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Bayesian interval robust optimization for sustainable energy system planning in Qiqihar City, China

Author

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  • Dong, Cong
  • Huang, Guohe
  • Cai, Yanpeng
  • Cheng, Guanhui
  • Tan, Qian

Abstract

Old industrial bases in Northeastern China have been experiencing a series of problems such as energy scarcity, economic slack and environmental pollution due to lack of scientific planning of energy systems. Effective energy system management is desirable for avoiding occurrence of these problems in the following decades under the national policy of revitalizing these industrial bases. This task is challenged by system complexity, data unavailability, and inaccurate demand forecasting which can hardly be resolved in existing studies. For improving energy system management in Qiqihar City, a representative industrial base in Northeastern China, under these challenges, a large-scale and fine-resolution Bayesian interval robust energy system optimization (BIRESO) approach is developed in this study. In detail, the structure, characteristics, problems and complexities of the energy system in this city are identified and analyzed. Based on these efforts, a series of optimized energy system management schemes are generated through construction of a BIRESO model and development of a solution algorithm. The obtained solutions can be ensured to remain robust when input data changes through controlling the degree of solution conservatism in accordance with probabilistic bounds on constraint violation. Furthermore, Bayesian estimation method was employed for effectively achieving dynamic forecast of energy demands. The tradeoff among energy security, economic development and environmental conservation under multiple uncertainties is revealed, and the optimal balance among them is identified. A scenario analysis approach is used to quantify the effects of economic growth on energy system schemes based on several developed scenarios. Results show that the BIRESO approach is capable of providing scientific support for energy supply planning, renewable energy development, technological advancement, facility expansion, energy allocation, pollution control, economic impacts assessment, and other energy related activities in Qiqihar City.

Suggested Citation

  • Dong, Cong & Huang, Guohe & Cai, Yanpeng & Cheng, Guanhui & Tan, Qian, 2016. "Bayesian interval robust optimization for sustainable energy system planning in Qiqihar City, China," Energy Economics, Elsevier, vol. 60(C), pages 357-376.
  • Handle: RePEc:eee:eneeco:v:60:y:2016:i:c:p:357-376
    DOI: 10.1016/j.eneco.2016.10.012
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    More about this item

    Keywords

    Qiqihar energy system; Emissions controlling; Robust optimization; Bayesian forecasting; Decision making; Multiple uncertainties;

    JEL classification:

    • Q01 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Sustainable Development
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other
    • Q52 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Pollution Control Adoption and Costs; Distributional Effects; Employment Effects

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