Distributed Optimal Control of Large-Scale Wind Farm Clusters
Optimal Active and Reactive Power Control, and Fault Ride Through
- 1st Edition - March 29, 2025
- Imprint: Academic Press
- Authors: Qiuwei Wu, Sheng Huang, Juan Wei, Pengda Wang, Canbing Li, Vladimir Terzija
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 9 2 3 4 - 7
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 9 2 3 5 - 4
Distributed Optimal Control of Large-Scale Wind Farm Clusters: Optimal Active and Reactive Power Control, and Fault Ride Through, a new volume in the Elsevier Wind Energy Engineeri… Read more
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Request a sales quoteDistributed Optimal Control of Large-Scale Wind Farm Clusters: Optimal Active and Reactive Power Control, and Fault Ride Through, a new volume in the Elsevier Wind Energy Engineering series, explores the latest advances in distributed optimal control of large-scale wind farm clusters, also describing distributed optimal control techniques for high voltage ride through (HVRT). Both mathematical formulations and algorithm details are provided, along with MATLAB codes to replicate and implement distributed optimal control schemes. This is a valuable resource for anyone interested in the operation, control, and integration of wind power plants, wind farms, and electricity grids, both at research and operational levels.
Researchers, faculty, scientists, engineers, R&D, and other industry professionals, as well as graduate and postgraduate students studying and working in wind energy will find this comprehensive resource a valuable addition to their work.
Researchers, faculty, scientists, engineers, R&D, and other industry professionals, as well as graduate and postgraduate students studying and working in wind energy will find this comprehensive resource a valuable addition to their work.
- Presents the latest developments in the distributed optimal control of large-scale wind power plant clusters
- Covers both active and reactive power control, as well as techniques for high voltage ride through (HVRT)
- Provides methodologies to follow set-points from system operators in order to maintain expected voltages
- Includes control algorithms and codes for implementing the control schemes
Researchers, students, and faculty across wind energy, renewable energy, electrical engineering, sustainability
- Distributed Optimal Control of Large-Scale Wind Farm Clusters
- Cover image
- Title page
- Table of Contents
- Front Matter
- Copyright
- Foreword
- Part One: Introduction
- Chapter One Introduction of large-scale wind power integration
- Abstract
- 1.1 Centralized and distributed/decentralized optimal operation
- 1.2 Active power control, active and reactive power control, voltage control
- 1.2.1 Voltage control
- 1.2.2 Fatigue load suppression
- 1.3 Synthetic inertial response
- 1.4 Enhancing high-voltage ride-through
- 1.5 Hierarchical event-triggered coordinated control
- References
- Part Two: Optimal active power control of large-scale wind farm clusters
- Chapter Two Bi-level decentralized active power control for large-scale wind farm cluster
- Abstract
- 2.1 Introduction
- 2.2 Bi-level control architecture
- 2.2.1 Configuration of a voltage-source-converter high-voltage-direct-current connected WFC
- 2.2.2 Concept of the bi-level DAPC
- 2.3 Consensus-based distributed active power dispatch for WFC
- 2.3.1 Graph theory
- 2.3.2 Distributed estimation of available wind power of wind farms
- 2.3.3 Distributed active power dispatch of WFC
- 2.4 Centralized MPC-based active power control of a wind farm
- 2.4.1 Predictive model of a WT
- 2.4.2 MPC formulation
- 2.4.3 Objective function
- 2.4.4 Constraints
- 2.5 Case study
- 2.5.1 Test system
- 2.5.2 Upper-level control performance
- 2.5.3 Lower-level control performance
- 2.6 Conclusion
- References
- Chapter Three Optimal active power control based on MPC for DFIG-based wind farm equipped with distributed energy storage systems
- Abstract
- 3.1 Introduction
- 3.2 Control scheme architecture
- 3.2.1 Wind farm configuration
- 3.2.2 Control concept
- 3.3 DFIG system model
- 3.3.1 RSC model
- 3.3.2 GSC model
- 3.3.3 ESS model
- 3.3.4 WT mechanical system model ESS model
- 3.3.5 Wind farm model
- 3.4 Coordinated control for wind farm equipped with distributed ESSs
- 3.4.1 Energy management for ESSs
- 3.4.2 Objective function for the first stage
- 3.4.3 Objective function for the second stage
- 3.5 Case study
- 3.5.1 Test system
- 3.5.2 Control performance
- 3.6 Discussion
- 3.7 Conclusion
- Appendix A. Optimal active power control for a wind farm without ESSs
- References
- Chapter Four Hierarchical active power control of DFIG-based
- Abstract
- 4.1 Introduction
- 4.2 Control scheme architecture
- 4.2.1 Wind farm configuration
- 4.2.2 Control concept
- 4.3 DFIG system model
- 4.3.1 RSC model
- 4.3.2 ESS model
- 4.3.3 WT mechanical system model
- 4.3.4 Wind farm model
- 4.4 Hierarchical control based on ADMM
- 4.4.1 Objective function
- 4.4.2 Mode 1
- 4.4.3 Mode 2
- 4.4.4 Constraints
- 4.4.5 Hierarchical active power control based on ADMM
- 4.5 Case study
- 4.5.1 Dynamic control performance
- 4.5.2 Static performance
- 4.6 Conclusion
- References
