Modelling PV Clouding Effects Using a Semi-Markov Process with Application to Energy Storage

Barnes, Arthur K.; Balda, Juan Carlos; Hayes, Jonathan

doi:10.3182/20140824-6-za-1003.00825

Cited by 12 publications

(13 citation statements)

References 19 publications

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“…Another more precise example that considers 3 weather parameters is Chenni et al's model. The coefficients are average values of measurements performed on 6 different PV technologies (amorphous silicon, monocrystalline silicon, copper indium diselenide, EFGpolycrystalline silicon, polycrystalline silicon, and cadmium telluride) located on two different geographical sites [48]: (12) Nevertheless, this still remains an imperfect model as it does not take heat losses into account.…”

Section: Modeling the Cell/module Temperaturementioning

confidence: 99%

“…The same authors also propose to model the solar irradiance in a probabilistic manner. Climate science has established that the shape of cumulus cloud shadows contours have a fractal structure [12,29]. Cai et al use a midpoint displacement algorithm to model this structure.…”

Section: Cloud Coverage Modeling and Cloud Classificationmentioning

confidence: 99%

“…Indeed, the rapid progress and increasing accuracy of short-term solar generation forecast guarantees continued efficient and reliable system operations. Yet, it is interesting to note that large capacity utility-scale grid-connected PV farms account for 38% of the overall PV generation [12]. PV farms can also use tracking devices to optimize the harvest of solar energy.…”

Section: Introductionmentioning

confidence: 99%

“…Yet, the motion of clouds a dramatic impact on the performance of a solar generator [14] and thus need to be forecasted to avoid undesired issues and costs. Due to changes in the cloud cover, it must be noted that some significant drops in PV generated power output can occur, in the range of up to 70% in a matter of 5 seconds [12,15]. This may result in episodes of grid instability if not properly compensated.…”

Section: Introductionmentioning

confidence: 99%

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

Barbieri

Rajakaruna

Ghosh

2017

Renewable and Sustainable Energy Reviews

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: Modeling the Cell/module Temperaturementioning

confidence: 99%

Section: Cloud Coverage Modeling and Cloud Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Barbieri

Rajakaruna

Ghosh

2017

Renewable and Sustainable Energy Reviews

233

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“…This model exhibits wide range of applications like utilization of PV in small and large timescales, short term forecasting of PV power and stochastic scheduling in coordination with ESDs and other resources including ultra-capacitors. The effective use of ESDs can be obtained by predicting cloud intermittency variation at rooftops also using pyranometer [110]. A probability mass function (PMF) and cumulative distribution function (CDF) of PV generated energy are introduced.…”

Section: Discussion and Future Research Directionsmentioning

confidence: 99%

A Review of Strategies to Increase PV Penetration Level in Smart Grids

Aleem

Hussain²,

Ustun³

2020

Energies

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Due to environmental concerns, power system generation is shifting from traditional fossil-fuel resources to renewable energy such as wind, solar and geothermal. Some of these technologies are very location specific while others require high upfront costs. Photovoltaic (PV) generation has become the rising star of this pack, thanks to its versatility. It can be implemented with very little upfront costs, e.g., small solar home systems, or large solar power plants can be developed to generate MWs of power. In contrast with wind or tidal generation, PV can be deployed all around the globe, albeit with varying potentials. These merits have made PV the renewable energy technology with highest installed capacity around the globe. However, PV penetration into the grid comes with its drawbacks. The inverter-interfaced nature of the PV systems significantly impacts the power system operation from protection, power flow and stability perspectives. There must be strategies to mitigate these negative impacts so that PV penetration into the grid can continue. This paper gives a thorough overview of such strategies from different research fields: such as communication, artificial intelligence, power electronics and electric vehicle charging coordination. In addition, possible research directions are given for future work.

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