Modeling Optimization And Intelligent Control Techniques In Renewable Energy Systems

This book consists of two parts. The first part studies selected recent developed strategies of control and management for renewable energy resources. The strategies of control are tested in the presence of unbalance power, voltage faults, frequency deviation, wind speed variation and parametric uncertainties. The second part is especially focused on study of hybrid photovoltaic (PV)-Concentrated solar power (CSP) coupled to a thermal storage system. It gathers a set of chapters covering recent survey literature, modelling and optimization of hybrid PV-CSP power plants. In this part, a detailed model of hybrid PV-CSP with thermal storage system is presented and smart optimization techniques like particle swarm optimization (PSO) and genetic algorithm (GA) are also described and used to optimally design the hybrid PV-CSP renewable energy system. The book would be interesting to most academic undergraduate, postgraduates, researchers on renewable energy systems in terms of modeling, optimization and control, as well as the satisfaction of grid code requirements. Also, it provides an excellent background to renewable energy sources, it is an excellent choice for energy engineers, researchers, system operators, and graduate students. This book can used as a good reference for the academic research on the smart grid, power control, integration of renewable energy sources, and related to this or used in Ph.D study of control, optimisation, management problems and their application in field engineering.

This book consists of two parts. The first part studies selected recent developed strategies of control and management for renewable energy resources. The strategies of control are tested in the presence of unbalance power, voltage faults, frequency deviation, wind speed variation and parametric uncertainties. The second part is especially focused on study of hybrid photovoltaic (PV)-Concentrated solar power (CSP) coupled to a thermal storage system. It gathers a set of chapters covering recent survey literature, modelling and optimization of hybrid PV-CSP power plants. In this part, a detailed model of hybrid PV-CSP with thermal storage system is presented and smart optimization techniques like particle swarm optimization (PSO) and genetic algorithm (GA) are also described and used to optimally design the hybrid PV-CSP renewable energy system. The book would be interesting to most academic undergraduate, postgraduates, researchers on renewable energy systems in terms of modeling, optimization and control, as well as the satisfaction of grid code requirements. Also, it provides an excellent background to renewable energy sources, it is an excellent choice for energy engineers, researchers, system operators, and graduate students. This book can used as a good reference for the academic research on the smart grid, power control, integration of renewable energy sources, and related to this or used in Ph.D study of control, optimisation, management problems and their application in field engineering.

INTELLIGENT RENEWABLE ENERGY SYSTEMS This collection of papers on artificial intelligence and other methods for improving renewable energy systems, written by industry experts, is a reflection of the state of the art, a must-have for engineers, maintenance personnel, students, and anyone else wanting to stay abreast with current energy systems concepts and technology. Renewable energy is one of the most important subjects being studied, researched, and advanced in today’s world. From a macro level, like the stabilization of the entire world’s economy, to the micro level, like how you are going to heat or cool your home tonight, energy, specifically renewable energy, is on the forefront of the discussion. This book illustrates modelling, simulation, design and control of renewable energy systems employed with recent artificial intelligence (AI) and optimization techniques for performance enhancement. Current renewable energy sources have less power conversion efficiency because of its intermittent and fluctuating behavior. Therefore, in this regard, the recent AI and optimization techniques are able to deal with data ambiguity, noise, imprecision, and nonlinear behavior of renewable energy sources more efficiently compared to classical soft computing techniques. This book provides an extensive analysis of recent state of the art AI and optimization techniques applied to green energy systems. Subsequently, researchers, industry persons, undergraduate and graduate students involved in green energy will greatly benefit from this comprehensive volume, a must-have for any library. Audience Engineers, scientists, managers, researchers, students, and other professionals working in the field of renewable energy.

