Life-Aware Operation of Battery Energy Storage in Frequency Regulation

Ma, Qianli; Wei, Wu; Wu, Lei; Mei, Shengwei

doi:10.1109/tste.2023.3245197

Cited by 15 publications

(2 citation statements)

References 36 publications

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“…The severe power outages resulting from these extreme disasters have underscored the critical need for enhancing the resilience of distribution systems (DS) Resilience serves as the benchmark for evaluating a distribution system's ability to withstand and recover from severe disasters [3]. Energy storage systems are instrumental in bolstering the flexibility of power systems by offering various ancillary services to system operators, such as load shifting, frequency regulation, voltage support, and grid stabilization [4]- [7]. As an important form of energy storage system, lithium-ion batteries (LIBs) have gained considerable attention in recent years due to factors such as the fact that they have a long life, a high energy density, a high power density, and are environmentally friendly.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Resilience in Battery Charging through Co-Simulation with Reinforcement Learning

Seyedi,

Sheida,

Siner

et al. 2024

Preprint

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Controlling the charging and discharging procedures of Lithium-Ion Batteries is of paramount importance as violating safety constraints, such as current deviations, can lead to significant damage to the battery or circuit or interruption in service. Thus, it is crucial to employ a robust controller capable of handling uncertainties and unexpected scenarios. PI controllers have become prevalent in recent years for managing battery dynamics, but they exhibit limited robustness in unpredictable situations. In this paper, we propose a Reinforcement Learning (RL) driven control method as a substitute for the PI controller. The agent is trained using a co-simulation approach with simultaneous employment of Python and Matlab, ensuring an accurate estimation of the environment and, consequently, enhanced performance. A prototype of the proposed controller is developed using dSPACE rapid control prototyper. The performance is compared with the benchmark controller (PI) across different fault scenarios, considering three criteria: overshoot, undershoot, and stabilization time. The comparative analysis reveals that, in most scenarios, the RL agent outperforms the PI controller, exhibiting a remarkable 50% reduction in both overshoot and undershoot compared to the benchmark controller. This research contributes to advancing battery control systems by introducing an RL-based controller that proves to be a more robust alternative, delivering improved performance in the face of uncertainties and fault scenarios.

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Section: Introductionmentioning

confidence: 99%

Enhanced Resilience in Battery Charging through Co-Simulation with Reinforcement Learning

Seyedi,

Sheida,

Siner

et al. 2024

Preprint

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“…In ref. [25], Ma et al employed different techniques in online policy and offline operation to compromise the revenue of frequency regulation and battery life. In ref.…”

Section: Introductionmentioning

confidence: 99%

Optimal energy storage configuration for joint energy‐regulation market participating renewable energy plants with excess revenue recovery mechanism

Ren

Deng²,

Guo

et al. 2023

IET Energy Syst Integration

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The fluctuation and stochastic characteristics of renewable energy resources challenge the secure system operation and also impose significant financial risks for the market participating renewable energy plants (REPs). Energy storage systems (ESSs) can serve as effective tools in enhancing the operating flexibility of REPs, thus improving their profitability while making them grid‐friendly. However, current studies focussing on the energy market participation of ESS‐equipped REPs neglect ESSs' frequency regulation performance. Moreover, power output deviation and power curtailment of REPs bring difficulties to the integration of renewable energy. To address these challenges, an optimal ESS configuration method for REPs participating in the joint energy‐regulation market is proposed first. A method considering constraints on frequency regulation performance is applied. Then, to reduce power output deviation and power curtailment of REPs, an incentive mechanism based on the excess revenue recovery is implemented to induce the grid‐friendly power outputs of REPs. Numerical analysis on XJTU‐ROTS2017 systems demonstrates that the optimal ESS configuration results obtained from the proposed method could promote the profitability of ESS‐equipped REPs. The results also verify the effectiveness of the excess revenue recovery mechanism in facilitating the grid‐friendly operation behaviours of the ESS‐equipped REPs.

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Machine Learning Health Estimation for Lithium-Ion Batteries Under Varied Conditions

Leite,

Pérez-Aracil,

Marcelino

et al. 2024

Lecture Notes in Computer Science

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Life-Aware Operation of Battery Energy Storage in Frequency Regulation

Cited by 15 publications

References 36 publications

Enhanced Resilience in Battery Charging through Co-Simulation with Reinforcement Learning

Enhanced Resilience in Battery Charging through Co-Simulation with Reinforcement Learning

Optimal energy storage configuration for joint energy‐regulation market participating renewable energy plants with excess revenue recovery mechanism

Machine Learning Health Estimation for Lithium-Ion Batteries Under Varied Conditions

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