Short-term irradiance variability: Preliminary estimation of station pair correlation as a function of distance

Perez, Richard; Kivalov, Sergey; Schlemmer, James; Hemker, Karl; Hoff, Thomas

doi:10.1016/j.solener.2012.02.027

Cited by 135 publications

(85 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, spatial autocorrelation structures and decorrelation length scales of increments in irradiance and clear-sky index (i.e., irradiance normalized to clear-sky conditions), and PV power output have also been studied for a range of spatial scales and increment values (Arias-Castro et al, 2014;Elsinga and van Sark, 2014;Hinkelman, 2013;Hoff and Perez, 2012;Mills, 2010;Perez et al, 2012;Perpiñán et al, 2013). For all quantities and methods considered, increment correlations at different locations have been shown to decrease with increasing distance, with a smaller rate of decrease (longer decorrelation distances) for larger time increments.…”

Section: Introductionmentioning

confidence: 99%

Local short-term variability in solar irradiance

2016

View full text Add to dashboard Cite

Abstract. Characterizing spatiotemporal irradiance variability is important for the successful grid integration of increasing numbers of photovoltaic (PV) power systems. Using 1 Hz data recorded by as many as 99 pyranometers during the HD(CP) 2 Observational Prototype Experiment (HOPE), we analyze field variability of clear-sky index k * (i.e., irradiance normalized to clear-sky conditions) and sub-minute k * increments (i.e., changes over specified intervals of time) for distances between tens of meters and about 10 km. By means of a simple classification scheme based on k * statistics, we identify overcast, clear, and mixed sky conditions, and demonstrate that the last of these is the most potentially problematic in terms of short-term PV power fluctuations. Under mixed conditions, the probability of relatively strong k * increments of ±0.5 is approximately twice as high compared to increment statistics computed without conditioning by sky type. Additionally, spatial autocorrelation structures of k * increment fields differ considerably between sky types. While the profiles for overcast and clear skies mostly resemble the predictions of a simple model published by Hoff and Perez (2012), this is not the case for mixed conditions. As a proxy for the smoothing effects of distributed PV, we finally show that spatial averaging mitigates variability in k * less effectively than variability in k * increments, for a spatial sensor density of 2 km −2 .

show abstract

Section: Introductionmentioning

confidence: 99%

Local short-term variability in solar irradiance

2016

View full text Add to dashboard Cite

show abstract

“…An extension of this metric (calledP) proposed by Perez et al [18] is based on the dispersion of the quantity diffas shown in the next equation (equation 10). Note that if 0 so .…”

Section: (X(t)x(t-1)…x(t-p+1) )+ϵ(T+1)mentioning

confidence: 99%

“…In addition, forecasts based on persistence on the clear sky index exhibit rather good performance for forecast horizons < 1h. Also, the clear sky index is very important when one want to characterize the variability of a site [18,19], therefore a key component in solar forecasting is the clear sky index or equally the clear sky model. Several statistical parameters [18]aim at assessing the variability and consequently the difficulty to forecast the GHI [4,19,20].…”

Section: Introductionmentioning

confidence: 99%

“…Also, the clear sky index is very important when one want to characterize the variability of a site [18,19], therefore a key component in solar forecasting is the clear sky index or equally the clear sky model. Several statistical parameters [18]aim at assessing the variability and consequently the difficulty to forecast the GHI [4,19,20]. The goal of this paper is to find a metric that is correlated to the forecasting accuracy (nRMSE; nMAE): -Based on sound numerical experiments, we study the aforementioned metrics [18,19] or even simple metric (variation coefficient, mean, standard deviation, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Several statistical parameters [18]aim at assessing the variability and consequently the difficulty to forecast the GHI [4,19,20]. The goal of this paper is to find a metric that is correlated to the forecasting accuracy (nRMSE; nMAE): -Based on sound numerical experiments, we study the aforementioned metrics [18,19] or even simple metric (variation coefficient, mean, standard deviation, etc. ); -Conversely, other parameters related to financial econometric community are studied (return, absolute log return, etc.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Statistical parameters as a means to a priori assess the accuracy of solar forecasting models

Voyant

Soubdhan

Lauret

et al. 2015

Energy

View full text Add to dashboard Cite

In this paper we propose to determinate and to test a set ofstatistical parameters (20)to estimate the predictability of the global horizontal irradiation time series and thereby propose a new prospective tool indicating the expected error regardlessthe forecasting methodsa modeller can possibly implement. The mean absolute log return, which is a tool usually used in econometry, proves to be a very good estimator. Some examples of the use of this tool are exposed, showing the interest of this statistical parameter in concrete cases of predictions or optimizations.

show abstract

Empirical assessment of short‐term variability from utility‐scale solar PV plants

Haaren

Morjaria

Fthenakis

2012

Progress in Photovoltaics

View full text Add to dashboard Cite

Variability of solar power is a key driver in increasing the cost of integrating solar power into the electric grid because additional system resources are required to maintain the grid's reliability. In this study, we characterize the variability in power output of six photovoltaic plants in the USA and Canada with a total installed capacity of 195 MW (AC); it is based on minute‐averaged data from each plant and the output from 390 inverters. We use a simple metric, “daily aggregate ramp rate” to quantify, categorize, and compare daily variability across these multiple sites. With this metric, the effect of geographic dispersion is observed, while controlling for climatic differences across the plants. Additionally, we characterized variability due to geographical dispersion by simulating a step by step increase of the plant size at the same location. We observed maximum ramp rates for 5, 21, 48, and 80 MWAC plants, respectively, as 0.7, 0.58, 0.53, and 0.43 times the plant's capacity. Copyright © 2012 John Wiley & Sons, Ltd.

show abstract

Short-term irradiance variability: Preliminary estimation of station pair correlation as a function of distance

Cited by 135 publications

References 7 publications

Local short-term variability in solar irradiance

Local short-term variability in solar irradiance

Statistical parameters as a means to a priori assess the accuracy of solar forecasting models

Empirical assessment of short‐term variability from utility‐scale solar PV plants

Contact Info

Product

Resources

About