Resilient Community Microgrids E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 857

Veröffentlichungsjahr: 2025

Beschreibung

Discover how to empower your community with sustainable energy solutions with Resilient Community Microgrids, a comprehensive guide that explores the integration of innovative technologies and distributed energy resources to enhance local energy independence and resilience.

Resilient Community Microgrids emphasizes opportunities to incorporate distributed energy resources and communication networks to build a cyber-physical community microgrid system by modelling photovoltaics, energy storage units, micro-turbines, and wind energy. The microgrid proves itself as a sustainable archetype to improve the resilience and reliability of power distribution networks. High-distributed energy resources penetrate communities, unlocking the potential to build the resilience of microgrids. Neighborhoods, villages, towns, and cities can meet their local energy needs by utilizing community microgrids. Community microgrids are being considered as a possibility even in locations where a bigger grid already exists, primarily as a means of boosting local energy independence and resilience. The fundamentals of community microgrids are covered in this book, along with an outline of how to join one and the factors contributing to their rising popularity.

Novel technologies arrive with the potential to integrate with the physical microgrid to realize the next generation in cyber-physical microgrid systems, which can be used as a prototype to demonstrate and promote the development of next-generation microgrids. Resilient Community Microgrids will clarify the ways to enhance a cyber-physical system's resilience that significantly contributes to realizing innovative and sustainable development in the energy sector.

