Modelling and sizing techniques to mitigate the impacts of wind fluctuations on power networks: a review

Tejeswini, M. V.; Raglend, I. Jacob

doi:10.1080/01430750.2020.1839554

International Journal of Ambient Energy

2020

DOI: 10.1080/01430750.2020.1839554

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Modelling and sizing techniques to mitigate the impacts of wind fluctuations on power networks: a review

M. V. Tejeswini

I. Jacob Raglend

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Cited by 3 publications

(1 citation statement)

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“…Within this framework, the action-value function, denoted as Q, aims to emulate the optimal action-value function, q^* by bypassing the adherence to the present policy. Its update mechanism is articulated as in Equation ( 12) [12,[27][28][29][30]:…”

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confidence: 99%

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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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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Stochastic aggregated dynamic model of wind generation with correlated wind speeds

Adeen

Milano²

2022

Electric Power Systems Research

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Basalt fiber as a skeleton to enhance the multi-conditional tribological properties of epoxy coating

Huang

Cai

et al. 2023

Tribology International

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Modelling and sizing techniques to mitigate the impacts of wind fluctuations on power networks: a review

Cited by 3 publications

References 122 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

Stochastic aggregated dynamic model of wind generation with correlated wind speeds

Basalt fiber as a skeleton to enhance the multi-conditional tribological properties of epoxy coating

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