Design, Analysis and Applications of Renewable Energy Systems

Design, Analysis and Applications of Renewable Energy Systems covers recent advancements in the study of renewable energy control systems by bringing together diverse scientific breakthroughs on the modeling, control and optimization of renewable energy systems as conveyed by leading energy systems engineering researchers. The book focuses on present novel solutions for many problems in the field, covering modeling, control theorems and the optimization techniques that will help solve many scientific issues for researchers. Multidisciplinary applications are also discussed, along with their fundamentals, modeling, analysis, design, realization and experimental results.

This book fills the gaps between different interdisciplinary applications, ranging from mathematical concepts, modeling, and analysis, up to the realization and experimental work.

• Presents some of the latest innovative approaches to renewable energy systems from the point-of-view of dynamic modeling, system analysis, optimization, control and circuit design
• Focuses on advances related to optimization techniques for renewable energy and forecasting using machine learning methods
• Includes new circuits and systems, helping researchers solve many nonlinear problems

• Language: English
• Publisher: Academic Press
• Release date: Sep 9, 2021
• ISBN: 9780323859912

Book preview

Design, Analysis, and Applications of Renewable Energy Systems

Edited by

Ahmad Taher Azar

Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

Robotics and Internet-of-Things Lab (RIOTU), Prince Sultan University, Riyadh, Saudi Arabia

Nashwa Ahmad Kamal

Faculty of Engineering, Cairo University, Giza, Egypt

International Group of Control Systems (IGCS), Cairo, Egypt

Table of Contents

Cover image

Title page

Copyright

List of contributors

Chapter 1. Boiler controls and operation from the perspective of renewable energy integration to electrical grid

Abstract

1.1 Introduction

1.2 Boiler control: an overview

1.3 Challenges

1.4 Experiment exploration

1.5 Conclusion and future scope

References

Chapter 2. Hybrid renewable energy systems for energy supply to autonomous desalination systems on Isolated Islands

Abstract

2.1 Introduction

2.2 Description of Canarian Archipelago

2.3 Software selection

2.4 Autonomous desalination systems

2.5 Methodology for the islands selection for the study

2.6 HOMER software input data

2.7 Economic analysis

2.8 Results and discussions

2.9 Conclusion

Acknowledgment

References

Chapter 3. Storage systems in photovoltaic plants with delivery limitation

Abstract

3.1 Introduction

3.2 Theoretical background

3.3 Materials and methods

3.4 Simulation and results

3.5 Discussion

3.6 Conclusions

Acknowledgments

Appendix A Simulink model results

References

Chapter 4. Soft computing in renewable energy system modeling

Abstract

4.1 Introduction

4.2 Hard computing and soft computing

4.3 Soft computing techniques

4.4 Hybrid soft computing modeling

4.5 Applications in renewable energy systems

4.6 Conclusions and future perspectives

References

Chapter 5. Strategy to support renewable energy sources in Europe

Abstract

5.1 Introduction

5.2 Materials and methods

5.3 Theoretical background

5.4 Results

5.5 Discussion

5.6 Conclusion

Acknowledgment

References

Appendix A

Chapter 6. The complex dynamics of renewable energy innovation system in Tunisia

Abstract

6.1 Introduction

6.2 Innovation cycle and system dynamics modeling

6.3 Empirical framework

6.4 Discussion and policy implications

6.5 Conclusion

References

Chapter 7. Designing and performance analysis of solar tracker system: a case study of Çukurova region

Abstract

7.1 Introduction

7.2 Literature review about solar tracking system

7.3 Design and application area

7.4 Simulation and experimental analysis

7.5 Results and discussions

7.6 Conclusion

Acknowledgments

References

Chapter 8. Renewable energy, R&D, and economic complexity: new evidence for Latin America using quantile regressions

Abstract

8.1 Introduction

8.2 Literature review

8.3 The data and statistical sources

8.4 Econometric strategy

8.5 Empirical analysis

8.6 Conclusions and policy implications

Acknowledgments

References

Chapter 9. Techno-economic comparative assessment of an off-grid hybrid renewable energy system for electrification of remote area

Abstract

9.1 Introduction

9.2 Solar system

9.3 Maximum power point algorithm

9.4 Wind energy system

9.5 Biomass energy system

9.6 Diesel generator

9.7 Energy storage systems

9.8 Modeling of HRES elements

9.9 HOMER software

9.10 Results and discussion

9.11 Conclusion

References

Chapter 10. Dynamic self-recurrent wavelet neural network for solar irradiation forecasting

Abstract

10.1 Introduction

10.2 Preliminaries and related work

10.3 Dynamic wavelet-neural network design

10.4 Numerical experiments: irradiation and daily hour forecast

10.5 Discussion

10.6 Conclusion

References

Chapter 11. Developing energy management system considering renewable energy systems in residential community

Abstract

11.1 Introduction

11.2 Related work

11.3 Modeling renewable energy systems, thermal resources, and storage units

11.4 Studying and modeling the residential community

11.5 Developing energy management algorithm

11.6 Numerical results on a sample residential complex

11.7 Conclusion

References

Chapter 12. Techno-economic performance evaluation among different solar photovoltaic system configurations

Abstract

12.1 Introduction

12.2 Field system design

12.3 Solar PV system modeling

12.4 Techno-economic performance of all solar PV systems

12.5 Summary

References

Chapter 13. Assessment and decision-making of biomass energy conversion system by big data and game theory technique

Abstract

13.1 Introduction

13.2 Biomass energy conversion system

13.3 Assessment of biomass energy system by big data analysis

13.4 Prefeasibility assessment of biomass system with big data analysis through CRISP-DM methodology

13.5 Biomass energy system by Hadoop environment

13.6 Energy aware cluster assessment of biomass energy system

13.7 Application of Map–Reduce in biomass energy conversion system

13.8 Market basket model of biomass energy system

13.9 NoSQL to manage biomass energy data

13.10 Energy aware task scheduling of biomass energy system by critical path method

13.11 Assessment of biomass power plant by game theory concept

13.12 Game theory in biomass energy system

13.13 Conclusion

References

Chapter 14. Assessment of V–I droop mechanism to create power reserve for stand-alone microgrid

Abstract

14.1 Introduction

14.2 Emerging technology of microgrid

14.3 Conservative voltage reduction

14.4 Demand side management

14.5 Droop control in microgrid

14.6 Hierarchical control of microgrid

14.7 Evaluation of operation of control scheme in various reference frames

14.8 Design of control strategy

14.9 Results and discussion

14.10 Conclusion

References

Chapter 15. The importance of renewable energy expansion in power generation sector in Iran: a computable general equilibrium approach

Abstract

15.1 Introduction

15.2 Power generation mix in Iran

15.3 Application of feed in tariff policy for re development

15.4 Environmental policies for RE expansion in Iran

15.5 Conclusion and policy recommendation

Acknowledgments

References

Chapter 16. Operating mode management of renewable energy systems using event-driven hybrid bond graphs

