Local energy system design support using a renewable energy mix multi-objective optimization model and a co-creative optimization process. (August 2020) - 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. Highlights: A co-creative design support method for local renewable energy use was developed. Integration of participatory backcasting approaches and mathematical models. Projection of future local energy demand reflecting holistic local future vision. Multi-objective optimization of a renewable energy mix and interactive selection. The proposed method could be useful for local decision of renewable energy use.