Assessment of direct normal irradiance and cloud connections using satellite data over Australia

Prasad, Abhnil Amtesh; Taylor, Robert A.; Kay, Merlinde

doi:10.1016/j.apenergy.2015.01.050

Cited by 41 publications

(12 citation statements)

References 81 publications

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“…Knowledge of spatiotemporal distributions of this parameter is essential for understanding numerous processes in the climate system, such the water cycle or plant photosynthesis [2]. In this regard, Rs is crucial in hydrology, playing a key role in the modelling of evapotranspiration (ET) as well as air temperature [3][4][5][6][7][8]. Moreover, it is a critical input parameter to crop monitoring systems that rely on agrometeorologic models [9,10].…”

Section: Introductionmentioning

confidence: 99%

Seasonal Characterization of Solar Radiation Estimates Obtained from a MSG-SEVIRI-Derived Dataset and a RAMS-Based Operational Forecasting System over the Western Mediterranean Coast

2016

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Solar radiation is a key factor in the Earth's energy balance and it is used as a crucial input parameter in many disciplines such as ecology, agriculture, solar energy and hydrology. Thus, accurate information of the global downward surface shortwave flux integration into the grid is of significant importance. From the different strategies used for grid integration of the surface solar radiation estimates, satellite-derived and numerical weather prediction forecasts are two interesting alternatives. In the current work, we present a comprehensive evaluation of the global downward solar radiation forecasts provided by the Regional Atmospheric Modeling System (RAMS) and the Downwelling Surface Shortwave Flux (DSSF) product, derived from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Both solar radiation estimates are compared to thirteen ground-based weather station measurements for the winter 2010-2011 and the summer 2011 seasons. For these periods, the most recent versions of RAMS (4.4 and 6.0) were running in parallel within the real-time weather forecasting system implemented over the Valencia Region. The solar radiation performance and accuracy are evaluated for these datasets segmented into two atmospheric conditions (clear and cloudy skies) and two terrain classes (flat and hilly). DSSF shows a very good agreement over the study area. Statistical daily evaluations show that corresponding errors vary between seasons, with absolute bias ranging from´30 to 40 W¨m´2, absolute root mean square errors (RMSE) from 25 to 60 W¨m´2, relative bias ranging from´11% to 7% and relative RMSE from 7% to 22%, depending on the sky condition and the terrain location as well, thus reproducing the observations more faithfully than RAMS, which produces higher errors in comparison to the measurements. In this regard, statistical daily evaluations show absolute bias values varying from´50 to 160 W¨m´2, absolute RMSE from 60 to 240 W¨m´2, relative bias ranging from´30% to 40% and relative RMSE from 10% to 80%, also depending on the daily initialization and the forecast horizon. This bias variability demonstrates that there is a different trend in the deviation of the model results in relation to the observations, both for the DSSF product and RAMS forecasts, and considering the summer and the winter seasons independently. In this regard, although there is an overestimation of the observed solar radiation within the summer months, this magnitude is underestimated during the winter. Finally, comparing this solar radiation estimates for different atmospheric conditions and different terrain classes, the best results are found under clear skies over flat terrain. This result is achieved using both methodologies.

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Section: Introductionmentioning

confidence: 99%

Seasonal Characterization of Solar Radiation Estimates Obtained from a MSG-SEVIRI-Derived Dataset and a RAMS-Based Operational Forecasting System over the Western Mediterranean Coast

2016

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“…The satellite observations were made once every hour, and the time of observation varies smoothly with latitude and differs between satellites. Further information about this dataset can be found in Prasad et al [52] and Dehghan, Prasad, Sherwood and Kay [23].…”

Section: Satellite Datamentioning

confidence: 99%

Assessment of Simulated Solar Irradiance on Days of High Intermittency Using WRF-Solar

Prasad

Kay

2020

Energies

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Improvements in the short-term predictability of irradiance in numerical weather prediction models can assist grid operators in managing intermittent solar-generated electricity. In this study, the performance of the Weather Research and Forecasting (WRF) model when simulating different components of solar irradiance was tested under days of high intermittency at Mildura, a site located on the border of New South Wales and Victoria, Australia. Initially, four intermittent and clear case days were chosen, later extending to a full year study in 2005. A specific configuration and augmentation of the WRF model (version 3.6.1) designed for solar energy applications (WRF-Solar) with an optimum physics ensemble derived from literature over Australia was used to simulate solar irradiance with four nested domains nudged to ERA-Interim boundary conditions at grid resolutions (45, 15, 5, and 1.7 km) centred over Mildura. The Bureau of Meteorology (BOM) station dataset available at minute timescales and hourly derived satellite irradiance products were used to validate the simulated products. Results showed that on days of high intermittency, simulated solar irradiance at finer resolution was affected by errors in simulated humidity and winds (speed and direction) affecting clouds and circulation, but the latter improves at coarser resolutions; this is most likely from reduced displacement errors in clouds.

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“…Proper probability density functions (PDFs) have to be chosen for each DER to reflect the uncertainties and create simulation scenarios [42]. For example, the beta PDF can be used to describe the distribution of irradiance that influences the PV output at a specific location [43]. However, the uncertainties of PV generation and building peak-load consumption are omitted from the formulas in this study because their actual data are used for this study's experiment.…”

Section: Uncertainty Modeling Of Evmentioning

confidence: 99%

Optimal Scheduling of Distributed Energy Resources in Residential Building under the Demand Response Commitment Contract

Baek

Kim

2019

Energies

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This study proposes optimal day-ahead demand response (DR) participation strategies and distributed energy resource (DER) management in a residential building under an individual DR contract with a grid-system operator. First, this study introduces a DER management system in the residential building for participation to the day-ahead DR market. The distributed photovoltaic generation system (PV) and energy-storage system (ESS) are applied to reduce the electricity demand in the building and sell surplus energy on the grid. Among loads in the building, lighting (LTG) and heating, ventilation, and air conditioning (HVAC) loads are included in the DR program. In addition, it is assumed that a power management system of an electric vehicle (EV) charging station is integrated the DER management system. In order to describe stochastic behavior of EV owners, the uncertainty of EV is formulated based on their arrival and departure scenarios. For measuring the economic efficiency of the proposed model, we compare it with the DER self-consuming operation model without DR participation. The problem is solved using mixed integer linear programming to minimize the operating cost. The results in summer and winter are analyzed to evaluate the proposed algorithm’s validity. From these results, the proposed model can be confirmed as reducing operation cost compared to the reference model through optimal day-ahead DR capacity bidding and implementation.

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Assessment of direct normal irradiance and cloud connections using satellite data over Australia

Cited by 41 publications

References 81 publications

Seasonal Characterization of Solar Radiation Estimates Obtained from a MSG-SEVIRI-Derived Dataset and a RAMS-Based Operational Forecasting System over the Western Mediterranean Coast

Seasonal Characterization of Solar Radiation Estimates Obtained from a MSG-SEVIRI-Derived Dataset and a RAMS-Based Operational Forecasting System over the Western Mediterranean Coast

Assessment of Simulated Solar Irradiance on Days of High Intermittency Using WRF-Solar

Optimal Scheduling of Distributed Energy Resources in Residential Building under the Demand Response Commitment Contract

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