Coordinated Operation and Planning of Modern Heat and Electricity Incorporated Networks E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: IEEE Press Series on Power Engineering
• Sprache: Englisch

Seitenzahl: 775

Veröffentlichungsjahr: 2022

Beschreibung

Coordinated Operation and Planning of Modern Heat and Electricity Incorporated Networks A practical resource presenting the fundamental technologies and solutions for real-world problems in modern heat and electricity incorporated networks (MHEINs) Coordinated Operation and Planning of Modern Heat and Electricity Incorporated Networks covers the foundations of multi-carrier energy networks (MCENs), highlights potential technologies and multi-energy systems in this area, and discusses requirements for coordinated operation and planning of heat and electricity hybrid networks. The book not only covers the coordinated operation of heat and electricity networks (HENs) but also supports the planning of HENs to provide more clarity regarding HENs' presence in the future modern MCENs. The first part of Coordinated Operation and Planning of Modern Heat and Electricity Incorporated Networks provides a conceptual introduction with more emphasis on definition, structure, features, and challenges of the one and multidimensional energy networks as well as optimal operation and planning of the MHEINs. The second part of the book covers potential technologies and systems for energy production, communication, transmission and distribution, hybrid energy generation, and more. The third and fourth parts of the book investigate the optimal coordinated operation and planning of the MHEINs. Topics covered in the book also include: * Considerations of hybrid energy storage systems, business models, hybrid transitional energy markets, and decision-making plans * Requirements for switching from the traditional independent energy networks to modern interdependent energy grids * The key role of multi-carrier energy systems in the optimal integration of modern heat and electricity incorporated networks * Technical and theoretical analysis of the coordinated operation and planning of the modern heat and electricity incorporated networks, especially in terms of hybrid energy storage systems Coordinated Operation and Planning of Modern Heat and Electricity Incorporated Networks is an invaluable resource and authoritative reference for the researchers and the system engineers focusing on advanced methods for deployment of state of art technologies in the modern structure of the multi-carrier energy networks.

Leseprobe

Table of Contents

Cover

Series Page

Title Page

Copyright Page

Editor Biographies

List of Contributors

Preface

1 Overview of Modern Energy Networks

1.1 Introduction

1.2 Reliability and Resilience of Modern Energy Grids

1.3 Renewable Energy Availability in Modern Energy Grids

1.4 Modern Multi‐Carrier Energy Grids

1.5 Challenges and Opportunities of Modern Energy Grids

1.6 Summary

References

2 An Overview of the Transition from One‐Dimensional Energy Networks to Multi‐Carrier Energy Grids

Abbreviations

2.1 Introduction

2.2 Traditional Energy Systems

2.3 Background of Multi‐Carrier Energy Systems

2.4 The Definition of Multi‐Carrier Energy Grids

2.5 Benefits of Multi‐Carrier Energy Grids

2.6 Challenges of Moving Toward Multi‐Carrier Energy Grids

2.7 Conclusions

References

3 Overview of Modern Multi‐Dimension Energy Networks

Nomenclature

3.1 Introduction

3.2 Multi‐Dimension Energy Networks

3.3 Benefits of MDENs

3.4 Moving Toward Modern Multi‐Dimension Energy Networks

3.5 Coordinated Operation of Modern MDENs

3.6 Coordinated Planning of Modern MDENs

3.7 Future Plans for Increasing RERs and MDENs

3.8 Challenges

3.9 Summary

References

4 Modern Smart Multi‐Dimensional Infrastructure Energy Systems – State of the Arts

Abbreviations

4.1 Introduction

4.2 Energy Networks

4.3 Infrastructure of Modern Multi‐Dimensional Energy

4.4 Modeling Review

4.5 Integrated Energy Management System

4.6 Energy Conversion

4.7 Economic and Environmental Impact

4.8 Future Energy Systems

4.9 Conclusion

References

5 Overview of the Optimal Operation of Heat and Electricity Incorporated Networks

Abbreviations

5.1 Introduction

5.2 Integration of Electrical and Heat Energy Systems: The EH Solution

5.3 Energy Carriers and Elements of EH

5.4 Advantages of the EH System

5.5 Applications of the EH System

5.6 Challenges and Opportunities

5.7 The Role of DSM Programs in the EH System

5.8 Management Methods of the EH System

5.9 Conclusion

References

6 Modern Heat and Electricity Incorporated Networks Targeted by Coordinated Cyberattacks for Congestion and Cascading Outages

Abbreviations

6.1 Introduction

6.2 Proposed Framework

6.3 Problem Formulation

6.4 Case Study and Simulation Results

6.5 Conclusions and Future Work

References

7 Cooperative Unmanned Aerial Vehicles for Monitoring and Maintenance of Heat and Electricity Incorporated Networks: A Learning‐based Approach

Abbreviations

7.1 Introduction

7.2 Application of Machine Learning in Power and Energy Networks

7.3 Unmanned Aerial Vehicle Applications in Energy and Electricity Incorporated Networks

7.4 Cooperative UAVs for Monitoring and Maintenance of Heat and Electricity Incorporated Networks: A Learning‐based Approach

7.5 Simulation Results

7.6 Conclusions

References

8 Coordinated Operation and Planning of the Modern Heat and Electricity Incorporated Networks

Nomenclature

8.1 Introduction

8.2 Literature Review

8.3 Optimal Operation and Planning

8.4 Components and Constraints

8.5 Incorporated Heat and Electricity Structure

8.6 Case Study

8.7 Demand Profile

8.8 Economic and Environmental Features

8.9 Result and Discussion

8.10 Conclusion

References

9 Optimal Coordinated Operation of Heat and Electricity Incorporated Networks

Nomenclature

9.1 Introduction

9.2 Heat and Electricity Incorporated Networks Components and Their Modeling

9.3 Uncertainties

9.4 Optimal Operation of Heat and Electricity Incorporated Networks

9.5 Market/Incentives

9.6 Main Achievements on Heat and Electricity Incorporated Networks Operation

9.7 Conclusions

References

10 Optimal Energy Management of a Demand Response Integrated Combined‐Heat‐and‐Electrical Microgrid

Nomenclature

10.1 Introduction

10.2 CHEM Modeling

10.3 Coordinated Optimization of CHEM

10.4 Case Studies

References

11 Optimal Operation of Residential Heating Systems in Electricity Markets Leveraging Joint Power‐Heat Flexibility

11.1 Why Joint Heat‐Power Flexibility?

11.2 Literature Review

11.3 Intelligent Heating Systems

11.4 Flexibility Potentials of Heating Systems

11.5 Heat Controllers

11.6 Thermal Dynamics of Buildings

11.7 Economic Heat Controller in Dynamic Electricity Market

11.8 Flexible Heat Controller in Uncertain Electricity Market

11.9 Economic Heat Controller of Mixing Loop

11.10 Conclusion

11.6 Funding

References

12 Hybrid Energy Storage Systems for Optimal Operation of the Heat and Electricity Incorporated Networks

Nomenclature

12.1 Introduction

12.2 Methodology

12.3 Numerical Results and Discussions

12.4 Conclusion

11.9.1 Acknowledgment

References

13 Operational Coordination to Boost Efficiency of Complex Heat and Electricity Microgrids

Abbreviations

13.1 Introduction

13.2 Integrated Energy System Resources

13.3 Optimization Scheme for Energy Management in the Microgrid

13.4 Simulations and Analysis of Results

13.5 Conclusion

References

14 Techno‐Economic Analysis of Hydrogen Technologies, Waste Converter, and Demand Response in Coordinated Operation of Heat and Electricity Systems

