Sun Position Identification in Sky Images for Nowcasting Application

Alessandro Niccolai,Alfredo Nespoli

doi:10.3390/forecast2040026

Abstract

Very-short-term photovoltaic power forecast, namely nowcasting, is gaining increasing attention to face grid stability issues and to optimize microgrid energy management systems in the presence of large penetration of renewable energy sources. In order to identify local phenomena as sharp ramps in photovoltaic production, whole sky images can be used effectively. The first step in the implementation of new and effective nowcasting algorithms is the identification of Sun positions. In this paper, three different techniques (solar angle-based, image processing-based, and neural network-based techniques) are proposed, described, and compared. These techniques are tested on real images obtained with a camera installed at SolarTechLab at Politecnico di Milano, Milan, Italy. Finally, the three techniques are compared by introducing some performance parameters aiming to evaluate of their reliability, accuracy, and computational effort. The neural network-based technique obtains the best performance: in fact, this method is able to identify accurately the Sun position and to estimate it when the Sun is covered by clouds.

Highlights

The growth of Renewable Energy Sources (RES) is expected to increase in the following years; among all the sources, the expansion is going to be led by solar photovoltaic (PV) at both the distributed and centralized levels [1]
The aim of this paper is to introduce, describe, and compare three different Sun identification techniques that can be applied to the nowcasting problem
The third method is based on Artificial Neural Networks (ANNs) that are able to infer the correlation between the theoretical solar angles and the solar position on the images; the strength of this approach is that, regardless of the weather conditions, it is able to provide the correct position of the Sun

Summary

Introduction

The growth of Renewable Energy Sources (RES) is expected to increase in the following years; among all the sources, the expansion is going to be led by solar photovoltaic (PV) at both the distributed and centralized levels [1]. While the aggregate load fluctuations vary within few percentage around the expected values, the power generation from plants exploiting RES presents larger and generally faster variations. For this reason, the shortness they can cause is usually faced with some “hot standby” spare capacity, which affects the overall efficiency of the system [5]. The RES fluctuation is usually compensated by energy storage and backup technologies, which should be properly sized and coordinated, generally to increase efficiency, to reduce fuel consumption, or to wisely combine the two [6,7]

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Results

Conclusion