Leseprobe

Table of Contents

Cover

Table of Contents

Series Page

Title Page

Copyright Page

Preface

1 AI-Based Virtual Advisor for Smart Climate Farming

1.1 Introduction

1.2 Research on Smart Farming Technologies and AI Applications

1.3 AI and IoT in Smart Farming

1.4 Sustainable Agriculture and Climate-Smart Farming

1.5 Conclusion

References

2 Swappable Battery Pack System for Electric Two-Wheelers: Design, Infrastructure, and Implementation

2.1 Introduction

2.2 Swappable Battery Technology

2.3 Battery Swapping Infrastructure and Optimization

2.4 Battery Management System

2.5 Business Models and Economic Implications

2.6 Conclusion

References

3 Implementation of High Gain Bidirectional Interleaved DC/DC Converter for Electric Vehicles with Supercapacitors

3.1 Introduction

3.2 Proposed Converter

3.3 Operating Principle of the HGBID Converter

3.4 Design Considerations

3.5 Characteristics of SC

3.6 Simulation Results

3.7 Conclusion

References

4 Fault Over-Ride and Minimization of Losses in a PV Integrated Transmission Network Using STATCOM

4.1 Introduction

4.2 Problem Statement

4.3 Contingency Analysis and Contingency Selection

4.4 Test System, Software and Components Used

4.5 Results and Analysis

4.6 IEEE 14 Bus Network with Two STATCOMs Installed at Bus 2 and Bus 6

4.7 Conclusion

4.8 Future Scope

References

5 Oscillating Water Column as Clean Energy Source for Sustainable Power Generation

5.1 Introduction to Technology

5.2 Hardware Implementation

5.3 Three-Dimensional Design of Hardware Components in Solid Edge Software

5.4 Hardware Implementation Results and Performance Analysis of Oscillating Water Column (OWC)

5.5 Conclusion

5.6 Future Scope

References

6 Cloud-Based Big Data Architecture and Infrastructure

6.1 Introduction

6.2 Big Data Architecture for the Cloud Fundamentals

6.3 Overview of Methods for Ingesting Data, Including Batch Operations and Live Streaming

6.4 Technologies for Big Data on the Cloud

6.5 Overview of Server Less Computing and Its Benefits for Cost Optimization and Scaling

6.6 Big Data Architectural Models for the Cloud

6.7 Integration of Cloud Services and Big Data

6.8 Examining Data Integration and ETL (Extract, Transform, Load) Methods Based on the Cloud

6.9 Overview of Cloud-Based Big Data Environments’ Data Governance and Metadata Management

6.10 Analysis of Cloud-Based Big Data Architectures’ Scalability Issues

6.11 Examining Vertical and Horizontal Scaling Methods to Succeed in Processing Demands and Growing Data Volumes

6.12 Introduction to Cloud-Based Big Data Architectures’ Performance Optimization Strategies

6.13 Big Data Based on the Cloud is Secure and Private

6.14 A Description of the Mechanisms for Data Encryption, Access Regulation and Identity Administration

6.15 Examination of Privacy Issues and Data Protection Laws Compliance

6.16 Case Studies and Real-World Applications

6.17 Future Directions and Trends

6.18 Future Developments Prediction and Scalable and Efficient Data Processing Implications

6.19 Conclusion

6.20 Emphasis on Cloud-Based Big Data Architecture and Infrastructure’s Potential for Transformation

6.21 Motivating Companies to Adopt Cloud-Based Big Data Technologies

7 RISC-V Processor Hardware Modelling with Custom Instruction Set for SHA-3 Acceleration

7.1 Introduction

7.2 State of the Art

7.3 Keccak Algorithm in SHA-3

7.4 RISC-V Instruction Set Architecture

7.5 Custom Instructions for SHA-3 Hashing

7.6 Proposed Processor Microarchitecture

7.7 Results and Discussion

7.8 Conclusion

References

8 SSL Vulnerability Exploitation Analysis Tool to Provide a Secure and Sustainable Network for Smart Cities

8.1 Introduction

8.2 Related Work

8.3 Research Methodology

8.4 Experimental Results

8.5 Conclusion

References

9 Service-Oriented Smart City Vigilant Data Hub for Social Innovation

9.1 Introduction

9.2 Background and Literature Review

9.3 App Architecture and Technology Stack

9.4 User Registration and Authentication

9.5 Features and Functionality

9.6 User Experience and Interface Design

9.7 Data Privacy and Security

9.8 Real-Time Updates and Push Notifications from the App

9.9 Scalability and Performance Optimization

9.10 User Engagement Analytics

9.11 Impact and User Engagement

9.12 Citizen User Flow and Admin Access User Flow

9.13 Conclusion

9.14 Future Potential

References

10 A Survey on AI & ML for Autonomous Driving, User Behavior Monitoring, and Intelligent Navigation in EVs

10.1 Introduction

10.2 Survey Overview

10.3 Objectives of this Work

10.4 Methodologies

10.5 Outcome

10.6 Applications of the Proposed Model

10.7 Demonstration of Autonomous Driving Car Using Pygame

10.8 Conclusion

References

11 Deep Learning in Waste Management and Recycling in Digital Smart City

11.1 Introduction

11.2 Related Work

11.3 Deep Learning Applications in Waste Management

11.4 Methodology and Model Specifications

11.5 Experimental Results and Discussions

11.6 Conclusion

References

12 Home Automation Using Augmented Reality

12.1 Introduction

12.2 Literature Review

12.3 Hardware Analysis

12.4 Methodology

12.5 Results and Discussion

12.6 Conclusion

References

13 Detection and Mitigation Techniques for Defending DDoS Attacks in Cloud Environment

13.1 Introduction

13.2 Related Literature Survey

13.3 Related Work

13.4 Conclusion

References

14 Design and Implementation of Secure MQTT Protocol for Embedded IoT Device

14.1 Introduction

14.2 Need for Security in IoT Device

14.3 Comparison Between Messaging Protocols Used in IoT Environment

14.4 MQTT Architecture

14.5 Proposed System Objective

14.6 Related Work

14.7 Conclusion and Future Scope

References

15 Internet of Things in Smart Building Management System

15.1 Introduction

15.2 Components of Intelligent Building Management System

15.3 Choosing the Right Building Management System

15.4 Choosing a System

15.5 Conclusion

References

16 Comparative Study of Solid Waste Management in Rural Homestays and Urban Hotels in Sikkim, India

16.1 Introduction

16.2 Literature Review

16.3 Methodology

16.4 Result

16.5 Discussion

16.6 Conclusion

References

17 Load Response in the Smart Home Energy Management System

17.1 Introduction

17.2 Active Demand Response

17.3 Modeling the Effects of Reimbursement of Load Response Resources

17.4 Conclusion

References

18 Sustainable Agriculture Using IoT Based Smart Irrigation and Intelligent Watering System

18.1 Introduction

18.2 Methods and Material

18.3 Problem Statement

18.4 Proposed Methodology

18.5 Simulation Results and Analysis

18.6 Conclusion

References

19 Assessing the Impact of Green Spaces on Climate, Air Quality and Temperature in Urbanized Areas: A Case Study of Colombo

19.1 Introduction

19.2 Literature Review

19.3 Data and Methods

19.4 Findings of the Study

19.5 Discussion and Conclusion

References

20 Weed Rate Analysis and Crop Quality Assessment Using Deep Learning

20.1 Introduction

20.2 Overview of Deep Learning-Based Architecture

20.3 Deep Learning Models

20.4 Transfer Learning and Domain Adaption

20.5 Precision Agriculture System

20.6 Continual Research and Innovation

20.7 Conclusion

Bibliography

21 Synergizing Semantic Technology and Deep Learning for Transformative Advances in Digital Agricultural Systems

21.1 Introduction

21.2 Semantic Web Technology in Agriculture

21.3 Deep Learning in Agriculture

21.4 Semantic Deep Learning in Agriculture

21.5 Conclusion

References

22 Smart Agriculture Systems

22.1 Introduction

22.2 Methodology

22.3 User Interface

22.4 Implementation

22.5 Benefits

22.6 Resource Efficiency

22.7 Environmental Impact

22.8 Cost-Benefit Analysis

22.9 Conclusion

Bibliography

23 Berklekamp-Massey Algorithm in Reed Solomon Error Detection Technique for Smart Grid Applications

23.1 Introduction

23.2 Methodology

23.3 Proposed Method

23.4 Results and Discussion

23.5 Conclusion

References

24 Economically Viable Solar–Wind Hybrid Power Generation System for Small- and Medium-Scale Applications

24.1 Introduction

24.2 Proposed Model

24.3 Implementation of Hybrid Scheme

24.4 Working

24.5 Results and Analysis

24.6 Efficiency Calculations

24.7 Conclusion and Future Prospects

References

25 Modified Booth Multiplier with Hybrid Adder

25.1 Introduction

25.2 Methodology

25.3 Proposed Architecture

25.4 Results and Discussion

25.5 Conclusion

References

26 Novel Bidirectional Converter Topology for Electric Vehicle Onboard Battery Charger

26.1 Introduction

26.2 Bidirectional Charger Topology with Interleaved Boost Converter

26.3 Circuit Operation of the Charger

26.4 Modes of Operation

26.5 Design Approach

26.6 Simulation Result

26.7 Comparative Analysis

26.8 Conclusion

References

Index

Also of Interest

End User License Agreement

List of Tables

Chapter 3

Table 3.1 SC vs lithium-ion battery.

Table 3.2 Parameters considered for simulation.

Chapter 4

Table 4.1 The power generation, load and losses for test bus network.

Table 4.2 Power generation, load and losses for test bus network with PV integ...

Table 4.3 Contingency ranking.

Table 4.4 Continuation power flow results for one STATCOM.

Table 4.5 Voltage magnitude profiles before and after installation of STATCOM ...

Table 4.6 Continuation power flow results for two STATCOMs.

Table 4.7 Voltage magnitude profiles before and after installation of STATCOM ...

Table 4.8 Improvement in losses.

Chapter 5

Table 5.1 Specifications of the equipment.

Table 5.2 Dimensions of the wells turbine models used.

Table 5.3 Efficiency of the device.

Chapter 7

Table 7.1 List of implemented instructions.

Table 7.2 Custom instruction encodings.

Table 7.3 Custom instructions definition.

Table 7.4 Instructions used in XOR5 program with RV32I instructions.

Table 7.5 Instructions used in XOR5 program with custom instructions.

Table 7.6 Instructions used in rotation program with RV32I instructions.

Table 7.7 Instructions used in rotation program with custom instructions.

Table 7.8 Comparison of the number of instructions required to swap endianness...

Table 7.9 Instructions used to perform SHA-2 hashing using RV32I and K extensi...

Table 7.10 Instructions used to perform SHA-3 hashing using RV32I and K extens...

Table 7.11 Instructions used to perform SHA-3 hashing using custom instruction...

Table 7.12 Module-wise area and power distribution of the proposed processor w...

Table 7.13 Comparison of power distribution of the proposed processor with and...