Nomenclature

Abbreviations

14.1 Introduction

14.2 Methodology

14.3 Results and Discussion

14.4 Conclusion

14.A Appendix

References

15 Optimal Operational Planning of Heat and Electricity Systems Considering Integration of Smart Buildings

Nomenclature

15.1 Introduction

15.2 Problem Modeling and Formulation

15.3 Optimization Algorithm

15.4 Numerical Results

15.5 Conclusions

References

16 Coordinated Planning Assessment of Modern Heat and Electricity Incorporated Networks

16.1 Introduction

16.2 Definition of the Optimal Planning of Heat and Electricity Incorporated Network

16.3 Structure of HEINs for Optimal Planning

16.4 Advantages and Features of Optimal Planning of HEINs

16.5 Challenges and Future Research Opportunities in Optimal Planning for HEINs

16.6 Summary

References

17 Coordinated Planning of Thermal and Electrical Networks

17.1 Introduction

17.2 The Concept of Energy Hub

17.3 Power Flow Modeling of Energy Hub

17.4 Electricity Market Modeling

17.5 Introduction and Modeling of Components of the Energy Hub

17.6 Energy Hub Analyzing and Discussions

17.7 Conclusion

References

18 Hybrid Energy Storage Systems for Optimal Planning of the Heat and Electricity Incorporated Networks

List of Symbols

Abbreviations

18.1 Introduction

18.2 Description of the Proposed Model for Heat and Electricity Incorporated Networks

18.3 Problem Formulation

18.4 CCHP Strategy to Satisfy Total Demands

18.5 Results and Discussion

18.6 Conclusion

References

Index

IEEE Press Series on Power and Energy Systems

End User License Agreement

List of Tables

Chapter 4

Table 4.1 Energy software applications.

Table 4.2 Energy balancing and duration.

Chapter 5

Table 5.1 Summarizes of the published energy hub papers.

Chapter 6

Table 6.1 Average wind speeds associated with eleven distribution systems....

Table 6.2 Descriptions of CHP units integrated into the distribution system...

Table 6.3 TDs associated with the overcurrent relays embedded in IEEE 14‐bu...

Table 6.4 TDs associated with the overcurrent relays embedded in IEEE 33‐bu...

Table 6.5 Sets of tie‐lines/branches to be simultaneously targeted in Scena...

Table 6.6 FDIs on the transmission and distribution buses related to Scenar...

Chapter 7

Table 7.1 Major simulation parameters.

Chapter 8

Table 8.1 Relevant literature in incorporated energy systems.

Table 8.2 Detailed cost for the energy system's components.

Table 8.3 PV clusters and their probability.

Table 8.4 WT clusters and their probability.

Chapter 9

Table 9.1 The input/output carriers of HEINs resources.

Table 9.2 The characteristics of some typical types of CHPs.

Table 9.3 Different storages used in HEINs.

Table 9.4 Characteristics of materials used in sensible‐based TESs.

Table 9.5 The characteristics of PCM‐based TES.

Table 9.6 Existing probabilities in HEIN power management.

Table 9.7 Reserve impact on the HEIN cost.

Table 9.8 Ancillary services markets available for HEINs.

Chapter 10

Table 10.1 The levels for the PBDR.

Table 10.2 Parameters for the ESS.

Table 10.3 Parameters for the whole system.

Table 10.4 Parameters of all the componentst.

Table 10.5 Mass flow rate in the DHN (kg/h).

Table 10.6 Configuration of test systems.

Table 10.7 The total system operation cost (¥).

Table 10.8 Total system operation cost (¥).

Chapter 12

Table 12.1 Technical specifications of generation units and P2X technologie...

Table 12.2 Technical specifications of the HESS.

Table 12.3 Comparison of imbalance costs in all scenarios.

Table 12.4 Expected profit in different cases.

Chapter 13

Table 13.1 Simulation results for microgrid’s costs in different cases.

Table 13.2 Results for consumption of PV unit’s generated energy in differe...

Chapter 14

Table 14.1 Summary of the literature review.

Table 14.2 Heat demand of the intended countries.

Table 14.3 Details of different technologies capacity.

Table 14.4 Details of different demands.

Table 14.5 Annual electricity demand for seawater desalination.

Table 14.6 Results of FT scenario with the presence of DR.

Table 14.7 Summary of the results of the scenarios.

Table 14.A FOM, production cost, and the lifetime of technologies.

Table 14.B Fuel cost.

Table 14.C Emitted CO

2

price.

Chapter 15

Table 15.1 The input data for simulation.

Table 15.2 The 123‐bus system parameters of utility‐owned distributed energ...

Chapter 16

Table 16.1 Challenges of HEINs.

Chapter 17

Table 17.1 Specifications of the gas furnace and diesel generator.

Table 17.2 Specifications of CHP units.

Table 17.3 Technical specifications of wind turbines used in the energy hub...

Chapter 18

Table 18.1 The costs for each applied component.

Table 18.2 Simulation results of QPSO/PSO/GA.

Table 18.3 The amount of interrupted demands, LPSP, and dump energy during ...

List of Illustrations

Chapter 1

Figure 1.1 Key factors of the reliability and resiliency in MEGs.

Figure 1.2 Components of MEGs resilience.

Figure 1.3 Some of the most prominent solutions for increasing RERs penetrat...

Figure 1.4 Different sectors of multi‐carrier energy networks.

Figure 1.5 Opportunities of MEGs.

Chapter 2

Figure 2.1 Renewable energy sources potentials in generating electrical and ...

Figure 2.2 Comparing the efficiency of traditional power plants and the coge...

Figure 2.3 Sector coupling in multi‐carrier energy grids.

Chapter 3

Figure 3.1 Interactions between MDENs technologies and networks.

Figure 3.2 Benefits of MDENs.

