To introduce the renewable energy in regional communities, it is necessary to select a sustainable energy mix on the basis of evaluation from multiple viewpoints including complex environmental impacts. The purpose of this study is to develop a tool for multi-objective optimization and evaluation of renewable energy composition in municipalities considering multiple environmental criteria. This tool was developed by improving Renewable Energy Regional Optimization Utility Tool for Environmental Sustainability, REROUTES. The adjustable variables are the amount of deployed renewable energy resources from solar, wind, small and medium-scale hydro, geothermal and biomass energy. NSGA-II, a kind of genetic algorithms was applied and implemented to REROUTES to solve multiobjective optimization with six objective functions (proportion of developed renewable energy, economic balance, decrease in CO2 emissions, circulation rate of biomass resource, impacted ecosystem area, and diversity index). A case study for two municipalities showed that the developed tool successfully calculated pareto solutions having trade-off with reflecting the natural conditions and varying demand structures of case study areas. In addition, a process of selecting one best solution from the pareto solutions on the basis of local opinions could be demonstrated. In conclusion, this study could develop an useful tool to support decision-making regarding the development of renewable energy resources.
|ジャーナル||Transactions of the Japanese Society for Artificial Intelligence|
|出版ステータス||Published - 2018|
ASJC Scopus subject areas
- Artificial Intelligence