IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v88y2011i3p599-611.html
   My bibliography  Save this article

Planning regional energy system in association with greenhouse gas mitigation under uncertainty

Author

Listed:
  • Li, Y.P.
  • Huang, G.H.
  • Chen, X.

Abstract

Greenhouse gas (GHG) concentrations are expected to continue to rise due to the ever-increasing use of fossil fuels and ever-boosting demand for energy. This leads to inevitable conflict between satisfying increasing energy demand and reducing GHG emissions. In this study, an integrated fuzzy-stochastic optimization model (IFOM) is developed for planning energy systems in association with GHG mitigation. Multiple uncertainties presented as probability distributions, fuzzy-intervals and their combinations are allowed to be incorporated within the framework of IFOM. The developed method is then applied to a case study of long-term planning of a regional energy system, where integer programming (IP) technique is introduced into the IFOM to facilitate dynamic analysis for capacity-expansion planning of energy-production facilities within a multistage context to satisfy increasing energy demand. Solutions related fuzzy and probability information are obtained and can be used for generating decision alternatives. The results can not only provide optimal energy resource/service allocation and capacity-expansion plans, but also help decision-makers identify desired policies for GHG mitigation with a cost-effective manner.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:88:y:2011:i:3:p:599-611
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306-2619(10)00307-7
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Claudia Kemfert & Wietze Lise & Richard Tol, 2004. "Games of Climate Change with International Trade," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 28(2), pages 209-232, June.
    3. Druce, Donald J., 2007. "Modelling the transition from cost-based to bid-based pricing in a deregulated electricity-market," Applied Energy, Elsevier, vol. 84(12), pages 1210-1225, December.
    4. Shimazaki, Yoichi & Akisawa, Atsushi & Kashiwagi, Takao, 2000. "A model analysis of clean development mechanisms to reduce both CO2 and SO2 emissions between Japan and China," Applied Energy, Elsevier, vol. 66(4), pages 311-324, August.
    5. Sadeghi, Mehdi & Mirshojaeian Hosseini, Hossein, 2006. "Energy supply planning in Iran by using fuzzy linear programming approach (regarding uncertainties of investment costs)," Energy Policy, Elsevier, vol. 34(9), pages 993-1003, June.
    6. Siitonen, Sari & Tuomaala, Mari & Suominen, Markku & Ahtila, Pekka, 2010. "Implications of process energy efficiency improvements for primary energy consumption and CO2 emissions at the national level," Applied Energy, Elsevier, vol. 87(9), pages 2928-2937, September.
    7. 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.
    8. Lin, Q.G. & Huang, G.H., 2009. "A dynamic inexact energy systems planning model for supporting greenhouse-gas emission management and sustainable renewable energy development under uncertainty--A case study for the City of Waterloo,," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1836-1853, October.
    9. Kannan, Ramachandran & Strachan, Neil, 2009. "Modelling the UK residential energy sector under long-term decarbonisation scenarios: Comparison between energy systems and sectoral modelling approaches," Applied Energy, Elsevier, vol. 86(4), pages 416-428, April.
    10. Doukas, Haris & Mannsbart, Wilhelm & Patlitzianas, Konstantinos D. & Psarras, John & Ragwitz, Mario & Schlomann, Barbara, 2007. "A methodology for validating the renewable energy data in EU," Renewable Energy, Elsevier, vol. 32(12), pages 1981-1998.
    11. van Beeck, Nicole & Doukas, Haris & Gioria, Michel & Karakosta, Charikleia & Psarras, John, 2009. "Energy RTD expenditures in the European union: Data gathering procedures and results towards a scientific reference system," Applied Energy, Elsevier, vol. 86(4), pages 452-459, April.
    12. Ashina, Shuichi & Nakata, Toshihiko, 2008. "Energy-efficiency strategy for CO2 emissions in a residential sector in Japan," Applied Energy, Elsevier, vol. 85(2-3), pages 101-114, February.
    13. Wachsmann, Ulrike & Wood, Richard & Lenzen, Manfred & Schaeffer, Roberto, 2009. "Structural decomposition of energy use in Brazil from 1970 to 1996," Applied Energy, Elsevier, vol. 86(4), pages 578-587, April.
    14. Lean, Hooi Hooi & Smyth, Russell, 2010. "CO2 emissions, electricity consumption and output in ASEAN," Applied Energy, Elsevier, vol. 87(6), pages 1858-1864, June.
