IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v35y2010i12p4627-4644.html
   My bibliography  Save this article

An interval fixed-mix stochastic programming method for greenhouse gas mitigation in energy systems under uncertainty

Author

Listed:
  • Xie, Y.L.
  • Li, Y.P.
  • Huang, G.H.
  • Li, Y.F.

Abstract

In this study, an interval fixed-mix stochastic programming (IFSP) model is developed for greenhouse gas (GHG) emissions reduction management under uncertainties. In the IFSP model, methods of interval-parameter programming (IPP) and fixed-mix stochastic programming (FSP) are introduced into an integer programming framework, such that the developed model can tackle uncertainties described in terms of interval values and probability distributions over a multi-stage context. Moreover, it can reflect dynamic decisions for facility-capacity expansion during the planning horizon. The developed model is applied to a case of planning GHG-emission mitigation, demonstrating that IFSP is applicable to reflecting complexities of multi-uncertainty, dynamic and interactive energy management systems, and capable of addressing the problem of GHG-emission reduction. A number of scenarios corresponding to different GHG-emission mitigation levels are examined; the results suggest that reasonable solutions have been generated. They can be used for generating plans for energy resource/electricity allocation and capacity expansion and help decision makers identify desired GHG mitigation policies under various economic costs and environmental requirements.

Suggested Citation

  • Xie, Y.L. & Li, Y.P. & Huang, G.H. & Li, Y.F., 2010. "An interval fixed-mix stochastic programming method for greenhouse gas mitigation in energy systems under uncertainty," Energy, Elsevier, vol. 35(12), pages 4627-4644.
    • Handle: RePEc:eee:energy:v:35:y:2010:i:12:p:4627-4644
    DOI: 10.1016/j.energy.2010.09.045
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544210005244
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2010.09.045?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Iniyan, S & Sumathy, K, 2000. "An optimal renewable energy model for various end-uses," Energy, Elsevier, vol. 25(6), pages 563-575.
    2. Messner, S. & Golodnikov, A. & Gritsevskii, A., 1996. "A stochastic version of the dynamic linear programming model MESSAGE III," Energy, Elsevier, vol. 21(9), pages 775-784.
    3. Ichinohe, Masayuki & Endo, Eiichi, 2006. "Analysis of the vehicle mix in the passenger-car sector in Japan for CO2 emissions reduction by a MARKAL model," Applied Energy, Elsevier, vol. 83(10), pages 1047-1061, October.
    4. Chen, W.T. & Li, Y.P. & Huang, G.H. & Chen, X. & Li, Y.F., 2010. "A two-stage inexact-stochastic programming model for planning carbon dioxide emission trading under uncertainty," Applied Energy, Elsevier, vol. 87(3), pages 1033-1047, March.
    5. Hiremath, R.B. & Shikha, S. & Ravindranath, N.H., 2007. "Decentralized energy planning; modeling and application--a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(5), pages 729-752, June.
    6. Lin, Q.G. & Huang, G.H., 2009. "Planning of energy system management and GHG-emission control in the Municipality of Beijing--An inexact-dynamic stochastic programming model," Energy Policy, Elsevier, vol. 37(11), pages 4463-4473, November.
    7. Michael A. H. Dempster & Igor V. Evstigneev & Klaus Reiner Schenk-Hoppé, 2011. "Growing Wealth with Fixed-Mix Strategies," World Scientific Book Chapters, in: Leonard C MacLean & Edward O Thorp & William T Ziemba (ed.), THE KELLY CAPITAL GROWTH INVESTMENT CRITERION THEORY and PRACTICE, chapter 29, pages 427-455, World Scientific Publishing Co. Pte. Ltd..
    8. Iniyan, S & Suganthi, L & Jagadeesan, T.R & Samuel, Anand A, 2000. "Reliability based socio economic optimal renewable energy model for India," Renewable Energy, Elsevier, vol. 19(1), pages 291-297.