Abstract

16.1 Introduction

16.2 Hybrid renewable energy systems

16.3 Modeling hybrid renewable energy systems

16.4 Application

16.5 Results

16.6 Conclusion

References

Chapter 17. A review of energy management methods for residential renewable energy systems

Abstract

17.1 Introduction

17.2 Review on current technologies of residential renewable energy systems

17.3 A review on energy management techniques

17.4 Conclusion

References

Chapter 18. Reliability effects of the dynamic thermal rating system on wind energy integrations

Abstract

18.1 Introduction

18.2 Brief history of conductor thermal ratings

18.3 IEEE and CIGRE standards

18.4 Comparison between the IEEE and CIGRE standards

18.5 Prospect for DTR systems

18.6 DTR system monitoring devices

18.7 Industrial application of DTR systems

18.8 Development of DTR system modeling

18.9 Conclusion

18.10 Future work

References

Chapter 19. Frequency support by grid connected DFIG-based wind farms

Abstract

19.1 Introduction

19.2 Wind power system control

19.3 Frequency response

19.4 Grid codes

19.5 Methodology and test system

19.6 Results

19.7 Conclusion

Acknowledgments

References

Chapter 20. A holistic approach of the energy system to frame renewable and energy efficiency models,

Abstract

20.1 Introduction

20.2 Holistic approach for the energy system

20.3 The energy chains: Interconnection of decisions

20.4 Context dependence of energy systems

20.5 Holistic approach for the energy policy: why should models work together?

20.6 Discussion

20.7 Conclusion and future directions

References

Further reading

Chapter 21. Next generation of grid-connected photovoltaic systems: modeling and control

Abstract

21.1 Introduction

21.2 PV panel modeling

21.3 PV system architectures

21.4 Maximum power point tracking

21.5 Dc/dc stage control

21.6 Dc/ac stage modeling and control in single-phase systems

21.7 Ancillary services: Reactive power control

21.8 Ancillary services: Harmonic current compensation

21.9 Conclusions and future trends

References

Chapter 22. Quantum computing in renewable energy exploration: status, opportunities, and challenges

Abstract

22.1 Introduction

22.2 Brief description of quantum computing

22.3 Quantum machine learning

22.4 Quantum optimization and quantum algorithms

22.5 Scopes of computational intervention (QC) in renewable energy

22.6 Potential quantum algorithms for renewable energy applications

22.7 Drivers of quantum computing application in renewable energy

22.8 Challenges of quantum computing in renewable energy

22.9 Conclusions and future perspectives

References

Further reading

Chapter 23. Electric Vehicles charging strategy based on multimarket platforms for photovoltaic-powered workplace charging station

Abstract

23.1 Introduction

23.2 Related work

23.3 Optimization model

23.4 System modeling

23.5 Implementation of the proposed model

23.6 Results and discussions

23.7 Conclusion and future work

References

Chapter 24. Choice among alternatives—an evaluation of Indian energy basket

Abstract

24.1 Introduction

24.2 Literature review

24.3 Analytical framework

24.4 Data

24.5 Econometric methodology

24.6 Results and discussion

24.7 Conclusion

References

Chapter 25. Renewable energy in Bangladesh: status and potential

Abstract

25.1 Introduction

25.2 Renewable energy and sustainability

25.3 Status and prospects of renewable energy in Bangladesh

25.4 Ocean renewable energies

25.5 Renewable energy potentials in Bangladesh

25.6 Environmental impacts

25.7 Concluding remarks

References

Chapter 26. Hybrid operational deployment of renewable energy—a distribution generation approach

Abstract

26.1 Introduction

26.2 Comprehensive overview of related works

26.3 Current energy scenario of India

26.4 Distributed energy resources

26.5 Smart grid system

26.6 Renewable energized DER

26.7 Conclusion

References

Chapter 27. The role of distributed energy systems in electric vehicle wireless charging

Abstract

27.1 Introduction

27.2 Electric vehicles and sustainable mobility

27.3 Distributed system and storage system feeding electrical roads

27.4 Dimensioning and application

27.5 Discussion and result

27.6 Conclusion

References

Chapter 28. IoT-driven data extraction applications using common information model in a hybrid microgrid system

Abstract

28.1 Introduction

28.2 Common information model technology

28.3 Role of Internet of Things in building common information model interfaces

28.4 Hybrid alternating current–direct current microgrid system

28.5 Data extraction from hybrid microgrid system using common information model

28.6 Overall system performance

28.7 Summary

References

Chapter 29. Metering architecture of smart grid

Abstract

29.1 Introduction to smart grid

29.2 Role of smart meter technologies in smart grid

29.3 Conclusion

29.4 Future scope

References

Chapter 30. Solar energy sustainability in Bangladesh: tackling the management challenges

Abstract

30.1 Introduction

30.2 Solar energy in Bangladesh in the backdrop of increasing energy demand

30.3 Barriers to a sustainable solar energy base

30.4 An efficient business model for solar energy

30.5 Conclusions and policy recommendations

Acknowledgments

References

Appendix

Index

Copyright

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List of contributors

Ibrahim Abdallah,     CRIStAL - Research Center in Computer Science, Signal and Automatic Control of Lille, University of Lille, Lille, France

Paul A. Adedeji,     Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg, South Africa

Myisha Ahmad,     Department of Civil Engineering, Military Institute of Science and Technology, Dhaka, Bangladesh

Abiola O. Ajayeoba,     Department of Mechanical Engineering, Ladoke Akintola University of Technology, Ogbomosho, Nigeria

Kammogne Soup Tewa Alain,     Laboratory of Condensed Matter, Electronics and Signal Processing (LAMACETS), Department of Physic, Faculty of Sciences, University of Dschang, Dschang, Cameroon

Nada Ali,     Department of Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi, India

Hakan Alici,     Department of Machine Energy, Kıvanc Textile, Adana, Turkey

Mohsen Alimi,     University of Kairouan, Kairouan, Tunisia

Rafael Alvarado,     Carrera de Economía, Universidad Nacional de Loja, Loja, Ecuador

Deivis Avila,     Higher Polytechnic School of Engineering (EPSI), University of La Laguna, Tenerife, Spain

Soumia Ayyadi,     Mohammadia School of Engineers, Mohammed V University, Rabat, Morocco

Ahmad Taher Azar

Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

College of Computer and Information Sciences, Prince Sultan University, Riyadh, Kingdom of Saudi Arabia

Prabodh Bajpai,     Department of Electrical Engineering, IIT Kharagpur, Kharagpur, India

Prashant Baredar,     Energy Centre, Manit Bhopal, India

Jayesh Barve,     Controls and Optimization, GE Global Research, Bangalore, India

Javed Ahmad Bhat,     Department of Economics and Finance, Birla Institute of Technology & Science, Pilani, K K Birla Goa Campus, India