    15. 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.
    16. Pekala, Lukasz M. & Tan, Raymond R. & Foo, Dominic C.Y. & Jezowski, Jacek M., 2010. "Optimal energy planning models with carbon footprint constraints," Applied Energy, Elsevier, vol. 87(6), pages 1903-1910, June.
    17. Turton, Hal & Barreto, Leonardo, 2006. "Long-term security of energy supply and climate change," Energy Policy, Elsevier, vol. 34(15), pages 2232-2250, October.
    18. 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.
    19. Atkins, Martin J. & Morrison, Andrew S. & Walmsley, Michael R.W., 2010. "Carbon Emissions Pinch Analysis (CEPA) for emissions reduction in the New Zealand electricity sector," Applied Energy, Elsevier, vol. 87(3), pages 982-987, March.
    20. Karlsson, Magnus & Gebremedhin, Alemayehu & Klugman, Sofia & Henning, Dag & Moshfegh, Bahram, 2009. "Regional energy system optimization - Potential for a regional heat market," Applied Energy, Elsevier, vol. 86(4), pages 441-451, April.
    21. Allen, S.R. & Hammond, G.P. & McManus, M.C., 2008. "Prospects for and barriers to domestic micro-generation: A United Kingdom perspective," Applied Energy, Elsevier, vol. 85(6), pages 528-544, June.
    22. 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.
    23. Chaabene, Maher & Ammar, Mohsen Ben & Elhajjaji, Ahmed, 2007. "Fuzzy approach for optimal energy-management of a domestic photovoltaic panel," Applied Energy, Elsevier, vol. 84(10), pages 992-1001, October.
    24. 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.
    25. Cranston, G.R. & Hammond, G.P., 2010. "North and south: Regional footprints on the transition pathway towards a low carbon, global economy," Applied Energy, Elsevier, vol. 87(9), pages 2945-2951, September.
    26. Li, Y.P. & Huang, G.H. & Guo, P. & Yang, Z.F. & Nie, S.L., 2010. "A dual-interval vertex analysis method and its application to environmental decision making under uncertainty," European Journal of Operational Research, Elsevier, vol. 200(2), pages 536-550, January.
    27. 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.
    28. Cao, M.F. & Huang, G.H. & Lin, Q.G., 2010. "Integer programming with random-boundary intervals for planning municipal power systems," Applied Energy, Elsevier, vol. 87(8), pages 2506-2516, August.
    29. Tolis, Athanasios & Doukelis, Aggelos & Tatsiopoulos, Ilias, 2010. "Stochastic interest rates in the analysis of energy investments: Implications on economic performance and sustainability," Applied Energy, Elsevier, vol. 87(8), pages 2479-2490, August.
    30. Aydinalp-Koksal, Merih & Ugursal, V. Ismet, 2008. "Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector," Applied Energy, Elsevier, vol. 85(4), pages 271-296, April.
    31. Amiri, S. & Moshfegh, B., 2010. "Possibilities and consequences of deregulation of the European electricity market for connection of heat sparse areas to district heating systems," Applied Energy, Elsevier, vol. 87(7), pages 2401-2410, July.
    32. Muela, E. & Schweickardt, G. & Garces, F., 2007. "Fuzzy possibilistic model for medium-term power generation planning with environmental criteria," Energy Policy, Elsevier, vol. 35(11), pages 5643-5655, November.
    33. John R. Birge, 1985. "Decomposition and Partitioning Methods for Multistage Stochastic Linear Programs," Operations Research, INFORMS, vol. 33(5), pages 989-1007, October.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    3. Cao, M.F. & Huang, G.H. & Lin, Q.G., 2010. "Integer programming with random-boundary intervals for planning municipal power systems," Applied Energy, Elsevier, vol. 87(8), pages 2506-2516, August.
    4. 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.
    5. Zhu, Y. & Huang, G.H. & Li, Y.P. & He, L. & Zhang, X.X., 2011. "An interval full-infinite mixed-integer programming method for planning municipal energy systems - A case study of Beijing," Applied Energy, Elsevier, vol. 88(8), pages 2846-2862, August.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Li, Y.P. & Huang, G.H. & Li, M.W., 2014. "An integrated optimization modeling approach for planning emission trading and clean-energy development under uncertainty," Renewable Energy, Elsevier, vol. 62(C), pages 31-46.
    15. Cai, Y.P. & Huang, G.H. & Tan, Q. & Yang, Z.F., 2009. "Planning of community-scale renewable energy management systems in a mixed stochastic and fuzzy environment," Renewable Energy, Elsevier, vol. 34(7), pages 1833-1847.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.

    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:appene:v:88:y:2011:i:3:p:599-611. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.