    9. Heinrich, G. & Howells, M. & Basson, L. & Petrie, J., 2007. "Electricity supply industry modelling for multiple objectives under demand growth uncertainty," Energy, Elsevier, vol. 32(11), pages 2210-2229.
    10. Li, Y.F. & Huang, G.H. & Li, Y.P. & Xu, Y. & Chen, W.T., 2010. "Regional-scale electric power system planning under uncertainty--A multistage interval-stochastic integer linear programming approach," Energy Policy, Elsevier, vol. 38(1), pages 475-490, January.
    11. Klaassen, Ger & Riahi, Keywan, 2007. "Internalizing externalities of electricity generation: An analysis with MESSAGE-MACRO," Energy Policy, Elsevier, vol. 35(2), pages 815-827, February.
    12. Zhang, Chi & May, Michael M. & Heller, Thomas C., 2001. "Impact on global warming of development and structural changes in the electricity sector of Guangdong Province, China," Energy Policy, Elsevier, vol. 29(3), pages 179-203, February.
    13. Thomas Unger & Tomas Ekvall, 2003. "Benefits from increased cooperation and energy trade under CO 2 commitments-The Nordic case," Climate Policy, Taylor & Francis Journals, vol. 3(3), pages 279-294, September.
    14. Chinese, Damiana & Meneghetti, Antonella & Nardin, Gioacchino, 2005. "Waste-to-energy based greenhouse heating: exploring viability conditions through optimisation models," Renewable Energy, Elsevier, vol. 30(10), pages 1573-1586.
    15. Kanudia, Amit & Loulou, Richard, 1998. "Robust responses to climate change via stochastic MARKAL: The case of Quebec," European Journal of Operational Research, Elsevier, vol. 106(1), pages 15-30, April.
    16. Chung, Whan-Sam & Tohno, Susumu & Shim, Sang Yul, 2009. "An estimation of energy and GHG emission intensity caused by energy consumption in Korea: An energy IO approach," Applied Energy, Elsevier, vol. 86(10), pages 1902-1914, October.
    17. Crilly, Damien & Zhelev, Toshko, 2008. "Emissions targeting and planning: An application of CO2 emissions pinch analysis (CEPA) to the Irish electricity generation sector," Energy, Elsevier, vol. 33(10), pages 1498-1507.
    18. Gustavsson, Leif & Madlener, Reinhard, 2003. "CO2 mitigation costs of large-scale bioenergy technologies in competitive electricity markets," Energy, Elsevier, vol. 28(14), pages 1405-1425.
    19. Fleten, Stein-Erik & Hoyland, Kjetil & Wallace, Stein W., 2002. "The performance of stochastic dynamic and fixed mix portfolio models," European Journal of Operational Research, Elsevier, vol. 140(1), pages 37-49, July.
    20. Cai, Y.P. & Huang, G.H. & Yang, Z.F. & Tan, Q., 2009. "Identification of optimal strategies for energy management systems planning under multiple uncertainties," Applied Energy, Elsevier, vol. 86(4), pages 480-495, April.
    21. Cormio, C. & Dicorato, M. & Minoia, A. & Trovato, M., 2003. "A regional energy planning methodology including renewable energy sources and environmental constraints," Renewable and Sustainable Energy Reviews, Elsevier, vol. 7(2), pages 99-130, April.
    22. Kolstad, Charles D., 1994. "George Bush versus Al Gore : Irreversibilities in greenhouse gas accumulation and emission control investment," Energy Policy, Elsevier, vol. 22(9), pages 771-778, September.
    23. Yasumasa Fujii & Kenji Yamaji, 1998. "Assessment of technological options in the global energy system for limiting the atmospheric CO 2 concentration," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 1(2), pages 113-139, December.