Figure 3.3 P2P market mechanism in blockchain‐based network.

Figure 3.4 Challenges of modern MDEN.

Chapter 4

Figure 4.1 Structure of multi-dimensional energy network.

Figure 4.2 Energy storage technologies.

Figure 4.3 Modeling of energy transformation.

Figure 4.4 Conceptual model of a multi‐energy system.

Figure 4.5 Markal‐Macro model.

Figure 4.6 Times model.

Figure 4.7 3E model.

Figure 4.8 Future energy challenges.

Chapter 5

Figure 5.1 The input and output matrix of the energy hub.

Figure 5.2 Energy hub system.

Figure 5.3 CHP technology.

Figure 5.4 Power to gas technology.

Source:

caracterdesign/E+/Getty Images, ...

Figure 5.5 CAES technology.

Figure 5.6 Water desalination technology.

Figure 5.7 Residential energy hub.

Figure 5.8 Energy management in the industrial hub energy system.

Figure 5.9 Renewable energy resources in the agriculture sector.

Figure 5.10 Demand response program.

Figure 5.11 Comparison of various energy hub control models.

Chapter 6

Figure 6.1 The proposed framework.

Figure 6.2 Feasible operating region (FOR) associated with a typical convex‐...

Figure 6.3 Transmission/distribution systems as the case study.

Figure 6.4 FORs associated with CHP units: (a) Small‐scale unit and (b) larg...

Figure 6.5 Selected sections of systems showing the installed relays: (a) A ...

Figure 6.6 Comparison of LMPs before and after cyberattack associated with S...

Figure 6.7 The importance of tie‐lines to be targeted in Scenario I.

Figure 6.8 FDIs on load centers to result in congestions of lines #8, #10, a...

Figure 6.9 Power flow of targeted lines before/after cyberattack associated ...

Figure 6.10 Comparison of LMPs on subsystem connected to bus #4 in Scenario ...

Figure 6.11 Comparison of LMPs on subsystem connected to bus #6 in Scenario ...

Figure 6.12 Comparison of LMPs on subsystem connected to bus #14 in Scenario...

Figure 6.13 Importance of lines to be targeted in Scenario II: (a) IOs and (...

Figure 6.14 FDIs on the distribution buses to result in congestions in the m...

Figure 6.15 Power flows of the subsystem connected to bus #10 in Scenario II...

Figure 6.16 Comparison of LMPs on main system before/after cyberattack in Sc...

Figure 6.17 Power flows of targeted lines of main system before/after attack...

Figure 6.18 Power flows of the subsystem connected to bus #5 in Scenario III...

Figure 6.19 Targeted branches/regions in subsystem connected to bus #5 in Sc...

Chapter 7

Figure 7.1 A summary of sustainable energy system applications where ML has ...

Figure 7.2 System model. The solar EH‐enabled SUAV informs the GUAVs deploye...

Figure 7.3 SUAV’s energy coverage probability vs. the deterministic componen...

Figure 7.4 The average of the maximum

Q

value for a particular state and ove...

Figure 7.5 The obtained flight trajectories of the SUAV when using the QL, d...

Figure 7.6 The normalized number of informed/activated GUAVs when using the ...

Figure 7.7 The mission success metric vs. the SUAVs flying height (

h

SUAV

) an...

Chapter 8

Figure 8.1 Conceptual model for optimal operation and planning.

Figure 8.2 GA steps.

Figure 8.3 MCS and k-means procedure in scenario-based modeling.

Figure 8.4 WT operation curve.

Figure 8.5 Structure and components of the proposed model.

Figure 8.6 Daily electricity demand for the studied energy system.

Figure 8.7 Daily heating demand for the studied energy system.

Figure 8.8 Solar irradiation for the case study during one year.

Figure 8.9 Wind speed for the case study during one year.

Figure 8.10 Electricity and natural gas price.

Figure 8.11 1000 generated scenarios for PV production.

Figure 8.12 1000 generated scenarios for WT production.

Figure 8.13 Optimal operation of the proposed incorporated energy network in...

Figure 8.14 Optimal operation of the proposed incorporated energy network in...

Figure 8.15 Optimal operation of the proposed incorporated energy network in...

Figure 8.16 Optimal operation of the proposed incorporated energy network in...

Figure 8.17 Daily electricity generation by source in each season.

Figure 8.18 Daily heat generation by source in each season.

Chapter 9

Figure 9.1 The framework of a HEIN.

Figure 9.2 The effective factors on different loads of HEINs.

Figure 9.3 The effective factors on the different loads of HEINs.

Figure 9.4 The overall structure of HEINs thermal loads.

Figure 9.5 The relationship amongst different equipment used in HEINs.

Figure 9.6 Overall structure of concentrating solar power.

Figure 9.7 A typical operational curve of wind turbines.

Figure 9.8 The connection between different layers of HEINs.

Figure 9.9 The goals in HEINs exploitation through optimal operation.

Figure 9.10 Voltage regulation within a model with power flow.

Figure 9.11 Congestion elimination within a model with power flow.

Figure 9.12 The connection between different levels of HEINs.

Figure 9.13 Cost per building in a HEIN including 720 buildings with thermal...

Figure 9.14 Peer‐to‐peer energy transactions in a HEIN or amongst HEINs.

Figure 9.15 Internal and external potential for optimizing HEINs performance...

Chapter 10

Figure 10.1 Schematic diagram of the CHEM and thermal network. (a) CHEM. (b)...

Figure 10.2 The schematic diagram of the pipeline section.

Figure 10.3 The flow chart of the proposed method.

Figure 10.4 Structure of our IEEE‐33 bus system.

Figure 10.5 Power exchanging price.

Figure 10.6 The structure of the second CHEM test system.

Figure 10.7 Power demands with/without PBDR strategy.

Figure 10.8 Energy balance in the 33‐bus CHEM. (a) Case 1. (b) Case 2. (c) C...

Figure 10.9 Results of Heat generation and consumption. (a) Comparisons of h...

Figure 10.10 Energy balance in the 69‐bus CHEM. (a) Case 1. (b) Case 2. (c) ...

Chapter 11

Figure 11.1 Demand‐side flexibility in different sectors (PEV: plug‐in elect...

Figure 11.2 Different types of flexibility potentials in heating systems....

Figure 11.3 Schematic structure of the heat controller (PLC: power line carr...

Figure 11.4 General floor plan of a building with

R

temperature zones.

Figure 11.5 Floor plan of the test house with four temperature zones.

Figure 11.6 Optimized exploitation of heating system in four rooms in respon...

Figure 11.7 Participation of SEMPC in three uncertain electricity markets....

Figure 11.8 Integration of energy flexibility of the single‐family house int...