Table 7.14 Comparison of area distribution of the proposed processor with and ...

Table 7.15 Comparison of area and power consumption of the proposed processor ...

Chapter 10

Table 10.1 Classification of automated driving.

Chapter 11

Table 11.1 Accuracy, precision, recall and F1 score of the proposed CNN models...

Chapter 14

Table 14.1 Comparison between messaging protocols used in IoT environment. Tab...

Chapter 16

Table 16.1 Level of education of respondents according to facility type.

Table 16.2 Implementation of policies by rural homestay and urban hotels for r...

Table 16.3 Disposal of waste using various approaches such as nearby municipal...

Table 16.4 Service-related issues that can be improved arranged by facility ty...

Table 16.5 Service-related issues that can be improved arranged by facility ty...

Chapter 17

Table 17.1 Time period of using secondary services of electricity market reser...

Chapter 19

Table 19.1 Climate in the city of Colombo.

Table 19.2 City of Colombo green spaces from 1985-2018 and the temperature inc...

Table 19.3 Greenspace improvement by using envi-met simulation.

Chapter 21

Table 21.1 Details of agriculture-based ontologies.

Table 21.2 Deep learning architectures in agriculture.

Table 21.3 Publicly available datasets for deep learning.

Chapter 23

Table 23.1 Decimal equivalent of primitive variable.

Table 23.2 Comparison of power and area.

Chapter 24

Table 24.1 Technical information of the solar panel.

Table 24.2 Technical information of the wind turbine.

Table 24.3 Technical information of the generator.

Table 24.4 Technical information of the battery.

Table 24.5 Technical specifications of the load.

Table 24.6 Output of the solar panel unit.

Table 24.7 Output of the wind turbine unit.

Table 24.8 Obtained output powers.

Chapter 26

Table 26.1 Parameters of the 4-channel interleaved boost converter.

List of Illustrations

Chapter 3

Figure 3.1 Proposed HGBID converter.

Figure 3.2 Theoretical waveforms: (a) waveforms for FM, (b) waveforms for BM.

Figure 3.3 Equivalent circuits: (a) time instant 1 and 3 (FM), (b) time instan...

Figure 3.4 Battery – SOC.

Figure 3.5 Battery current and voltage waveforms.

Figure 3.6 Supercapacitor results: (a) SOC, (b) supercapacitor current and vol...

Figure 3.7 FM: (a) inductor current waveform, (b) voltage across capacitor (c)...

Figure 3.8 MOSFET and SOC forward mode results: (a) MOSFET parameters, (b) – S...

Figure 3.9 BM: (a) inductor currents, (b) capacitor voltages, (c) gate pulses,...

Figure 3.10 FM performance: (a) duty cycle vs voltage gain, (b) duty cycle vs ...

Figure 3.11 BM performance: (a) duty cycle vs step-down voltage gain (b) duty ...

Figure 3.12 Duty cycle vs output voltage ripple: (a) FM (b) BM.

Chapter 4

Figure 4.1 Single/one line diagram of IEEE 14 bus network.

Figure 4.2 IEEE 14 bus network with solar photo-voltaic generator.

Figure 4.3 Test network with one STATCOM installed at bus 6.

Figure 4.4 Voltage magnitude profiles before and after installation of STATCOM...

Figure 4.5 PV curves for one STATCOM.

Figure 4.6 Test bus network with two STATCOMs installed at buses 2 and 6.

Figure 4.7 Voltage magnitude profile before and after installation of STATCOM ...

Figure 4.8 PV curves for two STATCOMs.

Chapter 5

Figure 5.1 Oscillating water column.

Figure 5.2 3D view of hardware model.

Figure 5.3 Dimensions of panel shell.

Figure 5.4 Dimensions of generator shell.

Figure 5.5 Front end of turbine blade.

Figure 5.6 Dimensions of wells turbine blades.

Figure 5.7 Dimensions of kaplan turbine blades.

Figure 5.8 Hardware prototype.

Figure 5.9 Alignment of turbine generator setup.

Chapter 7

Figure 7.1 Contents of the register file at the end of rotation operation usin...

Figure 7.2 Contents of the register file at the end of rotation operation usin...

Figure 7.3 Proposed RISC-V processor pipelined datapath.

Figure 7.4 Proposed SHA-3 module.

Figure 7.5 Physical design layout of the proposed processor with SHA-3 acceler...

Chapter 8

Figure 8.1 Flowchart of SSL vulnerability analysis tool.

Figure 8.2 Vulnerability analysis.

Figure 8.3 Generated SSL scan report.

Chapter 9

Figure 9.1 App architecture.

Figure 9.2 (Onboarding screen).

Figure 9.3 Registration process.

Figure 9.4 OTP based authentication.

Figure 9.5 Voting.

Figure 9.6 Search window.

Figure 9.7 Feedback and suggestions window.

Figure 9.8 User activity monitoring window.

Figure 9.9 App home screen.

Figure 9.10 Admin process flow.

Figure 9.11 Citizen process flow.

Chapter 10

Figure 10.1 System on lane detection.

Figure 10.2 System on object detection.

Figure 10.3 Signal detection.

Figure 10.4 System on stop sign detection.

Figure 10.5 Starting position.

Figure 10.6 Vehicle stops on barricade/stop sign ahead.

Figure 10.7 Car navigates on removal of barricade/stop sign.

Figure 10.8 Animal avoidance.

Figure 10.9 Lane detection upon obstacle.

Figure 10.10 Navigation of vehicle upon closed road.

Figure 10.11 Reaching destination after autonomous safe navigation.

Chapter 11

Figure 11.1 Design of the proposed model.

Figure 11.2 Sample datasets for training the model.

Figure 11.3 Sample augmented images.

Figure 11.4 Created CNN model with added layers.

Figure 11.5 Proposed CNN model.

Figure 11.6 Training and validation accuracy.

Figure 11.7 Training and validation loss.

Figure 11.8 Confusion matrix obtained for the proposed CNN model.

Figure 11.9 Accuracy, precision, recall and F1 score of the proposed CNN model...

Chapter 12

Figure 12.1 Image of tools used in the project setup.

Figure 12.2 Flowchart for augmented buttons.

Figure 12.3 Target image detection using OpenCV using ESPcam footage.

Figure 12.4 Flowchart for augmented data projection.

