Microgrids for Commercial Systems E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 630

Veröffentlichungsjahr: 2024

Beschreibung

MICROGRIDS for COMMERCIAL SYSTEMS This distinct volume provides detailed information on the concepts and applications of the emerging field of microgrids for commercial applications, offering solutions in the design, installation, and operation of this new, cutting-edge technology. The microgrid is defined as Distributed Energy Resources (DER) and interconnected loads with clearly defined electrical boundaries that act as a single controllable entity concerning the grid as per IEEE standard 2030.7-2017. It provides an uninterrupted power supply to end-user loads with high reliability. Commercial systems like IT/ITES, shopping complexes, malls, the banking sector, hospitals, etc., need an uninterrupted input power supply with high reliability. Microgrids are more suitable for commercial systems to service their clients with no service discontinuity. The microgrid enables both connection and disconnection from the grid. That is, the microgrid can operate both in grid-connected and islanded modes of operation. The microgrid controller plays an important role in microgrid systems. It shall have an energy management system and real-time control functions that operate in the following conditions: both grid-connected and islanded modes of operation, automatic transfer from grid-connected mode to islanding mode, reconnection and re-synchronization from islanded mode to grid-connected mode, optimization of both real and reactive power generation and consumption by the energy management system, grid support, ancillary services, etc. Whenever a microgrid is in islanded mode, it will work as an autonomous system without a distribution grid power supply. In this mode of operation, fault in the transmission or distribution grid will not propagate into the microgrid. Whenever a microgrid operates in grid-connected mode, power flows bi-directionally between the distribution grid and microgrid at the point of interconnection. Hence, microgrids ensure the interrupted power supply to the end-user loads with high reliability. This book aims to bring together the design, installation, operation, and new research that has been carried out in the field of microgrid applications for commercial power systems.

Leseprobe

Table of Contents

Cover

Table of Contents

Series Page

Title Page

Copyright Page

Acknowledgements

1 Smart Energy Source Management in a Commercial Building Microgrid

1.1 Introduction

1.2 Motivations of the Study

1.3 State of the Art of the System

1.4 Overview of the Proposed Methodology

1.5 DSM Approach

1.6 Background for HOMER Simulation

1.7 Conclusion

References

2 Renewable Power Generation Price Prediction and Forecasting Using Machine Learning

2.1 Introduction

2.2 Literature Review

2.3 Data Mining Models

2.4 Objectives

2.5 Conclusions

References

3 Energy Storage System for Microgrid for Commercial Systems

3.1 Introduction

3.2 State of the Art

3.3 Energy Storage Systems

3.4 Batteries for Microgrids in Commercial Applications

3.5 Future Trends

3.6 Summary

References

4 Emerging Topologies of DC–DC Converters for Microgrid Applications

4.1 Introduction

4.2 Microgrid

4.3 DC–DC Converter Topologies

4.4 Modulation of DC–DC Converters With Different Control Strategies

4.5 Comparative Analysis

4.6 Conclusion

Appendix

References

5 Analysis of PWM Techniques on Multiphase Multilevel Inverter for PV Applications in Microgrids

5.1 Introduction

5.2 Cascaded H-Bridge Multiphase Multilevel Inverter

5.3 Modulation Techniques for Multilevel Inverter

5.4 Simulation Results

5.5 Conclusion

References

6 Mathematical Modeling and Analysis of Solar PV–Electrolyzer–Fuel Cell‑Based Power Generation System

6.1 Introduction

6.2 Hybrid Renewable Energy Storage System

6.3 Modeling of the Hybrid Renewable Energy Storage System

6.4 Characteristic Study of Each Component of the Hybrid Renewable Energy Storage System

6.5 Energy Management System

6.6 Result and Discussion

6.7 Summary and Future Scope

References

7 Design of DC EV Charging Infrastructure in a Commercial Building Using the Solar PV System

7.1 Introduction

7.2 Methodological Analysis

7.3 Result Analysis

7.4 Conclusion

References

8 Design and Simulation of a Rooftop Stand-Alone Photovoltaic Power System for an Academic Institution

8.1 Introduction

8.2 System Design

8.3 Design Methodology

8.4 Conclusion

References

9 Integration of Wind Energy Control with Electric Vehicle

9.1 Introduction

9.2 PID Controller

9.3 Wind Power System Dynamics

9.4 PID Control in Frequency Regulation

9.5 Integrating Wind Power Systems into EV

9.6 Conclusion

References

10 Interactive Use of D-STATCOM and Storage Resource to Maintain Microgrid Stability for Commercial Systems

10.1 Introduction

10.2 The Proposed Structure

10.3 Simulation

10.4 Conclusion

References

11 Power System Studies for Microgrids

11.1 Introduction

11.2 Description of a Microgrid Model Operating in Islanded Mode

11.3 Harmonic Load Flow Analysis in Islanded Mode

11.4 Transient Analysis of a Microgrid System in Islanded Mode

11.5 Load Flow Analysis of a Microgrid Operating in Grid-Tied Mode

11.6 Transient Analysis of Microgrid System in Grid-Tied Mode

11.7 Comparative Analysis of a Microgrid Operating in Islanded and Grid-Tied Mode

11.8 Conclusion

References

12 EV Charging Infrastructure in Microgrid

12.1 Introduction

12.2 An Overview of EV Charging Infrastructure

12.3 Importance of Charging Station and Charge Point

12.4 EV Integration to the Microgrid

12.5 Industrial Microgrid and Subsystem

12.6 Summary

References

13 Operation and Control of EV Infrastructure for Microgrid

13.1 Introduction

13.2 Proposed Electric Vehicle Charging Infrastructure for Enhancing Microgrid Operation

13.3 Implementation of Proposed Commercial EV Charging Stations on Microgrids

13.4 Validation of the Proposed Commercial EV Charging Stations on Microgrids

13.5 Conclusion

References

14 Renewable-Energy-Powered EV Charging Station for Microgrid PSO-Based Controller for PV-Powered EV Charging Station

14.1 Introduction

14.2 Renewable-Energy-Powered EV Charging Station for Microgrid

14.3 EV Charging Station

14.4 System Description

14.5 Proposed PSO Optimized IC MPPT Algorithm

14.6 Case Study—MATLAB Simulation

14.7 Conclusion

References

Appendix

15 Closed-Loop Control of Microgrids With Wind and Battery Storage System in Islanding Mode

15.1 Introduction

15.2 Wind Turbine Modeling

15.3 Designing a Permanent Magnet Synchronous Generator

15.4 Three-Phase Rectifier

15.5 Modeling of a Boost Converter With MPPT

15.6 Battery Energy Storage System

15.7 Modeling of Lithium-Ion Battery

15.8 Modeling of a Bidirectional DC–DC Converter

15.9 Modeling a Three-Phase Voltage Source Inverter

15.10 Vector Control Structure for Inverter Control

15.11 Modeling a Passive Filter

15.12 Simulation Results

15.13 Conclusion

References

Index

Also of Interest

End User License Agreement

List of Tables

Chapter 1

Table 1.1 Categorization of loads.

