Intelligent Renewable Energy Systems

Integrating Artificial Intelligence Techniques and Optimization Algorithms

Neeraj Priyadarshi, Akash Kumar Bhoi, P. Sanjeevikumar, S. Balamurugan, Jens Bo Holm-Nielsen, Sanjeevikumar Padmanaban

• Publisher: Wiley-Scrivener
• Year: 2021
• Print ISBN: 9781119786276
• eBook ISBN: 9781119786283
• Edition: 1
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

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.

1
Optimization Algorithm for Renewable Energy Integration

Bikash Das¹, SoumyabrataBarik²*, Debapriya Das³ and V. Mukherjee⁴

¹Department of Electrical Engineering, Govt. College of Engineering and Textile Technology, Berhampore, West Bengal, India
²Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani, K. K. Birla Goa Campus, Goa, India
³Department of Electrical Engineering, Indian Institute of Technology, Kharagpur, West Bengal, India
⁴Department 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 algorith...

Table of contents

• Cover
• Table of Contents
• Title Page
• Copyright
• Preface
• 1 Optimization Algorithm for Renewable Energy Integration
• 2 Chaotic PSO for PV System Modelling
• 3 Application of Artificial Intelligence and Machine Learning Techniques in Island Detection in a Smart Grid
• 4 Intelligent Control Technique for Reduction of Converter Generated EMI in DG Environment
• 5 A Review of Algorithms for Control and Optimization for Energy Management of Hybrid Renewable Energy Systems
• 6 Integration of RES with MPPT by SVPWM Scheme
• 7 Energy Management of Standalone Hybrid Wind-PV System
• 8 Optimization Technique Based Distribution Network Planning Incorporating Intermittent Renewable Energy Sources
• 9 User Interactive GUI for Integrated Design of PV Systems
• 10 Situational Awareness of Micro-Grid Using Micro-PMU and Learning Vector Quantization Algorithm
• 11 AI and ML for the Smart Grid
• 12 Energy Loss Allocation in Distribution Systems with Distributed Generations
• 13 Enhancement of Transient Response of Statcom and VSC Based HVDC with GA and PSO Based Controllers
• 14 Short Term Load Forecasting for CPP Using ANN
• 15 Real-Time EVCS Scheduling Scheme by Using GA
• About the Editors
• Index
• End User License Agreement