Figure 12.5 Augmented buttons projection.

Figure 12.6 Augmented button ON button used.

Figure 12.7 LED turned ON.

Figure 12.8 Augmented button OFF button used.

Figure 12.9 LED turned OFF.

Figure 12.10 Visualisation and detection of fiducial marker.

Chapter 13

Figure 13.1 DDoS attack model.

Chapter 14

Figure 14.1 MQTT architecture from [5].

Figure 14.2 “Message queuing telemetry transport protocol model” from [12].

Figure 14.3 Proposed architecture from [9].

Figure 14.4 “IomaTic development board” from [12].

Figure 14.5 MQTT protocol operation from [10].

Figure 14.6 AES algorithm from [15].

Chapter 16

Figure 16.1 Cluster bar chart of work experience by facility type.

Figure 16.2 Cluster bar chart of level of education by facility type.

Figure 16.3 Cluster bar chart of media raised awareness by facility type.

Figure 16.4 Waste burning place in rural homestays.

Figure 16.5 Waste bins installed in locality around rural homestays and urban ...

Chapter 17

Figure 17.1 Division of electricity markets according to decision time.

Figure 17.2 Additional service capacities of today’s modern electricity market...

Figure 17.3 Energy balance.

Figure 17.4 Energy management techniques.

Figure 17.5 Load response classification.

Figure 17.6 Classification of the consumer side when the price offer is not ac...

Figure 17.7 Additional services by answering the active load.

Figure 17.8 Determining market price with constant consumption.

Figure 17.9 Consumption and production curve.

Figure 17.10 The impact of demand response resources.

Figure 17.11 The effect of the participation of four load response sources on ...

Chapter 18

Figure 18.1 Proposed flow diagram for minimum wastage of water.

Figure 18.2 Proposed circuit diagram using proteus software.

Figure 18.3 Proposed circuit diagram using proteus software representing for t...

Figure 18.4 Proposed circuit diagram using proteus software representing for m...

Figure 18.5 Proposed circuit diagram using proteus software representing for r...

Figure 18.6 Proposed circuit diagram using proteus software representing for r...

Figure 18.7 Proposed circuit diagram using proteus software representing for m...

Figure 18.8 Proposed circuit diagram using proteus software representing for m...

Figure 18.9 Control operation of the proposed method with few terminal outputs...

Figure 18.10 Data analyzed the KNN machine learning algorithm.

Figure 18.11 (a) Temperature monitoring (b) moisture level monitoring (c) moto...

Chapter 19

Figure 19.1 City climatic modifiers.

Figure 19.2 Case study areas.

Figure 19.3 Satellite heat map of city of Colombo (2018).

Figure 19.4 Air pollution in the city of Colombo.

Figure 19.5 Air pollution since 2009 in city of Colombo.

Figure 19.6 CO

2

level with and without green spaces.

Chapter 20

Figure 20.1 Crop and weed in the same image.

Figure 20.2 Crop and weed in classified images.

Figure 20.3 Fundamental elements for the analysis of deep learning architectur...

Figure 20.4 Custom CNN architecture.

Figure 20.5 VGG architecture.

Figure 20.6 ResNet architecture.

Figure 20.7 Mobile net architecture.

Figure 20.8 DenseNet architecture.

Figure 20.9 Precision farming market size (2018 to 2025).

Figure 20.10 Sustainable food future by 2050.

Chapter 22

Figure 22.1 Steps of implementation.

Figure 22.2 User interface.

Figure 22.3 User interface.

Figure 22.4 Result.

Figure 22.5 Importing dataset.

Figure 22.6 Attributes for the dataset.

Figure 22.7 Features and labels.

Figure 22.8 Using random forest algorithm.

Figure 22.9 Using joblib library.

Chapter 23

Figure 23.1 Simulation of the Galois arithmetic.

Figure 23.2 Simulation of the Berlekamp-Massey algorithm.

Chapter 24

Figure 24.1 Types of VAWT.

Figure 24.2 Savonius type VAWT.

Figure 24.3 Block diagram representation.

Figure 24.4 Component representation.

Figure 24.5 Working model.

Figure 24.6 Performance of the solar panel.

Figure 24.7 Performance of the wind turbine.

Figure 24.8 Performance of the hybrid model.

Figure 24.9 Comparison of the solar and wind power with the hybrid model.

Chapter 25

Figure 25.1 Block diagram of proposed multiplier.

Figure 25.2 Architecture of Weinberger CSELA (WCSELA).

Figure 25.3 Architecture modified linear Ling CSELA.

Figure 25.4 8 bit hybrid adder (Ling-Weinberger) simulation result.

Figure 25.5 8 bit modified booth multiplier simulation result.

Figure 25.6 Technology schematic of proposed CESLA adder with Ling and Weinber...

Figure 25.7 Technology schematic of proposed modified booth multiplier.

Figure 25.8 Device summary of proposed.

Figure 25.9 Device used.

Figure 25.10 Delay summary.

Figure 25.11 Power analysis.

Chapter 26

Figure 26.1 Circuit diagram of conventional MOBC.

Figure 26.2 Proposed MOBC circuit.

Figure 26.3 4-channel interleaved boost rectifier.

Figure 26.4 Circuit diagram for G2V operation.

Figure 26.5 Circuit diagram for V2G operation.

Figure 26.6 Circuit diagram for HV and LV charging.

Figure 26.7 Measured voltage of HV battery during mode 1 operation.

Figure 26.8 Current waveform of inductor (a) L1 (b) L2 (c) L3 (d) L4 during mo...

Figure 26.9 (a) SoC of HV battery during mode 2 operation (b) SoC of HV batter...

Figure 26.10 Measured voltage of LV battery during mode 3 operation.

Figure 26.11 (a) Input current ripple of the bidirectional OBC (b) Input curre...

Figure 26.12 SoC of HV battery during mode 3 operation.

Figure 26.13 Efficacy vs. duty cycle of the charger.

Figure 26.14 Efficiency vs. duty cycle.

