Electric vehicle charging service operations: A review of machine learning applications for infrastructure planning, control, pricing and routing

Fescioglu-Unver, Nilgun; Yıldız Aktaş, Melike

doi:10.1016/j.rser.2023.113873

Cited by 16 publications

(1 citation statement)

References 153 publications

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“…The analysis of investment project feasibility studies confirms that the highest levels of complexity and risk are shown by projects for the development of infrastructure and low-carbon propulsion technologies as well as solutions to foster greater cross-border interoperability and data exchange opportunities (Wappelhorst, 2021;Fescioglu-Unver & Yıldız, 2023). At the same time, these projects can be credited with a high degree of efficiency in the deployment of EVs.…”

Section: F2mentioning

confidence: 83%

Determinants of electromobility development from the perspective of a zero emission, innovative and resilient economy

Motowidlak,

Bukowska-Piestrzyńska

2024

EiS

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The aim of this article is to identify the critical factors and assess the specific actions conditioning the development of electromobility from the perspective of a zero-carbon, innovative and resilient economy. These issues have a particular dimension in relation to individual mobility. The study used a combination of primary and secondary data, using various research methods and techniques, such as descriptive analysis, desk research, diagnostic survey, cause-and-effect analysis and statistical analysis. Additionally, in-depth interviews were conducted with experts in managerial positions. The literature review and the results of our own research confirmed the importance of the identified factors in the uptake of electric cars. At the same time, the study highlighted the high complexity of problems regarding investment decisions determining the development of electromobility. Taking active steps to increase the level of sustainability and resilience of the electromobility system should first focus on further development of charging infrastructure, uptake of electric vehicles, development of renewable energy sources and creation of an electric vehicle battery value chain. The main expectations for the development of electromobility are to reduce CO2 emissions, reduce dependence on fossil fuel supplies, increase the competitiveness and innovation of the economy and reduce external costs generated by transport. Attempts were made to achieve the originality of the research carried out through its measurable nature. The proposed electromobility development model may contribute to the improvement of decision-making tools regarding the allocation of public funds and other sources for investments so that they contribute to the sustainable development of mobility systems.

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Section: F2mentioning

confidence: 83%

Determinants of electromobility development from the perspective of a zero emission, innovative and resilient economy

Motowidlak,

Bukowska-Piestrzyńska

2024

EiS

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Electric vehicles as facilitators of grid stability and flexibility: A multidisciplinary overview

Štogl,

Miltner,

Zanocco

et al. 2024

WIREs Energy & Environment

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Electric vehicles (EVs), as facilitators of grid stability and flexibility, provide a critical solution to the energy infrastructure's evolving demands, underscored by the growing integration of renewable energy sources (RES) and the rapid increase in EV adoption worldwide. This trend is particularly evident in Europe which is experiencing dramatic increases in both the adoption of RES and EVs. Vehicle‐to‐grid (V2G) technology allows EVs to operate as a two‐way power flow to both draw and feed electricity into the grid. This multidisciplinary overview examines the role of V2G systems in enhancing grid performance, identifying corporate vehicle fleets as key flexibility providers, and integration with Smart Grid technologies as a key element for successful V2G implementation. In a scoping analysis of recent literature (2005–2024), we identify challenges such as privacy, security, and regulatory compliance as well as a critical gap in establishing economically sustainable models for aggregators, distribution system operators (DSOs), generation companies (GENCOs), and end‐users. Drawing from these insights, we then discuss the necessity for future research to develop models that ensure equitable benefits across stakeholders and the importance of models that can adapt to country‐specific mechanisms. The findings from our overview argue that the integration of EVs, V2G, and RES are essential components for developing future energy systems that are resilient, adaptable, decarbonized, and sustainable.This article is categorized under: Cities and Transportation > Electric Mobility Energy and Power Systems > Energy Infrastructure Energy and Power Systems > Energy Management

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A survey on applications of reinforcement learning in spatial resource allocation

Zhang,

Wang,

Mango

et al. 2024

Comput.Urban Sci.

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The challenge of spatial resource allocation is pervasive across various domains such as transportation, industry, and daily life. As the scale of real-world issues continues to expand and demands for real-time solutions increase, traditional algorithms face significant computational pressures, struggling to achieve optimal efficiency and real-time capabilities. In recent years, with the escalating computational power of computers, the remarkable achievements of reinforcement learning in domains like Go and robotics have demonstrated its robust learning and sequential decision-making capabilities. Given these advancements, there has been a surge in novel methods employing reinforcement learning to tackle spatial resource allocation problems. These methods exhibit advantages such as rapid solution convergence and strong model generalization abilities, offering a new perspective on resolving spatial resource allocation problems. Despite the progress, reinforcement learning still faces hurdles when it comes to spatial resource allocation. There remains a gap in its ability to fully grasp the diversity and intricacy of real-world resources. The environmental models used in reinforcement learning may not always capture the spatial dynamics accurately. Moreover, in situations laden with strict and numerous constraints, reinforcement learning can sometimes fall short in offering feasible strategies. Consequently, this paper is dedicated to summarizing and reviewing current theoretical approaches and practical research that utilize reinforcement learning to address issues pertaining to spatial resource allocation. In addition, the paper accentuates several unresolved challenges that urgently necessitate future focus and exploration within this realm and proposes viable approaches for these challenges. This research furnishes valuable insights that may assist scholars in gaining a more nuanced understanding of the problems, opportunities, and potential directions concerning the application of reinforcement learning in spatial resource allocation.

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Electric vehicle charging service operations: A review of machine learning applications for infrastructure planning, control, pricing and routing

Cited by 16 publications

References 153 publications

Determinants of electromobility development from the perspective of a zero emission, innovative and resilient economy

Determinants of electromobility development from the perspective of a zero emission, innovative and resilient economy

Electric vehicles as facilitators of grid stability and flexibility: A multidisciplinary overview

A survey on applications of reinforcement learning in spatial resource allocation

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