Agent-based modelling of electric vehicle charging for optimized charging station operation

Chaudhari, Kalpesh Subhash

doi:10.32657/10220/48574

Cited by 3 publications

(6 citation statements)

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“…The ABM simulation determined whether an electric van fleet with various charging options could equal the performance of a diesel fleet (Utomo et al, 2019). The impacts of influencing variables such as driver behaviour, charging station location, and electricity pricing on EV charging demand were evaluated on EV charging demand using an ABM-simulation model in (Chaudhari, 2019). A multilevel agent-based model was proposed for buying, charging, and driving EVs (the ABCD model) (Hoekstra & Hogeveen, 2017).…”

Section: Related Workmentioning

confidence: 99%

Agent-based modelling of electric vehicle behaviour in a university environment

Jaslin,

Navaratne,

Ekanayake

2023

Ceylon J. Sci.

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As the number of electric vehicles (EVs) increases, the strategic planning of charging infrastructure becomes a crucial matter. Vehicle parking time takes the longest at home and work. The residential is responsible for 75% of EV charging time, while the workplace is for 14%. The combination of EVs with intermittent energy sources has attracted considerable attention in recent years. It has several advantages, including significantly greening the entire EV usage cycle and attaining financial viability by lowering the direct peak demand on the grid. This study has described the agent-based infrastructure of the EV charging station model on university premises. It lets us obtain the best possible energy supply from solar PV, external batteries, and grid agents. Three charging scenarios (uncontrolled, vehicle-to-grid (V2G), and grid-to-vehicle (G2V)) are constructed and simulated with varying percentages of EV resemblance. Slow charging is included in the G2V scenario to improve the PV benefits in the EV charging model. The simulation result shows that slow charging in the workplace infrastructure increases the PV benefits of EV charging while reducing grid dependency.

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Section: Related Workmentioning

confidence: 99%

Agent-based modelling of electric vehicle behaviour in a university environment

Jaslin,

Navaratne,

Ekanayake

2023

Ceylon J. Sci.

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“…This introduction has brought about numerous advantages for the transportation industry. The increasing demand for EVs has also brought attention to the need for establishing a robust charging infrastructure to meet the changing needs of EVs [11][12][13]. The charging infrastructure is an essential component of the EV ecosystem, and its optimal placement and allocation would be crucial for the adoption and widespread use of EVs [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…The increasing demand for EVs has also brought attention to the need for establishing a robust charging infrastructure to meet the changing needs of EVs [11][12][13]. The charging infrastructure is an essential component of the EV ecosystem, and its optimal placement and allocation would be crucial for the adoption and widespread use of EVs [11,12]. However, the inadequate charging infrastructure and uneven spatial and temporal demands for charging have been identified as significant problems that would need to be addressed [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…However, the inadequate charging infrastructure and uneven spatial and temporal demands for charging have been identified as significant problems that would need to be addressed [13,14]. To resolve these problems, several studies have proposed various solutions to introduce EV station recommendation systems providing the most appropriate charging stations for an EV driver to use based on their current location, destination, and other factors [1,2,[11][12][13]. The goal of this system would be to improve the convenience and efficiency of EV charging by helping drivers find the nearest, most available, and fastest charging stations [1, 12,13].…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Electric Vehicle Charging Recommendation in Smart Cities: A Multi-Agent Reinforcement Learning Approach

Suanpang,

Jamjuntr

2024

WEVJ

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As global awareness for preserving natural energy sustainability rises, electric vehicles (EVs) are increasingly becoming a preferred choice for transportation because of their ability to emit zero emissions, conserve energy, and reduce pollution, especially in smart cities with sustainable development. Nonetheless, the lack of adequate EV charging infrastructure remains a significant problem that has resulted in varying charging demands at different locations and times, particularly in developing countries. As a consequence, this inadequacy has posed a challenge for EV drivers, particularly those in smart cities, as they face difficulty in locating suitable charging stations. Nevertheless, the recent development of deep reinforcement learning is a promising technology that has the potential to improve the charging experience in several ways over the long term. This paper proposes a novel approach for recommending EV charging stations using multi-agent reinforcement learning (MARL) algorithms by comparing several popular algorithms, including the deep deterministic policy gradient, deep Q-network, multi-agent DDPG (MADDPG), Real, and Random, in optimizing the placement and allocation of the EV charging stations. The results demonstrated that MADDPG outperformed other algorithms in terms of the Mean Charge Waiting Time, CFT, and Total Saving Fee, thus indicating its superiority in addressing the EV charging station problem in a multi-agent setting. The collaborative and communicative nature of the MADDPG algorithm played a key role in achieving these results. Hence, this approach could provide a better user experience, increase the adoption of EVs, and be extended to other transportation-related problems. Overall, this study highlighted the potential of MARL as a powerful approach for solving complex optimization problems in transportation and beyond. This would also contribute to the development of more efficient and sustainable transportation systems in smart cities for sustainable development.

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“…An EV powered by an intermittent power source is an excellent way to ensure lowcost, emission-free mobility. An energy storage management hybrid optimization algorithm was presented in [21]. This algorithm flipped between deterministic and rule-based modes of operation depending on the power pricing band allocation.…”

Section: Introductionmentioning

confidence: 99%

Modeling Electric Vehicle Charging Station Behavior Using Multiagent System

Khan¹,

Navaratne²,

Ekanayake³

2023

Multi-Agent Technologies and Machine Learning

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Agent-based models(ABMs) are a type of simulation in which a large number of self-sufficient agents interact in a way that combines stochastic and deterministic behavior. Recently, there have been reestablished interests in utilizing multiagent systems (MASs) to get more granular data relating to specific conditions. MESA is an ABM framework for Python. It enables users to quickly develop ABMs with built-in core components, view them with a browser-based interface, and evaluate their findings with Python’s data analysis capabilities. This chapter depicts an ABM of a photovoltaic (PV)-powered electric vehicle (EV) charging station in a university car park modeled using MESA. The goal is to determine the preliminary requirements for PV-powered EV charging stations, which would result in increased PV and cost benefits.

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Agent-based modelling of electric vehicle charging for optimized charging station operation

Cited by 3 publications

References 58 publications

Agent-based modelling of electric vehicle behaviour in a university environment

Agent-based modelling of electric vehicle behaviour in a university environment

Optimizing Electric Vehicle Charging Recommendation in Smart Cities: A Multi-Agent Reinforcement Learning Approach

Modeling Electric Vehicle Charging Station Behavior Using Multiagent System

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