Guide

Cover Page

Table of Contents

Series Page

Title Page

Copyright Page

Preface

Begin Reading

Index

Also of Interest

WILEY END USER LICENSE AGREEMENT

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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106

Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Resilient Community Microgrids

Edited by

O.V. Gnana Swathika

and

K. Karthikeyan

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-394-27251-8

Front cover images supplied by Adobe FireflyCover design by Russell Richardson

Preface

The microgrid has proven itself as a sustainable archetype to improve the resilience and reliability of power distribution networks. High distributed energy resources (DER) penetration into communities unlocks the potential of building the resilience of microgrids.

Neighborhoods, villages, towns, and cities can meet their local energy needs by utilizing community microgrids. Community microgrids are being considered more and more as a possibility, even in locations where a bigger grid already exists, primarily as a means of boosting local energy independence and resilience. The fundamentals of community microgrids are covered in this book, along with an outline of how to join one and the factors contributing to their rising popularity.

Community microgrids are microgrids created specifically to serve a given community. They might or might not be linked to the main grid. The goal of this kind of microgrid is to maximize energy independence by combining local resources, primarily small-scale renewables. For financial reasons, such as high electricity prices on the primary grid, they may also be developed or connected.

This book will illustrate an in-depth view of cyber-physical microgrid systems. An elaborate discussion on concepts about photovoltaics, batteries, micro-turbines, wind generation, power-electronic interfaces, modeling and analysis of microgrids, power electronic interface and control, protection, cyber-communication networks and security are done. The book offers different frontiers of theoretical understanding and practical deployment of cyber-physical systems, paving the way to discover various opportunities and operational mechanisms of cyber-physical microgrids. The book will include case studies, examples and test systems that enable problem-solving and will be an essential resource for students, researchers, and professionals in power engineering.

Salient Features:

Sustainable and Green Spaces in Smart Cities, Smart Homes and Smart Buildings.

Sustainable Agriculture and Waste Management Systems.

Power Electronics Interface and Control in Community Microgrid.

IoT and Cloud Based Community Microgrid.

Cyber-physical Microgrid System.

Electric Vehicles.

1AI-Based Virtual Advisor for Smart Climate Farming

S. Ramanan1, Mekala Sujan1, Swati Kumari1 and O.V. Gnana Swathika2*

1School of Computer Science Engineering, Vellore Institute of Technology, Chennai, India

2Centre for Smart Grid Technologies, Vellore Institute of Technology, Chennai, India

Abstract

The following content includes the research and reviews of 96 articles, along with their citations and descriptions. Smart climate farming involves different techniques, such as agroforestry, vertical farming, sustainable water management, and precision agriculture. In simple terms, anything that benefits the production and income of individuals involved in the agricultural sector by using emerging technology is known as smart climate farming. With the help of smart farming, the agricultural industry can undergo transformative change. Smart climate farming is not only focused on technology development but also involves far-reaching impacts, such as livelihoods, food security, and ecological balance. Moreover, smart climate farming reduces greenhouse gases emitted when compared to traditional agriculture. Below are some of the content and advantages of smart climate farming.

Keywords: IoT in agriculture, smart agriculture, sensors, sustainable agriculture, climate, resilience

1.1 Introduction

Smart farming can also be called precision agriculture. Big data plays a major role in developing agriculture due to its extraordinary capabilities; it allows for the usage of various tools to provide farmers with rainfall patterns, helping them decide whether they can plant a particular crop or not, as well as outlining the water cycles to be used by the farmer. Heterogeneity and the plethora of agricultural data remove the challenges in classifying existing resources; without big data, collecting this information becomes difficult for future generations [1].

Satellite sensors have the capability to capture imagery from a distance, covering vast areas. The same procedure has been used with high-level drones to take aerial shots. Such defects include the canopy chlorophyll content (CCC) in wheat; the satellite sensors can detect red-colored edges in wheat and inform farmers that this wheat is not suitable for sale. The same applies to canopy nitrogen content (CNC) in rice. By using this method, we can enhance the efficiency of available materials and labor [2].

Unmanned farms represent the technology we will be able to see in the near future. In the 21st century, we observe that most people are interested in production but not in farming, so to create innovative ideas, unmanned farms have emerged. Gradually, we will transition into unmanned farms, and in the upcoming 50 years, we may see AI completely handling production tasks. However, this may not be as efficient as human labor due to unreliable robots and unstable sensors [3].

This paper presents a comprehensive review of smart technology applications in agriculture, focusing on artificial intelligence, machine learning, cloud computing, and the Internet of Things (IoT). It discusses how these technologies are used in crop and animal production, as well as post-harvest processes. Additionally, the paper addresses the impact of climate change on agriculture. It highlights challenges and gaps in current research related to smart farming using IoT and provides recommendations for further study to increase global food production, management, and sustainability [4].

The rapid digitalization of data has led to an overwhelming influx of information across various industries, including data-driven enterprises. This surge has been further intensified by the rise of machine-to-machine (M2M) processing of digital data. As a result, digital crop management applications have emerged, utilizing ICT (information and communication technology) to assist both farmers and customers and bring technical solutions to rural areas. Despite the difficulties it might present, this research examines ICT’s potential for traditional agriculture. It explores subjects like robots, IoT devices, machine learning, artificial intelligence, and agricultural sensors. It also discusses how to maximize yield and monitor crops using drones. The paper also highlights global IoT-based platforms and farming systems and reviews recent literature in these domains. Ultimately, it concludes by presenting artificial intelligence (AI) trends for the present and future while identifying research challenges in AI for agriculture, drawing from this comprehensive assessment [5].

Artificial intelligence, particularly deep learning, time series analysis, and machine learning, plays a vital role in addressing today’s sustainability challenges in agriculture. These technologies are employed for tasks such as crop selection, yield forecasting, soil compatibility evaluation, and water management. Time series analysis is essential for forecasting crop demand, commodity pricing, and yield production. Machine learning assists with crop selection and management, while deep learning simplifies crop forecasting. With these techniques, crop selection is based on factors such as soil quality. With the global population increasing, accurate crop production forecasting is essential to combat food shortages. This article provides a comprehensive overview of how artificial intelligence and deep learning methods can be utilized in agriculture, leveraging crop data sets for tasks like soil fertility classification and crop selection. Time series analysis is also explored, offering insights into future crop production, ultimately helping alleviate food scarcity by making informed crop recommendations based on yield estimation [6].

