PV Fluctuation Balancing Using Hydrogen Storage – a Smoothing Method for Integration of PV Generation into the Utility Grid

Tesfahunegn, S. G.; Ulleberg, Øystein; Vie, Preben J. S.; Undeland, Tore

doi:10.1016/j.egypro.2011.10.133

Cited by 42 publications

(20 citation statements)

References 5 publications

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“…Moving average based smoothing: A moving average approach was used in [7] and [8] to control energy storage device. According to [18], a moving average is a well know low-pass filter for time series data and it is formulated as: y w (i) = (1/w) Σy(i -k ), 0 ≤ k ≤ w -1 (7) where y(i) is a time series data with a window length of w. Exponential moving average is an extension of simple moving average by giving more weights to recent values [9]. Although it is a classical way to reduce fluctuations in renewable resources, it exhibits a memory effect that generally depends on the window length w. A moving average with window length w contains (1/w) % of present values.…”

Section: B the Proposed Algorithm Vs Moving Average 1)mentioning

confidence: 99%

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Gaussian-Based Smoothing of Wind and Solar Power Productions Using Batteries

Desta

Courbin²,

Sciandra³

et al. 2017

IJMERR

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Intermittent nature of Distributed Energy Resources (DER) such as solar and wind cause significant power fluctuations and integrating them to a power systems requires control mechanisms to reduce fluctuations. One way to control the effect of fluctuation is to use Energy Storage Systems (ESS) for smoothing out their power productions. A typical method to attain this goal is to use ESS with classical moving average approaches. However, these methods are affected by peaks and troughs in power production due to cloud passing and wind gust effects on solar panels and wind turbines, respectively. In this paper, we propose a Gaussian-based smoothing method to alleviate pitfalls of moving average methods to smooth out forecasted values of solar and wind powers. Then, we determine a minimum ESS size required to maintain a smoothed power curve for a day-ahead period. From our experiments, the proposed algorithm requires smaller ESS size than the classical approaches. 

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Section: B the Proposed Algorithm Vs Moving Average 1)mentioning

confidence: 99%

“…Furthermore, the MA approach was also used in [3] for controlling battery energy to reduce PV power fluctuation. In [9], an Exponential Moving Average (EMA) with hydrogen storage system was used. The difference between MA and EMA is that the EMA gives more weights on recent values of a fluctuating variable.…”

Section: Introductionmentioning

confidence: 99%

Gaussian-Based Smoothing of Wind and Solar Power Productions Using Batteries

Desta

Courbin²,

Sciandra³

et al. 2017

IJMERR

View full text Add to dashboard Cite

show abstract

“…In [17], a second smoothing strategy based on the exponential moving average (EMA) is presented. The algorithm considers a 10-minute window in tandem with a hydrogen storage system.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Ramp-Rate Control Algorithms for PV with Energy Storage Systems

et al. 2019

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The high variability of solar irradiance, originated by moving clouds, causes fluctuations in Photovoltaic (PV) power generation, and can negatively impact the grid stability. For this reason, grid codes have incorporated ramp-rate limitations for the injected PV power. Energy Storage Systems (ESS) coordinated by ramp-rate (RR) control algorithms are often applied for mitigating these power fluctuations to the grid. These algorithms generate a power reference to the ESS that opposes the PV fluctuations, reducing them to an acceptable value. Despite their common use, few performance comparisons between the different methods have been presented, especially from a battery status perspective. This is highly important, as different smoothing methods may require the battery to operate at different regimes (i.e., number of cycles and cycles deepness), which directly relates to the battery lifetime performance. This paper intends to fill this gap by analyzing the different methods under the same irradiance profile, and evaluating their capability to limit the RR and maintain the battery State of Charge (SOC) at the end of the day. Moreover, an analysis into the ESS capacity requirements for each of the methods is quantified. Finally, an analysis of the battery cycles and its deepness is performed based on the well-established rainflow cycle counting method.

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“…The moving average method was also used in [14] for controlling battery energy storage to reduce PV fluctuations. An exponential moving average has been used in [16] that gives more weight on the recent values of the fluctuating PV output. A modified Euler type moving average model is proposed in [11] for prediction of moving average values for a hybrid system with PV, fuel cell, EDLC and battery.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Ramp-Rate Control of Solar PV Using Energy Storage to Mitigate Output Fluctuations Caused by Cloud Passing

Alam

Muttaqi

Sutanto

2014

IEEE Trans. Energy Convers.

260

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The variability of solar irradiance with a high ramp-rate, caused by cloud passing, can create fluctuation in the PV output. In a weak distribution grid with a high PV penetration, this can create significant voltage fluctuations. Energy storage devices are used to smooth out the fluctuation using traditional moving average control. However, moving average does not control the ramp-rate directly; rather the ramp-rate depends on previous values of PV output. This paper proposes a strategy where the ramp-rate of PV panel output is used to control the PV inverter ramp-rate to a desired level by deploying energy storage (which can be available for other purposes, such as storing surplus power, countering voltage rise, etc.). During the ramping event, the desired ramp-rate is governed by controlling the energy storage based on an inverse relationship with the PV panel output ramp-rate to improve the fluctuation mitigation performance. In contrast to the moving average method, the proposed strategy is able to control the desired ramp-rate independent of the past history of the PV panel output. A dynamic model of the PVstorage integrated system is developed to verify the proposed strategy in the presence of physical device time lags. The proposed strategy is verified using simulation results based on an Australian distribution system. A laboratory experiment is also conducted to validate the concept of the proposed control strategy.

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PV Fluctuation Balancing Using Hydrogen Storage – a Smoothing Method for Integration of PV Generation into the Utility Grid

Cited by 42 publications

References 5 publications

Gaussian-Based Smoothing of Wind and Solar Power Productions Using Batteries

Gaussian-Based Smoothing of Wind and Solar Power Productions Using Batteries

Comparative Study of Ramp-Rate Control Algorithms for PV with Energy Storage Systems

A Novel Approach for Ramp-Rate Control of Solar PV Using Energy Storage to Mitigate Output Fluctuations Caused by Cloud Passing

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