    24. Lehtila, A & Pirila, P, 1996. "Reducing energy related emissions : Using an energy systems optimization model to support policy planning in Finland," Energy Policy, Elsevier, vol. 24(9), pages 805-819, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lu, Yuehong & Wang, Shengwei & Yan, Chengchu & Shan, Kui, 2015. "Impacts of renewable energy system design inputs on the performance robustness of net zero energy buildings," Energy, Elsevier, vol. 93(P2), pages 1595-1606.
    2. Ji, Ling & Huang, Guo-He & Huang, Lu-Cheng & Xie, Yu-Lei & Niu, Dong-Xiao, 2016. "Inexact stochastic risk-aversion optimal day-ahead dispatch model for electricity system management with wind power under uncertainty," Energy, Elsevier, vol. 109(C), pages 920-932.
    3. Karan, Ebrahim & Asadi, Somayeh & Ntaimo, Lewis, 2016. "A stochastic optimization approach to reduce greenhouse gas emissions from buildings and transportation," Energy, Elsevier, vol. 106(C), pages 367-377.
    4. Zhang, Xiaoyue & Huang, Guohe & Xie, Yulei & Liu, Lirong & Song, Tangnyu, 2022. "A coupled non-deterministic optimization and mixed-level factorial analysis model for power generation expansion planning – A case study of Jing-Jin-Ji metropolitan region, China," Applied Energy, Elsevier, vol. 311(C).
    5. Chen, Yizhong & Lu, Hongwei & Li, Jing & Huang, Guohe & He, Li, 2016. "Regional planning of new-energy systems within multi-period and multi-option contexts: A case study of Fengtai, Beijing, China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 356-372.
    6. Liang, M.S. & Huang, G.H. & Chen, J.P. & Li, Y.P., 2022. "Development of non-deterministic energy-water-carbon nexus planning model: A case study of Shanghai, China," Energy, Elsevier, vol. 246(C).
    7. Li, G.C. & Huang, G.H. & Lin, Q.G. & Zhang, X.D. & Tan, Q. & Chen, Y.M., 2011. "Development of a GHG-mitigation oriented inexact dynamic model for regional energy system management," Energy, Elsevier, vol. 36(5), pages 3388-3398.
    8. Xie, Y.L. & Xia, D.H. & Ji, L. & Zhou, W.N. & Huang, G.H., 2017. "An inexact cost-risk balanced model for regional energy structure adjustment management and resources environmental effect analysis-a case study of Shandong province, China," Energy, Elsevier, vol. 126(C), pages 374-391.
    9. Yulei Xie & Linrui Wang & Guohe Huang & Dehong Xia & Ling Ji, 2018. "A Stochastic Inexact Robust Model for Regional Energy System Management and Emission Reduction Potential Analysis—A Case Study of Zibo City, China," Energies, MDPI, vol. 11(8), pages 1-24, August.
    10. Chen, Yizhong & He, Li & Li, Jing & Cheng, Xi & Lu, Hongwei, 2016. "An inexact bi-level simulation–optimization model for conjunctive regional renewable energy planning and air pollution control for electric power generation systems," Applied Energy, Elsevier, vol. 183(C), pages 969-983.
    11. Dong, C. & Huang, G.H. & Cai, Y.P. & Liu, Y., 2012. "An inexact optimization modeling approach for supporting energy systems planning and air pollution mitigation in Beijing city," Energy, Elsevier, vol. 37(1), pages 673-688.
    12. Oliveira, Carla & Antunes, Carlos Henggeler, 2011. "A multi-objective multi-sectoral economy–energy–environment model: Application to Portugal," Energy, Elsevier, vol. 36(5), pages 2856-2866.
    13. Wu, C.B. & Huang, G.H. & Li, W. & Xie, Y.L. & Xu, Y., 2015. "Multistage stochastic inexact chance-constraint programming for an integrated biomass-municipal solid waste power supply management under uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1244-1254.
    14. Guan, Panbo & Huang, Guohe & Wu, Chuanbao & Wang, Linrui & Li, Chaoci & Wang, Yuanyi, 2019. "Analysis of emission taxes levying on regional electric power structure adjustment with an inexact optimization model - A case study of Zibo, China," Energy Economics, Elsevier, vol. 84(C).