Figure 11.9 The schematic diagram of the control valves for buildings connec...

Figure 11.10 Optimized operation of mixing loop. (a) Electricity price, (b) ...

Figure 11.11 Optimized operation of mixing loop. (a) Mass flow of DH‐side, (...

Chapter 12

Figure 12.1 Structure of the IEHN.

Figure 12.2 (a) Electricity demand and (b) heat demand.

Figure 12.3 (a) Electricity price and (b) heat price.

Figure 12.4 Natural gas price.

Figure 12.5 IEHN’s DA exchanged energy in the presence of the HESS.

Figure 12.6 IEHN’s DA exchanged energy in the absence of the HESS.

Figure 12.7 Operating points of connected sources to the electricity sector ...

Figure 12.8 Operating points of connected sources to the heat sector in scen...

Figure 12.9 Gas flow of resources within the IEHN in scenario 4.

Figure 12.10 (a) Imbalance power in scenario 4 and (b) imbalance heat in sce...

Figure 12.11 (a) Powers with the HESS in scenario 7 and (b) powers without t...

Chapter 13

Figure 13.1 System design constraints.

Figure 13.2 Hybrid electrical and thermal energy system.

Figure 13.3 Scenarios for hourly (a) price of purchasing electricity from up...

Figure 13.4 Hourly electrical load in deterministic case.

Figure 13.5 Hourly electrical load in stochastic case considering DRP.

Figure 13.6 The amount of energy sold to upstream grid in deterministic case...

Figure 13.7 The expected value of energy sold to upstream grid in stochastic...

Figure 13.8 Battery charging state in deterministic case.

Figure 13.9 Expected value of battery charging state in stochastic case.

Figure 13.10 The change in microgrid’s total cost with respect to change in ...

Figure 13.11 Fuel cell’s hourly generated electrical energy in deterministic...

Figure 13.12 Fuel cell’s hourly generated electrical energy in stochastic ca...

Figure 13.13 Backup burner’s hourly generated thermal energy in deterministi...

Figure 13.14 Fuel cell’s hourly generated electrical energy in stochastic ca...

Figure 13.15 Total cost with respect to backup burner’s capacity in determin...

Figure 13.16 Stored energy in heat storage tank in deterministic case.

Figure 13.17 Supplied thermal energy with respect to backup burner capacity....

Chapter 14

Figure 14.1 Hydrogen technologies cycle.

Figure 14.2 Structure of the system.

Figure 14.3 Amount of changes in carbon dioxide and fossil fuel consumption....

Figure 14.4 The way of supplying electrical and thermal demand.

Figure 14.5 Suppliers’ share in providing the electrical energy demand in FT...

Figure 14.6 Sensitivity analysis results on RESs based on the minimal cost o...

Figure 14.7 (a) Suppliers’ share in providing the electrical energy demand i...

Figure 14.8 (a) Amount of changes in the total cost of the network. (b) Amou...

Chapter 15

Figure 15.1 Schematic diagram of an electrical and heating energy distributi...

Figure 15.2 The optimization stages and levels.

Figure 15.3 The proposed multi‐stage multi‐level optimization procedure.

Figure 15.4 The 123-bus energy distribution system.

Figure 15.5 The forecasted loads of the energy system.

Figure 15.6 The day‐ahead prices of the electricity market.

Figure 15.7 The forecasted electricity generation of photovoltaic arrays.

Figure 15.8 The forecasted electricity generation of wind turbines.

Figure 15.9 The estimated values of smart buildings’ submitted and accepted ...

Figure 15.10 The electricity generation of distributed generation facilities...

Figure 15.11 The electricity generation of CHPs.

Figure 15.12 The heating energy generation of CHPs.

Figure 15.13 The electricity transactions of parking lots with the energy sy...

Figure 15.14 The aggregated electricity generation of distributed energy gen...

Figure 15.15 The heating energy generation of CHPs and boilers.

Figure 15.16 The energy system costs for active and reactive power transacti...

Figure 15.17 The aggregated expected energy not supplied and operational cos...

Figure 15.18 The estimated values of served critical electrical loads for th...

Figure 15.19 The estimated values of LCI

EL

and LCI

HL

for the worst‐case cont...

Figure 15.20 The real‐time electrical and heating load mismatches for the wo...

Figure 15.21 The real‐time market prices.

Figure 15.22 The aggregated electricity generation of distributed energy gen...

Figure 15.23 The heating generation of boilers for compensating the real‐tim...

Figure 15.24 The costs of energy transactions with the real‐time electricity...

Figure 15.25 The electricity generation of distributed generation facilities...

Figure 15.26 The smart buildings active power injection into energy system i...

Figure 15.27 The parking lots active power injection into energy system in n...

Figure 15.28 The active power injection of energy storage facilities into th...

Figure 15.29 The real‐time values of committed electrical loads for the wors...

Figure 15.30 The real‐time values of the

LCI

EL

and

LCI

HL

for the worst‐case ...

Chapter 16

Figure 16.1 Elements and objectives of planning HIENs.

Figure 16.2 Isometric view of a general scheme for a HEIN.

Figure 16.3 Speed‐power (V‐P) characteristic of a general wind turbine.

Figure 16.4 Possible features of optimal planning of a HEIN.

Chapter 17

Figure 17.1 Example of an energy hub including transformer, CHP, furnace, ab...

Figure 17.2 Model nomenclature for the energy carrier exchanging energy hub,...

Figure 17.3 Example of a hybrid energy hub including micro turbine, heat exc...

Figure 17.4 Example of day‐ahead market prices and real‐time.

Figure 17.5 Schematic and structure of combined heat and power.

Figure 17.6 An example of a cogeneration unit (CHP) operating range.

Figure 17.7 Diesel generator diagram.

Figure 17.8 The energy hub under study.

Figure 17.9 CHP operating area used in the hub.

Figure 17.10 Wind speed used in the energy hub.

Figure 17.11 Electric and thermal load demand.

Figure 17.12 Real‐time and day‐ahead electricity prices.

Figure 17.13 Electric power purchased from real‐time and day‐ahead markets....

Figure 17.14 Electrical power generated by the diesel generator.

Figure 17.15 Electrical power generated by CHP units.

Figure 17.16 Heat power generated by CHP units.

Figure 17.17 Heat power generated by gas furnace.

Figure 17.18 Electrical power generated by wind turbines.

Figure 17.19 Electrical power sales of CHP units.

Figure 17.20 Electrical power of CHP units for energy hub demand.

Figure 17.21 Total electrical power sales of diesel generator units.

Chapter 18

Figure 18.1 The schematic of the studied heat and electricity incorporated n...

Figure 18.2 The yearly electrical demand.

Figure 18.3 The yearly space cooling demand.

