Smart Vehicle-to-Grid Operation of Power System based on EV User Behavior

Shin, Gwang-Su; Kim, Ho-Young; Mahseredjian, Jean; Kim, Chul-Hwan

doi:10.1007/s42835-024-01797-9

J. Electr. Eng. Technol.

2024

DOI: 10.1007/s42835-024-01797-9

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Smart Vehicle-to-Grid Operation of Power System based on EV User Behavior

Gwang-Su Shin,

Ho-Young Kim,

Jean Mahseredjian

et al.

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Cited by 2 publications

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A Feed-Forward Back-Propagation Neural Network Approach for Integration of Electric Vehicles into Vehicle-to-Grid (V2G) to Predict State of Charge for Lithium-Ion Batteries

Cervellieri

2024

Energies

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The accurate prediction and efficient management of the State of Charge (SoC) of electric vehicle (EV) batteries are critical challenges in the integration of vehicle-to-grid (V2G) systems within multi-energy microgrid (MMO) models. Inaccurate SoC estimation can lead to inefficiencies, increased costs, and potential disruptions in power generation. This paper addresses the problem of optimizing SoC estimation to enhance the reliability and efficiency of V2G scheduling and MMO coordination. In this work, we develop a Feed-Forward Back-Propagation Network (FFBPN) using MATLAB 2024 software, employing the Levenberg–Marquardt algorithm and varying the number of hidden neurons to achieve better performance; performance was measured by the maximum coefficient of determination (R2) and the minimum mean squared error (MSE). Utilizing the NASA Prognostics Center of Excellence (PCoE) dataset, we validate the model’s capability to accurately predict the life cycle of EV batteries. Our proposed FFBPN model demonstrates superior performance compared to existing methods from the literature, offering significant implications for future V2G system developments. The comparison between training, validation, and testing phases underscores the model’s validity and precisely identifies the characteristic curves of FFBPN, showcasing its potential to enhance profitability, efficiency, production, energy savings, and minimize environmental impact.

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A Feed-Forward Back-Propagation Neural Network Approach for Integration of Electric Vehicles into Vehicle-to-Grid (V2G) to Predict State of Charge for Lithium-Ion Batteries

Cervellieri

2024

Energies

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Integrating Evolutionary Game-Theoretical Methods and Deep Reinforcement Learning for Adaptive Strategy Optimization in User-Side Electricity Markets: A Comprehensive Review

Cheng,

Wei,

et al. 2024

Mathematics

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With the rapid development of smart grids, the strategic behavior evolution in user-side electricity market transactions has become increasingly complex. To explore the dynamic evolution mechanisms in this area, this paper systematically reviews the application of evolutionary game theory in user-side electricity markets, focusing on its unique advantages in modeling multi-agent interactions and dynamic strategy optimization. While evolutionary game theory excels in explaining the formation of long-term stable strategies, it faces limitations when dealing with real-time dynamic changes and high-dimensional state spaces. Thus, this paper further investigates the integration of deep reinforcement learning, particularly the deep Q-learning network (DQN), with evolutionary game theory, aiming to enhance its adaptability in electricity market applications. The introduction of the DQN enables market participants to perform adaptive strategy optimization in rapidly changing environments, thereby more effectively responding to supply–demand fluctuations in electricity markets. Through simulations based on a multi-agent model, this study reveals the dynamic characteristics of strategy evolution under different market conditions, highlighting the changing interaction patterns among participants in complex market environments. In summary, this comprehensive review not only demonstrates the broad applicability of evolutionary game theory in user-side electricity markets but also extends its potential in real-time decision making through the integration of modern algorithms, providing new theoretical foundations and practical insights for future market optimization and policy formulation.

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Smart Vehicle-to-Grid Operation of Power System based on EV User Behavior

Cited by 2 publications

References 11 publications

A Feed-Forward Back-Propagation Neural Network Approach for Integration of Electric Vehicles into Vehicle-to-Grid (V2G) to Predict State of Charge for Lithium-Ion Batteries

A Feed-Forward Back-Propagation Neural Network Approach for Integration of Electric Vehicles into Vehicle-to-Grid (V2G) to Predict State of Charge for Lithium-Ion Batteries

Integrating Evolutionary Game-Theoretical Methods and Deep Reinforcement Learning for Adaptive Strategy Optimization in User-Side Electricity Markets: A Comprehensive Review

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