A Deep Reinforcement Learning Method for Pricing Electric Vehicles With Discrete Charging Levels

Qiu, Dawei; Ye, Yujian; Papadaskalopoulos, Dimitrios; Štrbac, Goran

doi:10.1109/tia.2020.2984614

Cited by 73 publications

(32 citation statements)

References 29 publications

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“…The authors in papers [61]- [72], [74], [75], [76], [77], [65], [78], [79], [80]- [81], [82], [83], [84]- [67], [85] - [86], and [87]- [69], used the charging or discharging cost as a state parameter. However, other operational costs, like waiting cost and traveling, were used mainly in two of the surveyed papers [88] and [67].…”

Section: Inputsmentioning

confidence: 99%

“…The other three papers aimed for either controlling energy cost or, like [89], maximizing the distribution system's profit. V2G, as a state parameter, was used in [89], [64], [99], [100], [74]- [75], [78], [104], [82], [87], [70], and [63]. Like TOU, V2G was used by methods that aimed to control the energy cost or maximize the profit of the distribution system.…”

Section: Inputsmentioning

confidence: 99%

“…Another parameter that was used as a state parameter by many of the proposed methods in [96], [60], [97], [99], [74], [90], [76], [77], [65], [78], [79], [101], [102]- [103], [105], [82], [83]- [84], [95], [85], [107], [87], [69], and [70]- [71], is the EVs charging demand satisfaction. These proposed methods aimed at controlling the energy cost, reducing the prices for the customers, or even increasing the charger's profit.…”

Section: Inputsmentioning

confidence: 99%

“…Chargers included the EVCS or the energy distribution systems. Authors in [62] and [87] presented solutions that maximize the charging stations' revenue in distributed EV charging systems. Likewise, the studies in [97], [64], [99], [100], [72], [88], [74], [90], [91], [76], [102], [81], [83], [66], [85] and [107] presented a likely solution that minimizes the energy costs and increase EV charging profit but with the use of multi-agent RL (MARL) techniques instead of the single-agent techniques.…”

Section: Objectivesmentioning

confidence: 99%

“…For instance, [72] used EVCS routing topology, driving time, and waiting time, in addition to the charging demand satisfaction constraint. Price was used as a constraint in [107], [87], and [108]. Price is a target to be achieved through the algorithms, using it as a constraint might not be of significant need in most cases, as the agent is already learning to find the optimum prices.…”

Section: Constraintsmentioning

confidence: 99%

See 4 more Smart Citations

Reinforcement Learning Based EV Charging Management Systems–A Review

2021

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To mitigate global warming and energy shortage, integration of renewable energy generation sources, energy storage systems, and plug-in electric vehicles (PEVs) have been introduced in recent years. The application of electric vehicles (EV) in the smart grid has shown a significant option to reduce carbon emission. However, due to the limited battery capacity, managing the charging and discharging process of EV as a distributed power supply is a challenging task. Moreover, the unpredictable nature of renewable energy generation, uncertainties of plug-in electric vehicles associated parameters, energy prices, and the time-varying load create new challenges for the researchers and industries to maintain a stable operation of the power system. The EV battery charging management system plays a main role in coordinating the charging and discharging mechanism to efficiently realize a secure, efficient, and reliable power system. More recently, there has been an increasing interest in data-driven approaches in EV charging modeling. Consequently, researchers are looking to deploy model-free approaches for solving the EV charging management with uncertainties. Among many existing model-free approaches, Reinforcement Learning (RL) has been widely used for EV charging management. Unlike other machine learning approaches, the RL technique is based on maximizing the cumulative reward. This paper reviews the existing literature related to the RL-based framework, objectives, and architecture for the charging coordination strategies of electric vehicles in the power systems. In addition, the review paper presents a detailed comparative analysis of the techniques used for achieving different charging coordination objectives while satisfying multiple constraints. This paper also focuses on the application of RL in EV coordination for research and development of the cutting-edge optimized energy management system (EMS), which are applicable for EV charging.

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Section: Inputsmentioning

confidence: 99%

Section: Inputsmentioning

confidence: 99%

Section: Inputsmentioning

confidence: 99%

Section: Objectivesmentioning

confidence: 99%

Section: Constraintsmentioning

confidence: 99%

See 3 more Smart Citations

Reinforcement Learning Based EV Charging Management Systems–A Review

2021

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On the Pivotal Role of Artificial Intelligence Toward the Evolution of Smart Grids

Refaat¹,

Abu‐Rub²

2021

Smart Grid and Enabling Technologies

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Integration of Renewable Energy in Microgrids and Smart Grids in Deregulated Power Systems: A Comparative Exploration

Dawn,

Ramakrishna,

Ramesh

et al. 2024

Adv Energy and Sustain Res

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In recent years, the importance of deregulated power systems has grown significantly, resulting in positive effects on stability, reliability, innovation, and investment in new energy grid technology. The competitive landscape among energy providers and distributors has empowered consumers to not only save money on their energy bills but also incorporate sustainable energy sources into the grid. To efficiently manage electricity distribution, deregulated power systems must include a smart grid and microgrid (MG). Herein, the potential for sustainable expansion of these systems, as well as their economic and environmental implications, are examined. A comprehensive grid system that integrates smart grids and MGs can offer a complete solution, catering to the evolving energy needs of communities and businesses. The advantages of establishing such a system, including improved grid stability, reliability, and increased utilization of renewable energy sources (RES), are highlighted. Furthermore, the integration of MGs and smart grids enhances the management of distributed generation, allowing power companies to optimize system operations for profitability and efficiency. By following these suggestions, businesses and stakeholders in the power sector can enhance the efficiency and responsibility of their systems, resulting in benefits for both the economy and the environment.

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A Deep Reinforcement Learning Method for Pricing Electric Vehicles With Discrete Charging Levels

Cited by 73 publications

References 29 publications

Reinforcement Learning Based EV Charging Management Systems–A Review

Reinforcement Learning Based EV Charging Management Systems–A Review

On the Pivotal Role of Artificial Intelligence Toward the Evolution of Smart Grids

Integration of Renewable Energy in Microgrids and Smart Grids in Deregulated Power Systems: A Comparative Exploration

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