Modelling and performance assessment of large scale solar photovoltaic plants in rural Australia

Zieland, Lucas; Rajakaruna, Sumedha; Liyanage, Diana

doi:10.1109/aupec.2013.6725424

Cited by 1 publication

(1 citation statement)

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“…The efficiency rate is where ߟ is the nominal conversion rate in STC, which depends on the PV technology. The average nominal efficiency of the three main categories of mono-junction PV technologies is summarized in table 7 [49]. Monocrystalline PV have the highest nominal conversion rate whereas thin film amorphous silicon systems have the lowest.…”

Section: Further Considerations Of the Pv Modelmentioning

confidence: 99%

Very short-term photovoltaic power forecasting with cloud modeling: A review

Barbieri

Rajakaruna

Ghosh

2017

Renewable and Sustainable Energy Reviews

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233

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This paper endeavors to provide the reader with an overview of the various tools needed to forecast photovoltaic (PV) power within a very short-term horizon. The study focuses on the specific application of a large scale grid-connected PV farm. As a matter of fact, the solar resource is largely underexploited worldwide whereas it exceeds by far humans' energy needs. In the current context of global warming, PV energy could potentially play a major role to substitute fossil fuels within the main grid in the future. Indeed, the number of utility-scale PV farms is currently fast increasing globally, with planned capacities in excess of several hundred megawatts. This makes the cost of PV-generated electricity quickly plummet and reach parity with non-renewable resources. However, like many other renewable energy sources, PV power depends highly on weather conditions. This particularity makes PV energy difficult to dispatch unless a properly sized and controlled energy storage system (ESU) is used. An accurate power forecasting method is then required to ensure power continuity but also to manage the ramp rates of the overall power system. In order to perform these actions, the forecasting timeframe also called horizon must be first defined according to the grid operation that is considered. This leads to define both spatial and temporal resolutions. As a second step, an adequate source of input data must be selected. As a third step, the input data must be processed with statistical methods. Finally, the processed data are fed to a precise PV model. It is found that forecasting the irradiance and the cell temperature are the best approaches to forecast precisely swift PV power fluctuations due to the cloud cover. A combination of several sources of input data like satellite and land-based sky imaging also lead to the best results for very-short term forecasting.

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Section: Further Considerations Of the Pv Modelmentioning

confidence: 99%