SV2G-ET: A Secure Vehicle-to-Grid Energy Trading Scheme Using Deep Reinforcement Learning

Kumari, Aparna; Trivedi, Mihir; Tanwar, Sudeep; Sharma, Ravi

doi:10.1155/2022/9761157

Cited by 16 publications

(3 citation statements)

References 35 publications

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“…S. Tanwar [26], adopts AI techniques to maintain the robustness of the system by identifying energy theft, thereby preventing cost leakage. A. Kumari [27] leverage AI to optimize the scheduling algorithm for charging and discharging in electrical vehicle environments. On the other hand, several studies leverage AI for metaverse system optimization.…”

Section: B Related Workmentioning

confidence: 99%

Quantum Reinforcement Learning for Spatio-Temporal Prioritization in Metaverse

Park,

Baek,

Kim

2024

IEEE Access

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A metaverse is composed of a physical-space and virtual-space, with the aim of having users in both the virtual reality and the real world experience. Prioritization is essential, but it is not straight-forwarded due to the limitation of computing resources in real-world, making it impossible to synchronize all data. Therefore, it is crucial to allocate resources based on regional preferences in physical-space and content preferences of users in virtual-space. The referencing system consists of two-stage sub-computations, (i) spatial-prioritization for more data gathering under the consideration of avatar-popularity one top of physical-space and (ii) temporal-prioritization for virtual-space rendering under the avatar-popularity. Both prioritization tasks are combinatorics problems and are well-known NP-hard. The problem scale is also large, making it difficult to solve within given times. The classical deep learning cannot be the solution. Quantum-based learning algorithms can be the potential solutions due to high-performance computing capabilities, because a small number of qubits can represent an exponentially large amount of information. On top of these advantages, an improved quantum reinforcement learning (QRL) algorithm is proposed for reducing the control dimensions into a logarithmic scale. We corroborate that our proposed QRL-based algorithm for low-dimensional spatio-temporal prioritization improves convergence and performance.

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Section: B Related Workmentioning

confidence: 99%

Quantum Reinforcement Learning for Spatio-Temporal Prioritization in Metaverse

Park,

Baek,

Kim

2024

IEEE Access

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“…Effective management of electric vehicles has the potential to enhance the reliability and stability of power grids. Furthermore, they facilitate the integration of renewable sources and improve the overall efficiency of the system (Kumari et al, 2022). Energy Management Systems (EMS) enables the operation and supervision of Battery Energy Storage Systems (BESS) in standalone power systems (Thirugnanam et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Integration of electric vehicle into smart grid: a meta heuristic algorithm for energy management between V2G and G2V

Srihari,

Krishnam Naidu,

Falkowski-Gilski

et al. 2024

Front. Energy Res.

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Recently, Electric Vehicles (EV) have been providing fast response and substantial progress in the power generation model. Further, EVs are exploited as adaptable Energy Storage Systems (ESSs) and show a promising performance in ancillary service markets to increase the demand of Smart Grid (SG) integration. The expansion of Vehicle-to-Grid concept has created an extra power source when renewable energy sources are not available. Yet, numerous operational problems still are required to be considered for EV implementation to turn out to be extensive. Even the development of Photo-Voltaic (PV) technology creates a problem in SGs when used for EV charging. Because of this, the Energy Management System (EMS) is required to handle charging requirements and deal with the intermittent generation. Here, in this research, an Improved Honey Badger algorithm (IHBA) is proposed for integrating SGs with EV parking lot, solar panels, and dynamic loads at the Point of Common Coupling (PCC). The proposed IHBA uses a dynamic programming method to optimize the charging Grid-to-Vehicle (G2V) or discharging Vehicle-to-Grid (V2G) profiles of the EVs using the forecasts of PV generation. This algorithm considers user preferences while also lowering reliance on the grid and maximizing SG effectiveness. The study’s findings show that the Honey Badger method is efficient in resolving issues involving large search spaces. The developed method is used to optimize charging and discharging of EV which is tested in MATLAB to obtain a stable load profile. From the evaluation of obtained results, it is evident that the IHBA controller outperforms the WOA and EHO controllers in terms of total harmonic distortion voltage (3.12%), power loss (0.197 kW) and efficiency (98.47%).

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“…However, the use of advanced technologies such as V2G makes the power systems more prone to security threats [10], [11]. Generally, an attacker may target either the cyber layer or the physical components in the power systems.…”

Section: Introductionmentioning

confidence: 99%

A Robust Controller Design based on Kharitonov’s Theorem for Frequency Control in an Interconnected Power System

Raouf

Mousavian

2023

EJECE

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The load frequency control of power systems is often carried out using methods that are dependent on the system load and parameters. Therefore, the controller design is not robust in unforeseen cases such an attack on the power system, variations in system parameters, or changes in load. In such methods, there is a need for an attack detection tool, and moreover, the controller parameters need to be adjusted as the load and power system parameters change. In this paper, Kharitonov's theorem was applied to design a robust decentralized load frequency control for a two-area power system in the presence of electric vehicle fleets as a power source that were targeted by a cyberattack. Furthermore, the robustness of the system against system nonlinearities was demonstrated by testing the efficacy of the controller on both linear and nonlinear systems. The controller design was robust such that there was no need to change the gains of the controller even during an attack. This was compared with the performance of controllers designed using GWO algorithm and fuzzy logic that needed retuning for different case studies with different variations in system parameters, load, or inclusion of a cyberattack to the electric vehicle fleets.

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SV2G-ET: A Secure Vehicle-to-Grid Energy Trading Scheme Using Deep Reinforcement Learning

Cited by 16 publications

References 35 publications

Quantum Reinforcement Learning for Spatio-Temporal Prioritization in Metaverse

Quantum Reinforcement Learning for Spatio-Temporal Prioritization in Metaverse

Integration of electric vehicle into smart grid: a meta heuristic algorithm for energy management between V2G and G2V

A Robust Controller Design based on Kharitonov’s Theorem for Frequency Control in an Interconnected Power System

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