Distributed energy storage system‐based control strategy for hybrid DC/AC microgrids in grid‐connected mode

Souraki, Hassan Pourvali; Radmehr, Masoud; Rezanejad, Mohammad

doi:10.1002/er.4331

Cited by 12 publications

(5 citation statements)

References 29 publications

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“…In designing a hybrid nanogrid, the size of the battery plays a crucial role. Souraki et al [38] and Parida et al [39] highlighted three factors in determining suitable battery size. The first one was autonomy days (AD), defined as the number of consecutive days the desired load can be driven solely by the battery bank, without any external support.…”

Section: Fundamental Constituentsmentioning

confidence: 99%

Multi-Objective Optimization of a Hybrid Nanogrid/Microgrid: Application to Desert Camps in Hafr Al-Batin

et al. 2021

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This paper presents an optimal design for a nanogrid/microgrid for desert camps in the city of Hafr Al-Batin in Saudi Arabia. The camps were designed to operate as separate nanogrids or to operate as an interconnected microgrid. The hybrid nanogrid/microgrid considered in this paper consists of a solar system, storage batteries, diesel generators, inverter, and load components. To offer the designer/operator various choices, the problem was formulated as a multi-objective optimization problem considering two objective functions, namely: the cost of electricity (COE) and the loss of power supply probability (LPSP). Furthermore, various component models were implemented, which offer a variety of equipment compilation possibilities. The formulated problem was then solved using the multi-objective evolutionary algorithm, based on both dominance and decomposition (MOEA/DD). Two cases were investigated corresponding to the two proposed modes of operation, i.e., nanogrid operation mode and microgrid operation mode. The microgrid was designed considering the interconnection of four nanogrids. The obtained Pareto front (PF) was reported for each case and the solutions forming this front were discussed. Based on this investigation, the designer/operator can select the most appropriate solution from the available set of solutions using his experience and other factors, e.g., budget, availability of equipment and customer-specific requirements. Furthermore, to assess the quality of the solutions found using the MOEA/DD, three different methods were used, and their results compared with the MOEA/DD. It was found that the MOEA/DD obtained better results (nondominated solutions), especially for the microgrid operation mode.

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Section: Fundamental Constituentsmentioning

confidence: 99%

Multi-Objective Optimization of a Hybrid Nanogrid/Microgrid: Application to Desert Camps in Hafr Al-Batin

et al. 2021

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“…Lithium‐ion batteries as storage systems increase grid flexibility and stability; moreover, they enhance system management by compensating the intermittent renewable energy. However, these systems are continually maintained as the most delicate and costly component 11 . The battery's electrochemical degradation may occur because of the irreversible electrolyte dynamics 12 .…”

Section: Introductionmentioning

confidence: 99%

Robust parameter estimation approach of Lithium‐ion batteries employing bald eagle search algorithm

Fathy

Ferahtia

Rezk

et al. 2022

Intl J of Energy Research

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Summary Lithium‐ion batteries play an essential role in sustainable power systems as energy storage systems. They are employed for electrical power storage and balancing power in multisources power systems. However, due to the degradation phenomena, these systems are more exposed to parametric variation. For this reason, the identification of the battery parameters remains crucial to enhance the power system performance and extend the battery lifespan. A robust parameter identification strategy based on the bald eagle search algorithm (BES) is proposed in this research work. This article proposed a robust estimation approach that accurately estimates the battery parameters based on the BES optimizer. The proposed approach has been evaluated for two distinct batteries to confirm its superiority. A comparison with recent optimization methods has been performed to validate the performance of the proposed strategy. These algorithms include marine predator algorithm, COOT algorithm, artificial eco‐system optimizer, and other previous algorithms. The obtained results affirmed the reliability of the proposed BES‐based strategy to estimate the battery equivalent circuit variables with superior accuracy compared with other methods. It provided the smallest fitness function value for the first battery with 7.06 × 10−4 and an excellent SD value of 1.877 × 10−8. The proposed strategy achieved 100% efficiency in terms of optimization efficiency. The second battery has been tested with ArtUban and New European Driving Cycle (NEDC) profiles to confirm the performance of BES. The results confirm the superior performance for the two cases. The BES provides the smallest fitness values (0.0860 for ArtUrban and 0.1222 for NEDC) and lower identification total error (2.294 for ArtUrban and 2.6737 for NEDC).

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“…These smart grids combine multiple power sources, such as renewable, classical, and energy storage systems (ESSs). ESSs provide more grid flexibility and reliability, and they improve the management of the system by acting as a backup to irregular renewable power 3 . Such systems have the potential to facilitate the proper integration of renewable energy sources, speed up the decarbonization of the energy grid, enhance the security and productivity of electrical transmission and distribution, and ensure an uninterruptible power supply 4 .…”

Section: Introductionmentioning

confidence: 99%

Optimal parameter identification strategy applied to lithium‐ion battery model

et al. 2021

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Summary This paper presents an optimal parameter identification strategy of the lithium‐ion (Li‐ion) battery model applying a recent metaheuristic artificial ecosystem‐based optimization (AEO) algorithm, which proves its ability in terms of both convergence speed and complexity. The key idea is to update the battery model parameters using the optimizer outputs. In the current paper, the battery model is based on the Shepherd model. To demonstrate the superiority of the suggested method of identification, the test results are compared in terms of efficiency, convergence speed, and accuracy of identification with those obtained by the salp swarm algorithm, the political optimizer, the equilibrium optimizer, and particle swarm optimization. Through the optimization procedure, the undetermined parameters of the battery model are employed as decision variables, but the root‐mean‐square error between estimated data and battery data is assigned to be an objective function must be minimal. The results showed the superior identification ability of the AEO compared to the other optimizers. This optimizer achieved 99.9% identification efficiency, which makes it an ideal solution for battery identification. Besides its identification efficiency, the AEO is much faster than the other optimizers, as the results show.

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Distributed energy storage system‐based control strategy for hybrid DC/AC microgrids in grid‐connected mode

Cited by 12 publications

References 29 publications

Multi-Objective Optimization of a Hybrid Nanogrid/Microgrid: Application to Desert Camps in Hafr Al-Batin

Multi-Objective Optimization of a Hybrid Nanogrid/Microgrid: Application to Desert Camps in Hafr Al-Batin

Robust parameter estimation approach of Lithium‐ion batteries employing bald eagle search algorithm

Optimal parameter identification strategy applied to lithium‐ion battery model

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