Integrated Green Energy Solutions, Volume 2 E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
INTEGRATED GREEN ENERGY SOLUTIONS This second volume in a two-volume set continues to present the state of the art for the concepts, practical applications, and future of renewable energy and how to move closer to true sustainability. Renewable energy supplies are of ever-increasing environmental and economic importance in every country in the world. A wide range of renewable energy technologies has been established commercially and recognized as an important set of growth industries for most governments. World agencies, such as the United Nations, have extensive programs to encourage these emerging technologies. This book will bridge the gap between descriptive reviews and specialized engineering technologies. It centers on demonstrating how fundamental physical processes govern renewable energy resources and their applications. Although the applications are being updated continually, the fundamental principles remain the same, and this book will provide a useful platform for those advancing the subject and its industries. Integrated Resilient Energy Solutions is a two-volume set covering subjects of proven technical and economic importance worldwide. Energy supply from renewables is an essential component of every nation's strategy, especially when there is responsibility for the environment and sustainability. These two volumes will consider the timeless renewable energy technologies' principles yet demonstrate modern applications and case studies. Whether for the veteran engineer, student, or other professional, these two volumes are a must-have for any library.
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Table of Contents
Cover
Series Page
Title Page
Copyright Page
Preface
23 Energy Economics and Environment
Abbreviations
23.1 Introduction
23.2 Benefits and Drawbacks of Microgrids
23.3 Causes of Increase in Power Plants
23.4 Demand Side Management in Microgrids
23.5 Centralized Control of Smart Grid
23.6 Decentralized Smart Grid Control
23.7 DER Resource Control Strategies in the Smart Grid
23.8 DER Participation Strategy in Smart Grid
23.9 Topics Raised in the Smart Grid
23.10 Smart Grid Protection
23.11 Detection of Smart Grid Islands
23.12 Smart Grid Optimization
23.13 Power Quality
23.14 Frequency and Voltage Control
23.15 Balance between Production and Power Consumption
23.16 Ability to Easily Connect Distributed Generation Sources
23.17 Smart Network Security
23.18 Resynchronization after Network Connection
23.19 Smart Grid Control Glasses
23.20 Economic Dimensions
23.21 Losses
23.22 Non-Technical Network Losses
23.23 Power System Loss Analysis
23.24 The Impact of the Electricity Market on the Performance of Distribution Companies
23.25 Power Quality in the Restructured Electricity Market
23.26 Conclusion
References
24 Stringent Energy Management Strategy during Covid-19 Pandemic
24.1 Introduction
24.2 Energy Management
24.3 Smart Grid Design
24.4 Smart Grid Design and Testing
24.5 Implementation of Smart Grid
24.6 Energy Management to Check Overload Conditions
24.7 Features of Smart Grid System
24.8 Conclusion and Future Work
References
25 Energy Management Strategy for Control and Planning
25.1 Energy Management and Audit
25.2 The Different Steps of an Energy Management Approach
25.3 Preliminary Technical and Economic
25.4 Evaluation of Energy-Saving Investments
25.5 Off-Line and On-Line Procedures
25.6 Personnel Training
25.7 A Successful Energy Management Program
25.8 Centralize Control of Process and Facility Plants
25.9 Energy Security
25.10 Evaluate Energy Performances
25.11 Energy Action Planning
25.12 Energy Economics
25.13 Case Study
References
26 Day-Ahead Solar Power Forecasting Using Statistical and Machine Learning Methods
Abbreviations
26.1 Introduction
26.2 Durations of Forecasting
26.3 Forecasting Techniques
26.4 Statistical Methods
26.5 Machine Learning Techniques
26.6 Deep Learning (DL)
26.7 Evaluation Index and Metrics
26.8 Conclusions
References
27 A Review on Optimum Location and Sizing of DGs in Radial Distribution System
Abbreviations
27.1 Introduction
27.2 Proposed Location and Sizing of DGs in RDS Using Analytical and PSO Methods
27.3 Result
27.4 Conclusion
27.5 Appendix: List of Symbols
References
28 High Step Up Non-Isolated DC-DC Converter Using Active-Passive Inductor Cells
28.1 Introduction
28.2 Proposed Converter
28.3 Modes of Operation
28.4 Design Considerations
28.5 Simulation
28.6 Hardware Results
28.7 Conclusion
References
29 A Non-Isolated Step-Up Quasi Z-Source Converter Using Coupled Inductor
29.1 Introduction
29.2 Improved Quasi Z Source Converter with Coupled Inductor
29.3 Modes of Operation
29.4 Simulation Results
29.5 Comparison
29.6 Conclusion
References
30 Datalogger Aided Stand-Alone PV System for Rural Electrification
Abbreviations and Nomenclature
30.1 Introduction
30.2 Work Description
30.3 Design and Realisation of DL
30.4 Results
30.5 Conclusion
References
31 Working and Analysis of an Electromagnet-Based DC V-Gate Magnet Motor for Electrical Applications
31.1 Conceptual Introduction
31.2 Existing Technologies to Review
31.3 Proposed Design
31.4 Block Schematic
31.5 Motor Assembly and Control Structure
31.6 Control Operation of the V-Gate Magnet Motor
31.7 Results and Analysis
31.8 Conclusion and Further Scope of Research
References
32 Design and Realization of Smart and Energy-Efficient Doorbell
32.1 Introduction
32.2 Methodology
32.3 Design and Specification
32.4 Result and Discussion
32.5 Conclusion
References
33 Optimal Solar Charging Enabled Autonomous Cleaning Robot
33.1 Introduction
33.2 Methodology
33.3 Results
33.4 Conclusion
References
34 Real-Time Health Monitoring System of a Distribution Transformer
34.1 Introduction
34.2 Flow Diagram
34.3 Operating Principle
34.4 Observation and Result
34.5 IFTTT Email Notification (in case of a fault)
34.6 Conclusion
References
35 Analysis of Wide-Angle Polarization-Insensitive Metamaterial Absorber Using Equivalent Circuit Modeling for Energy Harvesting Application
