An advanced electrical vehicle charging station using adaptive hybrid particle swarm optimization intended for renewable energy system for simultaneous distributions

Renoald, A. Johny; Selvan, P.; Sivakumar, P.; Rajalakshmi, R.

doi:10.3233/jifs-220089

Cited by 41 publications

(14 citation statements)

References 34 publications

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“…The validation process involved analyzing the performance of PID controllers and a base case scenario where no control input was applied to each EV station placement bus. This comparison allowed for an assessment of the effectiveness by utilizing a PID controller with a Particle Swarm Optimization (PSO) model for EV stations [19] and comparing it with a PID controller using RL and with no control input. By evaluating these different control approaches, the study aimed to determine their respective performances and identify the most suitable control strategy for regulating and stabilizing the power system in varying conditions.…”

Section: Pid-rl Control Performance Validation and Testingmentioning

confidence: 99%

“…The study conducted a comparative analysis of the stability of the electric vehicle (EV) system with 1% capacity (30 MW) under three control scenarios: no control input, PID-PSO [19], and PID-RL (reinforcement learning). The highest PV penetration level was selected (50%) and a disturbance of 50 MW generation reduction occurred in the first second.…”

Section: Pid-rl Control Performance Validation and Testingmentioning

confidence: 99%

“…These findings indicated that the PID controller with RL optimization significantly enhanced the damping of the dominant oscillation mode within the EV system, resulting in improved stability when compared to both the EV system with PID-PSO and the EV system without any control input. Frequency difference responses between generation area and loading area for three penetration scenarios for EV system with no control, PID-PSO control [19], and PID-RL control.…”

Section: Pid-rl Control Performance Validation and Testingmentioning

confidence: 99%

“…Nevertheless, these fast charging stations' high power requirements can put pressure on the grid, causing variations in frequency and voltage. During times of heavy demand for EV charging, efficient frequency control is crucial to managing the charging load and maintaining grid stability [17][18][19][20]. Although it is much desired, intermittent generation is produced by renewable energy sources because they are weather-dependent.…”

Section: Introductionmentioning

confidence: 99%

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Studying the Optimal Frequency Control Condition for Electric Vehicle Fast Charging Stations as a Dynamic Load Using Reinforcement Learning Algorithms in Different Photovoltaic Penetration Levels

Altarjami,

Alhazmi

2024

Energies

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This study investigates the impact of renewable energy penetration on system stability and validates the performance of the (Proportional-Integral-Derivative) PID-(reinforcement learning) RL control technique. Three scenarios were examined: no photovoltaic (PV), 25% PV, and 50% PV, to evaluate the impact of PV penetration on system stability. The results demonstrate that while the absence of renewable energy yields a more stable frequency response, a higher PV penetration (50%) enhances stability in tie-line active power flow between interconnected systems. This shows that an increased PV penetration improves frequency balance and active power flow stability. Additionally, the study evaluates three control scenarios: no control input, PID-(Particle Swarm Optimization) PSO, and PID-RL, to validate the performance of the PID-RL control technique. The findings show that the EV system with PID-RL outperforms the other scenarios in terms of frequency response, tie-line active power response, and frequency difference response. The PID-RL controller significantly enhances the damping of the dominant oscillation mode and restores the stability within the first 4 s—after the disturbance in first second. This leads to an improved stability compared to the EV system with PID-PSO (within 21 s) and without any control input (oscillating more than 30 s). Overall, this research provides the improvement in terms of frequency response, tie-line active power response, and frequency difference response with high renewable energy penetration levels and the research validates the effectiveness of the PID-RL control technique in stabilizing the EV system. These findings can contribute to the development of strategies for integrating renewable energy sources and optimizing control systems, ensuring a more stable and sustainable power grid.

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Section: Pid-rl Control Performance Validation and Testingmentioning

confidence: 99%

Section: Pid-rl Control Performance Validation and Testingmentioning

confidence: 99%

Section: Pid-rl Control Performance Validation and Testingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Studying the Optimal Frequency Control Condition for Electric Vehicle Fast Charging Stations as a Dynamic Load Using Reinforcement Learning Algorithms in Different Photovoltaic Penetration Levels

Altarjami,

Alhazmi

2024

Energies

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“…Additionally, dynamically adapting the routing decisions based on real-time energy availability and charging station locations can further optimize energy consumption. Another avenue for research is to explore the integration of renewable energy sources and charging infrastructure planning into the routing optimization process [95]. This can involve incorporating data on renewable energy generation, such as solar or wind power availability, and identifying optimal routes that enable EnFVs to recharge at strategically located charging stations powered by renewable sources.…”

Section: Enhancing Energy Efficiency and Sustainability In Routingmentioning

confidence: 99%

Multi-Objective Routing Optimization in Electric and Flying Vehicles: A Genetic Algorithm Perspective

Alolaiwy,

Hawsawi,

Zohdy

et al. 2023

Applied Sciences

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The advent of electric and flying vehicles (EnFVs) has brought significant advancements to the transportation industry, offering improved sustainability, reduced congestion, and enhanced mobility. However, the efficient routing of messages in EnFVs presents unique challenges that demand specialized algorithms to address their specific constraints and objectives. This study analyzes several case studies that investigate the effectiveness of genetic algorithms (GAs) in optimizing routing for EnFVs. The major contributions of this research lie in demonstrating the capability of GAs to handle complex optimization problems with multiple objectives, enabling the simultaneous consideration of factors like energy efficiency, travel time, and vehicle utilization. Moreover, GAs offer a flexible and adaptive approach to finding near-optimal solutions in dynamic transportation systems, making them suitable for real-world EnFV networks. While GAs show promise, there are also limitations, such as computational complexity, difficulty in capturing real-world constraints, and potential sub-optimal solutions. Addressing these challenges, the study highlights several future research directions, including the integration of real-time data and dynamic routing updates, hybrid approaches with other optimization techniques, consideration of uncertainty and risk management, scalability for large-scale routing problems, and enhancing energy efficiency and sustainability in routing. By exploring these avenues, researchers can further improve the efficiency and effectiveness of routing algorithms for EnFVs, paving the way for their seamless integration into modern transportation systems.

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Accurate state of charge prediction for lithium-ion batteries in electric vehicles using deep learning and dimensionality reduction

Jayaraman,

Thottungal

2024

Electr Eng

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An advanced electrical vehicle charging station using adaptive hybrid particle swarm optimization intended for renewable energy system for simultaneous distributions

Cited by 41 publications

References 34 publications

Studying the Optimal Frequency Control Condition for Electric Vehicle Fast Charging Stations as a Dynamic Load Using Reinforcement Learning Algorithms in Different Photovoltaic Penetration Levels

Studying the Optimal Frequency Control Condition for Electric Vehicle Fast Charging Stations as a Dynamic Load Using Reinforcement Learning Algorithms in Different Photovoltaic Penetration Levels

Multi-Objective Routing Optimization in Electric and Flying Vehicles: A Genetic Algorithm Perspective

Accurate state of charge prediction for lithium-ion batteries in electric vehicles using deep learning and dimensionality reduction

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