Evaluation of Hybrid Energy Storage Systems Using Wavelet and Stretched-Thread Methods

Santos, Yago A.; Nunes, Matheus P. M.; Lemes, Lucas A. C.; Bortoni, Edson C.

doi:10.1109/access.2020.3024966

Cited by 9 publications

(6 citation statements)

References 42 publications

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“…The use of lithium-ion batteries over recent years has brought many advantages: low cost, reasonable processing time, high cell strength, excellent efficiency at low temperatures, long service life [49,50]. In addition, by using a Li-ion battery [5], researchers are focused on the storage management of plug-in (PEV) hybrid vehicles. In [51], a novel technology of EV wireless charging, which facilitates the on-move charging, can represent another important storage system, given the high number of EVs.…”

Section: Battery Energy Storage Unit (Besu)mentioning

confidence: 99%

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Comprehensive Survey of Various Energy Storage Technology Used in Hybrid Energy

et al. 2021

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Various power generation technologies, such as wind turbines and solar power plants, have been increasingly installed in renewable energy projects as a result of rising demand and ongoing efforts by global researchers to mitigate environmental effects. The sole source of energy for such generation is nature. The incorporation of the green unit into the power grid also results in volatility. The stabilization of frequencies is critical and depends on the balance of supply and demand. An efficient monitoring scheme called Load Frequency Monitoring (LFM) is introduced to reduce the frequency deviation from its natural state. Specific energy storage systems may be considered to improve the efficiency of the control system. The storage system contributes to the load rate, peak rushing, black start support, etc., in addition to high energy and rapid responsive features. A detailed study of different power storage systems, their current business scenario, and the application of LFM facilities, as well as their analysis and disturbance, is presented in this paper. According to the literature analysis, the current approaches can be divided into two categories: grid and load scale structures. This article also distinguishes between the organized aggregate system and the uncoordinated system control scheme, both of which have advantages and disadvantages in terms of technology.

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Section: Battery Energy Storage Unit (Besu)mentioning

confidence: 99%

“…The balance between production and demand is disturbed by one or more production units (also known as generation side) or abrupt changes in connected loads. The short-term loading technique is available daily for this type of scenario and cannot be expected much before the day ends [4,5].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Survey of Various Energy Storage Technology Used in Hybrid Energy

et al. 2021

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“…T HE worldwide energy transition will fundamentally transform the future electricity grid to integrate everincreasing shares of renewable energy sources (RES). At shorter time scales, the intermittency of RES will mainly be balanced by hybridized, fast-response energy storage solutions [1]- [3]. This is because the ramping capability of hydropower is usually limited to 10-30 % per minute to reduce wear and tear and avoid too high thermal stresses [4].…”

Section: Introductionmentioning

confidence: 99%

Loss Modeling of Large Hydrogenerators for Cost Estimation of Reactive Power Services and Identification of Optimal Operation

Karekezi

Melfald

Øyvang

et al. 2023

IEEE Trans. Energy Convers.

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As a result of the worldwide energy transition, reactive power generation has started to become a more scarce resource in the power grid. Until recently, reactive power has been an auxiliary grid service that classical power generation facilities have provided without necessarily allocating any cost for this valuable service. In this paper, a new approach for predicting the additional costs of reactive power services delivered by large hydrogenerators is proposed. We derive the optimal reactive power (ORP) with minimal losses as a function of the active power level within the generator's capability diagram. This pathway can then be used to calculate additional losses from operational regimes deviating from the ORP. To back up the analysis, a dedicated example study was handpicked consisting of four real-world generators scaled in terms of power rating, i.e., 15 MVA, 47 MVA, 103 MVA, and 160 MVA. The objective was to identify how the ORP scale from smaller to larger MVAsized generators. Moreover, a sensitivity analysis of the machine characteristics is conducted. We find the ratio between the rotor and stator losses as the determining factor. Finally, we show how our framework could justify profit for reactive power services, which are projected to increase in the future.

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“…Literature [58] proposes the use of a GF filter to smooth the power from different renewable energy sources. Nonetheless, it has been concluded in [59] that a Gaussian filter tends to over smooth the power which results in the loss of the power signal characteristics, this in turn causes more energy to be drawn from the battery thereby decreasing its lifetime. Finally, a Savitzky Golay filter coupled with a control technique in [48] proves that the SG filter with higher order overcomes the power tracking issue and yields a degree of smoothing similar to the MA filter with reduced battery capacity.…”

Section: Introductionmentioning

confidence: 99%

Moving Regression Filtering With Battery State of Charge Feedback Control for Solar PV Firming and Ramp Rate Curtailment

Syed

Khalid

2021

IEEE Access

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The surge in energy demand needs to be met with environmentally pleasant resources to reduce the production of greenhouse gases. Solar Photovoltaic (PV) power is a widespread choice as it is accessible in plenty and is comparatively inexpensive. However, the large-scale penetration of intermittent PV power causes multiple variabilities in the grid such as frequency issues and voltage deviations. To counteract these instabilities, Battery Energy Storage System (BESS) is integrated into the grid as it reduces the PV fluctuations and promotes optimal operation. Nevertheless, storage systems are expensive, and thus smoothing filters are coupled with the BESS for cost reduction and power smoothing. Formerly, traditional filters such as Low Pass Filters (LPF), Moving Average (MA), and Moving Median (MM) filters have been proposed for power smoothing. However, these filters have inadequate power tracking capabilities particularly with longer window sizes (W.S) and time constants, which subsequently depreciates the storage system performance. To compensate for the delayed power tracking, larger energy storage systems are required which in turn adds to the overall operational costs. This paper proposes the Moving Regression (MR) filter combined with state of charge (SoC) feedback control for solar PV variability reduction, reduced time delay, decreased battery charging/discharging power, and ramp rate. Simulation results attest that the MR filter achieves better solar power smoothing without increasing the BESS capacity. Also, the performance of the MR filter is less affected with the increase in window sizes. Additionally, the execution of the introduced smoothing filter was found to be superior when assessed against the LPF, MA, MM, Savitsky-Golay (SG), and the Gaussian filter. In comparison to the SG (W.S = 53), the MR (W.S = 45) filter reduces the battery charging/discharging power by approximately 30.48% and as opposed to the LPF (T.C = 48), the peak SoC is reduced by 19.1%.INDEX TERMS Battery energy storage system, low pass filter, machine learning, moving average filter, moving regression filter, renewable energy, solar power smoothing.

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Evaluation of Hybrid Energy Storage Systems Using Wavelet and Stretched-Thread Methods

Cited by 9 publications

References 42 publications

Comprehensive Survey of Various Energy Storage Technology Used in Hybrid Energy

Comprehensive Survey of Various Energy Storage Technology Used in Hybrid Energy

Loss Modeling of Large Hydrogenerators for Cost Estimation of Reactive Power Services and Identification of Optimal Operation

Moving Regression Filtering With Battery State of Charge Feedback Control for Solar PV Firming and Ramp Rate Curtailment

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