City information models for optimal EV charging and energy-resilient renaissance
Internet of energy (IoE) with building electrification provides multi-directional power interactions between E-mobility and power grid for smart and low-carbon transformations. However, the lack of a city-scale information model for planning infrastructures like electric vehicle charging stations le...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Nexus |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2950160125000038 |
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| author | Zhaohui Dan Aoye Song Yuyu Zheng Xinyue Zhang Yuekuan Zhou |
| author_facet | Zhaohui Dan Aoye Song Yuyu Zheng Xinyue Zhang Yuekuan Zhou |
| author_sort | Zhaohui Dan |
| collection | DOAJ |
| description | Internet of energy (IoE) with building electrification provides multi-directional power interactions between E-mobility and power grid for smart and low-carbon transformations. However, the lack of a city-scale information model for planning infrastructures like electric vehicle charging stations leads to insufficient renewable self-consumption and performance overestimations. Furthermore, city-scale models struggle with achieving high accuracy for material-energy-information nexus across thousands of buildings, vehicles, and infrastructures. Considering future interactive energy dynamics, an agent-based modeling (ABM) platform is developed for optimal urban cross-sector energy network planning, incorporating climate-adaptive resilience and parameter uncertainties such as electric vehicle traveling behaviors, charging preferences, etc. Applied in Nansha, China, the ABM platform demonstrates computational efficiency and accuracy. It enhances urban renewable energy penetration by up to 50.1%, reduces road traffic carbon emissions by up to 46.6%, and decreases power outage hours and simulation time. This study offers a city-scale IoE model for optimal infrastructure planning and climate-adaptive energy-resilient renascence. Broader context: Electric vehicles (EVs) are being increasingly adopted, particularly in China, Europe, and the United States, as they offer a cleaner alternative to conventional vehicles. The integration of EVs with buildings and power grids establishes an internet of energy, facilitating multi-directional energy flows from distributed renewable energy systems. Central to this integration are electric vehicle charging stations (EVCSs), which enhance renewable energy penetration by connecting various components. Optimal planning of EVCSs is vital to determine locations and power levels, promoting efficient energy interactions. Considering challenges in cross-sector energy network modeling and future electrification-driven energy interactions, this study develops an agent-based modeling (ABM)-based city information model for optimal planning of urban public charging networks with multiple agents (eg, EVs, EVCSs, buildings, and the grid), as well as combining climate-adaptive resilience and EV behavior uncertainties for sustainable future cities. |
| format | Article |
| id | doaj-art-9137cacf34fa41c6a4dd4d4e2ee7d528 |
| institution | Kabale University |
| issn | 2950-1601 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Nexus |
| spelling | doaj-art-9137cacf34fa41c6a4dd4d4e2ee7d5282025-02-08T05:01:51ZengElsevierNexus2950-16012025-03-0121100056City information models for optimal EV charging and energy-resilient renaissanceZhaohui Dan0Aoye Song1Yuyu Zheng2Xinyue Zhang3Yuekuan Zhou4Sustainable Energy and Environment Thrust, Function Hub, The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou 511400, Guangdong, ChinaSustainable Energy and Environment Thrust, Function Hub, The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou 511400, Guangdong, China; Division of Emerging Interdisciplinary Areas, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, ChinaSustainable Energy and Environment Thrust, Function Hub, The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou 511400, Guangdong, ChinaSustainable Energy and Environment Thrust, Function Hub, The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou 511400, Guangdong, ChinaSustainable Energy and Environment Thrust, Function Hub, The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou 511400, Guangdong, China; Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China; HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Futian, Shenzhen, China; Division of Emerging Interdisciplinary Areas, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, China; Corresponding authorInternet of energy (IoE) with building electrification provides multi-directional power interactions between E-mobility and power grid for smart and low-carbon transformations. However, the lack of a city-scale information model for planning infrastructures like electric vehicle charging stations leads to insufficient renewable self-consumption and performance overestimations. Furthermore, city-scale models struggle with achieving high accuracy for material-energy-information nexus across thousands of buildings, vehicles, and infrastructures. Considering future interactive energy dynamics, an agent-based modeling (ABM) platform is developed for optimal urban cross-sector energy network planning, incorporating climate-adaptive resilience and parameter uncertainties such as electric vehicle traveling behaviors, charging preferences, etc. Applied in Nansha, China, the ABM platform demonstrates computational efficiency and accuracy. It enhances urban renewable energy penetration by up to 50.1%, reduces road traffic carbon emissions by up to 46.6%, and decreases power outage hours and simulation time. This study offers a city-scale IoE model for optimal infrastructure planning and climate-adaptive energy-resilient renascence. Broader context: Electric vehicles (EVs) are being increasingly adopted, particularly in China, Europe, and the United States, as they offer a cleaner alternative to conventional vehicles. The integration of EVs with buildings and power grids establishes an internet of energy, facilitating multi-directional energy flows from distributed renewable energy systems. Central to this integration are electric vehicle charging stations (EVCSs), which enhance renewable energy penetration by connecting various components. Optimal planning of EVCSs is vital to determine locations and power levels, promoting efficient energy interactions. Considering challenges in cross-sector energy network modeling and future electrification-driven energy interactions, this study develops an agent-based modeling (ABM)-based city information model for optimal planning of urban public charging networks with multiple agents (eg, EVs, EVCSs, buildings, and the grid), as well as combining climate-adaptive resilience and EV behavior uncertainties for sustainable future cities.http://www.sciencedirect.com/science/article/pii/S2950160125000038city information modelinternet of energyelectric vehicle charging stationsenergy communities and societyenergy resiliencelow-carbon city |
| spellingShingle | Zhaohui Dan Aoye Song Yuyu Zheng Xinyue Zhang Yuekuan Zhou City information models for optimal EV charging and energy-resilient renaissance Nexus city information model internet of energy electric vehicle charging stations energy communities and society energy resilience low-carbon city |
| title | City information models for optimal EV charging and energy-resilient renaissance |
| title_full | City information models for optimal EV charging and energy-resilient renaissance |
| title_fullStr | City information models for optimal EV charging and energy-resilient renaissance |
| title_full_unstemmed | City information models for optimal EV charging and energy-resilient renaissance |
| title_short | City information models for optimal EV charging and energy-resilient renaissance |
| title_sort | city information models for optimal ev charging and energy resilient renaissance |
| topic | city information model internet of energy electric vehicle charging stations energy communities and society energy resilience low-carbon city |
| url | http://www.sciencedirect.com/science/article/pii/S2950160125000038 |
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