Table 1.2 Distinguished time slots.

Table 1.3 Economical inputs.

Table 1.4 HOMER simulation configurations before and after DSM.

Table 1.5 Comparative result analysis.

Table 1.6 Comparative cost analysis.

Chapter 2

Table 2.1 Comparative analysis for the four seasons in the Indian market.

Table 2.2 Daily MAPE results in percentage for the period June 16–30, 2019.

Chapter 4

Table 4.1 Comparative configuration analysis of unidirectional resonant DC–DC ...

Table 4.2 Comparative operational analysis of unidirectional resonant DC–DC co...

Table 4.3 Comparative configuration analysis of bidirectional isolated DC–DC c...

Table 4.4 Comparative operational analysis of bidirectional isolated DC–DC con...

Table 4.5 Comparative configuration analysis of bidirectional non-isolated DC–...

Table 4.6 Comparative operational analysis of bidirectional non-isolated DC–DC...

Table 4.7 Comparative analysis of the overall configuration of unidirectional ...

Table 4.8 Comparative analysis on the overall configuration of bidirectional i...

Table 4.9 Comparative analysis of overall configuration of bidirectional non-i...

Chapter 5

Table 5.1 Switching sequence for five-level CHB MLI per phase.

Table 5.2 Simulation parameters.

Table 5.3 THD and RMS value comparison for the phase and line voltages of a fi...

Table 5.4 THD and RMS value comparison for the phase and line voltages of a si...

Table 5.5 THD and RMS value comparison for the phase and line voltages of a se...

Chapter 6

Table 6.1 Various output values for the change in the irradiance level of the ...

Chapter 7

Table 7.1 List of the arrival of vehicles in a day.

Table 7.2 Characteristics of various types of electric vehicles.

Table 7.3 Equipment specifications and ratings for the proposed microgrid desi...

Table 7.4 Cost comparison analysis for base case and winning system using HOME...

Table 7.5 Comparison analysis for the base case and the winning case of grid....

Table 7.6 Proposed EV microgrid design emission levels.

Chapter 8

Table 8.1 Daily load estimation table for the ECE department.

Chapter 10

Table 10.1 Data of available resources in the microgrid.

Table 10.2 Microgrid consumer data.

Table 10.3 Conditions of the microgrid lines.

Chapter 11

Table 11.1 Voltage alert levels.

Table 11.2 Load flow report operating scenario 1 in islanded mode.

Table 11.3 Load flow report operating scenario 2 in islanded mode.

Table 11.4 Voltage harmonics bus information in islanded mode.

Table 11.5 Current harmonics bus information in islanded mode.

Table 11.6 Bus voltage during line-to-ground fault.

Table 11.7 Bus current during line-to-ground fault.

Table 11.8 Bus voltage and current during the three-phase fault.

Table 11.9 Bus voltage during line-to-ground fault at bus 3.

Table 11.10 Bus current during LG fault at bus 3.

Table 11.11 Load flow report during loss of PV 1 unit in islanded mode.

Table 11.12 Load flow table in grid-tied mode during operating scenario 1.

Table 11.13 Load flow table in grid-tied mode during operating scenario 2.

Table 11.14 Voltage harmonics bus information in grid-tied mode.

Table 11.15 Current harmonics bus information in grid-tied mode.

Table 11.16 Bus voltage during line-to-ground fault.

Table 11.17 Bus current during line-to-ground fault.

Table 11.18 Bus voltage during three-phase fault at the main bus.

Table 11.19 Bus voltage during line-to-ground fault at bus 3.

Table 11.20 Bus current during line-to-ground fault at bus 3.

Table 11.21 Load flow table during loss PV 1 in grid-tied mode.

Table 11.22 Summarized comparison of reports generated from electrical transie...

Chapter 12

Table 12.1 Comparison of chargers.

Table 12.2 Different segments’ charging power and its requirement.

Table 12.3 The DC microgrid device installed sizes.

Chapter 13

Table 13.1 Performance comparison of commercial EV charging stations on microg...

Table 13.2 Commercial systems on a weekly and monthly EV handling delay for ef...

Chapter 14

Table 14.1 PV cell and DC–DC converter model specification.

Table 14.2 PSO parameter settings.

Chapter 15

Table 15.1 Switching states [28, 29, 37].

Table 15.2 System parameters for variable-speed wind energy system [28–34].

List of Illustrations

Chapter 1

Figure 1.1 Overview of HOMER analysis.

Figure 1.2 Location of Thiagarajar College of Engineering.

Figure 1.3 Block diagram of the test system.

Figure 1.4 Process flow of the proposed methodology.

Figure 1.5 Working of ML algorithm.

Figure 1.6 Monthly patterns before and after DSM.

Figure 1.7 Location of the TCE-EEE department.

Figure 1.8 Availability of resources.

Figure 1.9 Monthly average energy demand of the EEE department (before and aft...

Figure 1.10 Configuration—cost analysis.

Chapter 2

Figure 2.1 Price forecasting model [47].

Figure 2.2 Electricity price forecasting [24].

Figure 2.3 Machine learning methods [43].

Figure 2.4 Forecasting results for the month of October to November and Februa...

Figure 2.5 Forecasting results for the months of June and December.

Figure 2.6 Forecasting results for January and October.