Figure 18.4 The yearly water heating demand.

Figure 18.5 The yearly space heating demand.

Figure 18.6 The yearly wind velocity data.

Figure 18.7 The yearly solar irradiation.

Figure 18.8 The convergence curves of QPSO, PSO, and GA algorithms.

Figure 18.9 The generated electricity of PVs.

Figure 18.10 The generated electricity of wind turbines.

Figure 18.11 The excess generated electricity of RESs.

Figure 18.12 The available energy in EES.

Figure 18.13 The hourly amount of electrical power used in the electrolyzer ...

Figure 18.14 The dump load at each hour during a year.

Figure 18.15 The generated electricity of FCs at each hour.

Figure 18.16 The available H

2

in hydrogen storage.

Figure 18.17 The optimal scheduling of electrical demand during a typical da...

Figure 18.18 The variations of energy in TES during a year.

Figure 18.19 The yearly generated energy by boilers.

Figure 18.20 The optimal scheduling of thermal power during a typical day.

Guide

Cover Page

Series Page

Title Page

Copyright Page

Editor Biographies

List of Contributors

Preface

Table of Contents

Begin Reading

Index

IEEE Press Series on Power and Energy Systems

Wiley End User License Agreement

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IEEE Press445 Hoes LanePiscataway, NJ 08854

IEEE Press Editorial BoardSarah Spurgeon, Editor in Chief

Jón Atli Benediktsson  

Andreas Molisch  

Diomidis Spinellis  

Anjan Bose  

Saeid Nahavandi  

Ahmet Murat Tekalp  

Adam Drobot  

Jeffrey Reed  

Peter (Yong) Lian  

Thomas Robertazzi  

Coordinated Operation and Planning of Modern Heat and Electricity Incorporated Networks

Edited by

Mohammadreza Daneshvar

Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

Behnam Mohammadi‐Ivatloo

Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

Kazem Zare

Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

Copyright © 2023 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.Published simultaneously in Canada.

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Editor Biographies

Mohammadreza Daneshvar does research in the field of energy and electrical engineering at the Smart Energy Systems Lab of the Department of Electrical and Computer Engineering at the University of Tabriz. He is the author of more than 40 top journal and conference papers in the field of multi‐energy systems, grid modernization, transactive energy, and optimizing the multi‐carrier energy grids. Moreover, he is the author and editor of more than four books in the mentioned fields of study that are published and are in the publication process in the Springer, Elsevier, and Wiley‐IEEE Press publishers. He serves as an active reviewer with more than 40 top journals of the IEEE, Elsevier, Springer, Wiley, Taylor & Francis, and IOS Press and was ranked among the top 1% of reviewers in Engineering and Cross‐Field based on Publons global reviewer database. His research interests include Smart Grids, Transactive Energy, Energy Management, Renewable Energy Sources, Multi‐Carrier Energy Systems, Grid Modernization, Electrical Energy Storage Systems, Microgrids, Energy Hubs, Machine Learning and Deep Learning, Blockchain Technology, and Optimization Techniques.

Behnam Mohammadi‐Ivatloo is a member of the Faculty of Engineering with the Department of Electrical and Electronics Engineering at Muğla Sıtkı Koçman University, Turkey. He was previously a Senior Research Fellow at Aalborg University, Denmark. He is also a Professor at the University of Tabriz, from where he is currently on leave. Before joining the University of Tabriz, he was a research associate at the Institute for Sustainable Energy, Environment and Economy at the University of Calgary. He obtained MSc and Ph.D. degrees in electrical engineering from the Sharif University of Technology. His main research interests are renewable energies, microgrid systems, and smart grids.

Kazem Zare received the B.Sc. and M.Sc. degrees in electrical engineering from the University of Tabriz, Tabriz, Iran, in 2000 and 2003, respectively, and a Ph.D. degree from Tarbiat Modares University, Tehran, Iran, in 2009. Currently, he is a Professor in the Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran. He was PI or CO‐PI in 10 national and international‐funded research projects. He is included in the 2018, 2019, and 2021 Thomson Reuters’ list of the top 1% most cited researchers. He is the associate editor of the Sustainable Cities and Society journal and the editor of the e‐prime journal. His research areas include power system economics, distribution networks, microgrid, energy management, smart building, demand response, and power system optimization.

List of Contributors

Mohammad Taghi AmeliDepartment of Electrical EngineeringShahid Beheshti UniversityTehran, Iranand Electrical Networks Research InstituteShahid Beheshti UniversityTehran, Iran

Jaber Fallah ArdashirDepartment of Electrical EngineeringTabriz Branch, Islamic Azad UniversityTabriz, Iran

Arash AsrariSchool of Electrical, Computer, and Biomedical Engineering, Southern Illinois University, CarbondaleIL, USA

Sasan AzadDepartment of Electrical Engineering Shahid Beheshti UniversityTehran, Iran andElectrical Networks Research InstituteShahid Beheshti University Tehran, Iran

Valentina CecchiDepartment of Electrical and Computer Engineering, University of North Carolina, CharlotteNC, USA

Yumin ChenChina Southern Grid Digital Power Grid Co., Ltd.Digital Power Grid Branch Engineering and Technology Division Building C, Yunsheng Science ParkGuangzhou, China

Mojtaba DadashiFaculty of Electrical and Computer Engineering University of TabrizTabriz, Iran

Mohammadreza DaneshvarFaculty of Electrical and Computer Engineering, University of TabrizTabriz, Iran

Mohammad Reza DehbozorgiDepartment of Power and ControlSchool of Electrical and Computer Engineering, Shiraz UniversityShiraz, Iran

Sobhan DorahakiDepartment of Electrical EngineeringShahid Bahonar University of KermanKerman, Iran

Poria FajriDepartment of Electrical and Biomedical EngineeringUniversity of Nevada, RenoNV, USA

Xue FengEngineering ClusterSingapore Institute of TechnologyDover, Singapore

Hadi Vatankhah GhadimDepartment of Electrical EngineeringTabriz Branch, Islamic Azad UniversityTabriz, Iran

Ensieh GhanbariDepartment of Electrical EngineeringIran University of Science and TechnologyTehran, Iran

Reza GharibiDepartment of Electrical EngineeringAmirkabir University of Technology (Tehran Polytechnic)Tehran, Iran

Hessam GolmohamadiDepartment of Computer ScienceAalborg UniversityAalborg, Denmark

Aminabbas GolshanfardEnergy Modelling and Sustainable Energy System (METSAP) Research Lab., Faculty of New Sciences and TechnologiesUniversity of TehranTehran, Iran

Sara HaghifamFaculty of Electrical and Computer EngineeringUniversity of TabrizTabriz, IranandSchool of Technology and InnovationsFlexible Energy ResourcesUniversity of VaasaVaasa, Finland

