser HE, Laguna M (1999) Minimising the maximum relative regret for linear programmes with interval objective function coefficients. J Oper Res Soc 50(10):1063–1070

    Article  Google Scholar 

  • Mavrotas G, Diakoulaki D, Florios K, Georgiou P (2008) A mathematical programming framework for energy planning in services’ sector buildings under uncertainty in load demand: the case of a hospital in Athens. Energy Policy 36(7):2415–2429

    Article  Google Scholar 

  • Mu Y, Cai W, Evans S, Wang C, Roland-Holst D (2018) Employment impacts of renewable energy policies in China: a decomposition analysis based on a CGE modeling framework. Appl Energy 210(15):256–267

    Article  Google Scholar 

  • Rivers N (2013) Renewable energy and unemployment: a general equilibrium analysis. Resour Energy Econ 35(4):467–485

    Article  Google Scholar 

  • Savage LJ (1951) The theory of statistical decision. J Am Stat Assoc 46(253):55–67

    Article  Google Scholar 

  • Shimizu K, Aiyoshi E (1980) Necessary conditions for min–max problems and algorithms by a relaxation procedure. IEEE Trans Autom Control 25(1):62–66

    Article  Google Scholar 

  • Sooriyaarachchi TM, Tsai I-T, Khatib SE, Farid AM, Mezher T (2015) Job creation potentials and skill requirements in PV, CSP, wind, water-to-energy and energy efficiency value chains. Renew Sust Energy Rev 52:653–668

    Article  Google Scholar 

  • Van der Pol TD, Gabbert S, Weikard H-P, van Ierland EC, Hendrix EMT (2017) A minimax regret analysis of flood risk management strategies under climate change uncertainty and emerging information. Environ Resour Econ 68(4):1087–1109

    Article  Google Scholar 

  • Varela-Vázquez P, del Carmen Sánchez-Carreira M (2015) Socioeconomic impact of wind energy on peripheral regions. Renew Sust Energy Rev 50:982–990

    Article  Google Scholar 

  • Wei M, Patadia S, Kammen DM (2010) Putting renewables and energy efficiency to work: how many jobs can the clean energy industry generate in the US? Energy Policy 38(2):919–931

    Article  Google Scholar 

  • Xidonas P, Mavrotas G, Hassapis C, Zopounidis C (2017) Robust multiobjective portfolio optimization: a minimax regret approach. Eur J Oper Res 262(1):299–305

    Article  Google Scholar 

  • Yager RR (2004) Decision making using minimization of regret. Int J Approx Reason 36(2):109–128

    Article  Google Scholar 

  • Yaman H, Karasan OE, Pinar MC (2004) Restricted robust optimization for maximization over uniform matroid with interval data uncertainty, Bilkent University Technical Report

  • Yokoyama R, Ito Κ (2001) Multiobjective robust optimal design of a gas turbine cogeneration plant under uncertain energy demands. In: ASME Turbo Expo 2001, Paper No. 2001-GT-208

  • Yokoyama R, Ito K (2002) Optimal design of energy supply systems based on relative robustness criterion. Energy Convers Manag 43(4):499–514

    Article  Google Scholar 

  • Yokoyama R, Fujiwara K, Ohkura M, Wakui T (2014) A revised method for robust optimal design of energy supply systems based on minimax regret criterion. Energy Convers Manag 84:196–208

    Article  Google Scholar 

  • Yokoyama R, Ohkura M, Wakui T (2015) Robust optimal design of a gas turbine cogeneration plant under uncertain energy demands and costs. In: ASME Turbo Expo 2015, Paper No. GT2015-42296

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Authors and Affiliations

  1. Decision Support Systems Laboratory (EPU-NTUA), School of Electrical and Computer Engineering, National Technical University of Athens, 9 Heroon Polytechniou Str, Zografou Campus, 15780, Athens, Greece

    Georgios P. Trachanas, Aikaterini Forouli, Nikolaos Gkonis & Haris Doukas

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  1. Georgios P. Trachanas
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  2. Aikaterini Forouli
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  3. Nikolaos Gkonis
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  4. Haris Doukas
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Correspondence to Georgios P. Trachanas.

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Trachanas, G.P., Forouli, A., Gkonis, N. et al. Hedging uncertainty in energy efficiency strategies: a minimax regret analysis. Oper Res Int J 20, 2229–2244 (2020). https://doi.org/10.1007/s12351-018-0409-y

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  • DOI: https://doi.org/10.1007/s12351-018-0409-y

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