Smart farming is crucial in addressing challenges related to feeding a growing population and ensuring food safety. It combines agriculture with information and communication technologies. A potential idea in smart farming is the Multiponics Vertical Farming (MVF) system, which offers space and financial savings. Advanced technologies can manage complex data, enhancing accuracy and efficiency. Artificial intelligence (AI) is instrumental in solving dynamic and intricate agricultural problems. This study covers tasks like categorization, detection, and forecasting as it examines AI’s role in soil management and MVF. We explore neural network (NN), support vector machine (SVM), and decision tree (DT), which are three widely used AI and machine learning techniques. The abstract also touches on future prospects for urban farming [7].

Agribusiness is fundamental to India’s economy, directly or indirectly employing a significant portion of the population. The introduction of technology, particularly smart farming, holds great promise for improving agricultural outcomes. This shift from traditional methods is driven by the need to meet a 55% increase in global demand for agricultural products by 2050 while reducing the reliance on fertilizers and optimizing water use. Smart farming has become more energy-efficient due to factors like continuous cropping, increased fertilizer and chemical use, and advanced farm mechanization. The Internet of Things (IoT), data analytics, and satellite technology all contribute to the rapid expansion of smart farming. IoT-based precision agriculture involves real-time monitoring of agricultural parameters using sensors for soil, temperature, humidity, air quality, and drones equipped with cameras. AI is leveraged for analyzing images to detect crop health and pest/ disease outbreaks. This chapter explores how IoT and AI can enhance agricultural productivity and sustainability, focusing on their benefits and applications in farming processes and energy optimization [8].

Precision agriculture, with its advanced technologies like AI-driven equipment and robotic farm workers, is praised for its potential to enhance yields from crops, food security, economic development, and poverty alleviation. However, there’s growing concern about the biases and power dynamics ingrained in these technologies. While they may create opportunities for small-scale female farmers in East Africa, they also risk becoming tools of control over their labor and knowledge. Moreover, these technologies tend to view plants solely as objects to be optimized, overlooking their unique characteristics and ways of interacting with the environment. This essay examines how smart farming and precision agriculture might reinforce hierarchies and ignore indigenous viewpoints and expertise. It promotes a decolonial approach to governing these technologies to ensure greater inclusivity and respect for diverse ways of knowing and being [9].

Smart agriculture practices have gained significant attention among farmers due to the accessibility of cost-effective IoT-based wireless sensors for monitoring field conditions, climate, and crops. These sensors help manage resources efficiently, such as reducing water usage and minimizing pesticide application. Additionally, the surge in artificial intelligence (AI) enables the deployment of autonomous farming machinery and improved predictive capabilities to prevent crop diseases and pest infestations. These technologies have transformed traditional agriculture.

This survey study provides: (a) an in-depth lesson on developments in smart agriculture using IoT and AI, (b) a critical examination of these technologies and a discussion of obstacles to their general implementation, and (c) a detailed analysis of current trends, considering both technological and societal factors as smart agriculture becomes the norm among farmers worldwide [10].

Enhancing farming techniques and agricultural practices through advanced technology is increasingly vital, especially in today’s ecologically challenged world, with issues like energy shortages, droughts, and global warming affecting traditional agriculture. This paper explores the integration of renewable energy generation and artificial intelligence to optimize existing farming methods. The idea is to use Internet of Things (IoT) devices to power AI-driven smart agriculture monitoring systems by harnessing both natural and artificial wind energy, as well as the turbulence produced by moving objects like trains. This strategy aims to address challenges in suburbs and the agricultural sector, where wind turbines with a vertical axis can be used to improve the quality of life for farmers [11].

In various industries, the digitization of data has led to a massive influx of information, further amplified by machine-to-machine data handling. Agriculture has seen a significant increase in digital applications, driven by information and communication technology (ICT), benefiting both producers and consumers while extending technological solutions for rural areas. This essay examines the limitations and potential of ICT in conventional agriculture. The paper delves into the challenges and roles of robotics, IoT devices, sensors, artificial intelligence, and machine learning in agriculture, offering insights into how these technologies are shaping the sector. It also considers the monitoring and enhancement of crop yields using drones. Additionally, it highlights global IoT-based agricultural systems and platforms where applicable, summarizing present and upcoming trends in artificial intelligence (AI) while identifying research challenges in AI for agriculture based on an extensive review [12].

The widespread adoption of digital transformation in our daily lives is largely driven by the remarkable success of artificial intelligence (AI), particularly statistical machine learning (ML). AI and ML play crucial roles in analyzing, modeling, and managing agricultural and forest ecosystems, as well as in soil use and protection, contributing to the well-being of future generations. This digital transformation encompasses the entire agricultural and forestry value chain, empowered by cyber-physical systems, big data, and increased computing power. However, ensuring AI’s reliability in critical areas like agriculture and forestry necessitates two key components: explainability and robustness. To achieve this, human-centered AI (HCAI) combines artificial and natural intelligence, aiming to enhance human performance without substitution. This article emphasizes three research areas crucial for the success of agriculture and forestry: intelligent information fusion, robotics and embedded intelligence, and enhancement, clarification, and verification for reliable decision support. This approach spans three generations: G1 for immediate technology deployment, G2 for medium-term modifications, and G3 for adaptation and evolution beyond the current state [13].

Regenerative agriculture is a crucial component of smart farming. In this approach, there are many advantages, such as not damaging soil quality, improving fertility rates in production, and carefully increasing the use of internal resources; it prohibits the use of chemical pesticides. According to research, many farms have transitioned to climate-smart regenerative agriculture, while some farmers find the sudden change from conventional agriculture challenging [14].

Artificial intelligence has the potential to reduce carbon emissions in the agricultural sector and modernize the industry. However, the adoption of AI in Indian agriculture faces challenges due to small farm sizes, traditional practices, lack of infrastructure, and risk aversion among decision-makers. To make AI solutions effective, they should be accessible to farmers in their local languages, accompanied by training and support, and promoted through collective or cooperative efforts to achieve sustainable and eco-friendly agriculture [15].

1.2 Research on Smart Farming Technologies and AI Applications

AI and ML are essential for the sustainable development of the farming sector. AI and ML can be used to improve crop yields, decrease the utilization of pesticides, and conserve water resources. Agricultural automation using AI and ML is still in its infancy, but it has the potential to completely transform the farming industry [16].

