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Analysis of energy service systems in urban areas and their CO2 mitigations and economic impacts

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  • Aki, Hirohisa
  • Oyama, Tsutomu
  • Tsuji, Kiichiro

Abstract

Three kinds of energy service system were examined as alternative energy systems in urban areas. A comparison of these energy systems was performed by finding Pareto optimum solutions for a multi-objective model. The model had two objective functions: CO2 emission and cost to consumer. Various energy pricings were provided in the model as variables. The assumed alternative systems were those in which: (1) every consumer had a cogeneration system (CGS); (2) there was a centralized energy supply plant (ESP) that had a CGS installed and supplied energy (electricity, gas, cooling and heating) to consumers; and (3) both (1) and (2) were installed. Minimization of CO2 emissions and minimization of cost to consumers were assumed as objective functions of the multi-objective model. Energy prices that consumers paid were used in the model as variables. We performed an analysis and comparison of the three kinds of system from the viewpoints of CO2 emission, economic impact on consumers and ESP, using system operations as evaluation indexes.

Suggested Citation

  • Aki, Hirohisa & Oyama, Tsutomu & Tsuji, Kiichiro, 2006. "Analysis of energy service systems in urban areas and their CO2 mitigations and economic impacts," Applied Energy, Elsevier, vol. 83(10), pages 1076-1088, October.
    • Handle: RePEc:eee:appene:v:83:y:2006:i:10:p:1076-1088
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    Cited by:

    1. Tolis, Athanasios I. & Rentizelas, Athanasios A., 2011. "An impact assessment of electricity and emission allowances pricing in optimised expansion planning of power sector portfolios," Applied Energy, Elsevier, vol. 88(11), pages 3791-3806.
    2. Keirstead, James & Jennings, Mark & Sivakumar, Aruna, 2012. "A review of urban energy system models: Approaches, challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3847-3866.
    3. Chicco, Gianfranco & Mancarella, Pierluigi, 2009. "Distributed multi-generation: A comprehensive view," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(3), pages 535-551, April.
    4. Stojiljković, Mirko M., 2017. "Bi-level multi-objective fuzzy design optimization of energy supply systems aided by problem-specific heuristics," Energy, Elsevier, vol. 137(C), pages 1231-1251.
    5. Mancarella, Pierluigi, 2014. "MES (multi-energy systems): An overview of concepts and evaluation models," Energy, Elsevier, vol. 65(C), pages 1-17.
    6. Carpaneto, Enrico & Chicco, Gianfranco & Mancarella, Pierluigi & Russo, Angela, 2011. "Cogeneration planning under uncertainty: Part I: Multiple time frame approach," Applied Energy, Elsevier, vol. 88(4), pages 1059-1067, April.
    7. Dong, Cong & Huang, Guohe & Cai, Yanpeng & Li, Wei & Cheng, Guanhui, 2014. "Fuzzy interval programming for energy and environmental systems management under constraint-violation and energy-substitution effects: A case study for the City of Beijing," Energy Economics, Elsevier, vol. 46(C), pages 375-394.
    8. Kachirayil, Febin & Weinand, Jann Michael & Scheller, Fabian & McKenna, Russell, 2022. "Reviewing local and integrated energy system models: insights into flexibility and robustness challenges," Applied Energy, Elsevier, vol. 324(C).
    9. Ashina, Shuichi & Fujino, Junichi & Masui, Toshihiko & Ehara, Tomoki & Hibino, Go, 2012. "A roadmap towards a low-carbon society in Japan using backcasting methodology: Feasible pathways for achieving an 80% reduction in CO2 emissions by 2050," Energy Policy, Elsevier, vol. 41(C), pages 584-598.
    10. Mirko M. Stojiljković & Mladen M. Stojiljković & Bratislav D. Blagojević, 2014. "Multi-Objective Combinatorial Optimization of Trigeneration Plants Based on Metaheuristics," Energies, MDPI, vol. 7(12), pages 1-28, December.

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    Keywords

    Energy systems CO2 mitigation Energy pricing;

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