Chapter 3

Figure 3.1 Single-line diagram of a microgrid with renewable energy sources, e...

Figure 3.2 Classification of energy storage devices.

Figure 3.3 Cross-section of an individual UC and a hydrogen FC [14].

Figure 3.4 Battery models [30].

Figure 3.5 Block diagram of a generic BMS.

Chapter 4

Figure 4.1 Data from the World Resources Institute.

Figure 4.2 Data from the International Energy Agency.

Figure 4.3 Schematic diagram of a hybrid microgrid.

Figure 4.4 DC-DC converter topology.

Figure 4.5 Unidirectional resonant DC–DC converters.

Figure 4.6 Bidirectional isolated DC–DC converter.

Figure 4.7 Bidirectional isolated DC–DC converter.

Figure 4.8 Bidirectional non-isolated DC–DC converter.

Figure 4.9 Bidirectional non-isolated DC–DC converter.

Figure 4.10 Classical switching with linear control.

Figure 4.11 Classical switching with model predictive control.

Figure 4.12 Classical switching with intelligent control.

Figure 4.13 Classical switching with digital control.

Figure 4.14 Efficiency vs switching frequency of unidirectional resonant DC–DC...

Figure 4.15 Efficiency vs switching frequency of bidirectional isolated DC–DC ...

Figure 4.16 Efficiency vs switching frequency of bidirectional non-isolated DC...

Figure 4.17 Efficiency vs power rating of unidirectional resonant DC–DC conver...

Figure 4.18 Efficiency vs power rating of bidirectional isolated DC–DC convert...

Figure 4.19 Efficiency vs power rating of bidirectional non-isolated DC–DC con...

Figure 4.20 Total parts count of unidirectional resonant DC–DC converter.

Figure 4.21 Total parts count of bidirectional isolated DC–DC converter.

Figure 4.22 Total parts count of bidirectional non-isolated DC–DC converter.

Chapter 5

Figure 5.1 Classification of multilevel inverters.

Figure 5.2 Structure of five-phase five-level MLI.

Figure 5.3 Structure of six-phase five-level MLI.

Figure 5.4 Structure of seven-phase five-level MLI.

Figure 5.5 Multilevel modulation techniques.

Figure 5.6 Phase-shifted carrier waveforms, modulating waveform, regions, and ...

Figure 5.7 Carrier (triangular) and reference (sinusoidal) waveform for five-l...

Figure 5.8 Output voltage of a five-level converter with PD-LSPWM.

Figure 5.9 Offset signal.

Figure 5.10 Five-phase five-level CHB inverter phase voltage and line voltage ...

Figure 5.11 Six-phase five-level CHB inverter phase voltage and line voltage f...

Figure 5.12 Seven-phase five-level CHB inverter phase voltage and line voltage...

Figure 5.13 Five-phase five-level CHB inverter phase voltage and line voltage ...

Figure 5.14 Six-phase five-level CHB inverter phase voltage and line voltage f...

Figure 5.15 Seven-phase five-level CHB inverter phase voltage and line voltage...

Figure 5.16 Five-phase five-level CHB inverter phase voltage and line voltage ...

Figure 5.17 Six-phase five-level CHB inverter phase voltage and line voltage f...

Figure 5.18 Seven-phase five-level CHB inverter phase voltage and line voltage...

Chapter 6

Figure 6.1 Block diagram of hybrid renewable energy storage system.

Figure 6.2 Equivalent circuit of a PV cell.

Figure 6.3 Subsystem 1—simulink model of the photo current.

Figure 6.4 Subsystem 2—simulink model of the reverse saturation current.

Figure 6.5 Subsystem 3—simulink model of the saturation current.

Figure 6.6 Interconnected subsystems of the PV panel.

Figure 6.7 Simulink model of the boost converter.

Figure 6.8 Simulink model of the equilibrium voltage.

Figure 6.9 Simulink model of the ohmic over-potential.

Figure 6.10 Simulink model of the cathode activation potential.

Figure 6.11 Simulink model of the hydrogen pressure.

Figure 6.12 Simulink model of the Nernst equation.

Figure 6.13 Simulink model of the fuel cell.

Figure 6.14 (a) PV characteristics of the solar panel at a constant temperatur...

Figure 6.15 (a) PV characteristics of the solar panel at a constant irradiance...

Figure 6.16 J–V characteristics of the PEM electrolyzer.

Figure 6.17 Ohmic, anode, and concentration potential of the PEM electrolyzer ...

Figure 6.18 J–V characteristic curves obtained for PEM fuel cell at different ...

Figure 6.19 Flow chart for power regulation.

Figure 6.20 Simulink model of a hybrid renewable storage system.

Figure 6.21 Input of solar PV at various irradiance levels.

Figure 6.22 (a and b) Output voltage and current of the solar power system.

Figure 6.23 The excess power delivered to the electrolyzer.

Figure 6.24 The demand of power to turn on a fuel cell.

Figure 6.25 Hydrogen flow rate from an electrolyzer.

Figure 6.26 (a) DC output voltage of a PEM fuel cell and (b) DC output current...

Figure 6.27 (a) Inverter output voltage of a PEM fuel cell and (b) inverter ou...

Figure 6.28 (a and b) Inverter output voltage and current of a solar PV panel....

Chapter 7

Figure 7.1 Block diagram for overall representation.

Figure 7.2 Structural outline for the proposed system in HOMER.

Figure 7.3 Google map of the selected shopping complex.

Figure 7.4 Global horizontal insolation level for the chosen site.

Figure 7.5 Ambient temperature layout for the selected site.

Figure 7.6 Daily profile for the highest demand of the month of September.

Figure 7.7 Electrical energy supplied to the electric vehicles.

Figure 7.8 Single-line diagram for the solar power DC microgrid.

Figure 7.9 Overall energy production comparison.

Figure 7.10 Comparison between a base case grid and the proposed case grid lev...

Figure 7.11 Energy production difference between the conventional grid and the...

Figure 7.12 Battery state of charge

versus

time in the shopping complex.

Chapter 8

Figure 8.1 Block diagram representing a stand-alone PV system.

Figure 8.2 Flow chart reflecting the design steps.

Figure 8.3 Image illustrating the GIMT campus.

