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Development and application of the renewable energy regional optimization utility tool for environmental sustainability: REROUTES

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  • Hori, Keiko
  • Matsui, Takanori
  • Hasuike, Takashi
  • Fukui, Ken-ichi
  • Machimura, Takashi

Abstract

To expand the use of renewable energy, it is necessary to optimize energy production at a local level through an appropriate combination of different renewable energy resources. Additionally, the environmental impacts from the use of renewable resources must be considered from different perspectives. Thus, this study aims to develop a decision tool that evaluates and optimizes the renewable energy mix at a regional scale with regard to various environmental impacts. This tool was developed by creating a municipal energy database that includes five renewable energies (solar, wind, small- and medium-scale hydro, geothermal and biomass energy) and six evaluation indicators (proportion of developed renewable energy, economic balance, installed wind power, decrease in CO2 emissions, circulation rate of biomass resource, and impacted ecosystem area). Specifying different constraints in the model allowed investigation of the optimal renewable energy combination in a study area for two different scenarios, with the results highlighting the trade-offs between the scenarios. We also optimized the combination of different renewable energies for every municipality across Japan, while incorporating the natural conditions and varying demand structure. In conclusion, the tool developed in this study could support decision-making regarding the development of renewable energy resources.

Suggested Citation

  • Hori, Keiko & Matsui, Takanori & Hasuike, Takashi & Fukui, Ken-ichi & Machimura, Takashi, 2016. "Development and application of the renewable energy regional optimization utility tool for environmental sustainability: REROUTES," Renewable Energy, Elsevier, vol. 93(C), pages 548-561.
    • Handle: RePEc:eee:renene:v:93:y:2016:i:c:p:548-561
    DOI: 10.1016/j.renene.2016.02.051
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    References listed on IDEAS

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    Cited by:

    1. Ma, Weiwu & Xue, Xinpei & Liu, Gang, 2018. "Techno-economic evaluation for hybrid renewable energy system: Application and merits," Energy, Elsevier, vol. 159(C), pages 385-409.
    2. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    3. Song, Jeonghun & Oh, Si-Doek & Song, Seung Jin, 2019. "Effect of increased building-integrated renewable energy on building energy portfolio and energy flows in an urban district of Korea," Energy, Elsevier, vol. 189(C).
    4. Song, Jeonghun & Song, Seung Jin, 2020. "A framework for analyzing city-wide impact of building-integrated renewable energy," Applied Energy, Elsevier, vol. 276(C).
    5. Fang, Guochang & Tian, Lixin & Fu, Min & Sun, Mei & He, Yu & Lu, Longxi, 2018. "How to promote the development of energy-saving and emission-reduction with changing economic growth rate—A case study of China," Energy, Elsevier, vol. 143(C), pages 732-745.
    6. Spittler, Nathalie & Davidsdottir, Brynhildur & Shafiei, Ehsan & Diemer, Arnaud, 2021. "Implications of renewable resource dynamics for energy system planning: The case of geothermal and hydropower in Kenya," Energy Policy, Elsevier, vol. 150(C).
    7. Garcia Marrero, Luis Enrique & Arzola Ruíz, José, 2021. "Web-based tool for the decision making in photovoltaic/wind farms planning with multiple objectives," Renewable Energy, Elsevier, vol. 179(C), pages 2224-2234.
    8. Hori, Keiko & Kim, Jaegyu & Kawase, Reina & Kimura, Michinori & Matsui, Takanori & Machimura, Takashi, 2020. "Local energy system design support using a renewable energy mix multi-objective optimization model and a co-creative optimization process," Renewable Energy, Elsevier, vol. 156(C), pages 1278-1291.

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