35.1 Introduction
35.2 Absorber Theory and Proposed Unit Cell Design
35.3 Equivalent Circuit Model
35.4 Simulation Results
35.5 Experimental Results
35.6 Conclusion
References
36 World Energy Demand
36.1 Energy End Users
36.2 Rural Electrification
36.3 Residential and Non-Residential Buildings
36.4 Industry
36.5 Transport
36.6 Agriculture
36.7 Performance Mapping in Conjunction with Technological Evolution
References
37 Education in Energy Conversion and Management
37.1 Role of University
37.2 Personnel Training
37.3 Awareness of Energy Conversion and Management as an Intersectoral Discipline
37.4 Climate Change
37.5 Economic Policy Options
37.6 Policy in Practice
37.7 Green Economy
37.8 The Relationship between the Economy and the Environment
37.9 Industrial Ecology
37.10 Does Protecting the Environment Harm the Economy?
37.11 Creating a Green Economy
References
About the Editors
Index
Also of Interest
End User License Agreement
List of Tables
Chapter 26
Table 26.1 Recent publications on power forecasting.
Table 26.2 Evaluation indies.
Table 26.3 Evaluation metrics.
Chapter 37
Table 37.1 Comparative table of skeptics’ arguments and what the science say...
Table 37.2 Important events in international climate change negotiations.
List of Illustrations
Chapter 23
Figure 23.1 The structure of a microgrid.
Figure 23.2 Techniques for changing the characteristic demand curve in deman...
Figure 23.3 Information exchange path in centralized intelligent network con...
Figure 23.4 Schematic diagram of the MAS structure of decentralized intellig...
Figure 23.5 SMO control strategy.
Figure 23.6 MSO control strategy.
Chapter 24
Figure 24.1 Smart grid in the ground station.
Figure 24.2 Steps involved in smart grid design.
Figure 24.3 Digital data is sent to the cloud through a gateway.
Figure 24.4 Data flow from the smart grid to the cloud.
Figure 24.5 Data visualization.
Figure 24.6 Steps involved in data display.
Figure 24.7 Smart grid simulation using Proteus.
Figure 24.8 Power consumption display in Proteus.
Figure 24.9 Current measurement using Proteus.
Figure 24.10 Voltage measurement using Proteus.
Figure 24.11 Circuit is tested using Node-RED and MQTT Implementation of sma...
Figure 24.12 Microsoft azure as virtual machine.
Figure 24.13 Voltage and power consumed along with the status in dashboard....
Figure 24.14 Smart grid app in the client side.
Figure 24.15 Fusion 360 software to build the PCB for smart grid.
Figure 24.16 Smart grid.
Figure 24.17 Putty configuration.
Figure 24.18 CLI for secured access.
Figure 24.19 Smart grid with varying load.
Figure 24.20 Power displayed in Cloud.
Figure 24.21 Optimum power values displayed in Node-RED dashboard.
Figure 24.22 Varying voltage in Node-RED dashboard.
Figure 24.23 Google Firebase as the database.
Figure 24.24 Client app.
Figure 24.25 Overload condition in the power without load.
Figure 24.26 Cloud MQTT message for overload without load.
Figure 24.27 Node-RED indicating over-voltage.
Figure 24.28 Under-voltage indication.
Figure 24.29 Smart grid with load of 18W.
Figure 24.30 Status ‘ON’.
Figure 24.31 Node-RED-Status-‘ON’ and Penalty –‘OFF’.
Figure 24.32 Client app with power displayed and Penalty –‘OFF’.
Figure 24.33 Smart grid with overload.
Figure 24.34 Cloud MQTT with overload.
Figure 24.35 Node-RED with Status –OFF and Penalty –ON for overload.
Figure 24.36 Google firebase update for overload criteria.
Figure 24.37 Client app with Penalty ‘ON’ for overload.
Chapter 25
Figure 25.1.A Four basic steps for energy management.
Figure 25.2.A Evaluation of energy-saving investment.
Figure 25.3.A Flow chart.
Figure 25.4.A [6] Energy management program.
Figure 25.4.B [6] Flow chart: energy management program.
Figure 25.5.A Centralized control system.
Figure 25.6.A Schematic diagram evaluate of energy performance.
Chapter 26
Figure 26.1 Worldwide SPV installed capacity trends [5].
Figure 26.2 Classification of solar power forecasting.
Figure 26.3 Forecasting techniques.
Chapter 27
Figure 27.1 Flow chart for the optimization algorithm.
Figure 27.2 VP of 33-Bus RDS without and with DG placement at 0.85 p.f.
Chapter 28
Figure 28.1 Block diagram for grid interface with renewable energy sources....
Figure 28.2 Converter circuit for n cells.
Figure 28.3 Proposed converter ciruit for n=1 cell.
Figure 28.4 Operational mode of proposed converter for Ton.
Figure 28.5 Operational modes of the converter: Toff period.
Figure 28.6 Theoretical waveform for (a) FISM-CCM (b) PISM-CCM (c) PISM-DCM....
Figure 28.7 (a) Gating pulse for switches S, S1 and S’. (b) Voltage stress a...
Figure 28.8 (a) Output voltage (b) output current.
Figure 28.9 (a) Gating pulse for switches S,S1,S2,S’. (b) Input current. (c)...
Figure 28.10 (a) Voltage across switch S2. (b) Voltage across diode D. (c). ...
Figure 28.11 (a) Hardware setup. (b) Gating pulse. (c) Output voltage obtain...
Figure 28.12 Duty cycle versus voltage gain for n=1, n=2 and conventional co...