Figure 8.4 Geographical site parameters of Krishnagar over the year.

Figure 8.5 Optimum tilt angle of the PV panel.

Figure 8.6 Daily energy consumption in the ECE department (room-wise) on an ho...

Chapter 9

Figure 9.1 Block diagram of the PID controller [8].

Figure 9.2 Flow chart of the PSO algorithms.

Figure 9.3 Integration of the wind energy system [12].

Figure 9.4 Frequency deviations with and without the PID controller.

Figure 9.5 Wind output power.

Figure 9.6 Change in power.

Figure 9.7 EV output power.

Chapter 10

Figure 10.1 Microgrid model.

Figure 10.2 Compact system of the primary control system.

Figure 10.3 Drop controller block diagram.

Figure 10.4 Voltage controller’s block diagram.

Figure 10.5 Current controller’s block diagram.

Figure 10.6 Architecture of the distributed generation control system.

Figure 10.7 Block diagram of the compensator control section.

Figure 10.8 An illustration of a single line of a test network.

Figure 10.9 Point of connection circuit breaker status change.

Figure 10.10 Total power received by the microgrid from the power system.

Figure 10.11 Getting out of the allowed range of voltage and frequency.

Figure 10.12 Power injected by the storage source and distributed generation s...

Figure 10.13 Voltage fluctuations of distributed generation and storage source...

Figure 10.14 Microgrid frequency range.

Figure 10.15 Step changes of demand in the islanded microgrid.

Figure 10.16 Compatibility of the stored power.

Figure 10.17 Changes in the power of distributed generations.

Figure 10.18 Voltage energy storage source and distributed generations.

Figure 10.19 Microgrid frequency.

Figure 10.20 Changes in renewable source power.

Figure 10.21 The injected power of the storage source and distributed generati...

Figure 10.22 Voltage fluctuations of distributed generation and storage source...

Figure 10.23 Microgrid frequency.

Chapter 11

Figure 11.1 Schematic diagram of a microgrid operating in islanded mode.

Figure 11.2 Daily load profile of a school campus.

Figure 11.3 Load flow model in electrical transient analysis program for the i...

Figure 11.4 Load flow model in Electrical Transient Analysis Program for the i...

Figure 11.5 Voltage waveform in islanded mode.

Figure 11.6 Voltage harmonic spectrum in islanded mode.

Figure 11.7 Voltage profile during line-to-ground fault.

Figure 11.8 Current profile during line-to-ground fault.

Figure 11.9 Voltage profile during the three-phase fault.

Figure 11.10 Current profile during the three-phase fault.

Figure 11.11 Voltage profile during line-to-ground fault at bus 3.

Figure 11.12 Current profile during line-to-ground fault at bus 3.

Figure 11.13 Load flow analysis model in electrical transient analysis program...

Figure 11.14 Critical clearing time and critical clearing angle for the three-...

Figure 11.15 Critical clearing time and critical clearing angle for line-to-gr...

Figure 11.16 Block diagram of grid-tied microgrid.

Figure 11.17 Load flow model in electrical transient analysis program for grid...

Figure 11.18 Load flow model in electrical transient analysis program for grid...

Figure 11.19 Voltage waveform in grid-tied mode.

Figure 11.20 Voltage harmonic spectrum in grid-tied mode.

Figure 11.21 Voltage profile during line-to-ground fault at bus 1.

Figure 11.22 Current profile during line-to-ground fault at bus 1.

Figure 11.23 Voltage profile during three-phase fault at the main bus.

Figure 11.24 Current profile during three-phase fault at the main bus.

Figure 11.25 Voltage profile during line-to-ground fault at bus 3 in grid-tied...

Figure 11.26 Current profile during line-to-ground fault at bus 3 in grid-tied...

Figure 11.27 Load flow model in electrical transient analysis program during l...

Chapter 12

Figure 12.1 MG power balance on a sunny day.

Figure 12.2 A typical microgrid network.

Figure 12.3 Typical electric vehicle states.

Figure 12.4 Smart charging for solar and wind generation.

Figure 12.5 Types of electric vehicle charging infrastructure.

Figure 12.6 Electric vehicle charging chracteristics.

Figure 12.7 A microgrid design for domestic application.

Figure 12.8 Industrial microgrid concept.

Figure 12.9 V2G system block diagram for an industrial microgrid.

Figure 12.10 Typical electric vehicle charger configuration.

Figure 12.11 A DC microgrid system through an energy management system.

Figure 12.12 Electric vehicle charging optimization control strategy.

Chapter 13

Figure 13.1 The use of electric vehicles increases the level of microgrid reso...

Figure 13.2 System-level electric charging for battery and control: (a) level ...

Figure 13.3 Flow chart of the proposed electric vehicle charging scheme with m...

Figure 13.4 Voltage profile for the number of charging electric vehicles in a ...

Figure 13.5 State of charge at various EV charging stations: (a) village, (b) ...

Figure 13.6 Incremental cost saving (Rs/kWh) in EV charging station handling: ...

Figure 13.7 Simulated EV handling improvement for different levels of charging...

Figure 13.8 Energy saving after implementing the proposed charging: (a) level ...

Chapter 14

Figure 14.1 Layout of EV charging station for microgrid.

Figure 14.2 Types of plug.

Figure 14.3 The three main types of EV charging cable.

Figure 14.4 EV charging mode 1.

Figure 14.5 EV charging mode 2.

Figure 14.6 EV charging mode 3.

Figure 14.7 EV charging mode 4.

Figure 14.8 EV charger arrangement: (a) on-board and (b) off-board.

Figure 14.9 System block diagram.

Figure 14.10 Principle of the IC algorithm.

Figure 14.11 (a) Flow chart of the PSO. (b) Flow chart of the proposed IC-MPPT...

Figure 14.12 I–V and P–V curves.

Figure 14.13 Power vs time of PV output and DC–DC converter output.

Figure 14.14 Voltage vs time of PV output and DC–DC converter output.

Figure 14.15 Current vs time of PV output and DC–DC converter output.

Figure 14.16 Optimum voltage turned by PSO.

Figure 14.17 Maximum power tracking of IC MPPT with and without PSO optimum vo...

