Modeling PV fleet output variability

Hoff, Thomas; Perez, Richard

doi:10.1016/j.solener.2011.11.005

Cited by 156 publications

(108 citation statements)

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“…Security of energy supply and climate change are increasingly important drivers of energy policy and there are potential synergies between these two issues (Bazilian et al, 2011, p3750). While renewables are an essential part of the UK's commitments to reducing carbon emissions and increasing security of supply there are challenges associated with the intermittency of renewables, in particular wind and solar (Hoff andPerez, 2012, p2177).…”

Section: An Overview Of the Electricity Market In The Ukmentioning

confidence: 99%

A study on the effect of ground surface boundary conditions in modelling shallow ground heat exchangers

Bortoloni¹,

Bottarelli²,

2017

Applied Thermal Engineering

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Section: An Overview Of the Electricity Market In The Ukmentioning

confidence: 99%

A study on the effect of ground surface boundary conditions in modelling shallow ground heat exchangers

Bortoloni¹,

Bottarelli²,

2017

Applied Thermal Engineering

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“…Table 1 (last row) lists the solar variability of each site. This quantity is calculated as the the standard deviation of step changes in the clear sky index σ(∆kt * ∆T ) [19]. This metric was computed for hourly kt * values (∆T = 1 h) for the whole dataset (two years).…”

Section: Datamentioning

confidence: 99%

“…This metric was computed for hourly kt * values (∆T = 1 h) for the whole dataset (two years). The application of this metric to other locations [19] led to the conclusion that variability above 0.2 indicates very unstable GHI conditions. As the table indicates, variability for Le Tampon is above this threshold.…”

Section: Datamentioning

confidence: 99%

Probabilistic Solar Forecasting Using Quantile Regression Models

Lauret¹,

David²,

Pedro

2017

Energies

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Abstract:In this work, we assess the performance of three probabilistic models for intra-day solar forecasting. More precisely, a linear quantile regression method is used to build three models for generating 1 h-6 h-ahead probabilistic forecasts. Our approach is applied to forecasting solar irradiance at a site experiencing highly variable sky conditions using the historical ground observations of solar irradiance as endogenous inputs and day-ahead forecasts as exogenous inputs. Day-ahead irradiance forecasts are obtained from the Integrated Forecast System (IFS), a Numerical Weather Prediction (NWP) model maintained by the European Center for Medium-Range Weather Forecast (ECMWF). Several metrics, mainly originated from the weather forecasting community, are used to evaluate the performance of the probabilistic forecasts. The results demonstrated that the NWP exogenous inputs improve the quality of the intra-day probabilistic forecasts. The analysis considered two locations with very dissimilar solar variability. Comparison between the two locations highlighted that the statistical performance of the probabilistic models depends on the local sky conditions.

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“…Fleet management is a new approach within the operation of power systems, applied for the first time in PV generation [23,24]. The need of such approach is evident both due to the size of the considered PV stations and their geographical distribution.…”

Section: Fleet Management Of Pv Generationmentioning

confidence: 99%

“…An empirical model that illustrates this effect is illustrated by the correlation coefficient ρ, calculated in equation 1 [23]. The lower this correlation factor is the lower is the correlation between two stations and therefore a possible variation in the first station is not experienced simultaneously in the second station.…”

Section: Fleet Management Of Pv Generationmentioning

confidence: 99%