共享电动汽车充电基础设施的战略规划:基于强化学习的多阶段随机方法

IF 12 1区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Qiming Ye , Prateek Bansal , Bryan T. Adey
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引用次数: 0

摘要

网约车的快速电气化要求快速充电基础设施准备好支持共享电动汽车(SEV)车队的动态充电需求。在城市范围内部署快速充电站需要在多个规划阶段确定每个充电站的时间、位置和充电器数量,同时管理随机充电需求、增强服务的诱导需求以及电网容量等限制。与传统的基于静态需求和即时收益优化充电站配置的确定性和短视方法不同,本研究将充电站配置优化问题表述为一个随机顺序决策问题,并应用强化学习(RL)来近似考虑长期收益和不确定性的最优部署计划。开发了一个代表人工智能健身房环境的基于agent的模型来训练RL模型,模拟SEV与充电站的相互作用,以捕获跨空间和时间的随机充电需求。RL在规划范围内以最少数量的快速充电器最大化预期的充电服务效率。与近视基准相比,该模型减少了30.5%的充电器总数,同时实现了10.9%的充电服务效率提高。这种多阶段快速充电器规划方法为工程师和规划人员在随机充电需求下进行有效的长期基础设施配置和政策制定提供了一种新的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Strategic planning of charging infrastructure for shared electric vehicles: A multi-phase stochastic approach using reinforcement learning
The rapid electrification of ride-hailing mobility requires that fast-charging infrastructure be ready to support the dynamic charging needs of shared electric vehicle (SEV) fleets. Deploying city-wide fast-charging stations requires determining the timing, locations, and number of chargers at each station over multiple planning phases, while managing stochastic charging demand, induced demand from enhanced services, and constraints like grid capacity. In contrast to conventional deterministic and myopic methods that optimise charging stations’ configurations based on static demand and immediate benefits, this study formulates the problem as a stochastic sequential decision problem and applies reinforcement learning (RL) to approximate the optimal deployment plan considering long-term benefits and uncertainties. An agent-based model representing an AI gym environment is developed to train the RL model, simulating SEV interactions with charging stations to capture stochastic charging demand across space and time. RL maximises the expected charging service efficiency with the minimal number of fast chargers over the planning horizon. The model reduces the total number of chargers by 30.5% compared to the myopic benchmark, while achieving a 10.9% higher charging service efficiency. This multi-phase fast charger planning approach offers engineers and planners a novel tool for efficient long-term infrastructure allocation and policy development under stochastic charging demand.
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来源期刊
Sustainable Cities and Society
Sustainable Cities and Society Social Sciences-Geography, Planning and Development
CiteScore
22.00
自引率
13.70%
发文量
810
审稿时长
27 days
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;
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