Intelligent Renewable Energy Systems. Группа авторов
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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 resources are solar photovoltaic (PV), wind energy, biomass, fuel-cell, etc. Due to the various benefits of the renewable energy, the incorporation of renewable energy resources into the distribution network becomes an important topic in the field of the modern power system. The incorporation of renewable energy resources may reduce the network loss, improve voltage profile, and improve the reliability of the network. In this current research work, optimum placements of renewable distributed generations (RDGs) (viz. biomass and solar PV) and shunt capacitors have been highlighted. For the optimization of the locations and the sizes of the RDGs and the shunt capacitors, a multi-objective optimization problem is considered in this book chapter in presence of various equality and inequality constraints. The multi-objective optimization problem is solved using a novel mixed-discrete student psychology-based optimization algorithm, where the key inspiration comes from the behaviour of a student in a class to be the best one and the performance of the student is measured in terms of the grades/marks he/she scored in the examination and the efficacy of the proposed method is analyzed and compared with different other optimization methods available in the literature. The multi-objective DG and capacitor placement is formulated with reduction of active power loss, improvement of voltage profile, and reduction of annual effective installation cost. The placement of RDGs and shunt capacitors with the novel proposed method is implemented on two different distribution networks in this book chapter.
Keywords: Renewable energy integration, shunt capacitors, distributed generation, mixed discrete student psychology-based optimization algorithm, distribution networks
1.1 Introduction
In order to satisfy the increasing electricity load demand, electrical power generation needs to be scheduled properly [1–25]. Electrical power sources can be classified into two categories named as non-renewable and renewable sources. Non-renewable sources mainly include fossil fuels [26–45]. To generate electrical power from fossil-fuels, the fossil-fuels need to be burned. But the combustion of fossil-fuel causes pollution which affects the atmosphere. On the other hand, renewable energy resources cause zero or very little pollution. The main drawback of renewable energy resources is that the extraction of energy is dependent on nature [46–55]. In spite of having the disadvantages, the renewable energy resources are gaining more and more interest in the extraction of electrical power and to satisfy the increasing load demand.
To get better benefits, the placement of distributed generation (DG) to the distribution network needs proper strategy and planning [56–71]. Improper placement of DG may lead to increase in network loss, as well as may cause instability to the network. DG injects power into the distribution network. Based on the power injection, the DG sources can be classified into three categories