Figure 28.13 (a) Duty cycle vs. efficiency for n=1 and n=2. (b) Efficiency v...
Chapter 29
Figure 29.1 Modified Quasi ZSC.
Figure 29.2 (a) Improved qZSC using coupled inductor.
Figure 29.2 (b) Mode-1 circuit diagram.
Figure 29.2 (c) Mode-2 circuit diagram.
Figure 29.3 Theoretical waveforms of the proposed converter.
Figure 29.4 Improved qZSC simulation diagram.
Figure 29.5 Input waveforms.
Figure 29.6 Output waveforms.
Figure 29.7 (a) Current through inductors L
C
,
L
1
.
Figure 29.7 (b) Voltage across capacitors C
1
, C
2
, C
3
.
Figure 29.7 (c) Voltage across capacitors
C
4
.
Figure 29.7 (d) Voltage stress across diodes D
1
, D
2
.
Figure 29.8 Voltage stress across diodes D
3
,
D
4
.
Figure 29.9 (a) Load power vs. efficiency graph. (b) Gain vs. duty cycle gra...
Figure 29.10 Efficiency vs. duty cycle graph.
Chapter 30
Figure 30.1 Solar panel.
Figure 30.2 Equivalent circuit of a PV cell.
Figure 30.3 Arduino UNO.
Figure 30.4 F031-06 voltage sensor.
Figure 30.5 Voltage divider circuit of F031-06.
Figure 30.6 INA169 current sensor.
Figure 30.7 Internal circuit of the current sensor INA169 from the data shee...
Figure 30.8 Pin diagram of the current sensor INA169 from the data sheet.
Figure 30.9 PLX DAQ data logging.
Figure 30.10 Flowchart of system design.
Figure 30.11 Flowchart of data logger.
Figure 30.12 (a) Hardware Prototype (i). (b) Hardware Prototype (ii).
Figure 30.13 Predicted voltage values.
Figure 30.14 Predicted current values.
Figure 30.15 Predicted power values.
Chapter 31
Figure 31.1 Block schematic of V-gate magnet motor.
Figure 31.2 V-gate magnet motor.
Figure 31.3 Control circuit of the V-gate magnet motor using ATMEGA 328P.
Figure 31.4 Output-efficiency characteristics.
Figure 31.5 Voltage-current characteristics.
Figure 31.6 Speed-flux characteristics.
Figure 31.7 Losses-efficiency characteristics.
Figure 31.8 Speed-current characteristics.
Figure 31.9 Torque-current characteristics
Figure 31.10 Speed-torque characteristics.
Chapter 32
Figure 32.1 Flowchart of the working algorithm.
Figure 32.2 Tinkercad simulation circuit diagram of “Smart and Energy Effici...
Figure 32.3 Shows the hardware implementation of the simulation circuit on 0...
Figure 32.4 Simulation circuit showing “Welcome” message on LCD.
Figure 32.5 Shows the change of messages on LCD as the US distance sensor ge...
Figure 32.6 The working of the motor is indicated via glowing yellow LED.
Figure 32.7 Shows change of message on LCD as the algorithm now allows to ri...
Figure 32.8 The blue LED shows that the bell is ringing and thus the LCD dis...
Figure 32.9 Shows the UV lamp working as some object is kept in UV box which...
Figure 32.10 This is how the doorbell looks in real when implemented on hard...
Chapter 33
Figure 33.1 Isometric view of vehicle displaying unmanned vehicle.
Figure 33.2 Isometric view of motor clamping on chassis of vehicle.
Figure 33.3 Illustration of equidistant length and width of axles.
Figure 33.4 Lateral view of rack and pinion mechanism.
Figure 33.5 Image cropping.
Figure 33.6 Image augmentation.
Figure 33.7 Algorithm flow for robot movement.
Figure 33.8 Pictorial representation of path followed by robot.
Figure 33.9 Proteus simulation of wheel movement.
Figure 33.10 Circuit simulation for claw movement.
Figure 33.11 Flow chart for perturbation and observation.
Figure 33.12 MATLAB Simulink for MPPT using P&O algorithm for solar charging...
Figure 33.13 Circuit used to monitor the real-time condition of the robot.
Figure 33.14 Node-RED flow for monitoring.
Figure 33.15 Examples of R-CNN prediction.
Figure 33.16 MPP (Maximum Power Point) using P&O Algorithm for sample test...
Figure 33.17 Node-RED dashboard.
Chapter 34
Figure 34.1 Functional block diagram.
Figure 34.2 Voltage and location parameters displayed in IoT ThingSpeak plat...
Figure 34.3 Temperature, humidity, oil level and current parameters displaye...
Chapter 35
Figure 35.1 Proposed metamaterial absorber.
Figure 35.2 (a) Couplings between unit cells of the proposed MMA structure a...
Figure 35.3 Numerical absorbance, reflectance and transmittance of the desig...
Figure 35.4 Simulated results of designed absorber of normalized impedance....
Figure 35.5 Current distributions on (a) upper and (b) lower surface of the ...
Figure 35.6 Induced field distributions (a) electric and (b) magnetic.
Figure 35.7 Numerical absorption results for various polarization angle (ϕ)....
Figure 35.8 Numerical absorption response for varying θ under: (a) TE mode a...
Figure 35.9 Absorption response for various single resistive loads on the me...
Figure 35.10 Absorption response for various single resistive loads on the t...
Figure 35.11 Absorption response for various dual resistive loads on the med...
Figure 35.12 Absorption response for various dual resistive loads on the tra...
Figure 35.13 Absorption response for quad resistive loads on the median and ...
Figure 35.14 (a) fabricated proposed MMA sample and WR-430 waveguide and (b)...
Figure 35.15 Comparison of measured result with numerically simulated result...
Chapter 36
Figure 36.1 Types of energy sources.
Figure 36.2 Rural electrification.
