Analysis of the intra-day solar resource variability in the Iberian Peninsula

Rodríguez-Benítez, Francisco J.; Arbizu‐Barrena, Clara; Santos-Alamillos, Francisco J.; Tovar‐Pescador, J.; Pozo-Vázquez, D.

doi:10.1016/j.solener.2018.06.060

Cited by 21 publications

(11 citation statements)

References 49 publications

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“…Exploratory analysis reveals that there are multiple peaks in the data frequency, and that the variability in subdaily clearness indices is greater than in daily clearness indices. As noted by [40], subdaily variability is more sensitive to local features and meso-to micro-scale processes, while multiday variability is dependent on synoptic-scale systems. Our focus here was on the synoptic-scale, and so we analyzed only the distribution of daily indices.…”

Section: Discussionmentioning

confidence: 92%

“…We suggest that a better forecast may be achievable by combining knowledge of both daily and subdaily solar irradiance fluctuations to allow for both the synoptic driving conditions for daily variability, and microscale processes, e.g., governing convective cloud development [40]. As a first step in this analysis, we employ finite mixture models to explore whether it is possible to identify clear, overcast, or cloudy days and associated synoptic processes in an objective manner.…”

Section: Introductionmentioning

confidence: 99%

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Assessing Evidence for Weather Regimes Governing Solar Power Generation in Kuwait

et al. 2019

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With electricity representing around 20% of the global energy demand, and increasing support for renewable sources of electricity, there is also an escalating need to improve solar forecasts to support power management. While considerable research has been directed to statistical methods to improve solar power forecasting, few have employed finite mixture distributions. A statistically-objective classification of the overall sky condition may lead to improved forecasts. Combining information from the synoptic driving conditions for daily variability with local processes controlling subdaily fluctuations could assist with forecast validation and enhancement where few observations are available. Gaussian mixture models provide a statistical learning approach to automatically identify prevalent sky conditions (clear, semi-cloudy, and cloudy) and explore associated weather patterns. Here a first stage in the development of such a model is presented: examining whether there is sufficient information in the large-scale environment to identify days with clear, semi-cloudy, or cloudy conditions. A three-component Gaussian distribution is developed that reproduces the observed multimodal peaks in sky clearness indices, and their temporal distribution. Posterior probabilities from the fitted mixture distributions are used to identify periods of clear, partially-cloudy, and cloudy skies. Composites of low-level (850 hPa) humidity and winds for each of the mixture components reveal three patterns associated with the typical synoptic conditions governing the sky clarity, and hence, potential solar power.

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Section: Discussionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Assessing Evidence for Weather Regimes Governing Solar Power Generation in Kuwait

et al. 2019

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“…The annual VRE energy surplus percentage value regarding the electricity demand (i.e., the instantaneous sum of the wind and solar generation above the consumption) during the year 2015 was only 9.2e−04. As identified for other phenomena (e.g., precipitation [42]), due to the geographical location, along with its topographical features and weather conditions, different regional wind and solar power production patterns can be observed in Portugal [43]. The atmospheric circulation in Portugal is strongly influenced by the seasonal migration of the mid-latitude high pressure system [44].…”

Section: Characteristics Of the Portuguese Power System And Weather Conditionsmentioning

confidence: 94%

Exploring Wind and Solar PV Generation Complementarity to Meet Electricity Demand

Couto

Estanqueiro

2020

Energies

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Understanding the spatiotemporal complementarity of wind and solar power generation and their combined capability to meet the demand of electricity is a crucial step towards increasing their share in power systems without neglecting neither the security of supply nor the overall cost efficiency of the power system operation. This work proposes a methodology to exploit the complementarity of the wind and solar primary resources and electricity demand in planning the expansion of electric power systems. Scenarios that exploit the strategic combined deployment of wind and solar power against scenarios based only on the development of an individual renewable power source are compared and analysed. For each scenario of the power system development, the characterization of the additional power capacity, typical daily profile, extreme values, and energy deficit are assessed. The method is applied to a Portuguese case study and results show that coupled scenarios based on the strategic combined development of wind and solar generation provide a more sustainable way to increase the share of variable renewables into the power system (up to 68% for an annual energy exceedance of 10% for the renewable generation) when compared to scenarios based on an individual renewable power source. Combined development also enables to reduce the overall variability and extreme values of a power system net load.

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“…In such studies, different methods and metrics are presented to estimate and analyze the short-term variability in the PV system's power output. For instance, the correlation coefficient and standard deviation are employed in [9][10][11][12][13][14] while [15][16][17] utilized the variability score and variability index as mathematical metrics to calculate the power output variability of PV systems. Other studies introduced different metrics to investigate the power output variability, such as the Multifractal Detrended Fluctuation Analysis (MFDFA), which is a time series modeling method especially used for extreme ambient events [18].…”

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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Analysis of the intra-day solar resource variability in the Iberian Peninsula

Cited by 21 publications

References 49 publications

Assessing Evidence for Weather Regimes Governing Solar Power Generation in Kuwait

Assessing Evidence for Weather Regimes Governing Solar Power Generation in Kuwait

Exploring Wind and Solar PV Generation Complementarity to Meet Electricity Demand

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

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