Renewable Energy Systems: Modelling, Optimization and Control aims to cross-pollinate recent advances in the study of renewable energy control systems by bringing together diverse scientific breakthroughs on the modeling, control and optimization of renewable energy systems by leading researchers. The book brings together the most comprehensive collection of modeling, control theorems and optimization techniques to help solve many scientific issues for researchers in renewable energy and control engineering. Many multidisciplinary applications are discussed, including new fundamentals, modeling, analysis, design, realization and experimental results. The book also covers new circuits and systems to help researchers solve many nonlinear problems. This book fills the gaps between different interdisciplinary applications, ranging from mathematical concepts, modeling, and analysis, up to the realization and experimental work. Covers modeling, control theorems and optimization techniques which will solve many scientific issues for researchers in renewable energy Discusses many multidisciplinary applications with new fundamentals, modeling, analysis, design, realization and experimental results Includes new circuits and systems, helping researchers solve many nonlinear problems

Introduction to AI techniques for Renewable Energy System Artificial Intelligence (AI) techniques play an essential role in modeling, analysis, and prediction of the performance and control of renewable energy. The algorithms used to model, control, or predict performances of the energy systems are complicated, involving differential equations, enormous computing power, and time requirements. Instead of complex rules and mathematical routines, AI techniques can learn critical information patterns within a multidimensional information domain. Design, control, and operation of renewable energy systems require a long-term series of meteorological data such as solar radiation, temperature, or wind data. Such long-term measurements are often non-existent for most of the interest locations or, wherever they are available, they suffer from several shortcomings, like inferior quality of data, and in-sufficient long series. The book focuses on AI techniques to overcome these problems. It summarizes commonly used AI methodologies in renewal energy, with a particular emphasis on neural networks, fuzzy logic, and genetic algorithms. It outlines selected AI applications for renewable energy. In particular, it discusses methods using the AI approach for prediction and modeling of solar radiation, seizing, performances, and controls of the solar photovoltaic (PV) systems. Features Focuses on a significant area of concern to develop a foundation for the implementation of renewable energy system with intelligent techniques Showcases how researchers working on renewable energy systems can correlate their work with intelligent and machine learning approaches Highlights international standards for intelligent renewable energy systems design, reliability, and maintenance Provides insights on solar cell, biofuels, wind, and other renewable energy systems design and characterization, including the equipment for smart energy systems This book, which includes real-life examples, is aimed at undergraduate and graduate students and academicians studying AI techniques used in renewal energy systems.

ARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY SYSTEMS Renewable energy systems, including solar, wind, biodiesel, hybrid energy, and other relevant types, have numerous advantages compared to their conventional counterparts. This book presents the application of machine learning and deep learning techniques for renewable energy system modeling, forecasting, and optimization for efficient system design. Due to the importance of renewable energy in today’s world, this book was designed to enhance the reader’s knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business. Audience The primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology.

Focused on renewable energy systems and the development of information and communication technologies (ICTs) for their integration in smart grids, this book presents recent advances and methods that help to ensure that power generation from renewable sources remains stable, that power losses are minimized, and that the reliable functioning of these power generation units is maintained. The book highlights key topics and technologies for renewable energy systems including the intelligent control of power generators, power electronics that connect renewable power generation units to the grid, and fault diagnosis for power generators and power electronics. In particular, the following topics are addressed: • Modeling and control of power generators (PMSGs, DFIGs); • Modeling and control of power electronics (converters, inverters); • Modeling and fault diagnosis of the transmission and distribution Grid; and • Modelling and control of distributed power generation units (interconnected synchronous generators or photovoltaic units). Because of the above coverage, members of the wider engineering community will find that the nonlinear control and estimation methods presented provide essential insights into the functioning of renewable energy power systems, while the academic community will find the book a valuable textbook for undergraduate or graduate courses on renewable energy systems.

The editors of this Special Issue titled “Intelligent Control in Energy Systems” have attempted to create a book containing original technical articles addressing various elements of intelligent control in energy systems. In response to our call for papers, we received 60 submissions. Of those submissions, 27 were published and 33 were rejected. In this book, we offer the 27 accepted technical articles as well as one editorial. Authors from 15 countries (China, Netherlands, Spain, Tunisia, United Sates of America, Korea, Brazil, Egypt, Denmark, Indonesia, Oman, Canada, Algeria, Mexico, and the Czech Republic) elaborate on several aspects of intelligent control in energy systems. The book covers a broad range of topics including fuzzy PID in automotive fuel cell and MPPT tracking, neural networks for fuel cell control and dynamic optimization of energy management, adaptive control on power systems, hierarchical Petri Nets in microgrid management, model predictive control for electric vehicle battery and frequency regulation in HVAC systems, deep learning for power consumption forecasting, decision trees for wind systems, risk analysis for demand side management, finite state automata for HVAC control, robust μ-synthesis for microgrids, and neuro-fuzzy systems in energy storage.