Handbook of Artificial Intelligence Techniques in Photovoltaic Systems: Modeling, Control, Optimization, Forecasting and Fault Diagnosis

By Adel Mellit and Soteris Kalogirou

Handbook of Artificial Intelligence Techniques in Photovoltaic Systems: Modelling, Control, Optimization, Forecasting and Fault Diagnosis provides readers with a comprehensive and detailed overview of the role of artificial intelligence in PV systems. Covering up-to-date research and methods on how, when and why to use and apply AI techniques in solving most photovoltaic problems, this book will serve as a complete reference in applying intelligent techniques and algorithms to increase PV system efficiency. Sections cover problem-solving data for challenges, including optimization, advanced control, output power forecasting, fault detection identification and localization, and more.

Supported by the use of MATLAB and Simulink examples, this comprehensive illustration of AI-techniques and their applications in photovoltaic systems will provide valuable guidance for scientists and researchers working in this area.

• Includes intelligent methods in real-time using reconfigurable circuits FPGAs, DSPs and MCs
• Discusses the newest trends in AI forecasting, optimization and control applications
• Features MATLAB and Simulink examples highlighted throughout

• Language: English
• Publisher: Academic Press
• Release date: Jun 23, 2022
• ISBN: 9780128206423

Adel Mellit

Adel Mellit is Professor at the Faculty of Sciences and Technology, Jijel University, Algeria. He received his MS and PhD in electronics from the University of Sciences Technologies (USTHB), Algiers in 2002 and 2006, respectively. His research interests Q1 focus on the application of artificial intelligence techniques in photovoltaic systems and microgrids (control, fault diagnosis, optimization, and real-time applications). Dr. Adel Mellit has authored or coauthored more than 170 papers in international peer-reviewed journals (mostly with Elsevier), papers in conference proceedings (mostly with the IEEE) mainly on photovoltaic systems, six book chapters, and two books. He is the director of the Renewable Energy Laboratory at the Jijel University, Algeria, and is an associate member at the ICTP Trieste, Italy. He is serving on the editorial board of the Renewable Energy and is Editor of the IEEE Journal of Photovoltaic and of Energy (Elsevier Ltd). https://orcid.org/0000-0001-5458-3502

Book preview

1: Solar radiation and photovoltaic systems: Modeling and simulation

Abstract

This chapter is composed of two parts: the first part provides a short introduction to solar radiation, which play a very important role for solar energy systems, including photovoltaic systems. The main equations and models used for modeling and simulation of solar radiation components are presented in this section. Some examples of these applications are provided using Python and Matlab® languages. The second part of this chapter covers a brief introduction to photovoltaics, including solar cell conversion, photovoltaic technologies, latest solar cells efficiency, photovoltaic configuration, and type of photovoltaic systems (e.g., stand-alone, grid-connected, and hybrid photovoltaic systems). A total of 16 examples for modeling and simulation of photovoltaic systems, from solar cell to photovoltaic systems, using Matlab/Simulink and Python language are also presented.

Keywords

Solar radiation; Photovoltaic; Modeling; Prediction; Simulation; Photovoltaic issues

Chapter outline

1.1Introduction

1.2Solar radiation

1.3Photovoltaics

1.3.1Photovoltaic effect

1.3.2Photovoltaic technologies

1.3.3Modeling and simulation

1.3.4Photovoltaic systems

1.4Main issues of photovoltaic systems

1.5Summary

References

1.1: Introduction

Among the various renewable energy sources, solar energy utilization, through the use of photovoltaic (PV), is one of the most important sources of electrical energy production around the world. Over the past few years, photovoltaic installations have experienced rapid growth worldwide. At the end of 2020, the cumulative installed capacity exceeded 760.4 GW [1]. Fig. 1.1 shows the evolution of annual PV installations around the world. With reference to IRENA [2], photovoltaics (PV) is the leading energy technology in terms of annual growth rate (over 35% in 2010–19).

Fig. 1.1

Fig. 1.1 Evolution of annual photovoltaic installations (IEA) [1] .

