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

Abstract

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.

A Novel Approach for the Short-Term Forecast of the Effective Cloud Albedo

“…As shown in subsection 3.3 the utilization of optical flow estimation for a short-term forecast of the effective cloud albedo and hence of the solar surface irradiance shows promising results. Validation results reported in recent review publications by Voyant et al [9], Antonanzas et al [10] or Barbieri et al [11] or rather publications by other leading experts, for example Raza et al [12], Wolff et al [1] or Cros et al [34] do not provide any hints that the Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 28 April 2018 doi:10.20944/preprints201804.0367.v1 application of the widely used neuronal networks lead to a significant better accuracy for cloud motion vectors. For example in Cros et al [34] the RMSE of the 30-minute forecast of the effective cloud albedo is about 30 % for a neuronal network state of the art approach and a phase correlation method.…”

“…However they are not mentioned neither in the review of photovoltaic power forecasting performed by Antonanzas et al [10], nor by the review of very short PV-forecasting with cloud modelling by Barbieri et al [11]. Other leading experts, for example Raza et al [12] or Wolff et al [1], do not mention optical flow methods by OpenCV as an option or alternative.…”

Short-Term Solar Irradiance Forecasts Using Sky Images and Radiative Transfer Model

“…However, both NWP and satellite imagery lack the spatial and temporal resolutions necessary for providing detailed information regarding high frequency fluctuations of solar irradiance. To overcome this issue, ground-based sky imagers have been developed and deployed as a means of capturing local sky images with highly-refined spatial and temporal resolutions for intra-hour solar irradiance forecasts [5].…”

Prediction in Photovoltaic Power by Neural Networks

“…Starting from the input of past power measurements and meteorological forecasts of solar irradiance, the power output is estimated by means of ANN [25] or soft computing [26], while in [27], the ANN is applied only on past energy production values. Additionally, the ANN can be used in conjunction with numerical weather prediction (NWP) models [28], which can be based on satellite and land-based sky imaging [29]. NWP models have been also used to build an outperforming multi-model ensemble (MME) for day-ahead prediction of PV power generation [30].…”