The potential role of carbon capture and storage technology in sustainable electric-power systems under multiple uncertainties

One of the problems facing researchers in managing carbon capture and storage (CCS) technology is that complex energy systems accommodate the relevant social, economic, environmental, and political factors. Many system behaviors, factors, and parameters are associated with uncertainties. Effective management of such a complex system involves balancing tradeoffs among these key influencing factors under multiple uncertainties. In this study, an interval-fuzzy stochastic programming (IFSP) method is developed to deal with multiple uncertainties expressed as fuzzy sets, intervals and probability distributions. An IFSP-CCS model is formulated to plan CCS technology of power system in Bayingolin Mongol Autonomous Prefecture (Bazhou). Policy scenarios are introduced to investigate the potential role of CCS technology and sensitivity analyses are performed to assess the influence of various economic factors on system cost. Results indicate various uncertainties existed in CCS development and the related factors can affect the modeling outputs. Results also reveal that CO2-mitigation constraint can induce the development of renewable energy and CCS, and CCS technology can make a great contribution to CO2 emission reductions from a long-term planning perspective. The findings can provide support for CCS investment in fossil-fuel-dominated electric-power system and offer useful information for policy investigation under multiple uncertainties.

[1]  Peter Viebahn,et al.  Prospects of carbon capture and storage (CCS) in India’s power sector – An integrated assessment , 2014 .

[2]  Yanpeng Cai,et al.  A Fuzzy Evacuation Management Model Oriented Toward the Mitigation of Vehicular Emissions , 2016 .

[3]  Mahmud Fotuhi-Firuzabad,et al.  A comprehensive review on uncertainty modeling techniques in power system studies , 2016 .

[4]  Guohe Huang,et al.  Interval-fuzzy stochastic optimization for regional energy systems planning and greenhouse-gas emission management under uncertainty—a case study for the Province of Ontario, Canada , 2011 .

[5]  R. Saminathan,et al.  Uncertainty Assessment of Non-normal Emission Estimates Using Non-Parametric Bootstrap Confidence Intervals , 2015 .

[6]  C. W. Lee,et al.  Viability of CCS: A broad-based assessment for Malaysia , 2011 .

[7]  Robert T. Dahowski,et al.  A $70/tCO2 greenhouse gas mitigation backstop for China's industrial and electric power sectors: Insights from a comprehensive CCS cost curve , 2012 .

[8]  I. Sreedhar,et al.  A journey into the process and engineering aspects of carbon capture technologies , 2015 .

[9]  Mika Järvinen,et al.  Opportunities and obstacles for CO2 mineralization: CO2 mineralization specific frames in the interviews of Finnish carbon capture and storage (CCS) experts , 2015 .

[10]  N. Essounbouli,et al.  An Interval Fuzzy Optimization-Based Technique to Optimal Generation Scheduling with Load Uncertainty , 2016 .

[11]  Wim Turkenburg,et al.  Evaluating the development of carbon capture and storage technologies in the United States , 2010 .

[12]  Can Wang,et al.  An uncertainty analysis of subsidy for carbon capture and storage (CCS) retrofitting investment in China's coal power plants using a real-options approach , 2016 .

[13]  Can Wang,et al.  Employment impacts of CDM projects in China's power sector , 2013 .

[14]  Mikael Höök,et al.  Carbon capture and coal consumption: Implications of energy penalties and large scale deployment , 2015 .

[15]  Jiajun Chen,et al.  A novel modeling based real option approach for CCS investment evaluation under multiple uncertainties , 2014 .

[16]  Calin-Cristian Cormos,et al.  Oxy-combustion of coal, lignite and biomass: A techno-economic analysis for a large scale Carbon Capture and Storage (CCS) project in Romania , 2016 .

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

[18]  Guohe Huang,et al.  A hybrid fuzzy-stochastic programming method for water trading within an agricultural system , 2014 .

[19]  Luis M. Abadie,et al.  European CO2 prices and carbon capture investments , 2008 .

[20]  Guo H. Huang,et al.  An interval-parameter fuzzy two-stage stochastic program for water resources management under uncertainty , 2005, Eur. J. Oper. Res..

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

[22]  Ying Li,et al.  ENERGY AND ENVIRONMENTAL SYSTEMS PLANNING UNDER UNCERTAINTY—AN INEXACT FUZZY-STOCHASTIC PROGRAMMING APPROACH , 2010 .

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

[24]  Ronald R. Yager,et al.  A procedure for ordering fuzzy subsets of the unit interval , 1981, Inf. Sci..

[25]  Miguel Heleno,et al.  A linearized approach to the Symmetric Fuzzy Power Flow for the application to real systems , 2014 .

[26]  Bennaceur Kamel,et al.  Energy technology modelling of major carbon abatement options , 2009 .

[27]  Noradin Ghadimi,et al.  PSO Based Fuzzy Stochastic Long-Term Model for Deployment of Distributed Energy Resources in Distribution Systems With Several Objectives , 2013, IEEE Systems Journal.

[28]  Haslenda Hashim,et al.  Modelling and optimization of CO2 abatement strategies , 2014 .

[29]  Guohe Huang,et al.  An integrated approach for climate-change impact analysis and adaptation planning under multi-level uncertainties. Part II. Case study , 2011 .

