Artificial Intelligence-Based Energy Management Systems for Smart Microgrids
- Length: 374 pages
- Edition: 1
- Language: English
- Publisher: CRC Pr I Llc
- Publication Date: 2022-06-07
- ISBN-10: 0367754347
- ISBN-13: 9780367754341
- Sales Rank: #0 (See Top 100 Books)
Modeling and optimization of energy management systems for micro- and mini-grids play an important role in the fields of energy generation dispatch, system operation, protection coordination, power quality issues, and peak demand conflict with grid security. This comprehensive reference text provides an in-depth insight into these topics. This text discusses the use of meta-heuristic and artificial intelligence algorithms for developing energy management systems with energy use prediction for mini- and microgrid systems. It covers important concepts including modeling of microgrid and energy management systems, optimal protection coordination-based microgrid energy management, optimal energy dispatch with energy management systems, and peak demand management with energy management systems.
Key Features:
- Presents a comprehensive discussion of mini- and microgrid concepts
- Discusses AC and DC microgrid modeling in detail
- Covers optimization of mini- and microgrid systems using AI and meta-heuristic techniques
- Provides MATLAB(R)-based simulations on a mini- and microgrid
Comprehensively discussing concepts of microgrids with the help of software-based simulations, this text will be useful as a reference text for graduate students and professionals in the fields of electrical engineering, electronics and communication engineering, renewable energy, and clean technology.
Cover Half Title Title Page Copyright Page Contents Acknowledgments Editors 1. Flexibility of Microgrids with Energy Management Systems 1.1 Introduction 1.2 Flexible Energy Resources in Microgrids 1.2.1 Storage-Based Flexible Resources 1.2.2 Electric Vehicles (EVs) 1.2.2.1 Battery Energy Storage (BES) 1.2.2.2 Thermal Energy Storage (TES) 1.2.2.3 Flywheel 1.2.2.4 Fuel Cell (FC) 1.2.3 Demand-Based Flexible Resources 1.2.3.1 Thermostatically Controllable Load (TCL) 1.2.3.2 Shiftable Load 1.2.3.3 Curtailable Load 1.2.4 Fuel-Based Flexible Resources 1.2.4.1 Combined Heat and Power (CHP) 1.2.4.2 Diesel Generator (DiGen) 1.3 Modeling the Microgrid Energy Management 1.3.1 Microgrid Energy Management Methods 1.3.2 Microgrid Energy Management Objectives 1.3.2.1 Cost Reduction/Profit Maximization 1.3.2.2 Self-Sufficiency 1.3.2.3 Flexibility Provision 1.3.2.4 TSO-Level Flexibility Services 1.3.2.5 DSO-Level Flexibility Services 1.3.3 Microgrid Energy Management Tools and Techniques 1.3.3.1 Optimization Methods 1.3.3.2 Deterministic Optimization 1.3.3.3 Stochastic Optimization 1.3.3.4 Robust Optimization 1.3.3.5 Uncertainty Characterization 1.3.3.5.1 Uncertainty of Wind Units 1.3.3.5.2 Uncertainty of PV Units 1.3.3.5.3 Uncertainty of EV Owners' Behavior 1.3.3.5.4 Uncertainty of Flexibility Needs 1.3.3.5.5 Model Predictive Control 1.3.3.5.6 Game Theory 1.4 Conclusion References 2. Hybrid Particle Swarm Optimization - Artificial Neural Network Algorithm for Energy Management 2.1 Introduction 2.1.1 Energy Management (EM) 2.1.2 Fuel Switch 2.1.3 DERs 2.1.4 Demand Response (DR) 2.2 Energy Management Systems (EMS) 2.3 Artificial Neural Network (ANN) 2.3.1 Biological and Artificial Neural Network 2.3.2 Comparison between ANN and BNN 2.3.3 Model of ANN 2.3.3.1 Network Architecture 2.3.3.2 One-Layer Feed Forward N/W 2.3.3.3 Many-Layer Feed