{"title":"Electric vehicle charging emissions under different control strategies and temporal resolutions: Case study for Australia","authors":"Kriengsak Fungyai , Rob Passey , Baran Yildiz","doi":"10.1016/j.segy.2025.100190","DOIUrl":null,"url":null,"abstract":"<div><div>The increasing adoption of electric vehicles (EVs) presents both challenges and opportunities for reducing greenhouse gas (GHG) emissions. While EVs are essential for decarbonising the transport sector, the emissions from charging vary greatly depending on the generation mix at the time. This study investigates the impact of various EV charging strategies on GHG emissions in different regions in the Australian National Electricity Market (NEM). The study focuses on four key charging strategies–Control Tariff, Timer, Solar Soak, and Convenience Charging. Using real-world data, the analysis evaluates both average and marginal emissions across regions with varying levels of renewable energy integration. Sensitivity analysis showed that coarser temporal resolution in emissions calculations can lead to variances of up to 6.3 %, emphasising the importance of using higher resolution data when available. It was found that the Solar Soak strategy is the most effective in minimising EV charging emissions and can also help with challenges associated with increasing solar exports in the distribution network. The choice between average and marginal emissions intensity factors is also critical in determining outcomes. In Tasmania and South Australia, where renewable energy sources dominate, the use of marginal emission factors resulted in higher EV charging emissions than average emissions due to their reliance on coal and gas as the marginal generators. The sensitivity analysis carried out with emissions data between 2019 and 2023 revealed a negative relationship between renewable energy fraction and emissions intensity and highlighted the importance of aligning EV charging with high renewable generation periods to achieve maximum GHG reductions.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"19 ","pages":"Article 100190"},"PeriodicalIF":5.0000,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Smart Energy","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666955225000188","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
The increasing adoption of electric vehicles (EVs) presents both challenges and opportunities for reducing greenhouse gas (GHG) emissions. While EVs are essential for decarbonising the transport sector, the emissions from charging vary greatly depending on the generation mix at the time. This study investigates the impact of various EV charging strategies on GHG emissions in different regions in the Australian National Electricity Market (NEM). The study focuses on four key charging strategies–Control Tariff, Timer, Solar Soak, and Convenience Charging. Using real-world data, the analysis evaluates both average and marginal emissions across regions with varying levels of renewable energy integration. Sensitivity analysis showed that coarser temporal resolution in emissions calculations can lead to variances of up to 6.3 %, emphasising the importance of using higher resolution data when available. It was found that the Solar Soak strategy is the most effective in minimising EV charging emissions and can also help with challenges associated with increasing solar exports in the distribution network. The choice between average and marginal emissions intensity factors is also critical in determining outcomes. In Tasmania and South Australia, where renewable energy sources dominate, the use of marginal emission factors resulted in higher EV charging emissions than average emissions due to their reliance on coal and gas as the marginal generators. The sensitivity analysis carried out with emissions data between 2019 and 2023 revealed a negative relationship between renewable energy fraction and emissions intensity and highlighted the importance of aligning EV charging with high renewable generation periods to achieve maximum GHG reductions.