Figure 14.A2 Overall system in MATLAB/Simulink.

Chapter 15

Figure 15.1 Block diagram of VSWT stand-alone wind battery system with control...

Figure 15.2 PMSG with three-phase diode rectifier [3–7].

Figure 15.3 Block diagram of boost converter [8–13].

Figure 15.4 Control structure for MPPT [13].

Figure 15.5 Electrical equivalent of battery [14–17].

Figure 15.6 Schematic diagram of DC–DC bidirectional converter [17–22].

Figure 15.7 Schematic diagram of DC–DC bi-directional converter [16, 17].

Figure 15.8 Flow chart of control strategy for battery storage system [19–27]....

Figure 15.9 Equivalent circuit of two-level VSI [28, 29].

Figure 15.10 Vector control structure for inverter control [1, 2, 25, 26, 32]....

Figure 15.11 Block diagram of LC filter [40, 41].

Figure 15.12 Step change variation in wind speed (m/s) [1, 3, 5, 7].

Figure 15.13 Rotor speed for step change wind speed (rad/s) [5–7].

Figure 15.14 Mechanical torque for step change wind speed (N-m) [5–7].

Figure 15.15 DC current for step change wind speed (A) [5–7].

Figure 15.16 DC power output for step change wind speed (W) [5–7].

Figure 15.17 Staircase variation in wind speed (m/s) [1, 2, 4].

Figure 15.18 Staircase rotor speed (rad/s) [1, 2, 4].

Figure 15.19 Mechanical torque for staircase wind speed (N-m) [1, 2, 4].

Figure 15.20 DC current for staircase wind speed (A) [1, 2, 4].

Figure 15.21 DC power output for staircase wind speed (W) [1, 2, 4].

Figure 15.22 Random variation in speed of the wind (m/s) [8–13].

Figure 15.23 Rotor speed for random wind speed change (rad/s) [8–13].

Figure 15.24 Mechanical torque for random wind speed change (N-m) [8–13].

Figure 15.25 DC current for random wind speed change (A) [8–13].

Figure 15.26 DC power for random wind speed change (W) [8–13].

Figure 15.27 DC link voltage at constant load (volts) [14–21].

Figure 15.28 Inverter output voltage (volts) [22–27].

Figure 15.29 Inverter output current (A) [22–27].

Figure 15.30 Load voltage at constant load (volts) [22–27].

Figure 15.31 Load current at constant load (A) [22–27].

Figure 15.32 Active power at constant load (W) [22–27].

Figure 15.33 Reactive power at constant load (W) [22–27].

Figure 15.34 DC link voltage when the load is reduced to half (volts) [25–30]....

Figure 15.35 Load voltage when the load is reduced to half (volts) [25–30].

Figure 15.36 Load current when the load is reduced to half (A) [25–30].

Figure 15.37 Active power when the load is reduced to half (W) [25–30].

Figure 15.38 Reactive power when the load is reduced to half (VAR) [25–30].

Figure 15.39 Voltage of DC link when the load is increased to half (volts) [31...

Figure 15.40 Output voltage across load when the load is increased to half (vo...

Figure 15.41 Output current across load when the load is increased to half (A)...

Figure 15.42 Active power when the load is increased to half (W) [31–37].

Figure 15.43 Reactive power when the load is increased to half (VAR) [31–37]....

Figure 15.44 Voltage across resistive and inductive load under load switching ...

Figure 15.45 Load current across resistive and inductive load under load switc...

Figure 15.46 Active power across resistive and inductive load under load switc...

Figure 15.47 Reactive power across resistive and inductive load under load swi...

Figure 15.48 Voltage across practical inductive and non-linear load switching ...

Figure 15.49 Current across practical inductive and non-linear load switching ...

Figure 15.50 Active power across practical inductive and non-linear load switc...

Figure 15.51 Reactive power across practical inductive and non-linear load swi...

Guide

Cover Page

Table of Contents

Series Page

Title Page

Copyright Page

Acknowledgements

Begin Reading

Index

Also of Interest

WILEY END USER LICENSE AGREEMENT

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Microgrids for Commercial Systems

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Acknowledgements

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Warm regards,Editors

1Smart Energy Source Management in a Commercial Building Microgrid

A. C. Vishnu Dharssini*, S. Charles Raja and P. Venkatesh

Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Tamil Nadu, Madurai, India

Abstract

In the most recent times, renewable resource-based local power generation and effective utilization are evolving as a hot research topic, as it mainly focuses on ensuring a reliable energy supply to the consumers. It mitigates the challenges of the volatile nature of renewable sources and uncertainties in demand rise. To ensure the sustainable operation of microgrids specifically, a deep knowledge of demand and generation trends is required. The proposed research framework highlights the promising supply-side management approach of available renewable sources along with the existing grid connection, the optimal energy configuration of which is found with the help of Hybrid Optimization of the Multiple Energy Resources simulation software. The scope of the chapter is to assure the continual supply to meet the load of a particular department block in an educational institution, Thiagarajar College of Engineering (TCE), Madurai (9.8821° N, 78.0816° E). The feasibility study deals with prioritizing available sources such as solar photovoltaic generation, diesel generation, and backup facilities in TCE. Based on seasonal energy trends and availability, a significant demand-side management technique (DSM) technique is feasible for educational buildings: load shifting is implemented. The approach tracks the optimal energy configuration among four configurations and compares the configuration before and after the DSM incorporation. It also pinned out the probability of cost reduction by the optimal source allocation strategy.

Keywords: Building energy management, feasibility analysis, demand side management, optimal source allocation, ToU tariff

1.1 Introduction

In this modern era, the local power generation and utilization concept is considered an asset and efficacious approach to have a better energy management system [1]. This encourages the penetration of renewable energy resources and considerably reduces the usage of conventional fossil fuels [2]. The real-time challenge with the approach is adopting it to an existing grid-connected system without collapsing the system and also ensuring optimal source utilization [3, 4]. The alarming usage of conventional resources which are emitting greenhouse gas paves the way to emphasize the need of incorporating an appropriate demand-side management (DSM) strategy [5]. The DSM techniques encompass various kinds of strategies such as load shifting, peak clipping, valley filling, strategic load growth, strategic load conservation, etc. [6]. Some DSM implemented reduce loads in peak hours, resulting in a demand-response approach [7]. The applicability of DSM with respect to available resources in a location provides a clear insinuation on conventional sources [8]. Some strategies are also tariff-based, aimed to reduce additional charges and switch load in reference to the time of use principle [9]. The preliminary requirement for introducing building energy management is possible with smart energy meters which enable to model the demand pattern of that particular building. The process is generally termed modeling demand, involving the procedural gathering of available information as a numerical note and updating knowledge on the state of existing energy systems [10].