Forward N/W 2.3.3.4 Recurrent Network 2.3.3.5 Adjustment of Weights (or) Learning 2.3.3.6 Supervised Learning 2.3.3.7 Unsupervised Learning 2.3.3.8 Reinforcement Learning 2.3.3.9 Activation Function 2.4 Particle Swarm Optimization 2.4.1 Flow Chart 2.5 ANN-PSO-EMS 2.6 Conclusion References 3. Community Microgrid Energy Scheduling Based on the Grey Wolf Optimization Algorithm 3.1 Introduction 3.2 CMG Energy Scheduling Problem Formulation 3.2.1 Community Microgrid System Model 3.2.2 Problem Formulation 3.2.3 Constraints 3.3 Grey Wolf Optimization Algorithm 3.3.1 GW Hierarchy 3.3.2 Prey Encircling 3.3.3 Hunting 3.3.4 Prey Attacking (Exploitation) 3.3.5 Prey Searching (Exploration) 3.3.6 GWO Application for CMG Energy Scheduling 3.4 Results and Discussions 3.5 Conclusions Acknowledgments References 4. Different Optimization Algorithms for Optimal Coordination of Directional Overcurrent Relays 4.1 Introduction 4.1.1 Literature Review 4.1.2 Main Goals of this Chapter 4.2 DOCRs' Coordination Problem 4.2.1 Boundaries of the Coordination Problem 4.2.1.1 Limits on Relay Characteristics 4.2.1.1.1 Limits on Pickup Current Setting 4.2.1.1.2 Limits on TDS 4.2.1.1.3 Boundaries on DOCRs' Coordination 4.3 Optimization Techniques 4.3.1 GWO and EGWO 4.3.1.1 Conventional GWO 4.3.1.2 EGWO Algorithm 4.3.2 WOA and HWGO 4.3.2.1 WOA Technique 4.3.2.2 HWGO Algorithm 4.4 Results and Discussion 4.4.1 Description of Test System 4.4.1.1 The Eight-Bus Network 4.4.1.2 IEEE 30-Bus Test System 4.4.2 Using the EGWO for Solving the Coordination Problem 4.4.3 Using the HWGO for Solving the Coordination Problem 4.5 Conclusions References 5. Microgrids—A Future Perspective 5.1 Introduction 5.2 A Note from NREL 5.3 Workings of a Microgrid 5.3.1 Grid-Connected Mode 5.3.2 Island Mode 5.4 Microgrid Control 5.4.1 Centralized Microgrid Control 5.4.2 Decentralized Microgrid Control 5.4.3 Primary Control Strategy 5.4.4 Secondary Control Strategy 5.4.5 Tertiary Control Strategy 5.5 Need for Microgrids 5.6 Issues Related to Microgrid Installations 5.7 Faults and Their Classification 5.8 FACTS Devices 5.8.1 STATCOM 5.8.2 SVC 5.8.3 SSSC 5.8.4 UPFC 5.8.5 IPFC 5.9 Conclusion References 6. Control Techniques for the Operation and Power Management of Smart DC Microgrids 6.1 Introduction 6.1.1 Architecture of DC Microgrids 6.1.2 Power Control Algorithm for Smart DCMG 6.1.3 Dynamic Modeling of Single-Phase VSI with LC Filter 6.1.4 Determination of Transfer Functions of Single-Phase VSI 6.2 Developed Control Technique of Single-Phase VSI 6.3 Operational Analysis with Simulation Results 6.3.1 Performance Analysis of Smart DCMG for Power Control Technique 6.3.1.1 Performance Analysis for Developed Control Technique of Single-Phase VSI 6.4 Conclusions References 7. Analysis and Optimization of a PV-Integrated Rural Distribution Network 7.1 Introduction 7.2 Modeling of a Rural Distribution Network 7.3 Formulation of Optimal Power Flow Problem 7.3.1 Formulation of Objective Functions 7.3.2 Formulation Constraints Functions 7.3.2.1 Equality Constraints 7.3.2.2 Inequality Constraints 7.4 Solution Methodology 7.5 Results and Discussion 7.6 Conclusion References 8. Fuzzy C-Means Clustering and K-NN Regression-Based Protection Scheme for Transmission Lines 8.1 Introduction 8.2 Machine Learning Algorithm Used in the Proposed Algorithm 8.2.1 Fuzzy C-Means Clustering (FCM) 8.2.2 K-Nearest Neighbor (K-NN) 8.3 Proposed Algorithm 8.3.1 Detection and Classification Method 8.3.2 Fault Location Detection 8.4 Simulation and Results 