By 2050, two billion more people are anticipated to live on the planet, yet arable land is only expected to increase by 5%. This means that we need to find ways to improve agricultural productivity in order to feed the growing population. One way to do this is by using smart farming techniques, such as land suitability assessment. Land suitability assessment is the process of determining how suitable a piece of land is for a particular crop or use. Traditional methods of land suitability assessment are manual and time-consuming. New technologies such as sensor networks and artificial intelligence (AI) are making it possible to conduct land suitability assessments more efficiently and accurately. The proposed system integrates sensor networks with AI to assess land suitability. The system has several benefits over conventional approaches, including efficiency, accuracy, and scalability. The system is still under development, but it has the potential to revolutionize land suitability assessment [17].

With the aid of artificial intelligence (AI), we can predict crop yields and manage irrigation systems. Crop yields are predicted using AI-based yield prediction systems that utilize data on climate, soil characteristics, and crop types. Utilizing this information, farmers can decide how best to plant, fertilize, and irrigate their fields. AI-based irrigation systems use sensors to monitor various conditions, including soil moisture. This information is used to automatically adjust irrigation schedules, which can help save water and energy. An AI-based yield prediction system could be used to forecast the yield of a wheat crop based on weather conditions, soil type, and crop variety. Farmers can then use this data to determine when to grow the crop, how much fertilizer to apply, and how much water to irrigate the crop with. An AI-based irrigation system could automatically adjust irrigation schedules based on soil moisture levels to ensure that crops are not over- or under-irrigated, ultimately saving water and energy [18].

AI has the potential to revolutionize many industries, including agriculture, healthcare, transportation, and manufacturing. However, there are also risks associated with the development and use of AI. These dangers consist of: Bias: If AI systems are trained on biased data, they may become biased, leading to prejudice against specific groups of individuals. Security: AI systems can be vulnerable to cyberattacks, allowing attackers to gain control of the system and use it for malicious purposes. Privacy: AI frameworks can collect and store a significant amount of data about individuals, which can be used to track their activities or to identify them. The study concluded that it is important to carefully consider the risks and benefits of AI before deploying it in any application. It is also essential to develop security measures to protect AI systems from attacks [19].

Data on crop conditions, weather, and other variables can be gathered via the IoT. These data can then be used to make informed decisions about crop management. Agriculture may benefit from the IoT by becoming more productive, efficient, and sustainable. It can also help reduce human intervention, costs, and time. The IoT is a powerful tool that can improve the way we grow food [20].

Artificial intelligence (AI) can be used to detect pest infestations, nutrient levels, and various types of soil. This knowledge can improve the efficiency of fertilizer application and pest control. Crop management: AI can help farmers better time the planting, watering, and harvesting of their crops, potentially increasing crop yields and reducing waste. Weed management: AI can be used to identify and control weeds, helping to reduce herbicide use and improve crop yields. Disease management: AI can be utilized to identify and control diseases, thereby increasing crop yields and decreasing pesticide consumption [21].

Crop yields may increase as a result of AI’s assistance in streamlining the timing of crop planting, irrigation, and harvesting. It can help identify and control pests and diseases, reducing food waste. Additionally, it can contribute to making agriculture more sustainable by decreasing the use of water, pesticides, and fertilizers. Farmers can leverage it to boost their harvests and lower their costs, ultimately leading to increased income [22].

Due to their excellent climate and fertile land, South Asian nations rank among the world’s top producers of crops. However, there is still a shortage of technological investment and reliance on conventional farming methods. This has led to issues such as crop diseases not being diagnosed in a timely and efficient manner. The system can diagnose crop diseases accurately, even with limited input data. It can communicate with farmers in their local language. It is affordable and easy to use. It can be applied in underdeveloped nations with restricted access to expert counsel [23].

Improved precision agriculture provides farmers with more accurate information about what is happening on their farms, such as soil moisture, crop health, and pest infestation. Better crop management choices can be made using this knowledge, determining when to irrigate, fertilize, or harvest. Increased productivity can develop robots and automated systems that may handle weeding and harvesting, allowing farmers to focus on other tasks. This may result in higher productivity and lower labor expenses. Improved sustainability can be achieved by developing crop varieties that exhibit higher pest and disease resistance, or by optimizing irrigation practices to reduce water use. This can help to make agriculture more sustainable [24].

The paper highlights the challenges of digital farming techniques, such as sensor optimization, task planning algorithms, and item detection. It also addresses the potential for multi-robot farming, collaboration between humans and robots, and environmental reconstruction using aerial photographs and ground-based sensors. A trend in agricultural field robotics, according to the paper’s conclusion, is toward the creation of a swarm of small-scale robots and drones that cooperate to maximize farming inputs and uncover hidden information. While robots are a vital part of modern farms, the essay concludes that it is unrealistic to think that a fully automated farming system will be implemented in the near future [25].

The study covers the causes and moderating variables that affect the value that a crop farmer and their advisors can gain from data. The authors contend that a system promoting crop producers’ demand for learning, as well as their interactions with dependable advisors, is a necessary component of an efficient decision-support system. Crop protection, a subset of crop management that helps farmers make decisions, is utilized in this research as an example of current methods. The authors explain how applying emerging platform technologies and developments in artificial intelligence—taking into account farmers’ decision-making styles, knowledge capture and maintenance, and integrating technology into humancentered services—could improve awareness of situations and actionable knowledge [26].

Using AI, large volumes of data can be collected and analyzed about crops, the environment, and agricultural practices. This information can be used to identify trends and patterns that will aid farmers in making wiser decisions. It can be used to develop models that predict crop yields and identify potential problems. This information can help farmers take preventive measures to avoid crop losses. It may also be utilized to manage processes like harvesting and weeding, allowing farmers to focus on other responsibilities, such as management and planning. Additionally, it can be employed to create new crops that are more tolerant to pests and diseases. This could lead to decreased pesticide and herbicide applications. It also has the potential to optimize irrigation practices to reduce water use, contributing to more sustainable agriculture [27].

Smart climate farming is one of the prominent technologies used in the 21st century. The primary impetus behind smart climate farming is artificial intelligence (AI) and the Internet of Things (IoT). These networks can be involved in various practices, such as monitoring of the farm and irrigation, while AI can analyze the data, resolve real-time problems, and provide accurate decisions. Smart climate farming can triple the production rate and increase land usage and water resource efficiency [28].

