Quantifiers for the solar irradiance variability: A new perspective

Blaga, Robert; Paulescu, Marius

doi:10.1016/j.solener.2018.09.034

Cited by 33 publications

(17 citation statements)

References 24 publications

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“…Solar and wind resources present high variability intrinsically linked to the environmental and meteorological conditions, and this can significantly affect the quality and safety of the electrical system. The increasing share of such intermittent energy sources requires developing and implementing strategies to manage the electrical system [16], and this has mobilized the international scientific community [17,18].…”

Section: Brief Discussion Of the Published Articles In The Special Issuementioning

confidence: 99%

Editorial for the Special Issue: Assessment of Renewable Energy Resources with Remote Sensing

Martins

2020

Remote Sensing

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The development of renewable energy sources plays a fundamental role in the transition towards a low carbon economy. Considering that renewable energy resources have an intrinsic relationship with meteorological conditions and climate patterns, methodologies based on the remote sensing of the atmosphere are fundamental sources of information to support the energy sector in planning and operation procedures. This Special Issue is intended to provide a highly recognized international forum to present recent advances in remote sensing to data acquisition required by the energy sector. After a review, a total of eleven papers were accepted for publication. The contributions focus on solar, wind, and geothermal energy resource. This editorial presents a brief overview of each contribution.

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Section: Brief Discussion Of the Published Articles In The Special Issuementioning

confidence: 99%

Editorial for the Special Issue: Assessment of Renewable Energy Resources with Remote Sensing

Martins

2020

Remote Sensing

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“…The method is also able to generate synthetic data based on available similar climatic clustering. Moreover, to compare real and generated data, we use a recently published set of quantifiers (Blaga and Paulescu, 2018) that better highlight the different facets of solar irradiance variability. The overlap coefficient (OVC) (Inman and Jr, 1989) also called Szymkiewicz-Simpson coefficient, and Kullback-Leibler divergence (KLD) (Kullback and Leibler, 1951) called also the relative entropy measure in literature are used to quantify the good-fit between measured and simulated data.…”

Section: Main Ideas and Contributionmentioning

confidence: 99%

“…To validate our method, we use six quantifiers of variability in solar irradiance time-series as suggested in Blaga and Paulescu (2018). These quantifiers capture different facets of solar irradiance variability making the analysis complete.…”

Section: Data Generationmentioning

confidence: 99%

“…A low SDI indicates a smooth shape of daily solar irradiance curve, while a high SDI indicates a fluctuating solar irradiance curve. Also, we want to emphasize that the SDI for the clearness index has a wrong formula in the Blaga and Paulescu (2018) paper. The suggested correction in this paper is as follow:…”

Section: Data Generationmentioning

confidence: 99%

“…SSN indicates the frequency of the Sun occurrence in the sky. It is calculated using a random time-dependent binary variable that uses direct normal irradiance (DNI) values and zenith angle, for more details see Blaga and Paulescu (2018). Lastly, FD of a solar irradiance timeseries is a measure of how much it is jagged.…”

Section: Data Generationmentioning

confidence: 99%

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Nonparametric Bayesian-based recognition of solar irradiance conditions: Application to the generation of high temporal resolution synthetic solar irradiance data

Frimane

Soubdhan²,

Bright

et al. 2019

Solar Energy

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High resolution synthetic irradiance is of interest for theoretical studies such as grid integration of solar PV and battery storage analysis. Access to site-specific data is often limited to inadequate temporal resolutions for such application. A new model for producing synthetic solar global horizontal irradiance (GHI) time-series at up to 1-min resolution is presented as derived from >10-min input data. Briefly, it is a clustered-based method for daily clearness index distributions using Dirichlet process Gaussian mixture model (DPGMM). DPGMM is a nonparametric Bayesian (NPB) model indexed with an infinite-dimensional space of parameters. The key benefit of the NPB paradigm is the automatic adaptation to the correct complexity level and model size, suggesting a local adaptation of the model to all climatic conditions. A posterior inference using Markov chain Monte Carlo algorithm (namely Gibbs sampling) is applied. The model only requires a valid number of intraday data to construct daily distributions, then it can be applied worldwide. The synthetic GHI time series are validated against observed 1-min GHI data for four locations distributed throughout the world with different climatic conditions and significant geographic separation. Moreover, the presented method can generate data based on similar climatic conditions. A good fit between real and generated data is observed. We present an nRMSE ≤ 4% and nMBE < ±4% between generated and measured means at both daily and monthly scales for all sites. The agreement between the real and generated cumulative density distributions of six comparative variability metrics (defined in text) at four different sites is measured using the overlapping and the Kullback-Leibler coefficients, which are ≥ 75% and ≤ 10% respectively, in all cases. To ensure the reproducibility of the research presented in this paper, the methodology is freely available as an R-package downloadable from SolarClusGnr.

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Subordinated Gaussian processes for solar irradiance

2023

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Traditionally the power grid has been a one‐way street with power flowing from large transmission‐connected generators through the distribution network to consumers. This paradigm is changing with the introduction of distributed renewable energy resources (DERs), and with it, the way the grid is managed. There is currently a dearth of high fidelity solar irradiance datasets available to help grid researchers understand how expansion of DERs could affect future power system operations. Realistic simulations of by‐the‐second solar irradiances are needed to study how DER variability affects the grid. Irradiance data are highly non‐stationary and non‐Gaussian, and even modern time series models are challenged by their distributional properties. We develop a subordinated non‐Gaussian stochastic model whose simulations realistically capture the distribution and dependence structure in measured irradiance. We illustrate our approach on a fine resolution dataset from Hawaii, where our approach outperforms standard nonlinear time series models.

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Quantifiers for the solar irradiance variability: A new perspective

Cited by 33 publications

References 24 publications

Editorial for the Special Issue: Assessment of Renewable Energy Resources with Remote Sensing

Editorial for the Special Issue: Assessment of Renewable Energy Resources with Remote Sensing

Nonparametric Bayesian-based recognition of solar irradiance conditions: Application to the generation of high temporal resolution synthetic solar irradiance data

Subordinated Gaussian processes for solar irradiance

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