Energy Storage Baseline Requirements for Pulsed Power Loads

Weaver, Wayne W.; Bijaieh, Mehrzad Mohammadi; Robinett, Rush D.; Wilson, David G.

doi:10.1109/ests.2019.8847798

Cited by 9 publications

(10 citation statements)

References 17 publications

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“…i u,re f ,total is the reference current for the overall hybrid system and i u,batt,meas is the measured current injected by the overall BESS. The equivalent hybrid ESS [11] can be represented as…”

Section: Hybrid Battery and Flywheel System Control And Specificationmentioning

confidence: 99%

“…As a result a filter matching leakage may occur. In Section 7, it will be shown how a more detailed BESS (such as in Reference [11]) can be optimized so that its frequency response matches a first-order LPF.…”

Section: Power Requirementsmentioning

confidence: 99%

“…In previous sections, BESS and FESS systems were described as first-order LPFs such as in (34). However, the BESS in Reference [11] is clearly a higher order system. Mismatching the frequency responses of both systems may lead to substantial inaccuracies in power sharing based on frequency response.…”

Section: Battery Energy Storage System and First-order Lpf Matching Amentioning

confidence: 99%

“…where, H BESS (jw) and H f ilter (jw) represent the frequency responses of BESS and the first-order LPF respectively. Generally, H BESS (jw) is obtained from battery with its corresponding Device Layer Control (DLC) system such as in Reference [11]. ω cut−o f f represents the cut-off frequency for both BESS and LPF.…”

Section: Battery Energy Storage System and First-order Lpf Matching Amentioning

confidence: 99%

“…Another purpose of this work is to demonstrate the efficacy of the HSSPFC method to control and specify battery and flywheel hybrid storage systems as actuators while maintaining electrical levels of the system. Here, reduced-order models for MGs in Reference [10], and battery and flywheel systems in Reference [11] are used. The renewable sources such as solar or wind are modeled as ideal variable voltage sources.…”

Section: Introductionmentioning

confidence: 99%

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Energy Storage Power and Energy Sizing and Specification Using HSSPFC

2020

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The intermittent nature of renewable sources requires the integration of Energy Storage Systems (ESSs) with appropriate power and energy densities. One of the applications of Hamiltonian Surface Shaping and Power Flow Control (HSSPFC) is to size ESSs for power and energy densities by employing them as sole actuators of Microgrid (MG) systems. This Article provides a comprehensive yet simplified example of utilization of HSSPFC to size ESSs of inverter-based three-phase MG systems under hierarchical control. Here, the distributed Hamiltonian controller is expanded for control of parallel ESSs and power sharing metrics are defined to distribute power between hybrid storage systems according to their power and energy density capabilities. Simulated hybrid ESSs comprising battery and flywheel systems are used as examples to demonstrate the behaviour of the expanded control, verify the power sharing criteria and illustrate ESS design and specification by utilizing HSSPFC.

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Section: Hybrid Battery and Flywheel System Control And Specificationmentioning

confidence: 99%

Section: Power Requirementsmentioning

confidence: 99%

Section: Battery Energy Storage System and First-order Lpf Matching Amentioning

confidence: 99%

Section: Battery Energy Storage System and First-order Lpf Matching Amentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Energy Storage Power and Energy Sizing and Specification Using HSSPFC

2020

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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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