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A stochastic optimization approach to reduce greenhouse gas emissions from buildings and transportation


  • Karan, Ebrahim
  • Asadi, Somayeh
  • Ntaimo, Lewis


The magnitude of building- and transportation-related GHG (greenhouse gas) emissions makes the adoption of all-EVs (electric vehicles) powered with renewable power as one of the most effective strategies to reduce emission of GHGs. This paper formulates the problem of GHG mitigation strategy under uncertain conditions and optimizes the strategies in which EVs are powered by solar energy. Under a pre-specified budget, the objective is to determine the type of EV and power generation capacity of the solar system in such a way as to maximize GHG emissions reductions. The model supports the three primary solar systems: off-grid, grid-tied, and hybrid. First, a stochastic optimization model using probability distributions of stochastic variables and EV and solar system specifications is developed. The model is then validated by comparing the estimated values of the optimal strategies and actual values. It is found that the mitigation strategies in which EVs are powered by a hybrid solar system lead to the best cost-expected reduction of CO2 emissions ratio. The results show an accuracy of about 4% for mitigation strategies in which EVs are powered by a grid-tied or hybrid solar system and 11% when applied to estimate the CO2 emissions reductions of an off-grid system.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:energy:v:106:y:2016:i:c:p:367-377
    DOI: 10.1016/

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    References listed on IDEAS

    1. J. Javid, Roxana & Nejat, Ali & Hayhoe, Katharine, 2014. "Selection of CO2 mitigation strategies for road transportation in the United States using a multi-criteria approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 960-972.
    2. Koltsaklis, Nikolaos E. & Liu, Pei & Georgiadis, Michael C., 2015. "An integrated stochastic multi-regional long-term energy planning model incorporating autonomous power systems and demand response," Energy, Elsevier, vol. 82(C), pages 865-888.
    3. Liu, Ben-Chieh & Tzeng, Gwo-Hshiung & Hsieh, Chang-Tzeh, 1992. "Energy planning and environmental quality management : A decision support system approach," Energy Economics, Elsevier, vol. 14(4), pages 302-307, October.
    4. Rong, Aiying & Lahdelma, Risto, 2007. "CO2 emissions trading planning in combined heat and power production via multi-period stochastic optimization," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1874-1895, February.
    5. Kahane, Adam, 1991. "New perspectives for energy efficiency and system optimization," Energy Policy, Elsevier, vol. 19(3), pages 199-201, April.
    6. 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.
    7. 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.
    8. Cristóbal, Jorge & Guillén-Gosálbez, Gonzalo & Kraslawski, Andrzej & Irabien, Angel, 2013. "Stochastic MILP model for optimal timing of investments in CO2 capture technologies under uncertainty in prices," Energy, Elsevier, vol. 54(C), pages 343-351.
    9. Karan, Ebrahim & Mohammadpour, Atefeh & Asadi, Somayeh, 2016. "Integrating building and transportation energy use to design a comprehensive greenhouse gas mitigation strategy," Applied Energy, Elsevier, vol. 165(C), pages 234-243.
    10. Moradi, Mohammad H. & Hajinazari, Mehdi & Jamasb, Shahriar & Paripour, Mahmoud, 2013. "An energy management system (EMS) strategy for combined heat and power (CHP) systems based on a hybrid optimization method employing fuzzy programming," Energy, Elsevier, vol. 49(C), pages 86-101.
    11. Borges, Ana Rosa & Antunes, Carlos Henggeler, 2003. "A fuzzy multiple objective decision support model for energy-economy planning," European Journal of Operational Research, Elsevier, vol. 145(2), pages 304-316, March.
    12. Khondaker, A.N. & Hasan, Md. Arif & Rahman, Syed Masiur & Malik, Karim & Shafiullah, Md & Muhyedeen, Musah A, 2016. "Greenhouse gas emissions from energy sector in the United Arab Emirates – An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1317-1325.
    13. Krey, Volker & Martinsen, Dag & Wagner, Hermann-Josef, 2007. "Effects of stochastic energy prices on long-term energy-economic scenarios," Energy, Elsevier, vol. 32(12), pages 2340-2349.
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    Cited by:

    1. Sehar, Fakeha & Pipattanasomporn, Manisa & Rahman, Saifur, 2017. "Demand management to mitigate impacts of plug-in electric vehicle fast charge in buildings with renewables," Energy, Elsevier, vol. 120(C), pages 642-651.
    2. Quentin Hoarau & Yannick Perez, 2018. "Interactions between electric mobility and photovoltaic generation: a review," Working Papers 1802, Chaire Economie du climat.
    3. repec:eee:energy:v:145:y:2018:i:c:p:374-387 is not listed on IDEAS
    4. Tan, Kang Miao & Ramachandaramurthy, Vigna K. & Yong, Jia Ying, 2016. "Optimal vehicle to grid planning and scheduling using double layer multi-objective algorithm," Energy, Elsevier, vol. 112(C), pages 1060-1073.
    5. repec:eee:energy:v:140:y:2017:i:p1:p:1182-1197 is not listed on IDEAS


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