Figure 36.3 Urban residential buildings.
Figure 36.4 Rural residential building.
Figure 36.5 Classical non-residential buildings.
Figure 36.6 Modern non-residential building.
Figure 36.7 Water Framework Directive (WFD).
Figure 36.8 Environmental heterogeneity.
Figure 36.9 Renewable energy sources.
Figure 36.10 Asia Pacific Green Financing.
Figure 36.11 SAPI farming practises that conserve and enhance natural resour...
Figure 36.12 Cumulative energy-related carbon emission.
Figure 36.13 Energy-related CO2 emissions
Figure 36.14 Total primary energy supply
Chapter 37
Figure 37.1 Courses can be implemented in universities.
Figure 37.2 Personnel training.
Figure 37.3 Sustainable development and climate policy.
Guide
Cover Page
Series Page
Title Page
Copyright Page
Preface
Table of Contents
Begin Reading
About the Editors
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])
Integrated Green Energy Solutions Volume 2
Edited by
Milind Shrinivas DangateW.S. SampathO.V. Gnana Swathika
and
P. Sanjeevikumar
This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2023 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 9781394193660
Front cover images supplied by Pixabay.comCover design by Russell Richardson
Preface
Renewable energy supplies are of ever-increasing environmental and eco-nomic importance all over the world. A wide range of renewable energy technologies has been established commercially and recognized as growth industries. World agencies, such as the United Nations, have extensive pro-grams to encourage renewable energy technology.
This two-volume set, Integrated Green Energy Solutions, will bridge the gap between descriptive reviews and specialized engineering treatises on particular aspects. It centers on demonstrating how fundamental physi-cal processes govern renewable energy resources and their application. Although the applications are being updated continually, the fundamental principles remain the same, and we are confident that this book will pro-vide a useful platform for those advancing the subject and its industries. We have been encouraged in this approach by the ever-increasing com-mercial importance of renewable energy technologies.
Integrated Green Energy Solutions is a numerate and quantitative text covering subjects of proven technical and economic importance world-wide. Energy supply from renewables is an essential component of every nation’s strategy, especially when there is responsibility for the environ-ment and sustainability. These books will consider the timeless renewable energy technologies’ timeless principles yet seeks to demonstrate modern applications and case studies. This volumes will stress the scientific under-standing and analysis of renewable energy since we believe these are dis-tinctive and require specialist attention.
The five most important topics covered in these two books are:
Education in Energy Conversion and Management
Integrated Energy Systems
Energy Management Strategies for Control and Planning
Energy economics and environment
World Energy demand
23Energy Economics and Environment
P. Sanjeevikumar1, Morteza Azimi Nasab1*, Mohammad Zand1, Farnaz Hassani1 and Fatemeh Nikokar2
1 Department of Electrical Engineering, IT and Cybernetic, University of South-Eastern Norway, Porsgrunn, Norway
2 Department of Electrical and Computer Engineering, Shahrood Branch, Islamic Azad University, Shahrood, Iran
Abstract
Considering environmental issues and better utilization of facilities and having a superior level of service, a smart grid is one of the future requirements of the power system. The smart distribution network, which is the gateway between subscribers and the network, is one of the significant components of the smart grid that plays a vital role. Smart grids are able to confront unexpected events and they can disconnect the problematic part from the network; therefore the rest of the network can return to normal operation. Due to the fact that the smart grid is high priced, it must be implemented with great intelligence from the technical and economic and environmental perspective, to meet the requirements of subscribers, both. This chapter examines intelligent electrical systems and their function. The downside of the existing power grid system in comparison with smart power systems is presented and the main characteristics of smart grids are explained based on their capabilities. Uses of these networks are discussed regarding advanced measuring infrastructure system, meeting the demand, distributed generation and storage resources, distribution automation and comprehensive knowledge of the location of the area and electrical transportation.
Keywords: Environmental, optimization, microgrid, energy economics
Abbreviations
MT
Micro Turbine
FC
Fuel Cell
ESS
Energy Storage Systems
CHP
Combine Heat & Power
GB
Gas Boiler
DERs
Distributed Energy Resources
DG
Distributed Generation
RLD
Responsive Load Demand
NRLD
Non-Responsive Load Demand
DSM
Demand Side Management
MCC
Microgrid Central Controller
LC
Local Controller
MAS
Multi-Agent System
SMO
Single Master Operation
MMO
Microgrid Management System
23.1 Introduction
The conventional power system faces problems such as the gradual depletion of fossil fuel resources, poor energy efficiency, and environmental pollution, around the world.
On the other hand, numerous obstacles in the development and construction of centralized production resources, limitations in the capacity of transmission lines, etc., have reduced the role of centralized production. These problems lead to a new trend of electricity generation using unconventional or renewable energy sources, including MT, FC and ESS for storage of electrical and thermal energy, EHP, GB, CHP, etc., as these devices are considered controllable units [1].
Also, Renewable energy sources such as wind and photovoltaics and solar panels are uncontrollable units that are integrated with the distribution network. This type of power generation is called distributed generation (DG) and the mentioned energy sources are called distributed energy sources (DERs).
Regulatory commissions have initiated mandatory or optional policies to use advanced measurement infrastructure to enable demand-side responsiveness. The Federal Energy Regulatory Commission, in a ruling on October 17, 2008, adopted a policy aimed at removing barriers to load-sharing participation in electricity markets.
It is explained that accountability can be activated in creating competitive pressure to reduce the wholesale price; furthermore, it increases public awareness in energy consumption and provides better efficiency for electricity markets, and improves reliability. A distribution network integrated with DGs is called an active distribution network. Nowadays, DG products have several advantages. One of the most vital advantages of DG products is its proximity to consumers and as a result, the reduction of losses and related costs in energy distribution and transmission. Other benefits include eliminating the spatial and geographical constraints of small production compared to large power plants, needless for high risk, less installation time, better environmental conditions, higher quality production capacity, greater reliability and security [2].
