Optimal Planning and Operation of Distributed Energy Resources (Energy Systems in Electrical Engineering)

Preface
Contents
About the Editors
1 Fundamentals of Power System
1.1 General
1.2 Indian Power Sector
1.3 Distributed Generation (DG)
1.4 Impacts of DG
1.5 Outlines of the Book
References
2 The Energy Mix: An Emerging Trend in Distribution System
2.1 Introduction
2.2 DG Technologies
2.2.1 Microturbines
2.2.2 Solar
2.2.3 Wind Power
2.2.4 Hydroelectricity
2.2.5 Fuel Cells
2.2.6 Diesel Generators
2.2.7 Biomass
2.2.8 Geothermal Energy
2.2.9 Tidal Energy
2.2.10 Internal Combustion Engines
2.2.11 Combustion Turbines
2.2.12 Wave Energy Technology
2.2.13 Storage Systems
2.3 Challenges and Benefits
2.4 Conclusion
References
3 Design of Efficient Distributed Energy Resources (DER) Controller and Protection System
3.1 Introduction
3.2 Design of Efficient Controller for Distributed Energy Resources-Based Microgrids
3.2.1 Design of Decentralized Controller
3.2.2 Design of Centralized Controller
3.2.3 Design of Two-Level Hierarchical Controller
3.2.4 Performance Assessment of DER Controllers
3.3 Protection Systems for Distributed Energy Resources-Based Microgrids
3.4 Conclusion
References
4 Economic Dispatch for Unbalanced Active Distribution Systems Management
4.1 Introduction
4.2 Economic Dispatch Optimisation Model for ADSMs
4.2.1 Optimisation and Control Strategy
4.2.2 Objective Function
4.2.3 OLTC Control Algorithm
4.2.4 System Modelling
4.3 MATLAB® -DIgSILENT® Simulation Platform Implementation
4.3.1 First-Level Optimisation Algorithm
4.3.2 Second-Level Optimisation Algorithm
4.4 Application Examples
4.4.1 Test System
4.4.2 Simulation Results
4.5 Conclusion
References
5 Optimal Siting and Sizing of Renewable Energy Sources in Distribution System
5.1 Introduction
5.2 Load Modelling
5.2.1 Consumer Load
5.2.2 Charging Load of Electric Vehicle
5.3 Problem Formulation
5.4 Proposed Algorithm for Optimal Siting and Sizing of RES
5.5 Case Study and Results
5.6 Conclusion
References
6 Scheduling of Electric Vehicle’s Charging–Discharging: An Overview
6.1 Introduction
6.2 Charging–Discharging Strategies
6.2.1 Controlled and Uncontrolled CD Methods
6.2.2 Centralized Versus Decentralized CD Method
6.3 Scheduling Approaches and Applications Sites
6.3.1 Charging Stations
6.3.2 Parking Lots and Residential Parkings
6.3.3 ToU Pricing
6.3.4 Vehicle-To-Everything (V2X)
6.3.5 Scheduling Approaches
6.4 Optimization for EVs
6.4.1 Components of OP
6.4.2 Optimization Objectives
6.4.3 Optimization Algorithms
6.4.4 Steps in Optimization Problems
6.5 Literature Summary and Discussions
6.5.1 Formulation of Objective Function
6.5.2 EV Charging and Discharging
6.5.3 Discussions
6.6 Conclusion and Future Directions
References
7 Impact of Accurate Forecasting on Optimal Operation of Power System
7.1 Introduction
7.2 Wind and Solar Power Forecasting in Power Systems
7.2.1 Types of Forecasting
7.2.2 Classification of Forecasting Models
7.3 SCUC with Wind–Solar Generation and Energy Storage
7.3.1 Mathematical Formations
7.3.2 Real-Time SCUC Formulation
7.4 Case Study
7.4.1 Input Data and Test System
7.4.2 Wind, Solar Power and Demand Forecast Results
7.4.3 Case Study
7.4.4 Impact of Forecasting Errors on Balancing Cost
7.5 Conclusion
Appendix A: Notations
References
8 Optimal Design and Analysis of Standalone Hybrid Renewable Energy Sources
8.1 Introduction
8.1.1 Power Conversion Stage A: Boost Converter
8.1.2 Power Conversion Stage B: Buck Converter
8.1.3 Power Conversion Stage C: Full-Bridge Converter
8.2 Operation of the Integrated Configuration
8.3 Control Strategy Used for the Given Hybrid Standalone Configuration
8.4 Simulation Results
8.5 Experimental Results
8.6 Conclusion
References
9 Machine Learning Applications in Smart Grid
9.1 Introduction
9.2 Background
9.2.1 Supervised Learning
9.2.2 Unsupervised Learning
9.2.3 Popular Machine Learning Algorithms
9.2.4 Main Issues and Challenges in Smart Grid
9.2.5 Challenge to Select Appropriate Machine Learning Algorithm for Smart Grid
9.3 Machine Learning Applications in Smart Grid
9.4 Solutions and Recommendations
9.4.1 Machine Learning Approach to Power System Security Assessment
9.4.2 Machine Learning Approach to Calculate Available Transfer Capability of Tie Line
9.4.3 Machine Learning Approach for Electricity Price/Load Forecast
9.5 Future Directions
9.6 Conclusion
References
10 Energy Pool Management Mechanisms
10.1 Introduction
10.1.1 Benefits of DER
10.1.2 Challenges
10.2 Optimal Sizing
10.3 Energy Management of DER
10.4 A Case Study: DRM-Based Power Supply for Telecom Tower Base Station
10.4.1 Need for DER for Base Station Power Supply
10.4.2 System Modeling
10.4.3 Battery Charging and Discharging Strategy
10.4.4 Optimal Sizing Problem Formulation
10.4.5 Problem Formulation
10.4.6 Optimization Process
10.4.7 Energy Management in Base Station Power Supply
10.4.8 VES-Based Energy Management Strategy
10.4.9 VES Algorithm
10.4.10 Performance Analysis of Energy Management Algorithm
10.5 Conclusion
References
11 Reliability Analysis of Distribution System with Integration of Distributed Generation Resources
11.1 Introduction
11.2 Distribution System
11.2.1 Objectives of Distribution Systems
11.2.2 Classification of Distribution System
11.2.3 Radial Distribution System
11.2.4 Ring Main System
11.2.5 Interconnected System
11.3 Distributed Generation
11.3.1 Impacts of DG
11.4 Mathematical Modelling
11.4.1 Distribution System State
11.4.2 Reliability Indices of Load Point
11.4.3 Reliability Indices for the System
11.4.4 Cost Worth Reliability Indices
11.5 Problem Formulation
11.6 Solution Strategy
11.7 Network Topology
11.8 Computational Results
11.9 Conclusion
References