Sakib Bin Amin,     Department of Economics, North South University, Dhaka, Bangladesh

Fatemeh Chatri,     Department of Economics, Faculty of Economics and Management, National University of Malaysia, Bangi, Malaysia

Mainul Islam Chowdhury,     Department of Economics, North South University, Dhaka, Bangladesh

ClaraPérez-Molina,     Department of Electric, Electronic and Control Engineering, UNED, Ciudad Universitaria, Madrid, Spain

Antonio Colmenar-Santos,     Department of Electric, Electronic and Control Engineering, UNED, Ciudad Universitaria, Madrid, Spain

Allan Fagner Cupertino,     Department of Materials Engineering, Federal Center for Technological Education of Minas Gerais, Belo Horizonte, Brazil

Miguel de Simón-Martín,     Department of Electrical and Systems Engineering and Automation, Universidad de León, León, Spain

Tugce Demirdelen,     Department of Electrical and Electronics Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey

S.M. Asif Ehsan,     Department of Economics, North South University, Dhaka, Bangladesh

Burak Esenboga,     Department of Electrical and Electronics Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey

Arezki Fekik

Akli Mohand Oulhadj University, Bouira, Algeria

Electrical Engineering Advanced Technology Laboratory (LATAGE), Mouloud Mammeri University, Tizi Ouzou, Algeria

A.J.S. Filho,     Center for Engineering, Modeling and Applied Social Sciences (CECS), Federal University of ABC (UFABC), Santo Andre, Brazil

Marco A. Flores,     Institute of Energy Research IIE, Honduras National Autonomous University (UNAH), Tegucigalpa, Honduras

Anne-Lise Gehin,     CRIStAL - Research Center in Computer Science, Signal and Automatic Control of Lille, University of Lille, Lille, France

L.A.G. Gomez,     Laboratory of Advanced Electric Grids-LGrid, Polytechnic School, University of São Paulo (USP), São Paulo, Brazil

A.P. Grilo,     Center for Engineering, Modeling and Applied Social Sciences (CECS), Federal University of ABC (UFABC), Santo Andre, Brazil

Seyed Mehdi Hakimi,     Department of Electrical Engineering and Renewable Energy Research Center, Damavand Branch, Islamic Azad University, Damavand, Iran

G.M. Jahid Hasan,     Department of Civil Engineering, Military Institute of Science and Technology, Dhaka, Bangladesh

Arezoo Hasankhani,     Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, United States

Ángela Hernández,     Higher Polytechnic School of Engineering (EPSI), University of La Laguna, Tenerife, Spain

Amjad J. Humaidi,     Department of Control and Systems Engineering, University of Technology, Baghdad, Iraq

Ibraheem Kasim Ibraheem,     Department of Electrical Engineering, College of Engineering, University of Baghdad, Baghdad, Iraq

S.M. Zahid Iqbal,     Department of Economics, North South University, Dhaka, Bangladesh

Mohammad Jafari,     School of Electrical and Data Engineering, Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, Australia

Sumit Kumar Jha,     Department of Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi, India

Nashwa Ahmad Kamal

Faculty of Engineering, Cairo University, Giza, Egypt

International Group of Control Systems (IGCS), Cairo, Egypt

Inderpreet Kaur,     Department of Electrical Engineering, Guru Nanak Dev Engineering College, Ludhiana, India

Cheshta J. Khare,     Department of Electrical, SGSITS, Indore, India

Vikas Khare,     STME, NMIMS, Indore, India

Deepak Kumar,     Department of Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi, India

Mohit Kumar,     Department of Electrical Engineering, Bipin Tripathi Kumaon Institute of Technology, Dwarahat, India

Pankaj Kumar,     Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, India

Vineet Kumar,     Division of Instrumentation and Control Engineering, Netaji Subhas University of Technology, New Delhi, India

África López-Rey,     Department of Electric, Electronic and Control Engineering, UNED, Madrid, Spain

L.F.N. Lourenço,     Laboratory of Advanced Electric Grids-LGrid, Polytechnic School, University of São Paulo (USP), São Paulo, Brazil

Felipe San Luis,     Higher Polytechnic School of Engineering (EPSI), University of La Laguna, Tenerife, Spain

Mohamed Maaroufi,     Electrical Department, Mohammadia School of Engineers, Mohammed V University, Rabat, Morocco

Nkosinathi Madushele,     Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg, South Africa

Zahra Malekjamshidi,     School of Electrical and Data Engineering, Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, Australia

Graciliano N. Marichal,     Higher Polytechnic School of Engineering (EPSI), University of La Laguna, Tenerife, Spain

Shikha Mittal,     Department of Mathematics, Jesus and Mary College, University of Delhi, New Delhi, India

Mario Monteagudo-Mencucci,     Department of Electric, Electronic and Control Engineering, UNED, Ciudad Universitaria, Madrid, Spain

Antonio-Miguel Muñoz-Gómez,     Department of Electric, Electronic and Control Engineering, UNED, Madrid, Spain

P.S.V. Nataraj,     Systems and Control Engineering, IIT Bombay, Mumbai, India

Irfan Oktem,     Department of Electrical and Electronics Engineering, Adana Alparslan Turkes Science and Technology University, Adana, Turkey

Obafemi O. Olatunji,     Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg, South Africa

Belkacem Ould-Bouamama,     CRIStAL - Research Center in Computer Science, Signal and Automatic Control of Lille, University of Lille, Lille, France

Nitai Pal,     Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, India

Heverton Augusto Pereira,     Department of Electrical Engineering, Federal University of Viçosa, Viçosa, Brazil

K.P.S. Rana,     Division of Instrumentation and Control Engineering, Netaji Subhas University of Technology, New Delhi, India

Marina Y. Recalde

National Scientific and Technical Research Council (CONICET), Argentina

Bariloche Foundation, Argentina

Kengne Romanic,     Laboratory of Condensed Matter, Electronics and Signal Processing (LAMACETS), Department of Physic, Faculty of Sciences, University of Dschang, Dschang, Cameroon

Enrique Rosales-Asensio,     Department of Electrical Engineering, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

M.B.C. Salles,     Laboratory of Advanced Electric Grids-LGrid, Polytechnic School, University of São Paulo (USP), São Paulo, Brazil

Yashwant Sawle,     Vellore Institute of Technology, Vellore, India

Fernando E. Serrano,     Institute of Energy Research IIE, Honduras National Autonomous University (UNAH), Tegucigalpa, Honduras

Salma Sraidi,     Electrical Department, Mohammadia School of Engineers, Mohammed V University, Rabat, Morocco

P.U. Sunil,     Systems and Control Engineering, IIT Bombay, Mumbai, India

Jiashen Teh,     School of Electrical and Electronic Engineering, Universiti Sains Malaysia (USM), Nibong Tebal, Malaysia

