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Smart city article details
| Title | Quantifying Spatio-Temporal Carbon Intensity Within A City Using Large-Scale Smart Meter Data: Unveiling The Impact Of Behind-The-Meter Generation |
|---|---|
| ID_Doc | 43923 |
| Authors | Sugano S.; Fujimoto Y.; Ihara Y.; Mitsuoka M.; Tanabe S.-I.; Hayashi Y. |
| Year | 2025 |
| Published | Applied Energy, 383 |
| DOI | http://dx.doi.org/10.1016/j.apenergy.2025.125373 |
| Abstract | This study introduces a novel method for calculating spatio-temporal carbon intensity variations within a city using smart meter data. By integrating smart meter data with solar radiation data from weather satellites, the method predicts electricity demand and solar power generation across 1-km grid areas, achieving higher spatial resolution for carbon intensity distribution than existing models. Accounting for behind-the-meter self-consumption enables dynamic visualisation of carbon intensity variations driven by renewable energy adoption in localised urban areas, offering a more detailed assessment compared to conventional methods focusing solely on temporal fluctuations in the grid's energy mix. The method was applied to a dataset of approximately 410,000 smart meters in Utsunomiya City, Japan. Findings reveal that carbon intensity variations are affected by weather and seasonal changes. Notably, suburban areas with a higher proportion of prosumers exhibit lower carbon intensity than urban centres, highlighting significant intra-city variations linked to local renewable energy utilisation. This method can enhance the efficient use of distributed energy resources within cities and support prioritising low-carbon renewable energy through strategies such as demand response program development, optimising electric vehicle charging schedules, and identifying priority areas for photovoltaic and battery storage deployment. © 2025 The Author(s) |
| Author Keywords | CO<sub>2</sub> emission factor; Distributed energy resources; Energy management system; Power meters; Renewable energy; Smart city |
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