Ethics in agriculture encompasses the decisions made by individuals involved in the sector, such as farmers, industrial workers, and technology developers. An effort should be made to enhance the accountability of smart farming rather than allowing pollution from metal waste, etc. This consideration leads to questions about whom to share data with and establishing preconditions for stakeholders so that farmers do not bear the total loss and gradually develop guidelines and codes of conduct [29].

Smart farming in Europe: out of all the countries in Europe, France is the best suited for agriculture, with 35.5% of its land devoted to this sector. Globalization has affected European countries, particularly in agriculture. As a result, Europeans are trying to evolve in agriculture using smart farming techniques. They are employing technologies like UAVs, machine learning, image processing, and cloud computing. Furthermore, throughout most of Europe, agriculture is the main source of revenue [30].

Digital twins (DT) enhance farming productivity and sustainability. Digital twins have significant potential in agriculture; using this method, farmers can observe fields remotely rather than physically monitoring them. The framework of digital twins is based on the Internet of Things (IoT) approach. This type of framework can be targeted at dairy farming, organic vegetable farming, and livestock farming, enabling users to view situations from anywhere [31].

The current growing society, in terms of population, is seriously threatened in the role of food production due to a lack of agricultural land and the expansion of industrial areas. This article proposes a framework for precision agriculture using IoT gateways in terms of software. The aim of this software is to overcome the challenges faced in agriculture, providing a sensor cloud infrastructure-based architecture for mobile agriculture services in an intelligent farming system [32].

This research was conducted in Bangladesh, where agriculture contributes over 20% to the GDP and provides employment for 63% of the population. Thus, a web-based application for agriculture will be highly beneficial for smart farming, providing support in various agricultural activities. If any farmer is in need of help, there can be options for donations online to assist farming families. This application overcomes delays in communication, as it is based on an agile model and has great potential for improvement [33].

The data analysis and electronic applications play a vital role in smart farming, where everything we use is connected to technology, either directly or indirectly. Here are three ways to implement them: 1) Web, 2) Mobile, and 3) Hardware. The hardware is used to store information about the crops, the web can be designed to handle the details of the field and crop information, and finally, the mobile-based app can be used to manage field watering through a mobile device rather than requiring a visit to the farm [34].

This initiative has been implemented in Bangladesh, where an Internet of Things-based smart farming robotic system was developed. This IoT system has designed a robot that performs tasks such as monitoring humidity, field conditions, and air moisture. The data collected from every possible input will determine whether the water pump and cutter are automatically turned off or on. Smart agriculture using robotic systems can be a significant asset for future generations in production [35].

This survey addresses what smart agriculture means to members of the global alliance. The UN Food and Agriculture Organization has stated that Climate Smart Agriculture (CSA) not only boosts production but also helps reduce harmful greenhouse gas emissions. All stakeholders suggest that a bottom-up approach will be more suitable for CSA’s long-term use than the top-down approach, as it prioritizes shared governance and places farmers’ needs first [36].

The impact of smart agriculture advancements on household well-being is discussed in this essay. Rural households face numerous challenges due to a lack of income and food crises. This research examines the decisions made in crop management and the crop portfolio; the use of technology for crop storage increases agricultural performance in the field and enhances risk coping capacity. Smart farming can lead to rural development and prosperity [37].

This research involves Smart Herd Management, managing the poultry or dairy industry and other animals using Internet of Things-based technology, specifically fog computing. This software follows a microservices-oriented design approach to facilitate computing patterns in the management of dairy herds. With this fog-based setup, there has been an overall 85% reduction in data that needs to be moved to the cloud compared to standard cloud-based strategies [38].

This study was conducted in southern Ethiopia, focusing on the impacts on crop yield, soil fertility, and soil carbon. The impact on soil fertility due to the Climate Smart Agriculture (CSA) approach indicates a slight increase in soil pH values, which showed 2–2.5 times greater nitrogen and phosphate content compared to conventional farming. Additionally, the Normalized Difference Water Index (NDWI) demonstrated high soil moisture beneath the surface [39].

Agriculture, the foundation of nations, has evolved significantly with advancements in bioinformatics. Smart agriculture integrates various technologies throughout the farming process, from pre-cultivation to post-harvest stages. Precision farming, like smart irrigation, plays a crucial role, especially in developing countries. This communication encourages article submissions on smart farming and its applications, emphasizing areas such as irrigation and fertilization. Bioinformatics, at the core of smart agriculture, raises questions about its applications, models, and security, warranting further investigation [40].

The theoretical framework of “environing media” is introduced in this article to explore how mediation processes influence our perception, management, and utilization of the environment. It delves into the historical connection between environmental change and mediation, highlighting the role of technology in shaping human-environment interactions. Using the history of agriculture as an example, it demonstrates how contemporary developments like digitization and AI in precision agriculture represent the latest phase in this ongoing relationship between technology and the environment. The article emphasizes how media technologies not only shape our understanding of the environment but also drive interventions and changes within it [41].

A novel form of agriculture, vertical farming, is designed to address land and water scarcity, particularly in urban areas. This study employs the Preferred Reporting for Systematic Review and Meta-analysis (PRISMA) approach to evaluate the quality of literature. The study’s objective is to review scientific literature on vertical farming from the last six years, highlighting the integration of IoT, deep learning, machine learning, and artificial intelligence in precision agriculture, particularly in vertical farming. The findings provide insights into challenges, technological trends, and future prospects in vertical agriculture [42].

By 2050, there will be 9.73 billion people on the planet, leading to a growing demand for agriculture to meet food needs. Smart Agriculture, leveraging IoT, AI, and ML technologies, is replacing traditional farming to achieve higher productivity and precision. While previous literature reviews have explored Smart Agriculture, this paper focuses on the Operations Research (OR) perspective, which has been lacking. Using Biblioshiny, a data mining tool, the study analyzes 1,305 articles from the Scopus database (2000–2022) to identify trends and gaps in Agriculture 4.0. This analysis aids researchers, decision-makers, and policymakers in understanding the application of advanced OR theories and technologies in agriculture, including the use of UAVs, robotics, and satellite imagery for resource optimization and sustainability, especially in arid regions [43].