Dinesh Varma Tekumalla,     School of Energy Science and Engineering, IIT Kharagpur, Kharagpur, India

M. Thirunavukkarasu,     Vellore Institute of Technology, Vellore, India

Brayan Tillaguango

Carrera de Economía, Universidad Nacional de Loja, Loja, Ecuador

Esai Business School, Universidad Espíritu Santo, Samborondón, Ecuador

Elisa Toledo,     Departamento de Economía, Universidad Técnica Particular de Loja, Loja, Ecuador

Mehmet Tumay,     Department of Electrical and Electronics Engineering, Çukurova University, Adana, Turkey

Arash Vafaeizadeh,     Department of Electrical Engineering and Renewable Energy Research Center, Damavand Branch, Islamic Azad University, Damavand, Iran

Masoud Yahoo,     Faculty of Economics, Kharazmi University, Tehran, Iran

Chapter 1

Boiler controls and operation from the perspective of renewable energy integration to electrical grid*

P.U. Sunil¹, Jayesh Barve² and P.S.V. Nataraj¹,    ¹Systems and Control Engineering, IIT Bombay, Mumbai, India,    ²Controls and Optimization, GE Global Research, Bangalore, India

Abstract

Renewable energy-based electricity production made significant progress in the recent past, and in the future it is expected to have a considerable contribution in power generation demography. A persistent need of renewable energy and the slow retirement of less efficient thermal power plant is inevitable. However, the variation in renewable production poses a challenge to a smooth and stable operation of a power grid. Hence, the operability of a nonrenewable energy power plant, especially a thermal power plant, plays a crucial role in the successful integration of renewable and stable grid operation. Systems and control aspects like modeling, estimation, prediction, and optimization play an important role in successfully tackling and improving the reliability and availability of a future electrical ecosystem. This chapter gives an overview of the challenges and opportunities for control systems from the perspective of steam-based power plants, particularly boiler operability in lieu of renewable energy integration. Also, a case study of exploring robust MIMO (Multi-Input-Multi-Output) control during a part load of a boiler with bypass control is presented. This scenario is expected to be common during renewable integration.

Keywords

Boiler; steam generators; thermal power plant; control; renewable energy; robust control

1.1 Introduction

Energy production and integration is highly dynamic and has been unpredictable in recent years. The policies and environmental regulations across each country enabled research and development on renewable energy (RE), which impacted capability, viability, and reduction in production cost over a period of years. Also, the slow retirement of coal-based thermal power plants is slowly changing the grid landscape, which was holding for over a century. Systems and subsystem controls, such as optimization and estimation, are going to play a major role in this transition. The agility and stability of high-frequency components, such as excitation of synchronous generator, voltage control, stabilizers and power electronics control of inverter, gas turbine (GT) governor, wind turbine controls, and battery control, are going to perform a vital role in fast transient events. Control of boilers, nuclear reactors, duct burners and furnaces, and GTs are an important aspect from the process side of non-RE sources, which are relatively slow transient and contribute to delayed stability problems or contingency to power grid. Since these are slow time constant processes with a high degree of nonlinearity, their performance and robustness to handle a wide range of operations due the variability of non-RE source are critical to achieve seamless performance of the future electrical grid ecosystem.

Research and analysis by academic, independent agencies, and industries (Bird, Milligan, & Lew, 2013; Botterud, 2014; Eser, Singh, Chokani, & Abhari, 2016; Martinot, 2015; Shafiullah, Oo, Ali, & Wolfs, 2013; Ulbig, 2013) envisages the challenges of RE participating in electrical grid and its impact on overall stability and availability of grid. These studies suggest thermal power plants should be more flexible in their operation with respect to current state to achieve better grid stability and availability. Hence, operability studies of thermal power plants’ response to grid events are needed for each zonal grid region to understand the uncertainty and impact.

Boiler, furnace, and other energy transition equipment are key subsystems of thermal power plant. These systems are also expected to move out from the design zone (100% operation load) to partial loads and should support grid events by operating in wider region—that is, this equipment should be operationally flexible to enable the thermal power plant to contribute to and support renewable integration for a stable grid. This chapter specifically focuses on boiler or steam generation.Table 1.1 shows the future capacity projection of RE in India and the developed countries.

Table 1.1

Notes: Units have been representd as %.

A detailed study on the impact on the nonrenewable process side is important for successful integration of RE with the grid. This chapter focuses on only one component of this, the steam boiler, a piece of complex energy conversion equipment in all thermal power plants. Robust control and optimization of the boiler is important from the perspective of grid stability considering RE integration. Most of the developed countries are aiming for 50%–60% of RE contribution by 2050. One of the major findings of this study is increased electric system flexibility, which is needed to enable electricity supply and demand balance with high levels of RE generation, can come from a portfolio of supply- and-demand-side options, including flexible conventional generation, grid storage, new transmission, more responsive loads, and changes in power system operations. Similarly, the Scandinavian lands, Germany, and other countries in European Union have reformed to RE integration with the main power grid and reported that intermittency and variability of the RE source limit their ability to meet the grid need for a stable base load electricity supply. Greater flexibility in the system will hence be required to accommodate supply-side variability. The challenges expected by various researchers are as follows:

• load and generation management of the grid,

• variability of photovoltaics (PV) plants,

• cost impact on fossil units because of thermal cycle, and

• Flexible operation and generation sources.

This will set a new paradigm shift regarding operation and dispatch control for the electricity sector. Along with the penetration of RE, the installed base of cogeneration power plants is also increasing. Hence, better operational flexibility and electrical grid stability are needed. In general, Rankine-cycle-based power plants have a larger installed base and are typically currently operated in the base-load conditions (or at full-rated load). Even though less-efficient thermal power plant units might be retired or retrofitted, the thermal power plant share of total power-generation capacity globally, and particularly in India and China, is going to remain significant for decades to come. Hence, the operability of steam-based power plants is going to shortly play a crucial role in the successful integration and stable operation of the electrical grid. Academicians and industrial researchers are eagerly looking for RE integration and addressing its challenges while looking for potential opportunities. Since boiler systems are slow, that is, control loops have large system response and are lag dominant with nonlinearity and complex interactions. Hence, the operational response and robustness on widespread ranges are critical to achieve seamless operation of the power grid along with RE sources.

1.1.1 Impacts on steam-cycle-based power plant

Variation in solar-based PV is highly predictable if the installed base is large. Cloud cover can result in very rapid changes in the output of individual PV systems, but the impact on the electric grid is minimized when solar projects are spread out geographically so that clouds do not impact them at the same time. In this way, the variability of a vast number of systems is smoothed out, and a common variation with respect to time is more parabolic in nature. That is, the energy harvested will peak during noontime, see Fig. 1.1. Hence, with the spread of solar plants, the uncertainty or variability is transferred as a predictable affair with respect to time. A study conducted by a nonprofit organization called California ISO (CAISO) predicts a duck curve (CAISO, 2012) because of solar integration (see Fig. 1.2). The duck curve indicates severe challenges and will trigger innovation around the operability and control of the grid. The duck curve shows that during the absence of sunlight, other cogeneration fossil units need to ramp up to satisfy the power demand.

