Site Selection Criteria for Battery Energy Storage in Power Systems

Hameed, Zeenat; Hashemi, Seyedmostafa; Træholt, Chresten

doi:10.1109/ccece47787.2020.9255678

Cited by 6 publications

(3 citation statements)

References 34 publications

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“…Electric double-layer capacitors (EDLCs) and pseudocapacitors are two types of electrochemical capacitors (ECs), also known as supercapacitors, or ultracapacitors. Electrochemical cells and batteries are divided into four types based on their varied operating modes: main battery, secondary battery, reserve battery, and fuel battery [7].…”

Section: Electrochemical Energy Storage Systemsmentioning

confidence: 99%

Battery storage systems in electric power grid: A review

Mviri

2022

J. Phys.: Conf. Ser.

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Renewable energy within the utility grid has increased during the previous decade. Severe issues have been raised about the dependability, effectiveness, and efficiency of energy infrastructure. Therefore, energy storage devices have considerably enhanced the network’s accuracy and reliability. Battery energy storage systems (BESS) are among the greatest widely used storage solutions because they have several advantages over traditional power sources, including fast and accurate response, flexibility, and predictability. They are regarded as a reasonable alternative to the threat of climate change. This paper provides a comprehensive evaluation of the BESS’s optimum size targets, limitations, methodology, benefits and disadvantages. Furthermore, energy storage technologies and improved application targets have been presented and discussed for greater clarity. This study focuses primarily on BESS deployments, methodologies, and environmental impact. BEES innovations and achievements for electrical networks are also compared to other energy storage technologies. Concerns and obstacles are discussed to offer researchers in BESS a comprehensive picture. Finally, this study provides several essential ideas that economists and practitioners can use to design a fruitful, robust, efficient, and resilient BESS for a brighter and more optimal deployment.

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Section: Electrochemical Energy Storage Systemsmentioning

confidence: 99%

Battery storage systems in electric power grid: A review

Mviri

2022

J. Phys.: Conf. Ser.

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“…R ENEWABLE energy sources (RESs) are one of the technologies that enable covering a raising global energy consumption [1]. Battery energy storage systems (BESSs) are a critical part of an electrical grid since they can contribute to a smooth integration of RES [2]. A BESS can provide various services to the grid, such as frequency control, peak shaving, black start, and reactive power support.…”

mentioning

confidence: 99%

“…Since conventional power plants applied for frequency control are retiring, they have to be replaced with modern ones. The response time of a BESS, which is one of the most important features for frequency regulation services, is lower than conventional power plants [2]. Therefore, BESSs have the potential to become one of the possible replacements for conventional power plants.…”

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confidence: 99%

Evaluating Machine-Learning-Based Methods for Modeling a Digital Twin of Battery Systems Providing Frequency Regulation

Kharlamova

Hashemi

2023

IEEE Systems Journal

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Battery energy storage systems (BESSs) have the potential to become providers of frequency regulation services in future energy systems. In this case, the reliable operation of BESS has a significant impact on system stability. One of the tools that ensures the secure BESS operation is applying a digital twin to forecast the BESS state of charge (SOC). It allows to predict BESS behavior and identify potential failures or cyberattacks in BESS. In this article, we compare multiple data-driven approaches to forecast the SOC of a BESS based on realistic dataset and real battery operation. Recurrent neural networks, e.g., feedforward neural network, gated recurrent unit, long short-term memory, support vector regression, random forest, and AdaBoost methods are applied to evaluate each method's performance. We compare the methods based on the maximum and average forecast errors, the required computational capacity, as well as expected training speed. To validate the results, in addition to the data gathered from simulations, we used experimental setup with a BESS of 79 kWh providing containment reserve for normal operation, which is a BESS service with a high economical potential in Nordic Region. Furthermore, we provide recommendations for the methods that are suitable to be applied for modeling the digital twin of a utilityscale battery digital twin.Index Terms-Artificial intelligence (AI), battery energy storage system (BESS), digital twin, frequency regulation, neural network, state of charge (SOC). NOMENCLATUREConstants η BESS efficiency W Energy capacity of a BESS Δt Duration of the control period N Number of samples in the dataset MAE Mean absolute error ME Maximum error Variables ϕ(x i ) Input features vector

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