Priyadarshi
Akash Kumar Bhoi
Sanjeevikumar Padmanaban
S. Balamurugan
and
Jens Bo Holm-Nielsen
This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-78627-6
Cover image: Pixabay.com
Cover design by Russell Richardson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
Printed in the USA
10 9 8 7 6 5 4 3 2 1
Preface
This book presents intelligent renewable energy systems integrating artificial intelligence techniques and optimization algorithms. The first chapter describes placement of distributed generation (DG) sources including renewable distributed generation (RDGs) such as biomass, solar PV, and shunt capacitor has been considered for the study purpose. The second chapter develops a new approach to chaotic particle swarm optimization (CPSO) technique. In the third chapter, comprehensive reviews of different artificial intelligence and machine learning techniques have been explicated. To bring out its advantages over other methods used in island detection, the traditional methods are first explained and then compared with artificial intelligence and machine learning island detection techniques. The performance of the intelligent controller is found to be good under steady conditions for grid connected photovoltaic systems and has been discussed in chapter four. Chapter five explains various uses of Genetic Algorithms (GA) and Solar PV forecasting are described; further, many stimulated algorithms which have been used in optimization, controlling, and methods of supervising of power for renewable energy analysis, which include hybrid power generation strategies are discussed. Chapter six presents the integration of 100 kW solar PV source to the 25 kV AC grid by using generalized r-s based SVPWM algorithm. Chapter seven aims to discuss the idea of hybrid system configuration, dynamic modeling, energy management, and control strategies. A multi-stage planning framework is proposed in chapter eight to divide the planning period into several stages so that investments can be made in each stage as per the requirements. A unique and a novel GUI is presented to design the entire solar PV systems has been discussed in Chapter nine. Chapter ten addresses micro-grid situational awareness using micro PMU. Role of AI & ML in smart grid entities such as Home Energy Management System (HEMS), Energy Trading, Adaptive Protection, Load Forecasting and Smart Energy Meter are presented in Chapter eleven. Chapter twelve presents a new method for energy loss allocation in radial distribution network (RDN) with distributed generationin the context of deregulated power system. Chapter thirteen presents the optimization of controller parameters for FACTS and VSC based HVDC. Chapter fourteen describes Short Term load forecasting for a Captive Power Plant Using Artificial Neural Network. Chapter fifteen defines Real-time EV Charging Station Scheduling Scheme by using Global Aggregator.
Neeraj PriyadarshiAkash Kumar BhoiSanjeevikumar Padmanaban S. BalamuruganJens Bo Holm-Nielsen Editors
1
Optimization Algorithm for Renewable Energy Integration
Bikash Das1, SoumyabrataBarik2*, Debapriya Das3 and V. Mukherjee4
1 Department of Electrical Engineering, Govt. College of Engineering and Textile Technology, Berhampore, West Bengal, India 2 Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani, K. K. Birla Goa Campus, Goa, India 3 Department of Electrical Engineering, Indian Institute of Technology, Kharagpur, West Bengal, India 4 Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, India
*Corresponding author: [email protected]
Abstract
With the development of society, the electrical power demand is increasing day by day. To overcome the increasing load demand, renewable energy resources play an important role. The common examples of renewable energy