    15. Wang, Xingwei & Cai, Yanpeng & Chen, Jiajun & Dai, Chao, 2013. "A grey-forecasting interval-parameter mixed-integer programming approach for integrated electric-environmental management–A case study of Beijing," Energy, Elsevier, vol. 63(C), pages 334-344.
    16. Wu, Zhibin & Xu, Jiuping, 2013. "Predicting and optimization of energy consumption using system dynamics-fuzzy multiple objective programming in world heritage areas," Energy, Elsevier, vol. 49(C), pages 19-31.
    17. Guangxiao Hu & Xiaoming Ma & Junping Ji, 2017. "A Stochastic Optimization Model for Carbon Mitigation Path under Demand Uncertainty of the Power Sector in Shenzhen, China," Sustainability, MDPI, vol. 9(11), pages 1-12, October.
    18. Kim, Seunghyok & Koo, Jamin & Lee, Chang Jun & Yoon, En Sup, 2012. "Optimization of Korean energy planning for sustainability considering uncertainties in learning rates and external factors," Energy, Elsevier, vol. 44(1), pages 126-134.
    19. Xie, Y.L. & Huang, G.H. & Li, W. & Ji, L., 2014. "Carbon and air pollutants constrained energy planning for clean power generation with a robust optimization model—A case study of Jining City, China," Applied Energy, Elsevier, vol. 136(C), pages 150-167.
    20. Svensson, Elin & Strömberg, Ann-Brith & Patriksson, Michael, 2011. "A model for optimization of process integration investments under uncertainty," Energy, Elsevier, vol. 36(5), pages 2733-2746.
    21. Ji, L. & Niu, D.X. & Huang, G.H., 2014. "An inexact two-stage stochastic robust programming for residential micro-grid management-based on random demand," Energy, Elsevier, vol. 67(C), pages 186-199.
    22. Niknam, Taher & Narimani, Mohammad rasoul & Jabbari, Masoud & Malekpour, Ahmad Reza, 2011. "A modified shuffle frog leaping algorithm for multi-objective optimal power flow," Energy, Elsevier, vol. 36(11), pages 6420-6432.
    23. Zhu, Y. & Li, Y.P. & Huang, G.H., 2013. "Planning carbon emission trading for Beijing's electric power systems under dual uncertainties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 113-128.
    24. Pousinho, H.M.I. & Mendes, V.M.F. & Catalão, J.P.S., 2011. "A risk-averse optimization model for trading wind energy in a market environment under uncertainty," Energy, Elsevier, vol. 36(8), pages 4935-4942.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Y.F. & Li, Y.P. & Huang, G.H. & Chen, X., 2010. "Energy and environmental systems planning under uncertainty--An inexact fuzzy-stochastic programming approach," Applied Energy, Elsevier, vol. 87(10), pages 3189-3211, October.
    2. Li, Y.P. & Huang, G.H. & Chen, X., 2011. "Planning regional energy system in association with greenhouse gas mitigation under uncertainty," Applied Energy, Elsevier, vol. 88(3), pages 599-611, March.
    3. Cai, Y.P. & Huang, G.H. & Yang, Z.F. & Lin, Q.G. & Tan, Q., 2009. "Community-scale renewable energy systems planning under uncertainty--An interval chance-constrained programming approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(4), pages 721-735, May.
    4. Piao, M.J. & Li, Y.P. & Huang, G.H. & Nie, S., 2015. "Risk analysis for Shanghai's electric power system under multiple uncertainties," Energy, Elsevier, vol. 87(C), pages 104-119.
    5. Yong Zeng & Yanpeng Cai & Guohe Huang & Jing Dai, 2011. "A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty," Energies, MDPI, vol. 4(10), pages 1-33, October.
