Optimal Design of Wireless Charging Electric Bus System Based on Reinforcement Learning

Lee, Hyukjoon; Ji, Dong Jin; Cho, Dong-Ho

doi:10.3390/en12071229

Cited by 11 publications

(4 citation statements)

References 29 publications

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“…The availability of V2G (vehicle-to-grid) needs to be considered when modelling the possibility to sell energy from the vehicle batteries to the grid [14], but details such as grid inverters may be abstracted away at the discretion of the authors [15]. For a wireless EV charging system, the EV characteristics and the traffic environment need to be considered [16]. For optimizing revenues of a wind farm with battery storage, the environment simulates the settlement scheme of the electricity market [17].…”

Section: General Conceptual Overview For Reinforcement Learning Agent...mentioning

confidence: 99%

Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

2022

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The transition to renewable production and smart grids is driving a massive investment to battery storages, and reinforcement learning (RL) has recently emerged as a potentially disruptive technology for their control and optimization of battery storage systems. A surge of papers has appeared in the last two years applying reinforcement learning to the optimization of battery storages in buildings, energy communities, energy harvesting Internet of Things networks, renewable generation, microgrids, electric vehicles and plug-in hybrid electric vehicles. This article reviews these applications through 4 different perspectives. Firstly, the type of optimization problem is analyzed; the literature can be divided to approaches that optimize either financial targets or energy efficiency. Secondly, the approaches for handling user comfort are analyzed for applications that may impact a human user. Thirdly, this paper discusses the approach to model and reduce battery degradation. Fourthly, the articles are categorized by application context and applications likely to attract a high amount of research are identified. The paper concludes with a list of unresolved challenges.

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Section: General Conceptual Overview For Reinforcement Learning Agent...mentioning

confidence: 99%

Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

2022

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“…Hwang et al [18] propose an Mixed Integer Program (MIP) that allocates the charging infrastructure in the context of multiple-route environments, where independent bus routes share common road segments. Considering the OLEV shuttling system developed at KAIST, Lee et al [34] studied a Markov decision process-based optimization algorithm using reinforcement learning to estimate optimal battery capacities, pickup capacity and the number of ITUs. Mohamed et al [35] focused their work on the integration of a wireless charging system with automated driving.…”

Section: Related Literature and Research Questionsmentioning

confidence: 99%

Designing Dynamic Inductive Charging Infrastructures for Airport Aprons with Multiple Vehicle Types

et al. 2022

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In the effort to combat climate change, the CO2 emissions of the aviation sector must be reduced. The traffic caused by numerous types of ground vehicles on airport aprons currently contributes to those emissions as the vehicles typically operate with combustion engines, which is why an electrification of those vehicles has already begun. While stationary conductive charging of the vehicles is the current standard technology, dynamic wireless charging might be an attractive technological alternative, in particular for airport aprons; however, designing a charging network for an airport apron is a challenging task with important technical and economic aspects. In this paper, we propose a model to characterize the problem, especially for cases of multiple types of vehicles sharing the same charging network, such as passenger buses and baggage vehicles. In a numerical study inspired by real-world airports, we design such charging networks subject to service level constraints and evaluate the resulting structures via a discrete-event simulation, and thus, show the way to assess the margin of safety with respect to the vehicle batteries’ state of charge that is induced by the spatial structure of the charging network.

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“…RL can be used at investment time to determine the parameters of a smart energy system that incorporates batteries. Diverse application contexts have been encountered, including wireless EV charging systems [42], wind farms [43], microgrids [44] and isolated villages with microgrids [45].…”

mentioning

confidence: 99%

A Simulation Environment for Training a Reinforcement Learning Agent Trading a Battery Storage

et al. 2021

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Battery storages are an essential element of the emerging smart grid. Compared to other distributed intelligent energy resources, batteries have the advantage of being able to rapidly react to events such as renewable generation fluctuations or grid disturbances. There is a lack of research on ways to profitably exploit this ability. Any solution needs to consider rapid electrical phenomena as well as the much slower dynamics of relevant electricity markets. Reinforcement learning is a branch of artificial intelligence that has shown promise in optimizing complex problems involving uncertainty. This article applies reinforcement learning to the problem of trading batteries. The problem involves two timescales, both of which are important for profitability. Firstly, trading the battery capacity must occur on the timescale of the chosen electricity markets. Secondly, the real-time operation of the battery must ensure that no financial penalties are incurred from failing to meet the technical specification. The trading-related decisions must be done under uncertainties, such as unknown future market prices and unpredictable power grid disturbances. In this article, a simulation model of a battery system is proposed as the environment to train a reinforcement learning agent to make such decisions. The system is demonstrated with an application of the battery to Finnish primary frequency reserve markets.

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Optimal Design of Wireless Charging Electric Bus System Based on Reinforcement Learning

Cited by 11 publications

References 29 publications

Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

Designing Dynamic Inductive Charging Infrastructures for Airport Aprons with Multiple Vehicle Types

A Simulation Environment for Training a Reinforcement Learning Agent Trading a Battery Storage

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