Modeling the demand pattern of smart buildings is essential based on economic and environmental impact on energy utilization trends and sustainable use of available resources [11]. Enormous studies and research articles plotted the problem by various approaches and techniques, but it has a limitation as it varies from system to system and also relies upon system considerations [12]. Each work proposes a different and unique supply and demand-side management approach according to the dataset type, i.e., whether univariate or multivariate. Some researchers focused on comparing supply–demand result patterns [13] accompanied by an analysis of the cost involved in bidding for supply from each kind of source in case of the availability of multiple sources [14].

The study presented in [15] put forth a stochastic programming model for staging the performance of a smart microgrid by reducing the operating cost and emissions with non-conventional resources. Plenty of work dealt with this problem using optimization techniques which are found to be generic and primordial. Vishnupriyan and Manoharan [16] adopted the study with a HOMER simulation software for Hybrid Renewable Energy Systems under six different climatic conditions, and the results were optimized to improve the renewable fraction in both before and after the DSM strategies. Similar works are presented in [17, 18] for residential and commercial buildings, promoting optimal energy sources based on computed net present cost (NPC) and cost of energy (COE).

Figure 1.1 Overview of HOMER analysis.

From this extensive survey, it is clear that predictive DSM implementation by data pertaining to the demand load of higher education institutional buildings was not collected and approached to pick out optimum energy configuration with a standard analytical approach. This paves the way to the initiation of strategic DSM, i.e., load shifting and HOMER software solution as in Figure 1.1.

1.2 Motivations of the Study

To ensure a balancing operation of the microgrid from both the supply end and the customer end.

To prioritize the utilization of local generation with available renewable resources which, in turn, results in the reduction of transmission losses.

To promote building energy management.

To schedule the operation of each load and to trace corresponding source combinations to feed them.

To estimate the lifetime expenditure, i.e., over 25 years for each source configuration.

1.3 State of the Art of the System

The test system—Department of Electrical and Electronics Engineering in Thiagarajar College of Engineering—is the study location in the Madurai district of Tamil Nadu, as shown in the map (Figure 1.2). The study area has four sources of supply which are solar PV panels with a net capacity of 18 kW from an overall 450-kW system, a 10-kVA diesel generator system, battery storage of 1 kWh lead acid, and grid supply as depicted in Figure 1.3. The test system consists of highly equipped laboratories with Elmeasure smart meters affixed to trace the energy usage pattern. The data gets continually stored in the server which is reclaimed in the centralized server system on demand using Elmeasure’s ElNet software.

1.4 Overview of the Proposed Methodology

Figure 1.4 shows the overall process initiating from data fetching to optimal configuration selection. The data obtained from various smart meters will possess certain default shortcomings that can be offset by adopting proper data handling techniques. There exists a subsequent process to get coherent worthwhile data sources at the end of the data pre-processing approach. These data catered to the analytics part to draw insights from the data. In the proposed methodology, data is fed to the decision tree algorithm which decides on loads to be operated at a particular time. In short, it schedules the primary load and secondary loads in a building with respect to time. Following this, it incorporates the most probable DSM strategy, i.e., load shifting. Next, for effective supply side management, load patterns before and after DSM are laid hold of and fed to the HOMER software. It compares each configuration in terms of environmental, technical, and economic aspects, resulting in a best approach (before/after DSM with appropriate supply-side management).

Figure 1.2 Location of Thiagarajar College of Engineering.

Figure 1.3 Block diagram of the test system.

Figure 1.4 Process flow of the proposed methodology.

1.5 DSM Approach

The loads and appliances in the test system are tagged as primary and secondary loads based on customer preferences and utilization premises. The essential loads for the active functioning of laboratories apart from test kits and devices are tagged as primary loads, while the rest, for sophistication in utilization, are tagged as secondary loads. The categorization of loads in the building is presented in Table 1.1.

Lighting loads, fans, routers, projectors, and plug points are considered primary, while air conditioners, water dispensers, exhausters, printers, and coolants are secondary loads. With the knowledge of available loads and their importance, loads are preferred accordingly. Tracing the existing pattern of consumption is essential as it is one of the determining factors for implementing DSM.

The most probable and effective DSM approach—load shifting—is applicable as it involves the effective usage of loads without violating any kind of limitations. It prevents reaching the maximum demand limit and thereby ensures the neglect of penalties. The previous consumption record which is properly maintained in the server is retrieved and treated to feed the machine learning algorithm. The decision tree algorithm thus extracts insights from data and makes decisions on switching appliances. Its decision relies entirely on energy patterns obtained from smart meters, maximum demand limit, the peak time of consumption, and, finally, on the time of the day. The algorithm is designed to have average monthly patterns on a 24-h time scale for incorporating the appropriate DSM technique, and it also varies with academic semester schedules. Thus, net demand patterns before and after DSM over each month are required to be called out to comparatively visualize the role of the ML algorithm.

Table 1.1 Categorization of loads.

Primary load

Secondary load

Lights

Air conditioners

Fans

Water dispensers

Plug points

Exhausters

Projectors

Printers

Routers

Refrigerators

Table 1.2 Distinguished time slots.

Time slot

Period

11.15 a.m. to 01.15 a.m. and 02.15 p.m. to 04.15 p.m.

Peak hours

09.00 a.m. to 11.15 p.m.

Normal hours

01.15 p.m. to 2.15 a.m. and 04.15 p.m. to 09.00 a.m.

