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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, Canada

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  • Lin, Q.G.
  • Huang, G.H.

Abstract

In this study, a dynamic interval-parameter community-scale energy systems planning model (DIP-CEM) was developed for supporting greenhouse-gas emission (GHG) management and sustainable energy development under uncertainty. The developed model could reach insight into the interactive characteristics of community-scale energy management systems, and thus capable of addressing specific community environmental and socio-economic features. Through integrating interval-parameter and mixed-integer linear programming techniques within a general optimization framework, the DIP-CEM could address uncertainty (expressed as interval values) existing in related costs, impact factors and system objectives as well as facilitate dynamic analysis of capacity-expansion decisions under such a uncertainty. DIP-CEM was then applied to the City of Waterloo, Canada to demonstrate its applicability in supporting decisions of community energy systems planning and GHG-emission reduction management. One business-as-usual (BAU) case and two GHG-emission reduction cases were analyzed with desired plans of GHG-emission reduction. The results indicated that the developed DIP-CEM could help provide sound strategies for dealing with issues of sustainable energy development and GHG-emission reduction within an energy management system.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:rensus:v:13:y:2009:i:8:p:1836-1853
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    5. 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.
    6. Nkosi, Mfundo & Dikgang, Johane & Kutela Gelo, Dambala & Pholo, Alain, 2021. "Greening the vehicle fleet, how does South Africa’s tax reforms affect new car sales," EconStor Preprints 236726, ZBW - Leibniz Information Centre for Economics.
    7. Li, G.C. & Huang, G.H. & Sun, W. & Ding, X.W., 2014. "An inexact optimization model for energy-environment systems management in the mixed fuzzy, dual-interval and stochastic environment," Renewable Energy, Elsevier, vol. 64(C), pages 153-163.
    8. Iqbal, M. & Azam, M. & Naeem, M. & Khwaja, A.S. & Anpalagan, A., 2014. "Optimization classification, algorithms and tools for renewable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 640-654.
    9. Lin, Q.G. & Huang, G.H., 2010. "An inexact two-stage stochastic energy systems planning model for managing greenhouse gas emission at a municipal level," Energy, Elsevier, vol. 35(5), pages 2270-2280.
    10. Wang, Jingjing & Zhao, Xian & Guo, Xiaoxin, 2019. "Optimizing wind turbine's maintenance policies under performance-based contract," Renewable Energy, Elsevier, vol. 135(C), pages 626-634.
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