Figure 1.1 Variation in solar energy.

Figure 1.2 Duck curve.

The variability of wind PVs is highly unpredictable and poses a different problem about the operational flexibility of the base load station, see Fig. 1.3. Considering this situation, future energy ecosystem may require a more flexible generation from fossil-based steam units and combined cycle based on GT and steam turbine (ST) can help avoid grid instability and black out. The Western Wind and Solar Integration Study (Phase 1 and 2) recommends quick maneuvering required by combined-cycle plants (National Renewable Energy Laboratory NREL, 2012).

Figure 1.3 Wind variation.

One of the major hurdles for operational flexibility for the thermal power plant is the cost associated with the wear and tear owing to a thermal cycle, partial load efficiency, emission, and other factors. Also, process control of these power plants needs to be improved to achieve stable operability. Thermal steam plants have a substantial amount of thermal inertia in the boiler that limits their ability to ramp up or down quickly. Hence, the flexibility of the thermal power plant depends on boiler operational flexibility and availability. The next section gives an overview of controls literature around boilers.

1.1.2 Boiler control literature review

This section covers the recent direction of boiler controls from a research point of view. Application of a robust H∞ control is explored by Tan, Marquez, and Chen (2002), but a practical validation is not performed; they instead used a nonlinear simulation platform and their results are presented. Scheduling the gain for a turbine–boiler and formulating as an optimal control were done by Chen and Shamma (2004). Actuation saturation was considered in their design, which accounts some nonlinearity. An advanced process control using predictive control methods with dynamic matrix control algorithm was proposed by Kim, Moon, Lee, and Lee (2005); system identification methods on nonlinear are used and step response model was derived, which were tested on a simulation environment; similar exploration was done by Li, Li, Tan, and Liu (2006); Moon and Lee (2011) added an adaptive philosophy to DMC. On the other hand, a practical implementation of generalized predictive control on an industrial boiler was proposed by Xu, Li, and Cai (2005). A method proposed by Labibi, Marquez, and Chen (2009) represented boiler nonlinearity as uncertainty around a nominal plant and explored a decentralized PI control with some amount of robustness. A full operation of coal-based boiler using hybrid control is explored by Zheng, Bentsman, and Taft (2008). A PID control design and benchmarking was done by Morilla (2012). Chen (2013) investigated a new formulation that tackles multiple objective function for better performance of turbine–boiler dynamics. A fault tolerance control using sliding model control philosophy was explored by Aliakbari, Ayati, Osman, & Sam (2013). A comparative study of gain schedule on nonlinear controls technique using feedback linearization was conducted by Moradi, Abbasi, and Moradian (2016). An economic model predictive control formulation that is hierarchical in nature was projected by Kong, Liu, and Lee (2015). Lawryńczuk (2017) proposed an online dynamic optimization formulation that uses state space predictive control with an online linearization capability and solves a QP on a proposed time schedule. A plant-wide set point of reference management is proposed by Klaučo and Kvasnica (2017).

Boiler control is an interesting and challenging problem for the academic researcher because of the nonlinearity, nonminimum phase behavior, and multivariable interaction. Many control theory explorations such as, H-∞, sliding model, GPC, DMC, and artificial neural net are available in the boiler control literature. Many of these studies are limited to simulations only with little experimental validation to understand real implementation difficulties and obtain actual performance improvement. Approximately 95% of the studies performed on the boiler for advanced control use only simulation platforms. The irony is that industrial power plant boiler control still uses conventional control with some small percentage of the model-based advanced control done at the supervisory layer. A large set of advance control research showing superiority in the simulation is never tried because of the gap, disconnect, and risk associated in implementation. RE integration can pose a challenge in operational flexibility, which leads to a strategic solution for control and for this we need to bridge the gap between academic and industrial research.

1.1.3 Structure and focus

The focus of this chapter is to show academic researchers the high-level controls challenges in important control loops in boiler or steam generators with an aim to minimize the gap between academic and industry outcomes. The rest of this chapter is divided into three sections; Section 1.2 gives an overview of boiler control basics and abstracts to nominal controls terminology; Section 1.3 gives an overview of potential challenges due to wide range operability because of RE integration for major control loops; and Section 1.4 describes opportunities and results of the authors experimental research in this direction and also points future areas of exploration.

1.2 Boiler control: an overview

1.2.1 Boiler control basics

Boiler performance depends on the ability to transfer heat to waterside while maintaining operating specifications. Operating specifications are steam pressure, steam temperature, quality, and mass flow rate. Steady-state performance calculations are performed to design boiler optimally for maximum performance. However, real performance depends upon how these process parameters are being maintained at the desired operation points (based on performance calculations) during the boiler operation range. The boiler control system is the vehicle through which these parameters are maintained to ensure proper energy and mass balance to achieve desired performance. Mass balance and energy on the waterside and fire side (gas side) are shown in Fig. 1.4. All the major energy and mass inputs must be regulated/maintained to achieve the desired output conditions, for a smooth operation of the boiler with good performance. Hence, process variables such as pressure, level, and temperature need to be properly maintained. Other important requirements are safe, reliable, and environment-friendly operation. Fig. 1.5 shows the block diagram of the boiler control system to maintain boiler outputs and ensure good performance of the boiler (Dukelow, 1991).

Figure 1.4 Boiler mass energy balance. Courtesy Dukelow, S. G. (1991). The control of boilers. Instrument Society of America.

Figure 1.5 Main control loops. Courtesy Dukelow, S. G. (1991). The control of boilers. Instrument Society of America.

1.2.2 Steam pressure control

The objective of the steam pressure control is to maintain the desired pressure at the inlet of the ST. Steam pressure represents the proper energy balance of a boiler–turbine pair. Generally, boiler-based power plants operate at base-load condition, with a minimum margin for load fluctuation and control. When the throttle pressure is constant, the boiler supplies the optimal amount of energy to the turbine. A simple approach is to control the firing based on throttle pressure or steam flow (see Fig. 1.6). However, because of process latency, nonlinearity, and interaction, a control system that uses throttle pressure as its primary controlled variable will produce instability and oscillation. Stable operation can be achieved only by restricting the rate and magnitude of load changes. To overcome the instabilities and allow for faster load changes, feedforward signals, typically based on boiler steam flow or turbine first-stage pressure, are commonly added to the system. Fig. 1.7 shows a feed-forward-based approach. The demand or change in steam flow requirement is the trigger for pressure variations. Hence, typically pressure control and firing are tightly coupled with turbine load demand or load fluctuations. The next section discusses the load and boiler–turbine interactions.