Due to the high permeability of DGs in the network, new challenges have arisen in terms of their safe and effective performance. In order to increase reliability and flexibility, these must have capabilities such as restructuring design and performance independent of the global network. By using these microgrids (MGs), these challenges can be eliminated [3].
23.1.1 The Concept of Microgrids
A microgrid is a small network consisting of distributed and load products that are connected to the distribution network at a low voltage level and are an important part in the development of smart grids. By forming an alliance and exchanging power with each other in two functional modes, the mode connected to the Bo network, the island mode or the network independent mode can be exploited.
This structure is in the form of a set of some small controllable and uncontrollable sources such as solar panels and wind turbines, ESS and loads capable of responding to the central controller signals called responsive load (RLD). Interruptible loads and uncontrollable loads (NRLD) are modeled as shown in Figure 23.1.
Federal Energy Regulatory Commission Advanced Metering Infrastructure [4].
The usual goal pursued to control the microgrid in independent operation mode is to supply local loads; while in the case of grid-connected operation, the main purpose is to maximize profits or minimize the cost of electricity generation. Other additional goals such as minimizing greenhouse gas emissions can also be considered using multi-objective optimization techniques. One of the main technical issues in the processes of connection, control and management of renewable energy sources is the unpredictability of the production capacity by these resources. One of the main concerns of researchers in this regard is to assess the impact of these resources on the overall security and reliability of the network. Utilization of distributed energy sources installed in microgrids can potentially increase energy efficiency and improve voltage profiles, reduce power density in the distribution network and stabilize the system. As an example, a microgrid is compared to conventional centralized power plants as follows [5]:
Figure 23.1 The structure of a microgrid.
Production capacity in a microgrid is much less than conventional power plants.
The power generated at the distribution voltage level can be used directly to power the distribution system load.
The microgrid sources are installed in the vicinity of the consumption site, so the microgrid load can be provided with a suitable voltage and frequency profile and with small line losses.
The microgrid’s technical features make it a viable option for power supply in remote areas. Especially in places where various reasons such as topology or frequent outages due to bad weather conditions, it is difficult to supply power from the main network.
23.2 Benefits and Drawbacks of Microgrids
Environmental issues: There is no doubt that the environmental effects of microgrids are much less in terms of gas and particulate emissions than conventional thermal power plants. In addition, the proximity of consumers to production sources can increase the level of public awareness of fair energy consumption.
Investment and operation issues: diminishing the electrical and physical distance between production sources and consumption centers would have positive effects. These effects include optimizing the reactive power of the whole system and thus improving the voltage profile, reducing the distribution feeder density, reducing losses and reducing/delaying investment in the development of the transmission and production system.
Power quality: Factors such as decentralization of generated power, better matching of supply and demand, reducing the effect of large-scale transmission and production exit can be effective in improving power quality and system reliability by eliminating voltage harmonics, and if properly positioned can Reduce energy supply costs
[6]
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Cost saving: Resource integration can be cost effective. Because these resources are installed at the point of consumption, transmission and distribution costs are significantly reduced. In addition, the energy produced is distributed locally among consumers, which reduces power through long feeders.
Despite the potential benefits, the development of microgrids has the following challenges:
High costs of distributed energy sources: The high cost of installing microgrids is a major challenge. This issue can be partially addressed with government funding to encourage investment in this sector.
Technical issues: These problems are related to the lack of executive experience in controlling a large number of distributed generation sources, electric vehicles and the use of load response programs.
Lack of Standard: Since microgrids are a new field, there are still no standards that can be used to address the protection and performance of microgrids. Therefore, standards and protocols should be used to integrate distributed generation resources, safety and protection strategies.
Legal and administrative barriers: In many countries, despite government funding for microgrids, there are no standard rules for setting up and operating microgrids.
Market monopoly: Microgrids may be allowed to independently supply power for essential loads when the mains is not available due to an emergency. In this case the current electricity market will lose control of the energy rate, and microgrids will be able to retail energy at a very high rate. Therefore, designing and implementing appropriate market infrastructure for the development of microgrids is essential
[7]
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23.3 Causes of Increase in Power Plants
In the last decade, the number of network-connected DGs has been increasing due to the high benefits of distributed generation, and the need for a network-independent source.
Several factors have led to an increase in DGs [8]:
The ever-increasing demand for electricity around the world is leading to the search for new sources of energy.
Concerns about climate change limiting fossil fuel storage have led to increased interest in renewable energy.
Advances in DG technologies, such as the simultaneous generation of electricity and heat, require the production of energy close to the consumer.
Liberalization of the electricity market allows entry into the energy production business even with small power plants.
Consumer demand for high-reliability electricity has increased, necessitating storage and backup systems.
DGs include a wide range of generators such as PV photovoltaics, wind turbines, micro turbines, fuel cells, CHPs, water units, etc., whose production capacity is in the range of MW and KW.
23.4 Demand Side Management in Microgrids
Electricity companies and power grid companies have been forced to change their operation from a vertically integrated structure to a competitive market structure for various reasons. With the restructuring of the electricity industry, the philosophy of operating the power system also changed. The traditional method was to supply all power demand, no matter where it was located but the new philosophy states that system efficiency will increase if demand fluctuations are as low as possible.
Demand side management (DSM) is designed to plan, execute and monitor network activities to affect customer power consumption. As a result, the DSM can change the time pattern and amount of network load [9]. Usually, the main goal of demand-side management is to encourage users to either consume less power or shift energy consumption to off-peak times during peak hours, thus smoothing the demand curve. Reliable grid performance is primarily dependent on the perfect balance between output and load at any given time.