The contribution of PV to decarbonizing the energy mix is progressing, with PV savings for as much as 720 million tons of CO2eq. At the end of 2019, PV contributed to the reduction of the global CO2 emissions by 1.7% or 2.2% of the energy-related emissions and 5.3% of the electricity-related emissions as compared to a world without PV [3]. The role played by PV in the reduction of the CO2 emissions from electricity is continuously increasing [1].

According to the latest cost data from the IRENA, the global weighted-average levelized cost of electricity (LCOE) of utility-scale solar photovoltaics fell by 82% between 2010 and 2019 [2]. Currently, power purchase agreements (PPAs) have been announced for large-scale PV systems at below 2 USD cents/kWh/m². The share of monocrystalline technology is now about 66%, achieved by refining the architecture and the manufacturing of PV modules [4]. As reported in Ref. [1] for several countries, the PV contribution to the electricity demand has passed the 5% mark. PV is now the most competitive electricity source in some market segments. The availability of this cheap electricity is starting to allow the breakthrough of green fuels [1].

Photovoltaic applications include many areas: (1) solar farms (e.g., large PV systems), (2) remote locations (sites with no access to grid, such as electrification of Sahara regions, mountains, rural locations, etc.), (3) stand-alone power (e.g., water pumping, telecommunication, parking meters, radio transmitter, etc.), (4) space (e.g., satellite), (5) transportation (e.g., electrical vehicles and boats), (6) buildings (e.g., PV on the roof, integrating transparent PV window, parking structures, etc.), (7) military sector (e.g., drones and other equipment), and (8) supply power to electronic devices.

In this chapter, a short introduction to solar radiation is initially given, which plays a very important role for solar energy systems, including photovoltaic systems. The main equations and models used for modeling and simulation of solar radiation components are presented in this section. Some examples of these applications are provided using Python and Matlab® languages. Next, we present a brief introduction to photovoltaics, including solar cell conversion, PV technologies, latest solar cell efficiency, PV configuration, and type of photovoltaic systems (e.g., stand-alone, grid-connected, and hybrid photovoltaic systems). Examples for modeling and simulation of photovoltaic systems, from solar cell to PV systems, using Matlab/Simulink are also presented.

1.2: Solar radiation

Solar radiation is the energy radiated from the Sun in the form of electromagnetic waves, including visible and ultraviolet light and infrared radiation. Fig. 1.2 shows the extraterrestrial solar spectrum (ETS) at the mean Sun-Earth distance [5].

Fig. 1.2

Fig. 1.2 Extraterrestrial solar spectrum in the shortwave at low resolution (0.5 − 5 nm).

Example 1.1: Solar spectrum

The following code (Listing 1.1) shows an example of how to plot the solar spectrum [6] using the open-source PVLib-Python library [7] with the use of the Spectrum.spectrl2 function.

Listing 1.1: Solar spectrum modeling

lat = 36;lon = 5 ;tilt = 36;azimuth = 180pressure = 9 ;water_vapor_content = 0.5tau500 = 0.1;ozone = 0.31 ;albedo = 0.2times = pd.date_range('2020-03-20 12:00', freq ='h', periods = 4, tz ='Etc/GMT + 2')solpos = solarposition.get_solarposition(times, lat, lon)aoi = irradiance.aoi(tilt, azimuth, solpos.apparent_zenith, solpos.azimuth)spectra = spectrum.spectrl2(  apparent_zenith = solpos.apparent_zenith,  aoi = aoi,  surface:tilt = tilt,  ground_albedo = albedo,  surface:pressure = pressure,  relative_airmass = relative_airmass,  precipitable_water = water_vapor_content,  ozone = ozone,  aerosol_turbidity_500nm = tau500)

So to execute the above code, first the PVLib library should be installed and then the required functions such as spectrum, solar position, irradiance, and atmosphere should be imported; see the following command line.

from pvlib import spectrum, solarposition, irradiance, atmosphere

There is a need to also import Pandas and Matplotlib libraries to plot the curve using the lines below:

import pandas as pdimport matplotlib.pyplot as plt

The plot of the modeled spectral irradiance for different air mass (AM) and zenith values is shown in Fig. 1.3.

Fig. 1.3

Fig. 1.3 Plot of spectral irradiance for different values of AM and zenith (latitude = 36°, longitude = 5°, and tilt