Figure 1.6 Steam pressure control (feedback). Courtesy Dukelow, S. G. (1991). The control of boilers. Instrument Society of America.

Figure 1.7 Steam pressure control (feedback + feedforward). Courtesy Dukelow, S. G. (1991). The control of boilers. Instrument Society of America.

1.2.3 Boiler–turbine load control

Boiler load control is based on steam demand or electrical load. The purpose of this control is to match firing and electrical load demand without compromising the boiler performance. That is, pressure and temperature are to be properly maintained for optimum boiler operation. There are multiple ways to configure boiler–turbine control for load demand or disturbances. Four common methods used in industries for boiler–turbine controls are described as follows.

1.2.3.1 Boiler follow mode

Most conventional drum-type steam units are operated in the boiler follow mode, where changes in a steam flow are initiated by the turbine control valves responding to change in load reference or machine speed.

The boiler control responds with the necessary control action, upon sensing the resulting changes in steam flow and deviations in pressure. Fig. 1.8 describes the boiler follow mode of operation. Steam generation is controlled by the inputs to the boiler (fuel and air), where the fuel is controlled to correct the pressure error, that is, the difference between throttle pressure and the pressure set point.

Figure 1.8 Control scheme for boiler follow mode. Courtesy Dukelow, S. G. (1991). The control of boilers. Instrument Society of America.

1.2.3.2 Turbine follow mode

The turbine follow mode involves the use of the turbine control valves to regulate boiler pressure. Stored energy in the boiler is not used. Steam flows through the turbine and therefore the turbine power closely follows the amount of steam generation, that is, the input to the boiler. The response of turbine power is considerably slower than conventional control. Fuel is no longer controlled by pressure error but by the desired MW signal. This type of control focuses on the boiler and its heat input in such a way that small fluctuations on steam demand are not handled by the heat input to boilers. Nuclear steam generators and some classes of heat recovery boilers usually operate in this mode. Hence, huge variations of pressure due to demand variation or load rejections are typically handled with bypass systems, which will be described in next section. Fig. 1.9 shows the boiler–turbine control in turbine follow mode.

Figure 1.9 Control scheme for turbine follow mode. Courtesy Dukelow, S. G. (1991). The control of boilers. Instrument Society of America.

1.2.3.3 Coordinate control mode and variable pressure or sliding pressure

The coordinated mode recognizes the advantages and disadvantages of the conventional and turbine follow modes. Fig. 1.10 shows the block diagram of the coordinated control mode. In the variable pressure (often called sliding pressure) control mode, the pressure set point is proportional to the MW demand. The pressure error between the set point and actual throttle pressure drives steam generation through the fuel controls. The amount of coupling between boiler and turbine is based on the overall plant configuration.

Figure 1.10 Control scheme for coordinated control mode. Courtesy Dukelow, S. G. (1991). The control of boilers. Instrument Society of America.

1.2.4 Boiler–turbine bypass system

A steam bypass system is a common subsystem to bypass steam during imbalance and startups. This system plays a crucial role in nuclear boilers and combined cycle power plant boilers, HRSGs. Generally, these power plants operate at 100% load. Hence, simple control solutions are sufficient to meet these operations. Whenever there is a mismatch between steam demand and steam production, the coupling of ST and the boiler becomes an issue. Further, startup, shutdown, load step down, load setback, and load rejection also need attention because of the mismatch between transient steam load and supply. Even though these operations are not frequent these days, studies show frequent startups and partial loading for HRSG-based plants in lieu of renewable energy integration. A boiler with a bypass system is required to handle transient conditions, especially when there is an imbalance between steam production and demand. In nuclear boilers, when there is a steam demand loss, the reactor is set back to 70%, and the reactor is parked at this power state using the bypass. Another option is called step back, which is a fast power reduction technique. However, in combined cycle power plants during this imbalance conditions, multiple solutions are recommended. One solution is to install a bypass damper and a bypass stack upstream of the HRSG, enabling the GT exhaust gas to be vented directly to the atmosphere. This permits simple cycle operation but can still allow serious thermal transients in the HRSG. This solution also is capital and maintenance intensive, so it is installed in very few plants. Another solution is to allow the HRSG to generate steam but to vent it directly to the atmosphere until the steam-side metal is properly warmed up. However, the routine use of so-called sky vents during every plant startup incurs costly losses of demineralized water, not to mention problems with environmental regulators and plant neighbors who object to the imposing noise and plume. The most common method to manage the thermal imbalance during times when the ST cannot use all the steam produced by the bypass system is shown in Fig. 1.11.

Figure 1.11 Boiler–turbine configuration.

Fast opening of the bypass system is provided from a contact signal, such as loss of power export. The bypass can be controlled to maintain inlet steam pressure set point. The bypass maintains an upstream pressure to manage the energy balance. Operational requirements of the turbine bypass system include (1) startup and run-up of boiler, (2) continued boiler operation in the event of sudden load shedding or tripping of the turbine generator, (3) continued operation of the turbine generator at house load or partial load after sudden loss of export without the need to operating the main steam or drum safety relief valve, and (4) a relief valve for partial turbine load rejections (power/load unbalance), eliminating the operation of the ERV or spring-loaded safety valves. The sizing of a turbine bypass system depends on the operational requirements of the plant and acceptability of fluid loss from the cycle through safety valves or atmospheric dump valves. Two main areas must be considered to size the HP and LP turbine bypass system properly:

• Startup requirements: The startup of the boiler–turbine is performed in a variable pressure mode, and the turbine bypass system must handle the requirements, bearing in mind that the pressure drop in steam or velocity limits for valves, piping, and so on, is directly proportional to the specific volume of the steam.

• Load rejection requirements: The size of the bypass system depends on the magnitude of the partial load rejection that the boiler can sustain without tripping. The rate of fluid loss from the cycle during a load rejection through either high pressure or intermediate pressure relief devices along with firing rate reduction is used to size the load rejection scheme to achieve a stable operation.

1.2.5 Drum-level control

This section covers an overview of current industrial control schemes and the challenges associated with the drum-level control. Generally, the feedwater flow is used as a control variable to maintain proper level in the boiler drum. Dukelow (1991) provides a detailed overview of controls for all the boiler variants used in industries. The objective of drum level control is to (1) maintain drum level at the desired set point, (2) minimize the interaction with heat transfer system, (3) maintain smooth change of inventory balance during load variation, and (4) reject disturbance during pressure transients. Two major challenges posed by drum-level control are (1) the dynamic of swell and shrink on drum level due to steam flow disturbance and feedwater pressure disturbance and (2) robust control for wide-range boiler load. For the first problem, currently, a feedback plus feedforward control structure is practiced and widely accepted in the industry. These structures are popularly known as two-element and three-element controls for drum-type boilers. Two-element is used during low load or low steam productions condition, whereas three-element is used during normal operation, generally 30% to full load range. This section gives an overview of feedwater control in drum-type boilers. The main objective of this control is to maintain desired inventory in the boiler drum. This loop should minimize the interaction with boiler load change (steam flow) and boiler water inventory during transients. In a steady state, this controller should balance between steam flow outlet and feedwater. In industry, three basic control structures are practiced that primarily use a PI controller in the feedback loop and a simple gain in the feedforward loop.

