PV System Design and Performance

Photovoltaic solar energy technology (PV) has been developing rapidly in the past decades, leading to a multi-billion-dollar global market. It is of paramount importance that PV systems function properly, which requires the generation of expected energy both for small-scale systems that consist of a few solar modules and for very large-scale systems containing millions of modules. This book increases the understanding of the issues relevant to PV system design and correlated performance; moreover, it contains research from scholars across the globe in the fields of data analysis and data mapping for the optimal performance of PV systems, faults analysis, various causes for energy loss, and design and integration issues. The chapters in this book demonstrate the importance of designing and properly monitoring photovoltaic systems in the field in order to ensure continued good performance

A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

The use of artificial intelligence (AI) is increasing in various sectors of photovoltaic (PV) systems, due to the increasing computational power, tools and data generation. The currently employed methods for various functions of the solar PV industry related to design, forecasting, control, and maintenance have been found to deliver relatively inaccurate results. Further, the use of AI to perform these tasks achieved a higher degree of accuracy and precision and is now a highly interesting topic. In this context, this paper aims to investigate how AI techniques impact the PV value chain. The investigation consists of mapping the currently available AI technologies, identifying possible future uses of AI, and also quantifying their advantages and disadvantages in regard to the conventional mechanisms

A review of tools, models and techniques for long-term assessment of distribution systems using OpenDSS and parallel computing

Many distribution system studies require long-term evaluations (e.g. for one year or more): Energy loss minimization, reliability assessment, or optimal rating of distributed energy resources should be based on long-term simulations of the distribution system. This paper summarizes the work carried out by the authors to perform long-term studies of large distribution systems using an OpenDSS-MATLAB environment and parallel computing. The paper details the tools, models, and procedures used by the authors in optimal allocation of distributed resources, reliability assessment of distribution systems with and without distributed generation, optimal rating of energy storage systems, or impact analysis of the solid state transformer. Since in most cases, the developed procedures were implemented for application in a multicore installation, a summary of capabilities required for parallel computing applications is also included. The approaches chosen for carrying out those studies used the traditional Monte Carlo method, clustering techniques or genetic algorithms. Custom-made models for application with OpenDSS were required in some studies: A summary of the characteristics of those models and their implementation are also included.Peer ReviewedPostprint (published version

Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications

This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and systems; PV modeling; reconfiguration algorithms; fault diagnosis; mismatching detection; decision processes for grid operators

Designing a Solar Photovoltaic System for Generating Renewable Energy of a Hospital: Performance Analysis and Adjustment Based on RSM and ANFIS Approaches

One of the most favorable renewable energy sources, solar photovoltaic (PV) can meet the electricity demand considerably. Sunlight is converted into electricity by the solar PV systems using cells containing semiconductor materials. A PV system is designed to meet the energy needs of King Abdulaziz University Hospital. A new method has been introduced to find optimal working capacity, and determine the self‐consumption and sufficiency rates of the PV system. Response surface methodology (RSM) is used for determining the optimal working conditions of PV panels. Similarly, an adaptive neural network based fuzzy inference system (ANFIS) was employed to analyze the performance of solar PV panels. The outcomes of methods were compared to the actual outcomes available for testing the performance of models. Hence, for a 40 MW target PV system capacity, the RSM determined that approximately 33.96 MW electricity can be produced, when the radiation rate is 896.3 W/m2, the module surface temperature is 41.4 °C, the outdoor temperature is 36.2 °C, the wind direction and speed are 305.6 and 6.7 m/s, respectively. The ANFIS model (with nine rules) gave the highest performance with lowest residual for the same design parameters. Hence, it was determined that the hourly electrical energy requirement of the hospital can be met by the PV system during the year.Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant No. (D1441‐135‐626

Dynamic Modeling and Renewable Integration Studies on the U.S. Power Grids

Wind and solar generation have gained a significant momentum in the last five years in the United States. According to the American Wind Energy Association, the installed wind power capacity has tripled from 25,410 MW in early 2009 to 74,472 MW as of the end of 2015. Meanwhile, solar photovoltaic (PV) is reported that its capacity has skyrocketed from 298 MW in 2009 to 7,260 MW in 2015 by the Solar Energy Industries Association. Despite the fact that wind and solar only make up 4.4% and 0.4% , respectively, of total electricity generation in 2014, the nation is right on its track to the Department of Energy (DOE)’s goal of 20% wind and 14% solar by year 2030. The future of renewable energy is aspiring. The rapid growth in renewable generation results in an urge to studying the reliability implication of renewable integration. For this purpose, two DOE projects were funded to the University of Tennessee, Knoxville, and the Oak Ridge National Laboratory. The first project, Grid Operational Issues and Analyses of the Eastern Interconnection (EI), is aimed at studying the dynamic stability impact of high wind penetration on the U.S. EI system in year 2030. The second project, Frequency Response Assessment and Improvement of Three Major North American Interconnections due to High Penetrations of Photovoltaic Generation, concentrates on the influence of high solar penetration on primary frequency response. This thesis documents the efforts of the above-mentioned two projects. Chapter 1 gives an introduction on power system dynamic modeling. Chapter 2 describes the process of dynamic models development. Chapter 3 discusses the adoption of synchro-phasor measurement for system-level dynamic model validation and the impact of turbine governor deadband on system dynamic response. Chapter 4 presents a stability impact study of high wind penetration on the U.S. Eastern Grid. Chapter 5 documents the modeling and simulation of the EI system under high solar penetration. Chapter 6 summaries two dynamic model reduction studies on the EI system. Conclusions, a summary of the major contribution of the Ph.D. work, and a discussion of possible future work are given in Chapter 7

Application of adaptive models for the determination of the thermal behaviour of a photovoltaic panel

The use of reliable forecasting models for the PV temperature is necessary for a more correct evaluation of energy and economic performances. Climatic conditions certainly have a remarkable influence on thermo-electric behaviour of the PV panel but the physical system is too complex for an analytical representation. A neural-network-based approach for solar panel temperature modelling is here presented. The models were trained using a set of data collected from a test facility. Simulation results of the trained neural networks are presented and compared with those obtained with an empirical correlation