Assuming very little control on the demand side, the production side can be controlled according to the load. (Maintaining this balance is not easy.) This may become even more difficult as the distribution of distributed energy production increases. Since the amount of production produced by renewable sources varies according to weather conditions, it is not easy to follow the output of renewable sources from a particular load form.
Therefore, since the peak of production in renewable sources does not necessarily correspond to the peak of consumption in the demand side, energy must either be consumed artificially or stored for future consumption.
The system can rely on fossil fuels at peak times, but due to increased production diversity, the grid has to hold more reserves, which will significantly increase the total cost of electricity.
Alternative to this balance is the use of new methods and technologies that are mainly in interaction with users. Thus, unlike classical methods, which determined the amount of production capacity, the demand response (DR) can play a key role in balancing power. Due to the nature of renewable resources, it is not possible to control or demand power from these resources. The main goals of DR techniques are to reduce the peak load and create the ability to control consumption according to production.
Figure 23.2 Techniques for changing the characteristic demand curve in demand side management.
In other words, when cheap renewable energy is available and when there is a shortage of electricity, there must be a way for end consumers to be aware of it and react. There is a significant scope for DSM to help increase system productivity and utilization. Demand side management is planned for early 2018 [10, 11].
Demand side management can be used as a tool to achieve various load shaping goals such as peak cutting, canyon filling, load shifts, strategic maintenance, strategic load growth and flexible load shaping as shown in Figure 23.2.
23.5 Centralized Control of Smart Grid
Centralized control is performed with the aim of optimizing power exchange with the upstream network, and the production levels of the smart grid change according to market prices and security constraints. Centralized control can only be implemented with controllable resources and loads.
Figure 23.3 shows the data exchange path between the central controller and the local controller of the smart grid.
According to this figure, information is exchanged in two directions. This data exchange can be through telephone lines or wirelessly. The central controller makes decisions every 15 minutes according to market conditions, the capacity of the available units and the prices given by the local controller.
Figure 23.3 Information exchange path in centralized intelligent network control.
MCC considers the following requirements based on market policies [12]:
DER resource prices
Market prices
Network security restrictions
Load forecasts and even forecasts of renewable energy production
Birth control points of DER resources
Adjust loads in terms of supply or blackouts
Market price for the next period in order to enable local controllers to bid
LCs receive control signals from the MCC to determine their generation and consumption as well as the price of electricity. To achieve this, studies are needed to predict the load, production and heat energy required.
23.6 Decentralized Smart Grid Control
Decentralized control approach enables autonomous operation of DER units and loads in the smart grid. Since each DER resource can have a separate owner, it will be difficult to control the smart grid in a centralized manner for this situation. In decentralized control, each of the local controllers intelligently controls the production operation and can communicate with each other. In this case, maximizing the revenue corresponding to each unit is not necessarily the main task of any controller, but improving the overall performance of the smart grid is of paramount importance. The use of a multi-firm system solves many problems related to the operation and performance of the system, so MAS is selected as the first candidate for decentralized control to expand the smart grid. The MAS system is schematically shown in Figure 23.4.
In fact, this system has the ability to perform decentralized control tasks using special software and methods. An intelligent network requires a strong telecommunications infrastructure to transfer information to its various parts. Strong telecommunication systems help make data transfer easier and faster [13].
Figure 23.4 Schematic diagram of the MAS structure of decentralized intelligent network control.
23.7 DER Resource Control Strategies in the Smart Grid
DERs in smart grids are exploited with three control strategies: PQ, PV and VF. PQ control delivers constant active and reactive power to the pseudo and is especially used to extract maximum energy from DGs such as photovoltaics and wind turbines. PV control injects a constant amount of active power into the grid by keeping the bus voltage constant. This control strategy is often used for conventional synchronous DGs that are connected directly to the grid. The VF control acts like a slack bus and keeps the voltage and frequency of the smart grid constant by controlling the active and reactive power injected into the network.
It should be noted that in the case of network connection, all DG units are controlled in PQ or PV mode. In this case, DG units are involved in active power injection and voltage control. With the occurrence of the island, the intelligent reference network loses its voltage and frequency, and the task is to control the voltage and stabilize the frequency to some of its DGs. In this case, if the scattered products continue to operate in pre-island modes, we will see the instability of the smart grid in the transition to island mode. Therefore, in island mode, at least one DG unit must be in VF mode [14].
23.8 DER Participation Strategy in Smart Grid
There are two strategies, SMO and MMO, for the participation of distributed energy sources in the operation and control of smart grids. In the SMO method, a DER unit provides voltage and frequency reference for other doors, and the rest of the sources continue to operate in constant active and reactive power control mode by receiving voltage and frequency references from this unit. The SMO strategy is shown in Figure 23.5.
In the MMO strategy shown in Figure 23.6, several DERs work together to determine voltage and frequency references. Power sources for other sources are transmitted by the MGCC [15].
Figure 23.5 SMO control strategy.
Figure 23.6 MSO control strategy.
23.9 Topics Raised in the Smart Grid
Protection, safe and economical operation, power quality, dynamics and control of smart grids are the most important issues raised in smart grid research. In this section, the most important issues discussed in the smart grid are briefly studied.
23.10 Smart Grid Protection
If an error occurs in the upstream network, the protection equipment must be able to quickly disconnect the smart grid from the upstream network in order to protect the smart grid loads. Most methods of protection of conventional distribution systems are based on short circuit current. The presence of electronic power interfaces between the micro-sources and the network prevents the creation of the required level of short circuit current. Therefore, some overcurrent sensors are not responsive to such a level of overcurrent. The unique nature of smart grids necessitates new measures to protect smart grids [16].
23.11 Detection of Smart Grid Islands
The importance of the island detection operation is due to network security reasons. It can be very dangerous to have a power feeder that is separate from the mains while network workers are in maintenance operations. Many power grids use automatic reopening. In this way, when a short circuit occurs, the network is disconnected.