8.4.1 Various Case Studies of Fault Identification and Classification 8.4.2 Simulation of Fault Location Estimation 8.5 Conclusion References 9. Estimation of Solar Insolation Along with Worldwide Airports Situated on Different Latitude Locations: A Case Study of Rajasthan State, India 9.1 Introduction 9.1.1 Solar Energy 9.1.2 Solar Radiation 9.1.3 Solar Irradiance 9.1.4 Solar Constant 9.1.5 Effect of Atmosphere on Solar Radiation 9.1.6 Insolation 9.1.7 The Sun-Earth Relationship 9.1.8 Solar Array Orientation 9.1.9 Solar Radiation Measurement Device 9.2 Calculation for Cumulative Inclined Radiation by Using Instant Radiation with Classical Approach (Average Method) 9.3 Relative Study of Global Solar Insolation in Bikaner, Rajasthan Desert 9.3.1 Selection of PV Module Direction (Azimuth Angle) to Get Maximum Solar Insolation 9.3.2 Selection of Tilt Angle of a Solar PV Module at a Particular Location 9.4 Relative Study of Cumulative Inclined Radiation on Different Latitudes Using METEONORM Software 9.5 Conclusion References 10. An Algorithm for Identification of Multiple Power Quality Disturbances 10.1 Introduction 10.2 Formulation of PQ Issues and Methodology 10.2.1 Formulation of PQ Disturbances 10.2.2 Algorithm Adopted to Identify and Categorize the PQ Disturbances 10.3 Results and Discussion 10.3.1 Voltage Signal with Sag and Harmonics 10.3.2 Voltage Signal with Swell and Harmonics 10.3.3 Voltage Signal with Momentary Interruption and Harmonics 10.3.4 Voltage Signal with Oscillatory Transient and Voltage Sag 10.3.5 Voltage Signal with Oscillatory Transient and Voltage Swell 10.3.6 Voltage Signal with Impulsive Transient and Voltage Sag 10.3.7 Voltage Signal with Impulsive Transient and Voltage Swell 10.3.8 Voltage Signal with Simultaneou Occurrence of Voltage Swell, Oscillatory Transient, and Harmonics 10.3.9 Voltage Signal with Simultaneous Occurrence of Voltage Sag, Oscillatory Transient, and Harmonics 10.3.10 Voltage Signal with Simultaneous Occurrence of Oscillatory Transient, Impulsive Transient, Voltage Sag, and Harmonics 10.4 Feature Estimation and Classification of PQ Events 10.4.1 Feature Extraction 10.4.2 Classification of Complex PQ Disturbances 10.5 Performance Validation 10.6 Conclusion References 11. Recognition of Simple Power Quality Disturbances Using Wavelet Packet-Based Fast Kurtogram and Ruled Decision Tree Algorithm 11.1 Introduction 11.1.1 Related Work 11.1.2 Contribution of the Proposed Work 11.1.3 Organization of this Chapter 11.2 Methodology 11.2.1 Formulation of Problem 11.2.2 Formulation and Generation of Simple Nature PQ Disturbances 11.2.3 Wavelet Packet Supported Fast Kurtogram and Decision Rules-Based Algorithm for Identification and Classification of Simple Nature PQ Disturbances 11.3 Results and Discussion 11.3.1 Voltage Signal Without PQ Disturbance 11.3.2 Voltage Signal with Sag Disturbance 11.3.3 Voltage Signal with Swell Disturbance 11.3.4 Voltage Signal with Momentary Interruption Disturbance 11.3.5 Voltage Signal with Harmonic Disturbance 11.3.6 Voltage Signal with Oscillatory Transient Disturbance 11.3.7 Voltage Signal with Impuslive Transient Disturbance 11.3.8 Voltage Signal with Notches Disturbance 11.3.9 Voltage Signal with Spikes Disturbance 11.3.10 Classification of Power Quality Disturbances 11.3.11 Performance of Proposed Algorithm 11.3.12 Comparison of Performance of Proposed Algorithm with Reported Techniques 11.4 Conclusion References 12. Identification of Transmission Line Faults Using Voltage-Based Stockwell