[30]  Richard S. Middleton,et al.  The complex future of CO2 capture and storage: Variable electricity generation and fossil fuel power , 2013 .

[31]  John E. Bistline,et al.  Electric sector capacity planning under uncertainty: Climate policy and natural gas in the US , 2015 .

[32]  Raymond R. Tan,et al.  Power system planning with emission constraints: Effects of CCS retrofitting , 2014 .

[33]  Raul Poler Escoto,et al.  Fuzzy optimization for supply chain planning under supply, demand and process uncertainties , 2009, Fuzzy Sets Syst..

[34]  John R. Birge,et al.  Introduction to Stochastic programming (2nd edition), Springer verlag, New York , 2011 .

[35]  Wenji Zhou,et al.  Uncertainty modeling of CCS investment strategy in China’s power sector , 2010 .

[36]  Jamin Koo,et al.  Integration of CCS, emissions trading and volatilities of fuel prices into sustainable energy planning, and its robust optimization , 2011 .

[37]  S. W. Jin,et al.  Development of an integrated optimization method for analyzing effect of energy conversion efficiency under uncertainty – A case study of Bayingolin Mongol Autonomous Prefecture, China , 2015 .

[38]  Aimie L. B. Hope,et al.  The impact of religious faith on attitudes to environmental issues and Carbon Capture and Storage (CCS) technologies: A mixed methods study , 2014 .

[39]  Hassan Monsef,et al.  A multi-stage Smart Energy Management System under multiple uncertainties: A data mining approach , 2017 .

[40]  Lei Zhu,et al.  Analysis of Global CCS Technology, Regulations and Its Potential for Emission Reduction with Focus on China , 2011 .

[41]  Denny K. S. Ng,et al.  Targeting for optimal grid-wide deployment of carbon capture and storage (CCS) technology , 2014 .

[42]  Humberto Verdejo,et al.  Stochastic modeling to represent wind power generation and demand in electric power system based on real data , 2016 .

[43]  Dawei Han,et al.  Uncertainty Assessment in Environmental Risk through Bayesian Networks , 2015 .

[44]  Andrew Forbes Alexander Hoadley,et al.  Optimisation of power stations with carbon capture plants – the trade-off between costs and net power , 2012 .

[45]  Reinhard Madlener,et al.  Economics of CCS for coal plants: Impact of investment costs and efficiency on market diffusion in Europe , 2012 .

[46]  Vladimir Valentić,et al.  CCS (carbon capture and storage) investment possibility in South East Europe: A case study for Croatia , 2014 .

[47]  Marco Raugei,et al.  A comprehensive assessment of the energy performance of the full range of electricity generation technologies deployed in the United Kingdom , 2016 .

[48]  Lei Du,et al.  Study on carbon capture and storage (CCS) investment decision-making based on real options for China's coal-fired power plants , 2016 .

[49]  Alexandre Szklo,et al.  Will thermal power plants with CCS play a role in Brazil's future electric power generation? , 2014 .

[50]  Pei Liu,et al.  A multi-region optimization planning model for China's power sector , 2015 .

[51]  B. W. Ang,et al.  Carbon emission intensity in electricity production: A global analysis , 2016 .

[52]  Irene P. Koronaki,et al.  Modeling of CO2 capture via chemical absorption processes − An extensive literature review , 2015 .

[53]  Guillermo A. Mendoza,et al.  A fuzzy multiple objective linear programming approach to forest planning under uncertainty , 1993 .

[54]  Lei Zhu,et al.  CO2 mitigation potential of CCS in China – an evaluation based on an integrated assessment model , 2015 .

[55]  Saša Žiković,et al.  Can CCS save coal fired power plants – The European perspective , 2016 .

[56]  S. Fuss,et al.  Fuel price and technological uncertainty in a real options model for electricity planning , 2010 .

[57]  Alfons Kather,et al.  Post-combustion CO2-capture from coal-fired power plants: Preliminary evaluation of an integrated chemical absorption process with piperazine-promoted potassium carbonate , 2008 .

[58]  Guohe Huang,et al.  AN INEXACT TWO-STAGE STOCHASTIC PROGRAMMING MODEL FOR WATER RESOURCES MANAGEMENT UNDER UNCERTAINTY , 2000 .

[59]  Moritoshi Kato,et al.  A basic study of optimal investment of power sources considering environmental measures: Economic evaluation of CCS through a real options approach , 2011 .

[60]  Stein-Erik Fleten,et al.  Short-term hydropower production planning by stochastic programming , 2008, Comput. Oper. Res..

[61]  Guohe Huang,et al.  Planning regional energy system in association with greenhouse gas mitigation under uncertainty , 2011 .

[62]  Davoud Gholamiangonabadi,et al.  Dynamic changes in CO2 emission performance of different types of Iranian fossil-fuel power plants , 2015 .

[63]  Zeng Ming,et al.  CCS technology development in China: Status, problems and countermeasures—Based on SWOT analysis , 2014 .

[64]  I. Sreedhar,et al.  Process and engineering trends in membrane based carbon capture , 2017 .

[65]  Aie.  World Energy Outlook 2011 , 2001 .