Operation Method of Energy Storage System Replacing Governor for Frequency Regulation of Synchronous Generator without Reserve

Nguyen-Hoang, Nam-Du; Shin, Wooyoung; Lee, Choongman; Chung, In‐Young; Kim, Dongha; Hwang, Young‐Ha; Youn, Juyoung; Maeng, Jwayoung; Yoon, Michung; Hur, Kyeong; Shim, Jae Yeob

doi:10.3390/en15030798

Cited by 2 publications

(4 citation statements)

References 32 publications

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“…The SoC limitation scheme has been developed for FR-ESS applications, which involve a reference value that the ESS is continually attempting to adapt to with a set nominal frequency (60 Hz). This reference value, termed the initial SoC (SoC o ), was 50% in this study, indicating the highest potential energy of charging and discharging [18]. The absorption or supply of ESS power (P ESS ) results in a change in the SoC.…”

Section: R Essmentioning

confidence: 82%

“…Otherwise, the ESS absorbed the power to charge up to P ESS, min . The charging and discharging amounts of the power output can be calculated using the droop rate, as shown in Equation (18). The frequency deviation ∆ f can have both positive and negative values that define the ESS power injection and absorption, respectively, as expressed in Equation (10 In this study, the SoC limitation of the ESS was designed to operate based on the frequency deviation variation rate as well as the power output of the ESS.…”

Section: Ess Control Algorithmmentioning

confidence: 99%

“…A previous study demonstrated the advantage of using an ESS to replace the governor in a synchronous generator from the perspective of the SoC management scheme and FR performance. However, this study did not compare the proposed method with a conventional FR-ESS system [18].…”

Section: Introductionmentioning

confidence: 99%

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Coordinated Frequency Control of an Energy Storage System with a Generator for Frequency Regulation in a Power Plant

et al. 2022

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Considering the controllability and high responsiveness of an energy storage system (ESS) to changes in frequency, the inertial response (IR) and primary frequency response (PFR) enable its application in frequency regulation (FR) when system contingency occurs. This paper presents a coordinated control of an ESS with a generator for analyzing and stabilizing a power plant by controlling the grid frequency deviation, ESS output power response, equipment active power, and state of charge (SoC) limitation of the ESS in a power plant. The conventional generator and FR-ESS controllers were investigated and compared. To obtain the optimal frequency and power response, an ESS-based adaptive droop control method was proposed. The proposed control strategy was developed and implemented considering the changes and limitations of the dynamic characteristics of the system, FR requirements, and an ESS using the PSCAD/EMTDC software. The simulation results showed that the proposed method was more effective than the conventional droop-control-based FR-ESS, and the effectiveness of this method was validated.

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Section: R Essmentioning

confidence: 82%

Section: Ess Control Algorithmmentioning

confidence: 99%

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Coordinated Frequency Control of an Energy Storage System with a Generator for Frequency Regulation in a Power Plant

et al. 2022

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

Section: Design Adaptive Pid Controller Using Q-learningmentioning

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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Operation Method of Energy Storage System Replacing Governor for Frequency Regulation of Synchronous Generator without Reserve

Cited by 2 publications

References 32 publications

Coordinated Frequency Control of an Energy Storage System with a Generator for Frequency Regulation in a Power Plant

Coordinated Frequency Control of an Energy Storage System with a Generator for Frequency Regulation in a Power Plant

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

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