Seyed Mehdi HakimiDepartment of Electrical Engineering and Renewable Energy Research Center, Damavand BranchIslamic Azad UniversityDamavand, Iran

Arezoo HasankhaniDepartment of Computer and Electrical Engineering and Computer Science, Florida Atlantic UniversityBoca Raton, FL, USA

Hamid HassanzadehFardDepartment of Electrical EngineeringMiyaneh Branch, Islamic Azad UniversityMiyaneh, Iran

Alireza HeidariSchool of Electrical Engineering and Telecommunication, University of New South Wales, Sydney, Australia

Ali JalilianDeputy of Operation and DispatchKermanshah Power Electrical Distribution Company (KPEDC)Kermanshah, Iran

S. Mahdi Kazemi‐RaziDepartment of Electrical EngineeringAmirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

Javad KhazaeiDepartment of Electrical and Computer Engineering, Lehigh University, BethlehemPA, USA

Hannu LaaksonenSchool of Technology and Innovations Flexible Energy ResourcesUniversity of Vaasa, VaasaFinland

Zhengmao LiSchool of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang, Singapore

Sahar MobasheriDepartment of Electrical EngineeringShahid Bahonar University of KermanKerman, Iran

Mohammad MohammadiDepartment of Power and ControlSchool of Electrical and Computer Engineering, Shiraz UniversityShiraz, Iran

Behnam Mohammadi‐IvatlooFaculty of Electrical and Computer EngineeringUniversity of TabrizTabriz, Iran

Milad MohammadyariDepartment of Electrical EngineeringUniversity of TehranTehran, Iran

Ehsan NaderiSchool of Electrical, Computer, and Biomedical EngineeringSouthern Illinois UniversityCarbondale, ILUSA

Hamed NafisiDepartment of Electrical EngineeringAmirkabir University of Technology (Tehran Polytechnic)Tehran, Iran

Mehrdad Setayesh NazarFaculty of Electrical EngineeringShahid Beheshti University Tehran, Iran

Younes NoorollahiEnergy Modelling and Sustainable Energy System (METSAP) Research Lab., Faculty of New Sciences and Technologies, University of TehranTehran, Iran

Saba NorouziFaculty of Electrical and Computer Engineering, University of TabrizTabriz, Iran

Mohammad Hossein PafeshordehDepartment of Power and ControlSchool of Electrical and Computer Engineering, Shiraz UniversityShiraz, Iran

Fereidoun H. PanahiDepartment of Electrical Engineering University of KurdistanSanandaj, Iran

Farzad H. PanahiDepartment of Electrical Engineering University of KurdistanSanandaj, Iran

Navid Talaei PashiriDepartment of Electrical EngineeringShahid Beheshti UniversityTehran, Iran

Masoud RashidinejadDepartment of Electrical EngineeringShahid Bahonar University of KermanKerman, Iran

Mohammad RastegarDepartment of Power and ControlSchool of Electrical and Computer EngineeringShiraz UniversityShiraz, Iran

Mohammad Reza SalehizadehDepartment of Electrical EngineeringMarvdasht BranchIslamic Azad UniversityMarvdasht, Iran

Miadreza Shafie‐khahSchool of Technology and InnovationsFlexible Energy ResourcesUniversity of VaasaVaasa, Finland

Ali SharifzadehDepartment of Electrical EngineeringShahid Beheshti UniversityTehran, Iran

Behrooz VahidiDepartment of Electrical EngineeringAmirkabir University of Technology (Tehran Polytechnic)Tehran, Iran

Yan XuSchool of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang, Singapore

Seyedeh Soudabeh ZadsarDepartment of Electrical EngineeringShahid Bahonar University of KermanKerman, Iran

Kazem ZareFaculty of Electrical and Computer Engineering, University of TabrizTabriz, Iran

Preface

Nowadays, the significant development in energy production systems has created an evolutionary trend in coupling multi‐carrier energy networks (MCENs) to each other. In addition to this progress in energy networks with different vectors, the day‐by‐day growth in energy consumption has also more highlighted the inevitable dependencies between MCENs. To this end, future modern energy grids not only are targeted to be developed as a couple of multi‐energy systems, but also they will be structured with a high/full level of renewable energy resources (RERs). In this structure, hybrid energy systems such as combined heating, cooling, and power units play an undeniable role in reliable meeting the energy demand. However, how different types of energy networks can be integratively operated in the modern energy infrastructure is a key question that needs to be addressed in deep detail. As the reliable electrical and heating energy supply is critical for the energy network, a great need is felt for coordinated operation and planning of the heat and electricity networks (HENs) under the modern structure of MCENs. Therefore, as a pioneering book that presents the fundamental theories, technologies, and solutions for real‐world problems in modern heat and electricity incorporated networks, this book is targeted to not only cover the coordinated operation of HENs but also to support the planning of HENs and more clarify the HENs presence in the future modern MCENs.

The current book consists of 18 chapters. Chapter 1 wants to clarify what are the characteristics of future modern energy networks and why grid modernization is essential for the interdependent structure of networks that are going to be engaged with a large number of stochastic clean energy production devices. The related challenges and opportunities are also covered in this chapter to give a clear overview for the future modern multi‐carrier energy grid. Chapter 2 reviews the transition from conventional energy networks to multi‐carrier energy grids, introduces the background of multi‐carrier energy systems and definitions in the literature and investigates the benefits and challenges of these systems. Chapter 3 covers the definition and benefits of MCENs as well as proposes innovative solutions to overcome the uncertainties in optimal operation and planning of modern MCENs. Chapter 4 reviews the main topics of modern MCENs and the state of the arts by mostly focusing on implementing new financial concepts for providing broad flexibility to better coordination between operating and planning of modern heat and power incorporated networks. Chapter 5 provides an overview of the optimal operation of MCENs by concentrating on components, energy conversion technologies, advantages, challenges, and applications. Moreover, the economic and environmental benefits, as well as reliability and flexibility improvements of utilizing energy hubs systems, are discussed. Chapter 6 scrutinizes a cyberattack model, which can simultaneously target transmission and distribution sectors of a modern heat and electricity incorporated network leading to system congestions and possible power outages. Chapter 7 discusses an intelligent monitoring and maintenance system for multi‐unmanned aerial vehicle teams applicable to modern heat and electricity incorporated networks. Chapter 8 deals with the stages of operation and planning of heat and electricity incorporated networks with the aim of proposing a model for the coordinated operation and planning of them. Chapter 9 defines optimal operation in the heat and electricity incorporated networks as an operation with maximized flexibility and sustainability along with minimized cost and CO2 emission with the aim of optimal coordinated operation of them. Chapter 10 mainly presents the optimal energy management scheme of the combined‐heat‐and‐electrical microgrid by focusing on the operation side of modern heat and electricity incorporated networks. Chapter 11 investigates the role of intelligent heat controllers in power and heat networks by suggesting an economic heat controller for residential heat pumps with water tanks. Chapter 12 aims to model an electricity‐heat incorporated energy system in the presence of various sector coupling and storage technologies and investigate the impact of a hybrid energy storage system on the optimal performance of the considered network. Chapter 13 discusses the optimal operation of a microgrid including a hybrid energy system in the presence of a demand response program and also presents an intuitive perception of the microgrid through an optimization model. Chapter 14 provides a techno‐economic analysis of hydrogen technologies, waste converters, and demand response in the coordinated operation of heat and electricity systems. Chapter 15 introduces an algorithm for optimal operational planning of heat and electricity systems integrating the commitment scenarios of smart buildings. Chapter 16 provides effective information about the meaning of optimal planning of heat and electricity incorporated networks, its basic structure, advantages, and possible challenges. Chapter 17 presents a general approach to the simultaneous optimization and planning of the electrical and thermal energy systems. Chapter 18 evaluates the energy production of various types of (distributed generations) DGs and hybrid storage systems in the heat and electricity incorporated network set up for minimizing the total costs of the applied DGs, fuel consumption, and pollutant emissions considering reliability analysis.

