An interval-valued minimax-regret analysis approach for the identification of optimal greenhouse-gas abatement strategies under uncertainty

In this study, an interval-valued minimax regret analysis (IMRA) method is proposed for planning greenhouse gas (GHG) abatement under uncertainty. The IMRA method is a hybrid of interval-parameter programming (IPP) and minimax regret analysis (MMR) techniques. The developed method is applied to support long-term planning of GHG mitigation in an energy system under uncertainty. Mixed integer linear programming (MILP) technique with fixed-charge cost function is introduced into the IMRA framework to facilitate dynamic analysis for decisions of timing, sizing and siting in planning capacity expansions for power-generation facilities. The results obtained indicate that replacing fossil fuels with renewable energy sources (i.e. hydro, wind and solar power) can effectively facilitate reducing the GHG emissions. They can help decision makers identify an optimal strategy that can facilitate reducing the worst regret level incurred under any outcome of the uncertain GHG-abatement target.

[1]  E. Muela,et al.  Fuzzy possibilistic model for medium-term power generation planning with environmental criteria , 2007 .

[2]  L. J. Savage,et al.  The Foundations of Statistics , 1955 .

[3]  J. Houghton,et al.  Climate change 2001 : the scientific basis , 2001 .

[4]  Stephen C. Peck,et al.  Optimal CO2 control policy with stochastic losses from temperature rise , 1995 .

[5]  G H Huang,et al.  An inexact two-stage mixed integer linear programming method for solid waste management in the City of Regina. , 2006, Journal of environmental management.

[6]  V. Tummala.  Decision analysis with business applications , 1973 .

[7]  R. V. Mayorga,et al.  A Fuzzy Inference System Prototype for Indoor Air and Temperature Quality Monitoring and Hazard Detection , 2010 .

[8]  Carolien Kroeze,et al.  Cost-effective control of SO2 emissions in Asia. , 2004, Journal of environmental management.

[9]  Guo H Huang,et al.  Minimax Regret Analysis for Municipal Solid Waste Management: An Interval-Stochastic Programming Approach , 2006, Journal of the Air & Waste Management Association.

[10]  Amit Kanudia,et al.  Modelling of uncertainties and price elastic demands in energy-environment planning for India , 1996 .

[11]  Ye Xu,et al.  Regional-scale electric power system planning under uncertainty—A multistage interval-stochastic integer linear programming approach , 2010 .

[12]  Víctor M. Albornoz,et al.  A two‐stage stochastic integer programming model for a thermal power system expansion , 2004 .

[13]  G. Huang,et al.  Grey integer programming: An application to waste management planning under uncertainty , 1995 .

[14]  Amit Kanudia,et al.  Robust responses to climate change via stochastic MARKAL: The case of Québec , 1996, Eur. J. Oper. Res..

[15]  Guohe Huang,et al.  Grey fuzzy integer programming: An application to regional waste management planning under uncertainty , 1995 .

[16]  Alan S. Manne,et al.  MERGE. A model for evaluating regional and global effects of GHG reduction policies , 1995 .

[17]  Gordon H. Huang,et al.  Planning of energy system management and GHG-emission control in the Municipality of Beijing--An inexact-dynamic stochastic programming model , 2009 .

[18]  Leslie G. Fishbone,et al.  Markal, a linear‐programming model for energy systems analysis: Technical description of the bnl version , 1981 .

[19]  M. Sadeghi,et al.  Energy supply planning in Iran by using fuzzy linear programming approach (regarding uncertainties of investment costs) , 2006 .

[20]  Guohe Huang,et al.  A two-stage inexact-stochastic programming model for planning carbon dioxide emission trading under uncertainty , 2010 .

[21]  James G. Morris,et al.  Decision Problems Under Risk and Chance Constrained Programming: Dilemmas in the Transition , 1981 .

[22]  Amit Kanudia,et al.  Minimax regret strategies for greenhouse gas abatement: methodology and application , 1997, Oper. Res. Lett..

[23]  Ronald R. Yager,et al.  Decision making using minimization of regret , 2004, Int. J. Approx. Reason..

[24]  Abraham Charnes,et al.  Response to Decision Problems Under Risk and Chance Constrained Programming: Dilemmas in the Transition , 1983 .

[25]  James M. Gibbons,et al.  Modelling uncertainty in greenhouse gas emissions from UK agriculture at the farm level , 2006 .

[26]  J. Qiang,et al.  Probability distributions for carbon emissions and atmospheric response , 2008 .

[27]  G. H. Huang,et al.  A hybrid inexact-stochastic water management model , 1998, Eur. J. Oper. Res..

[28]  David S. Schimel,et al.  The climate system: an overview , 2001 .

[29]  Guohe Huang,et al.  ITCLP: An inexact two-stage chance-constrained program for planning waste management systems , 2007 .

[30]  Andrea Ramírez,et al.  Monte Carlo analysis of uncertainties in the Netherlands greenhouse gas emission inventory for 1990–2004 , 2008 .

[31]  Wilfried Winiwarter,et al.  Assessing the uncertainty associated with national greenhouse gas emission inventories:: a case study for Austria , 2001 .

[32]  John R. Birge,et al.  Introduction to Stochastic Programming , 1997 .

[33]  G H Huang,et al.  Inexact multistage stochastic integer programming for water resources management under uncertainty. , 2008, Journal of environmental management.

[34]  J. Bruce,et al.  Climate change, 1995 : economic and social dimensions of climate change , 1997 .

[35]  Q. Huang.  An Integrated MM5-CAMx Modeling Approach for Assessing PM10 Contribution from Different Sources in Beijing, China , 2010 .

[36]  Guohe Huang,et al.  Community-scale renewable energy systems planning under uncertainty—An interval chance-constrained programming approach , 2009 .

[37]  Dana L. Hoag,et al.  The impact matrix approach and decision rules to enhance index dimensionality, flexibility and representation , 2002 .

[38]  Guohe Huang,et al.  A GREY LINEAR PROGRAMMING APPROACH FOR MUNICIPAL SOLID WASTE MANAGEMENT PLANNING UNDER UNCERTAINTY , 1992 .

[39]  Masahiro Inuiguchi,et al.  Portfolio selection under independent possibilistic information , 2000, Fuzzy Sets Syst..

[40]  Dillip Kumar Swain,et al.  Climate Change Impact Assessment and Evaluation of Agro-Adaptation Measures for Rice Producti on in Eastern India , 2010 .