An intelligent-based framework for urban energy transition and sustainable development

Richao Cong, Mirei Abe, Atsushi Fujiyama and Toru Matsumoto

Renewable Energy, 2025, vol. 250, issue C

Abstract: To support regional decarbonization and achievement of sustainable development goals (SDGs), this study aims to develop a unique optimization framework. An intelligent framework of an energy system was designed in which supplies by renewable energy (RE), grid power, and the storage battery (SB) are optimized from its lifecycle carbon dioxide (CO2) emissions. We found that introducing SB increased RE utilization by 50.3 %, contributed to grid power suppression by 18.6 %, and reduced emissions by 28.7 ktCO2 year−1. It indicates that introducing SB could ensure greater utilization of RE in the optimization to use less grid power, contributing to a greener energy system and SDGs 7 and 13. Moreover, the optimal total capacity of the SB was determined as 0.585 PJ at the lower demand level (total demand without industrial sector) and 0.0115 PJ at higher demand level (including all sectors), respectively. We found that the present model was better to be applied to a system with more energy surplus and deficit hours. Sensitivity analysis results showed that reducing energy demand through energy-saving policies was the most effective pathway for decarbonization, comparing with other factors.

Keywords: Energy transition; Intelligent-based framework; Renewable energy promotion; System optimization; Urban energy system (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:250:y:2025:i:c:s0960148125009851

DOI: 10.1016/j.renene.2025.123323

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