1.2.5.1 Single-element control

This structure is a classical feedback control loop as shown in Fig. 1.12. The control action activates only when the level deviates from its set point, but if there is a change in steam flow pressure, the performance of this controller will be poor. So, there is one more element added as explained in the next.

Figure 1.12 One-element control. Courtesy Dukelow, S. G. (1991). The control of boilers. Instrument Society of America.

1.2.5.2 Two-element control

Two variants of two-element control are generally practiced in industry. One variant uses steam measurement as the second element, which acts as a feedforward control. A feedforward loop is implemented in the steam flow outlet. This control strategy holds the water level at set point. If there is any drop in the pressure, the feedforward loop anticipates the disturbance before it disturbs the level. During the shrink and swell period, a proper tuning action is required to balance the effects of steam flow and drum water level. The second variant uses feedwater flow as the second element, and it is structured as a cascade loop with level control as an outer loop and flow control as an inner loop. This scheme helps to counter any unpredictable change in the feedwater line before affecting the primary variable. This structure is typically used at low power operations and is shown in Fig. 1.13.

Figure 1.13 Two-element control. Courtesy Dukelow, S. G. (1991). The control of boilers. Instrument Society of America.

1.2.5.3 Three-element control

This control scheme is a combination of the two variants of two-element control discussed in the previous section. Both steam flow and feedwater flow are used in this structure, as second and third elements. A block diagram of the three-element control scheme is shown in Fig. 1.14.

Figure 1.14 Three-element control. Courtesy Dukelow, S. G. (1991). The control of boilers. Instrument Society of America.

1.3 Challenges

This section covers high-level challenges in the control and operation of the boilers, especially considering RE to grid. As we know this new shift will force thermal boiler power plants to operate a wide range of envelopes for which controls were not designed. Most of the control loops are tuned and designed to operate at 100% load or base load of a power plant. Hence, for a wide range the loops should be robust without compromising stability and availability. One of the key factors to study to understand the operability challenge is the distribution of steam bubbles in the evaporator during wide range operation, that is, two-phase mixture in a boiler. Boiler dynamics across startup involves complex physics between heat transfer, steam properties, two-phase flow, and so on. Astrom and Bell (2000) stated that the dynamics of the drum level in a boiler are highly influenced by the bubble redistribution inside evaporator tubes. Bubbles on the evaporator can be mathematically represented as void fractions. The steady-state relationship of void fraction (α) to exit steam quality (x) is shown in Eq. (1.1), and exit steam quality is a function of recirculation ratio and it is shown in Eq. (1.2). In these equations, ρs represents steam density in the evaporator and CR represents circulation ratio of flow between the evaporator and main steam.

(1.1)

(1.2)

It is noted that the void fraction has a variation of 30%–40% in the operation envelope for 40%–100% power variations. Fig. 1.15 shows the variation of a void fraction with power for cold and hot startup profile. The void fraction can be anywhere between the bound of hot and cold start profiles, which are the thermal constraints of the boiler for transition operations. For the earlier results, it is clear that when the power produced by thermal power plant changes the steam distribution in the evaporator changes, which leads to potential control stability and availability challenges. Since current boiler fleets are typically tuned or designed to work mostly on 100% operation, these changes in operation philosophy can lead to trip and catastrophic failure of grid. Hence, a closer look on the impact of this wide-range operation to each loop might be useful for identifying potential solutions. In the next section, some important loops are taken into consideration.

Figure 1.15 Variation of void fraction (A) during startup and (B) with respect to power.

1.3.1 Challenges in drum-level control

This section presents the current challenges in controlling the boiler drum level, industrial solutions, and future challenges with respect to RE integration.

1.3.1.1 Swell and shrink

Dynamic swell and shrink is a unique phenomenon of drum-level dynamics for a drum-type boiler. This dynamic of the level is primarily due to the variation of boiler demand or load, that is, steam flow on boiler level. The natural circulation of water and two phases is the cause of this behavior. The sudden increase in steam apparently redistributes the bubbles in the riser because of the sudden pressure drop in the drum. Because of this, an increase in level is experienced during the transient, which is an inverse response for steam flow increase. The increase in level for a steam flow is termed as dynamic swell. On the other hand, for a steam demand decrease, bubbles in the riser collapse due to a pressure increase in the drum resulting in a level decrease during the transient. This behavior is termed as dynamic shrink. Both these inverse responses pose a challenge for control design. A similar behavior is also experienced during feedwater perturbations; however, the magnitude is not as severe as for steam demand. Fig. 1.16 shows the simulation results of Astrom and Bell’s (2000) model based on Swedish power plant boiler data, with a 10% perturbation of steam and feedwater in both directions.

Figure 1.16 Dynamic simulation showing the shrink–swell phenomena.

The swell and shrink of an industrial boiler is typically handled by a three-element control strategy discussed earlier. Well-tuned constants of these PI controls minimize the impact of swell and shrink. Additionally, a lag-lead filter is added and tuned on the feedforward line to reduce the impact. However, there are practical challenges to tune this filter because of the asymmetric nature of swell and shrink, that is, in the real world, the zero-crossing times of inverse response for swell and shrink are different (even though the simulations may show they are the same). Even if with great difficulties, we can tune this control; the performance will degrade at some other operation point. This is because the boiler is nonlinear in nature, and the dynamic behavior changes significantly as we move to other operation points. This is an important aspect with respect to wide-range operation of the boiler for RE integration and needs considerable attention.

1.3.1.2 Nonlinearity

Typically, the boiler is operated at the base load, which makes control tuning relatively straightforward. However, we noticed that the studies of RE integration recommend flexible boiler operations that demand operation at multiple load points. The boiler is known to pose severe nonlinearity, and the severity of nonlinearity is reported in Tan et al. (2002) who recommend robust control approaches using linear control. These authors used Astrom and Bell’s (2000) model to perform the study. To visualize the nonlinearity and qualify the impact, Astrom and Bell’s model is revisited. In that work, there are two operation points experimented to validate the model, high load (100%) and low load (50%). The nonlinear model is linearized around those operation points and analyzed. A Bode analysis is performed to understand the impact of nonlinearity. Fig. 1.17 shows the Bode response of feedwater to level and steam flow to level at these two operation points.

Figure 1.17 Bode diagram of boiler model at two operation points.