As any research achievement may not be free of gaps, the Editors kindly welcome any suggestions and comments from the respectful readers to improve the quality of this work. The interested readers can share their valuable comments with the Editors via [email protected].

EditorsMohammadreza DaneshvarFaculty of Electrical and Computer EngineeringUniversity of TabrizTabriz, Iran

Behnam Mohammadi-IvatlooFaculty of Electrical and Computer EngineeringUniversity of TabrizTabriz, Iran

Kazem ZareFaculty of Electrical and Computer EngineeringUniversity of TabrizTabriz, Iran

1Overview of Modern Energy Networks

Mohammadreza Daneshvar Behnam Mohammadi‐Ivatloo and Kazem Zare

Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

1.1 Introduction

Nowadays, energy grids witness accelerating changes in their different domains from the generation sector to the distribution area. This evolution mostly relies on rapid developments in the technology of smart devices that is occurring in line with variations in the well‐being range of people. Another aspect of this transition backs to the need for delivering efficient, reliable, affordable, and sustainable energy to the consumer side [1]. The main motivation for this transformation was flourished when the drawbacks of traditional energy systems have appeared, especially in terms of technical, economical, and environmental aspects [2]. The smart grid was the first common term devoted for the pass that its main goal is built on overcoming the disadvantages of the conventional energy structure, especially focusing on improving power quality, resilience, efficiency, reliability, security, and environmental aspects [3]. In light of the main mission to answer the need for smartization of the power grid, the smart grid promoted widespread developments in the different sectors of energy grids. It has driven the focus of energy generation from the centralized mechanism to the decentralized one and has allowed end‐users to contribute to the grid services and participate in a variety of energy management programs [4]. Another key criterion of smartization lies in extending the appropriate volume of RERs in the generation sector [5]. The applicability, availability, and environmentally friendly nature of RERs have made it a thrust area of research, and the smart grid structure has made their applicability vast and more promising [6]. In this respect, increasing demand for energy has created a fundamental need for reinforcement of the energy generation sector, making it reliable in energy supply. Modern energy grids (MEGs) are targeted to equip their energy production part by a high/full level of RERs for maximum clean energy production [7]. The structure of MEGs is planned to be designed in a way to allow for involving advanced technologies for facilitating people’s daily life as well as address the challenges of the previous conventional system. MEGs are famous for their capabilities in advancing innovative ways for covering the current shortages and expected upcoming challenges by adopting intelligent devices and coordinated platforms [8]. As properly integrating different energy grids in the coupled structure alongside producing a high/full renewable energy are recognized as the great requirements for the future energy infrastructure [9], one of the main focuses of MEGs belongs to enable future grids to benefit from the mentioned advantages. This chapter aims at discussing the overview of MEGs and clarifying some critical aspects of their presence in the near future that can be considered in designing future roadmaps or modifying current ones for future modern grids. Additionally, the related opportunities and challenges are specified to give a simplistic and complete overview of MEGs properties.

1.2 Reliability and Resilience of Modern Energy Grids

Reliability and resilience are two key characteristics of MEGs that refer to the ability of the system to operate in a sustainable way under diverse operation conditions [10]. By targeting a high range of RERs for presence in MEGs, the reliability of the system needs to be kept under the huge uncertain fluctuations in the energy production process. Given the reliability lies on the function of the system under normal/prescribed operation conditions [11], its satisfaction requires more promising solutions in the presence of high RERs of MEGs. How several promising strategies can be coordinately engaged to provide an adequate level of reliability in the highly smartized area will be challengeable for the future operation of MEGs. Several main factors affect the system’s reliability that focusing on their effectiveness on the reliability index can properly highlight critical tips for filling the related system gaps against their bad effects. Some of the important ones are environmental barriers like vegetation and trees, autonomous monitoring of the system, effects of weather changes and their threats to the system’s reliability, the strength of the grid in providing a sufficient level of reliability, especially in the presence of numerous stochastic producers, the accuracy rate of predictions for the critical factors like RERs’ outputs and energy consumption, damages created by animals, humans, and their vehicles, etc.

On the other hand, the system’s resilience refers to the ability of the system to efficiently and swiftly recover its performance from its previous deterioration condition and resume normal operation [12]. The rapid advancements in energy technologies and widespread adoption of them along with RERs in the body of the grid expose them in the randomization operation, making it more prone to failures and instabilities [13]. The system’s situation can be even more acute in some cases when inevitable disruption of services has occurred under extreme environmental conditions that require a recovering in an efficient and agile manner. The inability property of the grid in withstanding adverse events is recognized as its vulnerability [14]. The effective factors in the reliability and resiliency of MEGs are illustrated in Figure 1.1.

Figure 1.1 Key factors of the reliability and resiliency in MEGs.

From the resilience perspective of the system, recovering ability, adaptability, and vulnerability are the properties that affect the system’s resilience as the three major components. In addition, several other factors also influence the aforementioned domains that are depicted in Figure 1.2[12]. Such factors need to be deeply scrutinized to develop reliable frameworks for MEGs with a full/high level of RERs to realize a sustainable energy structure.