    6. Dong, C. & Huang, G.H. & Cai, Y.P. & Liu, Y., 2012. "An inexact optimization modeling approach for supporting energy systems planning and air pollution mitigation in Beijing city," Energy, Elsevier, vol. 37(1), pages 673-688.
    7. Hu, Qing & Huang, Guohe & Cai, Yanpeng & Huang, Ying, 2011. "Feasibility-based inexact fuzzy programming for electric power generation systems planning under dual uncertainties," Applied Energy, Elsevier, vol. 88(12), pages 4642-4654.
    8. Li, G.C. & Huang, G.H. & Lin, Q.G. & Zhang, X.D. & Tan, Q. & Chen, Y.M., 2011. "Development of a GHG-mitigation oriented inexact dynamic model for regional energy system management," Energy, Elsevier, vol. 36(5), pages 3388-3398.
    9. Li, Y.F. & Huang, G.H. & Li, Y.P. & Xu, Y. & Chen, W.T., 2010. "Regional-scale electric power system planning under uncertainty--A multistage interval-stochastic integer linear programming approach," Energy Policy, Elsevier, vol. 38(1), pages 475-490, January.
    10. Jin, L. & Huang, G.H. & Fan, Y.R. & Wang, L. & Wu, T., 2015. "A pseudo-optimal inexact stochastic interval T2 fuzzy sets approach for energy and environmental systems planning under uncertainty: A case study for Xiamen City of China," Applied Energy, Elsevier, vol. 138(C), pages 71-90.
    11. Li, Y.P. & Huang, G.H. & Chen, X., 2011. "An interval-valued minimax-regret analysis approach for the identification of optimal greenhouse-gas abatement strategies under uncertainty," Energy Policy, Elsevier, vol. 39(7), pages 4313-4324, July.
    12. Sampaio, Henrique César & Dias, Rubens Alves & Balestieri, José Antônio Perrella, 2013. "Sustainable urban energy planning: The case study of a tropical city," Applied Energy, Elsevier, vol. 104(C), pages 924-935.
    13. Heinrich, G. & Basson, L. & Cohen, B. & Howells, M. & Petrie, J., 2007. "Ranking and selection of power expansion alternatives for multiple objectives under uncertainty," Energy, Elsevier, vol. 32(12), pages 2350-2369.
    14. Xie, Y.L. & Huang, G.H. & Li, W. & Ji, L., 2014. "Carbon and air pollutants constrained energy planning for clean power generation with a robust optimization model—A case study of Jining City, China," Applied Energy, Elsevier, vol. 136(C), pages 150-167.
    15. Chen, C. & Li, Y.P. & Huang, G.H., 2016. "Interval-fuzzy municipal-scale energy model for identification of optimal strategies for energy management – A case study of Tianjin, China," Renewable Energy, Elsevier, vol. 86(C), pages 1161-1177.
    16. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.
    17. Zhou, Xiong & Huang, Guohe & Zhu, Hua & Chen, Jiapei & Xu, Jinliang, 2015. "Chance-constrained two-stage fractional optimization for planning regional energy systems in British Columbia, Canada," Applied Energy, Elsevier, vol. 154(C), pages 663-677.
    18. Liu, Y. & Huang, G.H. & Cai, Y.P. & Cheng, G.H. & Niu, Y.T. & An, K., 2009. "Development of an inexact optimization model for coupled coal and power management in North China," Energy Policy, Elsevier, vol. 37(11), pages 4345-4363, November.
    19. Chen, C. & Li, Y.P. & Huang, G.H. & Zhu, Y., 2012. "An inexact robust nonlinear optimization method for energy systems planning under uncertainty," Renewable Energy, Elsevier, vol. 47(C), pages 55-66.
    20. Chen, C. & Li, Y.P. & Huang, G.H., 2013. "An inexact robust optimization method for supporting carbon dioxide emissions management in regional electric-power systems," Energy Economics, Elsevier, vol. 40(C), pages 441-456.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:35:y:2010:i:12:p:4627-4644. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.