As explained earlier, for drum-level control, feedwater to level transfer function is the open loop response from a control design point of view. Hence, from stability perspective, there is approximately an 18% reduction in system gain margin with respect to high and low loads. Similarly, there is a 47% reduction in phase margin. This shows a clear challenge to assure robust performance at both loads. Similarly, if we analyze the disturbance model, that is, steam flow to level model, there is a 100% increase in disturbance gain while moving to partial load. The impact of this variation can be interpreted as variations in the zero crossing of swell and shrink at these loads. This behavior also poses a challenge to tune the feedforward controller in three-element control and can impact control performance drastically.

1.3.2 Boiler bypass control

Boiler bypass sudden transition operation like part loading and frequent power maneuver needs boiler bypass control to act to maintain required header pressure to ST. There is a strong interaction between pressure and level in this operation and it is highly challenging due to the nonlinearity and plant operation. A study conducted by ASME (Duncan & Brown, 1984) on a set of combined cycle power plant reveals that 30%–40% of plant trip during startup is associated with the drum-level control problems. The coordination of drum level and bypass needs is an important area to be addressed considering renewable integration.

Fig. 1.18 shows a typical drum-level response during startup with bypass operation collected from various combined cycle power plants. The performance of the controller is poor at off load/partial load. To date, since these boilers operate at base load, these shortcomings have not been given much importance, but with the scenario slowly changing we need to pay attention to improve this coordination to avoid unnecessary trip, which can potentially lead to black outs. Hence, an appropriate control strategy during boiler startup or bypass operation needs to be explored thoroughly.

Figure 1.18 Response of drum level during bypass control in operation.

1.3.3 Steam temperature control

Steam temperature control is similar to bypass; it kicks in only during part load and transitions of load point. It is also called attemperation control and is a very lag-dominant loop, which takes time to stabilize. In current scenario, this loop comes into play only during startup and it is one of the important loops to stabilize; since high steam inlet temperature leads to ST trip and intern availability of power plant, careful attention is provided to tune this loop. Most of the control structure for this loop use cascade loop with two temperature loops as explained in the previous section. Hence, it is even more challenging to fine tune. In addition, precise control of these loops with less variability in steam inlet temperature can improve the overall efficiency of power plant. With RE integration to the grid, this loop may come very frequently in operation and we need to provide a better strategy for tight control of steam temperature.

1.4 Experiment exploration

From the previous sections, it is clear that we need to work on improving control at the lower level for each loop for a smooth operation of the boiler for a wide range. Since the boiler is highly nonlinear, we need a robust control for a wide range of operation; also, considering the existing fleets of conventional power we need to develop control that is deployable on standard PLC or DCS controls. A quantitative feedback theory (QFT)-based approach is explored by the authors to achieve a robust control. As a first step, a simulation-based study (Sunil, Barve, & Nataraj, 2017) was conducted to determine the feasibility of using a robust control for wide range, which provided great insight. Simulation is a good engineering practice to validate hypotheses and develop insight. A simulation-assisted workflow is very popular these days in academia and industrial research. However, outcomes of simulation-only studies are generally questioned because of the assumptions, approximation, validation, and gaps associated with overall system-level interactions. Hence, a better research workflow is to combine simulation studies and experimental research. From the literature review on boiler control, it is clear that the vast majority of boiler research is concentrated on simulation-only studies. This is typically true in many process industries, and very specifically for boilers. Many systems and control research papers are limited only up to simulation studies. It is unfortunate that the boiler-based industries (power and process industries) are not highly encouraged with the idea of direct experimentation on controls algorithms on running plants because of the cost and risk associated. This is another reason for simulation only type of research on boiler controls. Hence, there is a gap between academia and industry in terms of acceptance of new control laws, which needs to be addressed.

An approach to address this gap is to develop a scaled-down boiler plant from a system and controls perspective for experimental research. The authors designed a scaled-down version of a boiler to perform experimental studies and develop insight about wide-range operability of the boiler by varying steam bubble distribution. The laboratory boiler setup is designed to mimic real-world natural recirculation-type drum boilers. Fig. 1.19 shows the laboratory boiler designed and commissioned by the authors (Sunil, Desai, Barve, & Nataraj, 2017). The laboratory boiler has a unique feature to vary the steam bubble distribution by adjusting the circulation ratio. This feature is used to mimic real-world wide-range operation and to introduce uncertainty. The steady-state operation range of the boiler is shown in Table 1.2. The process variables of interest are drum pressure and drum level. The manipulative variables to maintain these process variables within the specified tolerance are steam flow and feedwater flow, as per the configuration proposed in this experiment. In this experiment, the boiler is operated in three modes based on variable circulation ratio: Mode A, Mode B, and Mode C. These three modes represent high bubble distribution, medium bubble distribution, and low bubble distribution in the riser, respectively. A wide range of operation dynamics and the transient operation challenge of the industrial boiler are mimicked by the experimental boiler with this configurability. Mode A represents the maximum void fraction, similar to a base-load condition for an industrial boiler, and is termed the nominal mode of operation. Modes B and C represent partial load conditions with medium and low void fractions. Also, the boiler can operate at two operating conditions based on the inlet feedwater temperature: operating point I (cold feed) and operating point II (hot feed). The nominal operation of the boiler is at Mode A with a hot feedwater inlet temperature.

Figure 1.19 Laboratory research boiler.

Table 1.2

An experimental feasibility study for robust control using SISO structure was conducted on this boiler and results are very promising to explore a QFT approach, for wide-range boiler operability. The idea is to use nominal operating condition transfer functions as nominal and the other transfer functions from envelope as an uncertain set of transfer functions.

QFT (Horowitz, 1963, 1993, 2001; Horowitz & Sidi, 1972; Houpis, 2003; Houpis, Garcia-Sanz, & Rasmussen, 2006) is a frequency-domain-based robust control technique. The basic idea in QFT is to convert the desired control performance specifications into frequency-domain constraint curves (called QFT bounds) on the Nichols chart. Using any gain-phase loop-shaping method, a controller is then designed to satisfy all the bounds at each design frequency. The aim of QFT is then to minimize the cost of feedback. The basic idea of QFT control is to design a linear control over a set of linear models; in the laboratory boiler case, the linear model set identified over the entire operation envelope. The experimental study is detailed in Sunil, Desai, Barve, & Nataraj (2020).

1.4.1 Linear model envelope

The laboratory-scale boiler can be configured as a multiinput or multioutput system, similar to a real-world industrial boiler. In this study, drum pressure and drum level were configured as primary controlled process variables of interest. Drum level was controlled by manipulating feedwater flow, and drum pressure was controlled by manipulating the steam flow rate. In the system, the feedwater pump and main steam-flow control valve act as actuators to manipulate feedwater flow and steam flow, respectively. Fig. 1.20 provides a block diagram of the laboratory boiler configured in this study; p11 represents the steam flow to pressure dynamic model and p12 represents the feedwater flow to pressure dynamic model. Similarly,p21 and p22 represent the steam flow to level and the feedwater