After a certain period of time and by fixing the error, the switching equipment closes the circuit again.
Now, if the smart grid remains electrified and reclosing occurs between the global grid and the smart grid, frequency, phase and magnitude distortion will form between the global grid and the island, which will damage the power system equipment.
In fact, the DG will connect to the main network when it is out of sync. The result is that island performance should be avoided for safety and network power quality. As a result, the occurrence of the island should be detected as soon as possible and the power switch between distributed generation and the network should be disconnected.
Detection methods are divided into two main groups: remote detection methods and local detection methods. Local methods, in turn, are divided into three subsets: passive, active, and hybrid [17].
23.12 Smart Grid Optimization
The optimization of the smart grid is done by the energy manager. The energy manager examines the needs of his electrical and thermal loads, power quality requirements, electricity and heat generation costs, wholesale and retail service needs, upstream network specific needs, manageable load requests, density levels, etc. He performs system optimization to determine the output power of micro-resources. Some of the key tasks of an energy manager are as follows [18]:
Determine voltage and power adjustment points for each micro source
Supply of electrical and thermal loads
Providing smart grid operation contracts with the transmission system
Minimize environmental pollution and system losses
Maximize the efficiency of exploiting micro-resources
Provide control signal to identify the island and reconnect the smart grid
23.13 Power Quality
Due to the presence of sensitive loads in the smart grid, which must be provided with high reliability and an appropriate level of power quality (voltage drop, flicker, harmonic, etc.), the smart grid must also provide good quality power in the island mode.
23.14 Frequency and Voltage Control
In the case of connecting to the global network, the voltage and frequency are determined by the upstream network. In this case, the generating sources are involved in injecting active power and voltage control. With the occurrence of the island, the reference smart grid loses its voltage and frequency. In this case, if DERs continue to operate in pre-island modes, we will see the smart grid become unstable in the transition to island mode. In order to stabilize the smart grid in the island mode, the island must be quickly detected and then receive at least one scattered energy source to regulate the frequency and voltage of the smart grid by receiving an island command from the protection devices. If the existing DGs and DS are not able to recover the frequency and voltage of the smart grid, part of the load of the smart grid will be cut off [19].
23.15 Balance between Production and Power Consumption
The small inertia and the slow response time to the load changes of the small generators cause a large frequency oscillation in the island’s smart grid for the occurrence of a small imbalance. If power is exchanged between the smart grid and the upstream network when connected to the global grid, the control strategy selected for DERs should be able to balance power generation and consumption by disconnecting the smart grid. This balance is generally maintained by rapid battery performance and the removal of unnecessary loads [20].
23.16 Ability to Easily Connect Distributed Generation Sources
It is desirable to be able to flexibly connect or disconnect distributed distribution source controllers anywhere in the electrical system without the need to redesign, which is called Plug and Play mode.
Such a system has protection and control parts that can operate in all situations.
23.17 Smart Network Security
Sometimes an error, even in a low voltage network, can lead to an increase in ground voltage.
Therefore, grounding of distributed energy sources and transformers of smart grid interface with upstream grid should be carefully considered and ground rules should be observed. LV grounding systems are defined based on the secondary grounding techniques of MV/LV transformers and within the framework of load-bearing equipment. Neutral ground connections LV are mostly classified into three samples, TI, IT and TN.
23.18 Resynchronization after Network Connection
After recovering the frequency and voltage, the control system must synchronize the smart grid and connect it to the grid so that all the loads can be supplied and power exchanges can be established between the smart grid and the global grid. The voltage range, frequency and phase angle between the mains voltage and the smart grid must be within range before reconnecting. Reconnection with improper phase difference can cause transient states and damage to network equipment by causing severe surges in transformers.
Smart grid reconnection can be done if the voltage error is less than 3%, the frequency error is less than 1% Hz and the phase error is less than 10 degrees.
23.19 Smart Grid Control Glasses
We expect to have a growing trend of smart grid presence in traditional networks in the future. As a result, the characteristics of distribution systems will be different from today’s distribution systems, and this difference will become more significant as the number of smart grids increases. Therefore, it is necessary to provide appropriate control strategies for such situations. Controlling a large number of micro-sources due to communication constraints is very challenging. The transition from a connected state to a separate one is likely to lead to a loss of balance between load and output and to voltage and frequency problems. If a large number of micro-sources are disconnected or connected at the same time, the Plug and Play feature may cause a number of serious problems [21, 22].
23.20 Economic Dimensions
In the field of distributed generation resources infrastructure, a lot of investment is needed for tools and network changes for control and security. Reimbursement in open, less risky markets will be complex and profitability will be achieved by avoiding price fluctuations or punishing peak loads or unexpected consumption patterns unless scattered sources of production are the only option (costly). Otherwise, methods that are considered a kind of retreat should be used. Even so, owning one is still beyond the reach of the average person. Artificial, unlike the free market, is always there.
The question here is who will financially support this support system, because it is a heavy investment with very long-term profitability.
Normally, the network budget can be paid to the power system operator through the approved transmission tariff, which will be registered in terms of power and energy exchanged. The installation of a distributed generation unit and the application of an energy island mean that less energy will be returned from the grid and the economic contribution to capital gains will be reduced.
At the same time, the network in which the transformers and lines designed for maximum capacitance will remain the same due to the balance and emergency support; therefore in some countries the cost of disconnection is charged from the units of distributed generation units. Otherwise, the economic burden of the remaining customers will be heavier with each unit of distributed generation source. It will be levied as a hidden tax to protect customers from scattered production sources.
On the other hand, additional marginal services related to PQ, such as voltage support, can also be provided to the network operator to provide an additional source of revenue and an indirect aid to the costs of network equipment, but suitable contexts in business mechanisms and economic incentives for Profitable use of technology capabilities has not yet been provided.
