Optimal Operation Parameter Estimation of Energy Storage for Frequency Regulation

Cho, Sung-Min; Kim, Jin-Su; Kim, Jae-Chul

doi:10.3390/en12091782

Cited by 10 publications

(8 citation statements)

References 26 publications

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“…SOC o and SOC N are the initial and last values of SOC. By setting the initial and last value of SOC as equal, the battery operation for one day becomes one cycle, and the cycle gradation of lithium batteries can be analyzed more accurately [29]. At time t, the SOC of ESS is represented as:…”

Section: Formulation For Milp Of Conventional Microgrid Schedulingmentioning

confidence: 99%

Energy Curtailment Scheduling MILP Formulation for an Islanded Microgrid with High Penetration of Renewable Energy

et al. 2021

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The efficiency of photovoltaic (PV) cells has improved significantly in the last decade, making PV generation a common feature of the sustainable microgrid. As the PV-powered microgrid reaches high penetrations of intermittent PV power, optimum scheduling of over-production is necessary to minimize energy curtailment. Failure to include an accurate assessment of curtailed energy costs in the scheduling process increases wasted energy. Moreover, applying an objective function without considering the cost coefficients results in an inefficient concentration of curtailed power in a specific time interval. In this study, we provide an optimization method for scheduling the microgrid assets to evenly distribute curtailment over the entire daily period of PV generation. Each of the curtailment intervals established in our optimization model features the application of different cost coefficients. In the final step, curtailment costs are added to the objective function. The proposed cost minimization algorithm preferentially selects intervals with low curtailment costs to prevent the curtailment from being concentrated at a specific time. By inducing even distribution of curtailment, this novel optimization methodology has the potential to improve the cost-effectiveness of the PV-powered microgrid

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Section: Formulation For Milp Of Conventional Microgrid Schedulingmentioning

confidence: 99%

Energy Curtailment Scheduling MILP Formulation for an Islanded Microgrid with High Penetration of Renewable Energy

et al. 2021

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“…As previous studies related to an FR BESS, researchers have adopted stage-of-charge (SoC) control techniques for an operational efficiency improvement and optimization [9][10][11][12], considered various operating factors, e.g., an aggregated BESS and demand-responsive (DR) resources [13][14][15], and conducted research based on the operation of large-capacity FR [16][17][18][19] research related to participation in the power exchange market [20], and operation research focusing on battery life management [21]. Optimization studies have been conducted (e.g., design capacity, economic efficiency, links with various systems, and lifetime management of the facilities) upon the results of the operational effectiveness of an FR BESS.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Distributed Autonomous Control Based Battery Energy Storage System for Frequency Regulation

Kim

Hong

Choi³

2021

Energies

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It has been mandated that 5% of the generation capacity of conventional fossil fuel power plants shall be used exclusively for frequency regulation (FR) purposes in South Korea. However, the rotational speed of generators cannot be controlled quickly, and thus the variation in the power generation for FR takes some time. Even during this short period of time, frequency fluctuations may occur, and the frequency may be out of range of its reference value. In order to overcome the limitations of the existing FR method, 374 MW (103 MWh) battery energy storage systems (BESSs) for FR have been installed and are in operation at 13 sites in South Korea. When designing the capacity of BESS for FR, three key factors, i.e., the deployment time, duration of delivery, and end of delivery, are considered. When these times can be reduced, the required capacity for BESS installation can be decreased, achieving the same operational effects with minimal investment in the facilities. However, because a BESS for FR (FR BESS) needs to be installed under a large capacity, providing a single output, a centralized control method is employed. The centralized control method has the advantage of being able to view and check the entire system at once, although in the case of FR BESS, a novel system design that can optimize the above three factors through a faster and more accurate control is required. Therefore, this paper proposes the implementation of a distributed autonomous control-based BESS for frequency regulation. For the proposed FR BESS, the central control system is responsible for the determination of external factors, e.g., power generation/demand forecasting; and the system is designed such that the optimal control method of renewable energy sources and BESS according to real-time frequency variations during practical operation is determined and operated using a distributed autonomous control method. Furthermore, this study was verified through the simulation that the proposed distributed autonomous control method conducts FR faster than an FR BESS with conventional centralized control, leading to an increase in the FR success rate, and a decrease in the deployment time required (e.g., 200 ms).

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“…Since the non-linearity of the battery life is neglected, the life span can be simply considered, but the accuracy is reduced. On the other hand, the optimal design of the energy storage system for frequency regulation considering the non-linear lifetime model of the battery was suggested in Reference [22]. However, this method of simulating and designing more than 20 years of operation in 1 s units has the disadvantage that it requires very long calculation time.…”

Section: Introductionmentioning

confidence: 99%

Optimum State-of-Charge Operating Range for Frequency Regulation of Energy Storage Systems Using a Master–Slave Parallel Genetic Algorithm

2020

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Lithium batteries are used for frequency regulation in power systems because of their fast response and high efficiency. Lithium batteries have different life characteristics depending on their type, and it is necessary to set the optimal state-of-charge (SOC) operating range considering these characteristics to obtain the maximum gain. In general, narrowing the operating range increases the service life but may lower the performance of charging and discharging operations in response to frequency fluctuations, and vice versa. We present performance assessment indicators that consider charging and discharging due to frequency variations and lifespan of the batteries. However, to evaluate the performance, while reflecting the non-linear life characteristics of lithium batteries, simulating the entire operation is necessary, which requires a long calculation time. Therefore, we propose a master–slave parallel genetic algorithm to derive the optimal SOC operating range with reduced calculation time. A simulation program was implemented to evaluate the computational performance that determines the optimal SOC range. The proposed method reduces the calculation time while considering the non-linear life characteristics of lithium batteries. It was confirmed that a more accurate SOC operating range could be calculated by simulating the entire life span.

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Optimal Operation Parameter Estimation of Energy Storage for Frequency Regulation

Cited by 10 publications

References 26 publications

Energy Curtailment Scheduling MILP Formulation for an Islanded Microgrid with High Penetration of Renewable Energy

Energy Curtailment Scheduling MILP Formulation for an Islanded Microgrid with High Penetration of Renewable Energy

Implementation of Distributed Autonomous Control Based Battery Energy Storage System for Frequency Regulation

Optimum State-of-Charge Operating Range for Frequency Regulation of Energy Storage Systems Using a Master–Slave Parallel Genetic Algorithm

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