Off-peak hours

The procedure adopted to train the ML algorithm is shown in Figure 1.5. The loads are shifted in correspondence to the time slot. The 24 h of a day are split into three distinguished time periods, namely, peak, normal and off-peak, as shown in Table 1.2. The algorithm checks the time period at the moment and cross-verifies the criteria as shown in the figure. Only primary loads are prioritized during peak hours, i.e., active laboratory sessions. During normal hours, loads are utilized based on the probability of maximum demand reach, and during off-peak hours, it is up to the consumer’s choice of load usage, accounting for both norms in the prior two slots.

The patterns of before and after DSM visualizes that a maximum reach of 80 kWh/day falls in January and is considered to be the peak consumption month, while in March and October the lowest consumption of below 40 kWh/day occurs. It was also observed that it is quite challenging to reduce the peak by shifting, as the possible time of shifting is only around morning (7:30 a.m.) to evening (6:30 p.m.). Still the ML was found to be effective in modifying the demand curve by reducing the peak.

Based on the decision, loads are shifted, and the demand curve is traced back on approximation, as shown in Figure 1.6. It was seen that reducing the peak over a working time slot will aid in the quality of energy utilization. In addition to ensuring this, both demand traces before and after DSM are fed to HOMER software, which comparatively analyzes the system to have suitable and profitable energy configurations.

Figure 1.5 Working of ML algorithm.

1.6 Background for HOMER Simulation

The HOMER software is initialized with the exact location of the EEE block in the TCE building, as shown in Figure 1.7. It falls at 9°53′ N latitude and 78°4.5′ E longitude and located at Thiruparankundram, Madurai, Tamil Nadu, 625015, India.

Figure 1.6 Monthly patterns before and after DSM.

Figure 1.7 Location of the TCE-EEE department.

After affixing the location details of the test system, data on renewable resources is obtained from the National Renewable Energy Laboratory and NASA’s prediction of worldwide energy resources. The data on solar global horizontal irradiance and average temperature persisting in the particular location is collected for estimating the effective utilization of available renewable energy resources. The yearly average solar irradiance is about 5.61 kWh/m2/day, and it is represented in Figure 1.8.

Figure 1.8 Availability of resources.

1.6.1 Economical Input Data for Simulation

The load data before DSM is incorporated initially to map out appropriate energy configuration without DSM, and then load data after DSM is fed for observation. The data on all three sources, excluding the grid which needed to be imputed for simulating the real-time system, is tabulated in Table 1.3.

The load categorizing and optimization with the DSM technique promotes the management of load consumption for achieving the desired objective. As per the DSM strategy, loads are prioritized and shifted with hourly data using the load shifting strategy. The comparative seasonal profile in Figure 1.9 shows the impact of DSM in the institution’s annual load profile.

1.6.2 Simulation—Energy Configurations

The HOMER software is accounted for comparing four energy configurations under two scenarios (before and after DSM) as depicted in Table 1.4. The configurations are depicted by tabulation.

Table 1.3 Economical inputs.

S. no.

Resource component

Manufacturer

Size (kW)

Initial cost ($)

Replacement cost ($)

O&M cost ($)

Lifetime

1

PV array

TATA Solar Power system

1–18

27,000

24,300

0

20 years

2

Converter

Schneider Electric

1–7

2,625

2,345

10.00

15 years

3

Diesel generator

Cummins

10

1,600

1,440

0.100

15,000 hours

4

Battery storage system

EnerSys

1 kWh

247.67

227

10.00

10 years

Figure 1.9 Monthly average energy demand of the EEE department (before and after DSM).

The results witnessed a reduction in the peak load and load factor from 153.90 kW and 0.16 to 133.63 kW and 0.19. Following it, the seasonal profile with a box plot comparatively depicts the variation in the range of consumption over each month. The average peak of every month is reduced to a greater extent and is notified clearly.

1.6.3 Comparative Analysis

The configurations are compared based on economical, technical, and environmental aspects considering the factors—components involved, net generation, consumption range, emissions, and, finally, cost—which are tabulated in Table 1.5. The ratio of components involved varies with system configuration, and the tabulation shows the base system to meet the load.

On the whole, the configurations after DSM are more reliable compared with the configurations before DSM. Based on production, total range of consumption, and rate of emission, DSM results in bounteous outcomes. Accounting for each combination of supplies, configurations 1 and 5, configurations 2 and 6, configurations 3 and 7, and configurations 4 and 8 are compared.

Configurations 1 and 2 and configurations 5 and 6 possess a high renewable fraction of 9.60 and 9.70, respectively. Hence, the optimal configuration from the aspect of the ratio of integrating renewable resources is among these pairs of configurations. Finally, concerning the economic cost involved in each configuration, the initial capital increases with the addition of new energy sources. The operating cost involved varies with configuration as it varies with accounted energy sources for that particular configuration, while levelized COE is found almost similar to each configuration, except configuration 4 and configuration 8. Next to that, NPC emerges as a determining factor to trace the optimal configuration among combos of four configurations.

Table 1.4 HOMER simulation configurations before and after DSM.

Configuration

Before DSM

After DSM

PV + grid

PV + battery + grid

PV + DG + grid

PV + battery + DG + grid

Table 1.5 Comparative result analysis.

Description

Without DSM

With DSM

Config 1

Config 2

Config 3

Config 4

Config 5

Config 6

Config 7

Config 8

Component (kW)

Solar PV

3

3

4

4

4

4

3

3

DG

0

0

2

2

0

0

2

2

Converter

2.42

2.44

2.42

2.56

2.42

2.42

2.42

2.42

Battery

0

1

0

1

0

1

0

1

Grid

148

148

148

148

128

128

128

128

Production (kWh/year)

PV array

28,367

28,367

28,367

28,367

28,367

28,367

28,367

28,367

Grid purchases

200,893

200,711

200,828

200,702

197,800

197,799

197,800

197,797

Total production

229,260

229,078

229,260

229,105

226,167

226,167

226,167

226,187

PV capacity factor (%)

18

18

18

18

18

18

18

18

Renewable fraction (%)

9.60

9.60

9.50

9.50

9.70

9.70

9.68

9.68

Consumption (kWh/year)

Total consumption

219,000

219,000

219,000

219,900

216,000

216,000

216,000

216,000

Emission (kg/year)

Carbon dioxide

126,964

126,849

126,964

126,964

125010

125010

125010

125010

Sulphur dioxide

550

550

550

550

542

542

542

542

Nitrogen oxides

269

269

269

269

265

265

265

265

Cost

Initial cost ($)

29,625

29,873

29,945

30,033

29,625

29,873

29,945

30,033

Operating cost ($/year)

20,423.2

20,415.0

20,397.2

20,584

20,114

20,137

20,088

20,261

Total NPC ($)

293,647

293,788

293,899

296,133

289,649

290,200

290,913

291,463

COE ($/kW)

0.1023

0.1023

0.1024

0.1031

0.102

0.103

0.103

0.1029

Table 1.6 Comparative cost analysis.

