Comparison of different correlational techniques in estimating the total generated power of neighboring photovoltaic systems

Al-Hilfi, Hasanain A. H.; Shahnia, Farhad; Abu-Siada, A.

doi:10.1109/aupec.2016.7749377

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…13,14 Some of these studies have explicitly noted the importance of accounting for delayed cross correlation, which is observed to increase the pairwise correlation in the along-wind direction. 11,13 One study by Al-Hilfi et al 15 directly compares the results from several of the published spatiotemporal correlation models. Using a slightly different approach, Klima and Apt 16 describe the variability using power spectral density (PSD) of the time series and observe that larger plants have a steeper drop-off in spectral density, leading to a greater smoothing effect at high frequency (i.e., short time scales).…”

Section: A Characterizing Variabilitymentioning

confidence: 99%

Cloud advection model of solar irradiance smoothing by spatial aggregation

Ranalli

Peerlings

2021

Journal of Renewable and Sustainable Energy

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Solar generation facilities are inherently spatially distributed and therefore aggregate solar irradiance in both space and time, smoothing its variability. To represent the spatiotemporal aggregation process, most existing studies focus on the reduced correlation in solar irradiance throughout a plant's spatial distribution. In this paper, we derived a cloud advection model that is instead based upon lagging correlations between upwind/downwind portions of a distributed plant, induced by advection of a fixed cloud pattern over the plant. We use the model to calculate a plant transfer function that can be used to predict the smoothing of the time series. The model was validated using the distributed HOPE-Melpitz measurement dataset, which consisted of 50 solar irradiance sensors at 1 s temporal resolution over a 3 Â 2 km 2 bounding area. The initial validation showed that the advection-based model outperforms other models at predicting the smoothed irradiance time series during manually identified, advection dominated conditions. We also conducted validation on the model against additional advection dominated periods in the dataset that were identified algorithmically. The cloud advection model's performance compared well to models in literature, but degraded slightly as larger cross-wind plant distributions were investigated. The results in this paper highlight the need to incorporate advection effects on spatial aggregation during advection dominated conditions. Future development of spatiotemporal aggregation models is needed to unify advective models with existing correlation reduction models and to identify regimes where each dominate.

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Section: A Characterizing Variabilitymentioning

confidence: 99%

Cloud advection model of solar irradiance smoothing by spatial aggregation

Ranalli

Peerlings

2021

Journal of Renewable and Sustainable Energy

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“…According to utilities guidelines, the PV system's power output reduction should not be more than 10% of the PV system's capacity over a minute [7]. Adopting an energy storage system to meet this requirement may be an expensive option for large PV plants [7,8]. Another cost-effective solution is through allocating the PV systems over a large geographical area.…”

Section: Introductionmentioning

confidence: 99%

Combined ANFIS–Wavelet Technique to Improve the Estimation Accuracy of the Power Output of Neighboring PV Systems during Cloud Events

2020

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The short-term variability of photovoltaic (PV) system-generated power due to ambient conditions, such as passing clouds, represents a key challenge for network planners and operators. Such variability can be reduced using a geographical smoothing technique based on installing multiple PV systems over certain locations at distances of meters to kilometers. To accurately estimate the PV system’s generated power during cloud events, a variability reduction index (VRI), which is a function of several parameters, should be calculated precisely. In this paper, the Wavelet Transform Technique (WTT) along with Adaptive Neuro Fuzzy Inference System (ANFIS) are used to develop new models to estimate the PV system’s power output during cloud events. In this context, irradiance data collected from one PV system along with other parameters, including ambient conditions, were used to develop the proposed models. Ultimately, the models were validated through their application on a 0.7 km2 PV plant with 16 rooftop PV systems in Brisbane, Australia.

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“…The key limitation of PVs is its intermittent characteristics that exhibit long‐term and short‐term variabilities in the generated power [2, 3]. The long‐term variability is the change in the PVs output power due to sun movement through the daytime.…”

Section: Introductionmentioning

confidence: 99%

“…Fig. 1 shows a schematic for the solar irradiance, corresponding to power generated, by one PV system, in a particular location along with the accumulated power generated by 5 and 23 PVs distributed over a neighbouring area [3]. The impact of geographic smoothing is evident from this figure.…”

Section: Introductionmentioning

confidence: 99%

“…This paper proposes a technique based on a non‐mathematical approach inspired by [2–4] and [7] and considers other parameters ignored in the VRI models presented in the literature to result in better calculation accuracy. The proposed technique employs practical collected data to determine a correlation between the total power generated by a group of neighbouring PV systems and solar irradiance, measured by a single pyranometer within that locality.…”

Section: Introductionmentioning

confidence: 99%

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Gene expression technique‐based approach to improve the accuracy of estimating the total generated power by neighbouring photovoltaic systems

Al-Hilfi

Shahnia

Abu-Siada

2020

IET Renewable Power Generation

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The penetration of photovoltaic systems (PVs) to existing power grids is increasing as they are considered attractive options for electricity generation in distribution networks. This paper focuses on estimating the total power generated by a group of neighbouring PVs, spread over a distribution network using a single pyranometer for measuring the solar irradiance. A new empirical‐based model that employs the Gene Expression Programming (GEP) technique is proposed to correlate the distribution of the PVs and the irradiance measured by the pyranometer and estimate the total power generated by the PVs. The geographic variability reduction index has been considered in developing the proposed model that also employs a Wavelet Transform technique to enhance its accuracy. The effective performance of the proposed model is validated using real data collected by the Solar Project at the University of Queensland, Brisbane, Australia. Results reveal that the proposed technique yields more accurate results when compared with other existing approaches in the literature.

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Comparison of different correlational techniques in estimating the total generated power of neighboring photovoltaic systems

Cited by 7 publications

References 16 publications

Cloud advection model of solar irradiance smoothing by spatial aggregation

Cloud advection model of solar irradiance smoothing by spatial aggregation

Combined ANFIS–Wavelet Technique to Improve the Estimation Accuracy of the Power Output of Neighboring PV Systems during Cloud Events

Gene expression technique‐based approach to improve the accuracy of estimating the total generated power by neighbouring photovoltaic systems

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