Transform Features and Decision Rules Supported Fault Classification 12.1 Introduction 12.1.1 Related Work 12.1.2 Contribution of the Work 12.1.3 Organization of this Chapter 12.2 Methodology and Test System 12.2.1 Problem Formulation 12.2.2 Test System Used for the Study 12.2.3 Voltage Supported Algorithm Used for the Estimation of Fault Conditions 12.3 Results and Discussion 12.3.1 Fault on Phase-A and Involvement of Ground 12.3.2 Fault on Phases-A and B without Involvement of Ground 12.3.3 Fault on Phases-A and B with Involvement of Ground 12.3.4 Fault on All the Phases without Involvement of Ground 12.3.5 Fault on All the Phases with Involvement of Ground 12.3.6 Impact of Variations in Fault Incidence Angle 12.3.7 Impact of Variations in Fault Impedance 12.3.8 Impact of Variations in Fault Location 12.3.9 Classification of the Faults Using Voltage-Based Features 12.4 Conclusion References 13. Algorithm Based on Harmonic Wavelet Transform and Rule-Based Decision Tree for Detection and Classification of Transmission Line Faults 13.1 Introduction 13.2 Proposed Test System 13.3 Simulation Results and Discussion 13.3.1 Line to Ground Fault 13.3.2 Double Line Fault 13.3.3 Double Line to Ground Fault 13.3.4 Three-Phase Fault with the Involvement of Ground 13.3.5 Classification of Transmission Line Faults 13.4 Conclusion References 14. A Voltage-Based Algorithm Using the Gabor Wigner Distribution and Rule-Based Decision Tree for the Detection of Transmission Line Faults 14.1 Introduction 14.2 Proposed Test System 14.3 Proposed Methodology 14.4 Simulation Results with Their Discussion 14.4.1 Line to Ground Fault 14.4.2 Double Line Fault 14.4.3 Double Line to Ground Fault 14.4.4 Three-Phase Fault Involving Ground 14.5 Performance Comparison 14.6 Conclusion References 15. Power Quality Estimation and Event Detection in a Distribution System in the Presence of Renewable Energy 15.1 Introduction 15.2 Test Distribution Grid with RE Generators 15.3 Proposed PQ Estimation and Event Detection Algorithm 15.4 Discussion of Simulation Results 15.4.1 Feeder Opening Event 15.4.2 Feeder Closing Event 15.4.3 Load Switching ON Event 15.4.4 Load Switching OFF Event 15.4.5 Capacitor Switching ON Event 15.4.6 Capacitor Outage Event 15.4.7 Solar Power Plant Outage Event 15.4.8 Solar Power Plant Grid Synchronization Event 15.4.9 Wind Power Plant Outage Event 15.4.10 Wind Power Plant Grid Synchronization Event 15.5 Classification of Events 15.6 Performance Comparison 15.7 Conclusion References 16. Recognition and Categorization of PQ Disturbances Using a Power Quality Index and Mesh Plots 16.1 Introduction 16.2 Research Method 16.2.1 Formulation of Single-Stage PQ Disturbances 16.2.2 Algorithm Adopted to Identify and Categorize the PQ Disturbances 16.3 Results and Analysis 16.3.1 Voltage Signal without PQ Disturbance 16.3.2 Sag Associated with the Voltage Signal 16.3.3 Swell Associated with the Voltage Signal 16.3.4 Momentary Interruption Associated with the Voltage Signal 16.3.5 Harmonics Associated with the Voltage Signal 16.3.6 Oscillatory Transient Associated with the Voltage Signal 16.3.7 Impulsive Transient Associated with the Voltage Signal 16.3.8 Notch Associated with the Voltage Signal 16.3.9 Spike Associated with the Voltage Signal 16.4 Extraction of Features from the PQI and PQTLI for the Classification of PQ Events 16.5 Classification of PQ Disturbances 16.6 Performance Validation 16.7 Conclusion References Index
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