Configurations

Year-0

Year-1

Year-15

Year-20

Year-25

Cap. cost

Op. cost

Op. cost

Rep. cost

Op. cost

Rep. cost

Op. cost

Salvage cost

Before DSM

Config 1

$29,625

$18,982

$8,525.9

$994.92

$6,407.79

$7,747.0

$4,814.9

$4,553.20

Config 2

$29,872

$18,974

$8,524.1

$994.92

$6,405.17

$7,747.0

$4,812.7

$4,553.20

Config 3

$29,785

$18,982

$8,527.6

$994.92

$6,407.79

$7,747.0

$4,814.9

$4,586.83

Config 4

$31,472

$19,124

$8,591.2

$994.92

$6,455.61

$7,819.4

$4,850.9

$4,916.73

After DSM

Config 5

$29,625

$18,690

$8,396.4

$994.92

$6,309.20

$7,747.0

$4,740.9

$4,553.20

Config 6

$29,872

$18,681

$8,400.6

$994.92

$6,312.39

$7,747.0

$4,743.3

$4,580.39

Config 7

$29,785

$18,690

$8,396.4

$994.92

$6,309.20

$7,747.0

$4,740.9

$4,889.54

Config 8

$31,472

$18,899

$8,412.6

$994.92

$6,328.39

$7,819.4

$4,792.2

$4,916.73

Focusing on the point of the Payback period or lifetime of the system under each configuration is another essential determining factor of optimal source selection depicted in Table 1.6. The HOMER simulation software estimates the system life over 25 years concerning capital investment cost, operating cost, replacement cost, and salvage cost as in Figure 1.10. As mentioned earlier, incorporating DSM upgrades the system’s effectiveness by reducing the operating cost to a notifiable extent. It is also evident that configurations operating all the available resources (configuration 4 and configuration 8) are considered to be the worst configuration under both DSM scenarios. Next to that configurations 3 and 7 involve diesel generation, operating which demands non-renewable fuel of higher economic cost and also results in an adverse impact on environmental aspects i.e., emission. Finally, the combo for optimal source configuration for the test system is configurations 1&5 and configurations 2&6 which slightly vary in economic aspects.

Despite all conflicts in choices, configuration 1 and 5 is found to be more reliable, efficient, favourable, economical, and environmental. Thus, energy configuration with solar PV panels and grid-tied systems with appreciable DSM strategy is found to be the best choice for promoting supply and demand side management in a building.

Figure 1.10 Configuration—cost analysis.

1.6.4 Highlights of the Proposed Framework

Simulated real-time hybrid renewable energy system with HOMER software.

Selected of optimal supply-side strategy along with existing Grid connection.

Traced the seasonality trend in energy consumption.

Compared energy configuration under both with and without DSM.

Done economic analysis with NPC and COE metrics.

1.7 Conclusion

This study presented hybrid supply and demand side management for collected smart energy meter data on consumption in the Electrical and Electronics Engineering department of Thiagarajar College of Engineering, Madurai. Four different source configurations under two scenarios in terms of the Demand side management approach is concerned. The test system adopted consists of different sources – solar PV, Diesel, and Battery systems along with a grid connection. The strategic DSM using a machine learning algorithm renders reduced peak demand compared with the system without DSM. The following suitable results are incorporated and compared using the HOMER software tool. It compares the four combinations of resources before and after DSM. The methodology adopted highlights that configuration 5 with PV solar generation along with grid after the application of DSM is the best configuration for energy requirements in the considered test location. On the whole, the chapter emphasizes that extracting in-depth knowledge on local generations and supply sources in a smart environment facilitates customer end in planning building energy strategies concerning economic and environmental aspects. The approach of Demand cum supply side management creates a possibility of increasing the usage of Renewable energy sources among the rest and also ensures effective energy utilization at minimal cost expenditure. Thus, adopting this study will definitely aid in the sustainable operation of the Microgrid.

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Note

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Corresponding author

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2Renewable Power Generation Price Prediction and Forecasting Using Machine Learning

Challa Krishna Rao1,2*, Sarat Kumar Sahoo2 and Franco Fernando Yanine3

1Department of Electrical and Electronics Engineering, Aditya Institute of Technology and Management Tekkali, A.P., India

2Dept. of Electrical Engineering, Parala Maharaja Engineering College, Berhampur, Affiliated to Biju Patnaik University of Technology, Rourkela, Odisha, India

3School of Engineering of Universidad, Finis Terrae, Providencia, Santiago, Chile

Abstract

In the power market, electricity price forecasting (EPF) is a crucial consideration for decision-making. The need to optimize earnings by adjusting bids in dayahead power markets is becoming more and more important to different market participants. Prior information is required for marketers to have an advantage over the competition while controlling the risk of pricing fluctuation. However, not all marketers must accurately predict the worth of future pricing when making decisions. To make a choice, it is necessary to determine whether the cost will be prohibitive. Thus, in order to determine the prices that have an impact on marketers, electricity price classification is first performed. Based on a threshold value, prices are categorized as low class prices and high class pricing. In order to determine the precise value of prices for utility maximization, the EPF has been explored next. In order to maximize advantage or utility, buying and selling bidding techniques rely on the accurate projections of prices for the following day. Effective forecasting models can improve the performance of producers and consumers, who play important roles in the electrical markets. The EPF approaches now in use produce complicated models and are not generalizable.The best method for forecasting prices with superior generalization performance, kernel functions, and distributive prediction is to use machine learning (ML)-based models. In order to forecast prices, machine learning-based electricity price forecasting has been studied.