In general, it is difficult to say where the limitations of the technology of distributed generation resources in distribution networks are. In fact, it is the subject of many projects that different parameters must be considered in this regard, such as voltage stability, power quality and inventory reliability.
All the issues raised depend on various features such as loads, network topology and transmission network support. The principle that the rules for connecting region to region differ makes the problem much more complicated. In practice, efforts are made to maintain inventory reliability, which is a conservative view. This is understandable in situations where reliability inventory is heavily dependent.
Generally, one should not only focus on the initial problems of developing distributed generation resources, but also the benefits of using them. In a situation with good conditions, the reliability of the whole system increases, the peak of power demand will put less pressure on the full load power system. One of the most important possibilities is to use local resources to reduce the cost of electricity by reducing transmission losses and eliminating (delayed) expensive investments in infrastructure.
It is almost certain that the central power system will undergo a revolutionary transformation in the coming years and decades, because electricity consumption has not been reduced at all and the problems of grid expansion can only be solved by installing distributed generation technology. However, crossing the boundaries and applying more or less economically and technically independent energy islands has not yet been confirmed as a good idea, and examples will be implemented in the near future that will not be without merit. Let’s follow, which shows how to use networks in the not too distant future [23].
23.21 Losses
Losses in electrical energy networks in the path of transmission and consumption are divided into two components: technical losses and non-technical losses, which are described below and the factors influencing it.
Network technical losses:
Technical losses are losses that occur naturally due to the nature of the components of the power system. Many solutions have been proposed to calculate the technical losses of the network.
The most common of these are load distribution, simultaneous meter reading, statistical methods, and so on. The source of technical losses are [24]:
Intrinsic equipment losses (losses due to resistance of transformer unloaded lines -copper losses or pregnancy of transformers)
Losses due to environmental conditions (losses due to moisture and corona, losses due to dust, etc. Air pollution - collision and connection losses - tree branches)
Meter waste and measuring instruments (inaccuracy in measurement)
Losses due to improper power factor
Losses due to unbalanced load in phases and low-pressure single-phase distribution
Losses due to the use of substandard supplies and equipment
Losses due to improper designs (failure to use transformers with suitable power -failure to install transformers in the center of gravity of the load - lack of optimal fit between transmission and over-distribution voltages and medium and low pressure)
Losses due to obsolescence of networks
Losses due to network connections and not using the correct grounding system
Losses due to the construction of long networks to supply electricity to subscribers
Loss in network equipment due to current leakage
23.22 Non-Technical Network Losses
Non-technical losses are due to factors other than inherent network losses. The only solution to calculate this part of the losses is to obtain the difference between the total losses and the technical losses. Nontechnical losses include unauthorized use of electricity, tampering with measuring devices and meters, incorrect reading of the officers, improper operation of the meters, and so on. Lower losses lead to lower energy costs and thus accelerate countries’ economic growth by lowering the cost of production.
Lower losses make distribution companies more flexible to compete in competitive markets. Studies show that loss reduction can be considered as one of the optimization activities that will delay the development and construction operations and provide huge investments related to it. In this regard, the issue of economic evaluation and prioritization of their implementation is discussed. There are various methods to reduce losses, which according to the amount of investment can be used to reduce losses, including [25]:
Capacitor placement and distributed generation sources
Optimal placement of distribution transformers
Install the distribution transformer in the center of gravity of the load
Load adjustment of low-pressure feeders
Replacing the neutral wire with a larger cross section and leveling the cross section of the neutral wire with the phase wire
Replacement of three-phase wire with larger cross section
Using a three-phase system instead of a single-phase system
Rearrangement of medium and weak pressure network
23.23 Power System Loss Analysis
Losses can increase the capacity of power plants as well as consumption needs. To reduce energy losses, it is worthwhile to consider reducing power losses.
The huge costs of losses are a major factor in persuading companies to look for ways to reduce it. Technical losses depend on the structure of the network, cannot be removed and can only be reduced by changing the structure and equipment, but since non-technical losses are due to a factor outside the power network can be identified and removed to recover lost payout. It can be boldly said that measuring and evaluating losses is the first and most important step in related studies. Because without accurate and accurate measurement of losses, we cannot expect other studies and planning such as intelligent design of transmission and distribution system, rearrangement, voltage leveling, etc., which aims to reduce losses and improve the quality of networks. Intelligently communicate to provide an appropriate, accurate, and practical response.
Although it is very common to use power losses in peak load and loss factor to calculate energy losses in the power system, it should be noted that the loss factor in each area depends on several parameters such as peak load, transmission energy and shape of the consumption curve. And therefore, its value is mainly a function of the type of consumption that will vary from region to region. In obtaining simple loss models for different types of power systems, the electrical network topology of the power systems has been identified and there is also relevant bus data. (Branch data and consumer data obtained from the databases and databases of the country in question.) Three-phase load distribution analysis will lead to solving power system power losses and creating training set data.
23.24 The Impact of the Electricity Market on the Performance of Distribution Companies
Improving distribution networks and reducing losses In the electricity market, when regional power companies sell energy to distribution companies, they are responsible for a number of things, including wasted energy, the improvement of the distribution network, and the non-abuse of electricity that they have. They will take these issues seriously and correct them
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Increase network reliability and reduce blackouts Energy purchases are regulated in the area covered by the distribution companies and they are paid according to this amount, so the distribution companies will be more sensitive and serious about increasing reliability and reducing blackouts.
Improving the methods of exploitation and adjustment of manpower Today, not only the electricity industry but also all fields should abandon traditional methods and turn to modern technologies, so electricity companies are thinking of reducing manpower and reducing costs.
Expanding consumption management programs and load factor correction In the regulations of the electricity company, they have multiplied the prices for buying electricity, so the distribution companies will be more inclined to use effective methods of consumption management and load factor correction.
