Applications for solar irradiance nowcasting in the control of microgrids: A review

Samu, Remember; Calais, Martina; Shafiullah, GM; Moghbel, Moayed; Shoeb, Asaduzzaman; Nouri, Bijan; Blum, Niklas

doi:10.1016/j.rser.2021.111187

Cited by 27 publications

(12 citation statements)

References 107 publications

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“…The demand for solar irradiance nowcasting has increased enormously and followed an exponential growth since 2011 [1]. Solar nowcasting can be referred to as forecasting up to 6 h ahead and offers various benefits for the better exploitation of solar energy systems.…”

Section: Introductionmentioning

confidence: 99%

“…Ground-based all-sky imagers or ASIs, along with the implementation of various image processing techniques, form a promising approach to provide reliable solar irradiance nowcasts with adequately high spatiotemporal resolution [1,12]. ASIs are able to nowcast all solar radiation components, namely Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), and Diffuse Horizontal Irradiance (DHI), at pixel-scale by processing consecutive images derived from the corresponding camera systems [13].…”

Section: Introductionmentioning

confidence: 99%

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Benchmarking of solar irradiance nowcast performance derived from all-sky imagers

Logothetis

Salamalikis

Wilbert³

et al. 2022

Renewable Energy

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Benchmarking of solar irradiance nowcast performance derived from all-sky imagers

Logothetis

Salamalikis

Wilbert³

et al. 2022

Renewable Energy

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“…Samu et al [30] summarize the usage of ASI-based nowcasting with power system modelling in mini-grids to increase the PV hosting capacity and to reduce costs. Ramprate control, predictions of spinning reserves, battery management and demand response management (DRM) have been highlighted as the major prospective applications.…”

Section: Grid Integrationmentioning

confidence: 99%

A network of all sky imagers (ASI) enabling accurate and high-resolution very short-term forecasts of solar irradiance

Schmidt¹,

Stührenberg²,

Blum³

et al. 2022

21st Wind &Amp; Solar Integration Workshop (WIW 2022)

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The Eye2Sky network is a measurement network in north-western Germany consisting of multiple all-sky imagers (ASI), meteorological and solar irradiance measurements. The network provides high temporal and spatial resolution data for meteorological and especially solar energy related applications. With increasing photovoltaic (PV) capacity in electrical grids fluctuations in solar irradiance due to changing cloud cover may have adverse effects on the grid stability. Within Eye2Sky, new technologies and methodologies facing the demand for more accurate solar irradiance forecasts are being developed. The ASIs used in Eye2Sky record 180° field of view hemispherical sky images from fish-eye lensed cameras. Accompanied with local measurements of solar irradiance components (global, direct and diffuse) a very short-term forecast of the solar resource is possible. These nowcasts provide minutely updated information up to 20 minutes ahead with 1-minute temporal and 50 m x 50 m spatial resolution. This approach shows more precise forecasting results for the next minutes ahead compared to traditional and less detailed methods based on satellite or numerical weather prediction models. In the network, multiple ASIs are used to enlarge the spatial coverage and the forecast horizon requested by many applications. Moreover, the forecast error can be reduced with a network of cameras. In this article, the Eye2Sky network, its research results and applications are introduced.

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“…The irregular and rapid movement of clouds can completely transform the sky within a few seconds, significantly altering photovoltaic production [4,5]. This variability complicates maintaining the balance between production and consumption and timely interventions in the power system, resulting in increased integration costs for PV [6].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Solar Irradiance Using a Neural Network Based on the Combination of Sky Camera Images and Meteorological Data

Barancsuk,

Groma,

Günter

et al. 2024

Energies

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In recent years, with the growing proliferation of photovoltaics (PV), accurate nowcasting of PV power has emerged as a challenge. Global horizontal irradiance (GHI), which is a key factor influencing PV power, is known to be highly variable as it is determined by short-term meteorological phenomena, particularly cloud movement. Deep learning and computer vision techniques applied to all-sky imagery are demonstrated to be highly accurate nowcasting methods, as they encode crucial information about the sky’s state. While these methods utilize deep neural network models, such as Convolutional Neural Networks (CNN), and attain high levels of accuracy, the training of image-based deep learning models demands significant computational resources. In this work, we present a computationally economical estimation technique, based on a deep learning model. We utilize both all-sky imagery and meteorological data, however, information on the sky’s state is encoded as a feature vector extracted using traditional image processing methods. We introduce six all-sky image features utilizing detailed knowledge of meteorological and physical phenomena, significantly decreasing the amount of input data and model complexity. We investigate the accuracy of the determined global and diffuse radiation for different combinations of meteorological parameters. The model is evaluated using two years of measurements from an on-site all-sky camera and an adjacent meteorological station. Our findings demonstrate that the model provides comparable accuracy to CNN-based methods, yet at a significantly lower computational cost.

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Applications for solar irradiance nowcasting in the control of microgrids: A review

Cited by 27 publications

References 107 publications

Benchmarking of solar irradiance nowcast performance derived from all-sky imagers

Benchmarking of solar irradiance nowcast performance derived from all-sky imagers

A network of all sky imagers (ASI) enabling accurate and high-resolution very short-term forecasts of solar irradiance

Estimation of Solar Irradiance Using a Neural Network Based on the Combination of Sky Camera Images and Meteorological Data

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