1.3 Renewable Energy Availability in Modern Energy Grids

Eco‐friendly energy production has become the first priority in the energy generation sector when fossil‐fuel‐based units highlighted their remarkable bad effects on environmental and economic issues [15]. In this regard, different technologies of RERs are advanced day by day to enable the energy grid to produce clean energy at a high level. Although RERs offer affordable and environmentally friendly energy generation, the main problem for them is their availability in energy production. Wind, solar, and hydro are three typical RERs that are widely planned for clean energy generation, and their technology has become enough matured for MEGs’ goals. Unlike the traditional controllable energy production units, the main drawback of RERs is that they are not dispatchable and cannot provide regulated energy to the grid [16]. Limited predictability along with the daily and seasonal changes have given intermittent sprite for their energy production [17]. As MEGs are promised for the operation of full/high RERs, innovative mechanisms and frameworks are required for realizing this great goal. This issue not only needs advanced information and communication technologies but also relies on the essentiality usage of several confident ways for ensuring the flexibility of energy supply in the system [18]. How such ways should be effectively developed and implemented is a key challenge regarding the widespread usage of RERs. However, the research world has witnessed significant advancements in the practical solutions for increasing the penetration of RERs in the body of the power generation process. Some of the most prominent of these solutions are indicated in Figure 1.3.

Figure 1.2 Components of MEGs resilience.

Source: Adapted from [12].

Figure 1.3 Some of the most prominent solutions for increasing RERs penetration in MEGs.

The denoted solutions in Figure 1.3 are the usual practical ways for facilitating the integration of RERs in the grid. RERs outputs directly depend on the climate conditions that provide uncertain situations for scheduling of the system. In the moments with desirable climate changes, the surplus clean produced power can be effectively used under the different processes. Energy conversion technologies like power‐to‐gas systems and hydrogen‐based procedures enable the grid to convert the surplus power to other carriers of energy for supporting various energy grids such as natural gas, district heating network, etc. [19]. On the other hand, energy storage systems in their diverse types such as batteries, pumped storage, and compressed air energy storage can store excess energy and play a supportive role for the system when it suffers from lower‐level energy production. Energy management schemes can effectively allow end‐users to be actively involved in the customer‐side services and provide a certain degree of flexibility by load shifting/shedding in return for specific rewards [20]. The common type of energy management scheme is demand response programs that are categorized into incentive‐based and price‐based programs. From another side, a variety of energy interaction strategies can be employed in the scheduling of the system that allows the grid to dynamically balance energy in the highly deregulated environment. In addition to the aforementioned approaches, optimal integration of RERs is known as one of the proper ways of using the benefits of RERs on a large scale. However, its feasibility requires coordinated operation of several intelligent devices that developing the advanced smart agents are expected as a primary condition for the implementation of related plans.

1.4 Modern Multi‐Carrier Energy Grids

Nowadays, by increasing the deployment of hybrid devices as well as the importance of flexibility due to the becoming of RERs as the target units for energy production, a strong coupling between the grid industries such as the electric power system, district heating and cooling networks, natural gas grid, and the water distribution system is necessitated more than ever before. Figure 1.4 shows different sectors of multi‐carrier energy networks (MCENs). In this regard, co‐ and tri‐generation energy systems alongside the multi‐carrier energy consumption devices have created undeniable dependences among multi‐vector energy structures and have made their relationship tight [21]. MCENs are the integrated version of several energy grid infrastructures that enable the system for cooperative interactions, multi‐vector energy sharing and conversion, and multi‐energy generation and management in line with the objectives of MEGs. The MCENs mechanisms allow the renewable‐based system to produce a huge volume of clean energy by offering sustainable ways for managing produced energy in all time periods. In the MCENs infrastructure, each energy sector can engage with other energy structures and develop energy interactions for maintaining the grid’s sustainability and reliability of the energy supply while pursuing specific economic, technical, and environmental goals. The future MEGs are targeted to be developed under MCENs paradigms for using different energy layers aiming to make the overall infrastructure of the grid reliable in energy supply, resilient and self‐healing, secure and stable, efficient in various energy processes, and flexible in the energy management in the presence and high penetration of uncontrollable and eco‐friendly energy generation systems.

Figure 1.4 Different sectors of multi‐carrier energy networks.

1.5 Challenges and Opportunities of Modern Energy Grids

As earlier described, MEGs are not only highly interdependent but also self‐sufficient in their operation. In light of the main targets of MEGs in equipping to the full/high level of RERs as well as coupling different energy structures and creating MCENs infrastructure, future MEGs require advanced technologies, innovative solutions, and a strong roadmap that clearly state what are the requirements for the grid modernization, how they can be procured for implementing the relevant plans, and which actions need to be adopted to close to overcome the challenges ahead. Due to this, thinking about the general and basic challenges can be the primary step in realizing the goals of modern grids. Effectively detecting and analyzing these challenges not only can able the system for making up the practical solutions but also can facilitate reaching the confident scheduling. On the other hand, identifying critical challenges can give suitable overviews regarding the feasibility of MEGs frameworks procuring opportunities for the possible modifications. Some of the most important of these challenges are listed as:

Controlling and managing this great transition.

Optimally evolving planning, operations, and marketing.

Optimally adopting informed, prudent, and future‐looking decision‐making.

Handling the complexity of MEGs with large‐scale and hybrid components.

Handling the computational burden of numerous control systems and expert computing.

Adopting diverse kinds of computational language and developing unique processes.

Developing a complete and sufficient system with the capability of addressing the requirements of future MEGs.

Efficiently recovering and rapidly fostering the large disruptions.

Integrally and optimally monitoring MCENs.

Developing a holistic platform to adopt emerging technologies.

Structuring an environment conducive to all energy market players.

Building market structure for different owners with various expectations and opinions.

Making an active energy interactions system for consumers with diverse consumption patterns.

Manipulating incorporated frameworks conducive to governments with diverse political policies and suitable for regions with various geographical forms.

Developing a comprehensive platform with logical justifications for persuading stakeholders, producers, and consumers with multifarious requirements to pursue MEGs protocols.

Advancing a fair energy area aiming to allow different players to participate in MEGs’ interactions, smartization schemes, and energy management programs.

Developing an innovative mechanism of interdependent MEGs.

Entirely presenting the dynamic process of restoration and failure.

Presenting a holistic system assessment mechanism for analyzing the availability/reliability of highly complex interdependent MEGs.

Optimally reconstructing the dynamic topology in the integrated MEGs.

Developing sustainable architecture of MEGs in line with grid modernization criteria.

Despite MEGs face some critical challenges in their implementation, realizing them can provide tremendous opportunities that can revolute all energy sectors. Investigating these opportunities can also provide a convenient overview of the modern structure benefits, especially in terms of economic, convenience, technical, and environmental. The most important of these opportunities is illustrated in Figure 1.5[22].

Figure 1.5 Opportunities of MEGs.

1.6 Summary