{"title":"Multi-period Charging Infrastructure Planning under Uncertainty: Challenges and Opportunities","authors":"Qiming Ye , Prateek Bansal , Bryan Adey","doi":"10.1016/j.scs.2024.105908","DOIUrl":null,"url":null,"abstract":"<div><div>Long-term charging infrastructure planning is imperative to sustain the rapid adoption of electric vehicles (EVs) in line with climate goals. While the literature on spatial planning of charging infrastructure is well documented, the temporal dimension has received limited attention. This paper comprehensively reviews the literature on multi-period charging infrastructure planning under uncertainty. It examines the complex interplay between EV mobility and the energy sector. Four gaps are identified after examining 44 pertinent studies published from January 1990 to March 2024. <em>Firstly</em>, current models are predominantly deterministic and myopic, lacking a forward-looking approach to accommodate future uncertainties. <em>Secondly</em>, most studies rely on EVs’ aggregated mobility and charging patterns, leading to inaccurate charging demand forecasts and suboptimal plans. Addressing this requires integrating vehicle-level agent-based models that accurately depict EVs’ charging patterns, and their interactions with charging stations and the grid. <em>Thirdly</em>, the impact of improved charging infrastructure on EV adoption is generally ignored. Joint consideration of charging demand forecasting with infrastructure planning is essential to incorporate such infrastructure-demand feedback loops. <em>Lastly</em>, current planning frameworks show limited integration of grid expansion, operations, and renewable energy sources To address these gaps, we propose a dynamic programming-based framework and solution approach to this planning problem.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105908"},"PeriodicalIF":10.5000,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Cities and Society","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210670724007327","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Long-term charging infrastructure planning is imperative to sustain the rapid adoption of electric vehicles (EVs) in line with climate goals. While the literature on spatial planning of charging infrastructure is well documented, the temporal dimension has received limited attention. This paper comprehensively reviews the literature on multi-period charging infrastructure planning under uncertainty. It examines the complex interplay between EV mobility and the energy sector. Four gaps are identified after examining 44 pertinent studies published from January 1990 to March 2024. Firstly, current models are predominantly deterministic and myopic, lacking a forward-looking approach to accommodate future uncertainties. Secondly, most studies rely on EVs’ aggregated mobility and charging patterns, leading to inaccurate charging demand forecasts and suboptimal plans. Addressing this requires integrating vehicle-level agent-based models that accurately depict EVs’ charging patterns, and their interactions with charging stations and the grid. Thirdly, the impact of improved charging infrastructure on EV adoption is generally ignored. Joint consideration of charging demand forecasting with infrastructure planning is essential to incorporate such infrastructure-demand feedback loops. Lastly, current planning frameworks show limited integration of grid expansion, operations, and renewable energy sources To address these gaps, we propose a dynamic programming-based framework and solution approach to this planning problem.
期刊介绍：
Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including:
1. Smart cities and resilient environments;
2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management;
3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management);
4. Energy efficient, low/zero carbon, and green buildings/communities;
5. Climate change mitigation and adaptation in urban environments;
6. Green infrastructure and BMPs;
7. Environmental Footprint accounting and management;
8. Urban agriculture and forestry;
9. ICT, smart grid and intelligent infrastructure;
10. Urban design/planning, regulations, legislation, certification, economics, and policy;
11. Social aspects, impacts and resiliency of cities;
12. Behavior monitoring, analysis and change within urban communities;
13. Health monitoring and improvement;
14. Nexus issues related to sustainable cities and societies;
15. Smart city governance;
16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society;
17. Big data, machine learning, and artificial intelligence applications and case studies;
18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems.
19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management;
20. Waste reduction and recycling;
21. Wastewater collection, treatment and recycling;
22. Smart, clean and healthy transportation systems and infrastructure;
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
