Éclairement énergétique

Résumé

vignette|Irradiance spectrale solaire (distribution du rayonnement solaire). L’éclairement énergétique ou irradiance est un terme radiométrique qui quantifie la puissance d'un rayonnement électromagnétique frappant par unité de surface perpendiculaire à sa direction. C'est la densité surfacique du flux énergétique arrivant au point considéré de la surface. Dans le Système international d'unités, elle s’exprime en watts par mètre carré (W/m ou ). La grandeur d'émission associée est l'exitance. La distribution hémisphérique de l'éclairement énergétique reçu sur un élément de surface est la luminance énergétique. L'équivalent en photométrie de l'éclairement énergétique est l'éclairement lumineux. L'éclairement énergétique ne considère que l'énergie arrivant sur l'élément de surface, par opposition à l'énergie émise par cet élément, qui est l'exitance. Photométrie En photométrie l'éclairement qualifie communément la puissance du flux lumineux reçu par unité de surface. Voici

Source officielle

https://fr.wikipedia.org/wiki/%C3%89clairement_%C3%A9nerg%C3%A9tique

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Tools for multi time horizon PV point forecasting and prediction intervals

Mario Paolone, Enrica Scolari, Fabrizio Sossan

The massive integration of stochastic renewable generation in modern power systems requires decentralized control schemes that cope with the uncertainties of the distributed energy resources (DERs). Among the DERs, small-scale photovoltaic (PV) systems are expected to represent most of the future power generation capacity. Therefore, solar resource assessment and the associated forecasting at various time scales are of fundamental importance for power systems operators. The activity here summarized focuses on developing forecasting methods based on the integrated use of time series, all-sky camera images and generation models of PV systems, considering short-term temporal horizons (below one hour) and fine spatial resolution (single site installations). We aim to improve the efficiency of forecasting methods developed by the DESL through prediction of clouds movement and associated cover of the sun disk. For this, we developed and validated different machine-learning techniques relying on the integrated use of Global Horizonal Irradiance (GHI) time series, all-sky image processing and cloud motion identification. Furthermore, a methodology to estimate the irradiance from all-sky images is proposed, investigating the possibility of using all-sky cameras as irradiance sensors.

SCCER-FURIES2020

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Solar irradiance estimations for modeling the variability of photovoltaic generation and assessing violations of grid constraints: A comparison between satellite and pyranometers measurements with load flow simulations

Rahul Kumar Gupta, Mario Paolone, Enrica Scolari, Fabrizio Sossan

Global horizontal irradiance (GHI) is typically used to model the power output of distributed photovoltaic (PV) generation. On the one hand, satellite estimations are nonpervasive and already available from commercial providers, but they have a limited spatiotemporal resolution. On the other hand, local estimations, e.g., from pyranometers, sky-cameras, and monitored PV plants, capture local irradiance patterns and dynamics, but they require in situ monitoring infrastructure and upgrading the asset of electrical operators. Considering that in most power systems, PV generation is typically the aggregated contribution of many distributed plants, are local GHI estimations necessary to characterize the variability of the power flow at the grid connection point (GCP) and detect violations of the limits of voltages and line currents accurately? To reply, we consider GHI measurements from a dense network of pyranometers (used to model the ground truth GHI potential), satellite estimations for the same area, and information about a medium and low voltage distribution system. We perform load flows at different levels of installed PV capacity and compare the nodal voltages, line currents, and the power at the GCP when the irradiance is from pyranometers and when from satellite estimations, deriving conclusions on the necessity, or not, of highly spatiotemporally resolved irradiance estimations.

2019

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Cloud motion is a cause of direct irradiance variations at ground level and determines significant fluctuations of PV generation. In this work, we investigate on how integrating information on clouds motion extracted from all-sky images into a time series-based forecasting tool for global horizontal irradiance (GHI) to enhance its prediction performance. We consider three different cloud motion algorithms: heuristic motion detection (HMD), particle image velocimetry (PIV), and a persistent model. The HMD method is originally proposed in this paper. It consists in choosing the cloud motion vector leading to the best cloud map prediction considering the most recent sky images. Results show that integrating the information of the predicted cloud coverage in the circumsolar area leads to a decrease of the width of the GHI prediction intervals up to 2% for prediction horizons in the range 1 to 10 minutes.

2017

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