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Local energy system design support using a renewable energy mix multi-objective optimization model and a co-creative optimization process

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  • Hori, Keiko
  • Kim, Jaegyu
  • Kawase, Reina
  • Kimura, Michinori
  • Matsui, Takanori
  • Machimura, Takashi

Abstract

When developing a sustainable local energy system, it is useful to apply backcasting to help select an appropriate renewable energy mix based on an evaluation by diverse stakeholders of multiple possible implementation impacts. The purpose of this study was to propose a co-creative design support method for local energy systems that includes (1) participatory development of a local future vision, (2) quantitative projection of future energy demand coupled with future vision, (3) multi-objective optimization of a regional renewable energy mix consistent with the future vision, and (4) a co-creative optimization process that encompasses local resident preferences. A case study in Takashima, Shiga Prefecture, Japan, was conducted in collaboration with the Takashima Community Promotion Council to test the proposed method. A participatory workshop was conducted with nine officers and 16 citizens to design a qualitative future vision for 2040. This vision was then quantified and the future energy demand was projected using the Extended Snapshot Tool model. Pareto solutions for an optimal renewable energy mix were visualized using the Renewable Energy Regional Optimization Utility Tool for Environmental Sustainability with a multi-objective evolutionary algorithm. One optimal solution was interactively selected according to the preferences of local residents surveyed using a pairwise comparison questionnaire. The proposed method was demonstrated to successfully derive an optimal renewable energy mix for Takashima using backcasting. In addition, it was shown to be a useful method for the co-creation of local energy systems.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:156:y:2020:i:c:p:1278-1291
    DOI: 10.1016/j.renene.2019.11.089
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    2. Khrisydel Rhea M. Supapo & Lorafe Lozano & Ian Dominic F. Tabañag & Edward M. Querikiol, 2022. "A Backcasting Analysis toward a 100% Renewable Energy Transition by 2040 for Off-Grid Islands," Energies, MDPI, vol. 15(13), pages 1-19, June.
    3. Mayer, Martin János & Biró, Bence & Szücs, Botond & Aszódi, Attila, 2023. "Probabilistic modeling of future electricity systems with high renewable energy penetration using machine learning," Applied Energy, Elsevier, vol. 336(C).
    4. Horasan, Muhammed Bilal & Kilic, Huseyin Selcuk, 2022. "A multi-objective decision-making model for renewable energy planning: The case of Turkey," Renewable Energy, Elsevier, vol. 193(C), pages 484-504.
    5. Karaaslan, Abdulkerim & Gezen, Mesliha, 2022. "The evaluation of renewable energy resources in Turkey by integer multi-objective selection problem with interval coefficient," Renewable Energy, Elsevier, vol. 182(C), pages 842-854.
    6. Gul, Eid & Baldinelli, Giorgio & Bartocci, Pietro & Shamim, Tariq & Domenighini, Piergiovanni & Cotana, Franco & Wang, Jinwen & Fantozzi, Francesco & Bianchi, Francesco, 2023. "Transition toward net zero emissions - Integration and optimization of renewable energy sources: Solar, hydro, and biomass with the local grid station in central Italy," Renewable Energy, Elsevier, vol. 207(C), pages 672-686.

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