- Chapter Five Hierarchical optimal control for synthetic inertial response of wind farm based on alternating direction method of multipliers
- Abstract
- 5.1 Introduction
- 5.2 Control architecture
- 5.2.1 Configuration of a wind farm
- 5.2.2 Control concept
- 5.3 Synthetic inertial response model for wind farm
- 5.3.1 Synthetic inertial response controller
- 5.3.2 WT model
- 5.3.3 Wind energy loss
- 5.3.4 Wind farm model
- 5.3.5 Objective function
- 5.4 Hierarchical solution method for the synthetic inertial response
- 5.4.1 ADMM-based hierarchical optimization problem
- 5.4.2 Hierarchical solution method based on ADMM
- 5.5 Case study
- 5.5.1 Test system
- 5.5.2 Control performance
- 5.6 Conclusion
- References
- Part Three: Optimal active and reactive power control of large-scale wind farm clusters
- Chapter Six Bi-level decentralized active and reactive power control for large-scale wind farm cluster
- Abstract
- 6.1 Introduction
- 6.2 Bi-level control architecture
- 6.2.1 Configuration of a WFC
- 6.2.2 Concept of the bi-level DARPC
- 6.3 Consensus-based distributed active power and reactive power dispatch for WFC
- 6.3.1 Graph theory
- 6.3.2 Distributed active power control of WFC
- 6.3.3 Distributed reactive power control of WFC
- 6.4 Centralized MPC-based active and reactive power control of wind farm
- 6.4.1 Modeling of wind farm
- 6.4.2 Sensitivity coefficient calculation
- 6.4.3 MPC formulation for active and reactive power control of wind farm
- 6.5 Case study
- 6.5.1 Test system
- 6.5.2 Control performance under normal operation
- 6.6 Conclusion
- Appendix
- CARPC method
- References
- Chapter Seven Two-tier combined active and reactive power controls for VSC–HVDC-connected large-scale wind farm cluster based on ADMM
- Abstract
- 7.1 Introduction
- 7.2 Distributed active and reactive power control structures
- 7.2.1 Structure of a VSC–HVDC-connected WFC
- 7.2.2 TCARPC concept
- 7.3 Distributed active and reactive power controls for WFC
- 7.3.1 Distributed active power control of WFC
- 7.3.2 Voltage sensitivity calculation
- 7.3.3 Predictive model of WFs
- 7.3.4 MPC formulation
- 7.3.5 ADMM-based solution method
- 7.4 Hierarchical active and reactive power controls for WFS
- 7.4.1 Modeling of WF
- 7.4.2 MPC formulation
- 7.4.3 ADMM-based solution
- 7.5 Case study
- 7.5.1 Control performance
- 7.6 Conclusion
- References
- Chapter Eight Distributed optimal active and reactive power control for wind farms based on ADMM
- Abstract
- 8.1 Introduction
- 8.2 DARPC control architecture
- 8.2.1 Configuration of the wind farm
- 8.2.2 Concept of the ADMM-based DARPC
- 8.3 Centralized problem formulation
- 8.3.1 Linearized DistFlow model of the WF
- 8.3.2 Optimization problem formulation for the WF
- 8.4 Distributed optimal control method
- 8.4.1 Decomposed optimization subproblem
- 8.4.2 Distributed solution method based on ADMM
- 8.4.3 Iterative solution process
- 8.5 Simulation results
- 8.5.1 Test system
- 8.5.2 Control performance of the DARPC scheme
- 8.6 Conclusion
- References
- Chapter Nine ADMM-based distributed active and reactive power control for regional AC power grid with wind farms
- Abstract
- 9.1 Introduction
- 9.2 Control strategy architecture
- 9.2.1 System configuration
- 9.2.2 Strategy concept
- 9.3 TS optimization model
- 9.3.1 The objective function in TS
- 9.3.2 The constraints in TS
- 9.3.3 Convex relaxation of OPF in TS
- 9.3.4 Transformation of the objective function
- 9.3.5 Transformation of the constraints
- 9.4 WF optimization model
- 9.5 ADMM formulation for the whole system
- 9.6 Case study
- 9.6.1 Test system
- 9.6.2 Control performance
- 9.7 Conclusion
- References
- Part Four: Optimal voltage control of large-scale wind farm clusters
- Chapter Ten Distributed voltage control based on ADMM for large-scale wind farm cluster connected to VSC-HVDC
- Abstract
- 10.1 Introduction
- 10.2 DVC control architecture
- 10.2.1 Configuration of a VSC-HVDC connected WFC
- 10.2.2 Control concept
- 10.3 Distribution control design for WFC
- 10.3.1 Model of WFC connected to VSC-HVDC
- 10.3.2 Sensitivity coefficient calculation
- 10.3.3 Sensitivity coefficient calculation
- 10.3.4 Sensitivity coefficient calculation
- 10.4 Wind farm control with ADMM
- 10.4.1 Modeling of wind farm
- 10.4.2 Modeling of wind farm
- 10.4.3 Objective function for wind farm voltage control
- 10.4.4 Voltage control based on ADMM
- 10.5 Case study
- 10.5.1 Test system
- 10.5.2 Control performance
- 10.6 Conclusion
- References
- Chapter Eleven Distributed optimal voltage control for VSC-HVDC-connected large-scale wind farm cluster based on analytical target cascading method
- Abstract
- 11.1 Introduction
- 11.2 Control architecture
- 11.2.1 Configuration of WFC
- 11.2.2 Control concept
- 11.3 Voltage control of large-scale WFC
- 11.3.1 WFCVSC and WT model
- 11.3.2 Objective function
- 11.4 Distributed solution method based on ATC
- 11.4.1 Optimization problem of the WFCVSC controller
- 11.4.2 Optimization problem of the Sub-WF controller
- 11.4.3 ATC-based solution method
- 11.5 Case study
- 11.5.1 Test system
- 11.5.2 Control performance
- 11.6 Conclusion
- References
- Chapter Twelve Adaptive droop-based hierarchical optimal voltage control scheme for VSC-HVDC-connected offshore wind farm
- Abstract
- 12.1 Introduction
- 12.2 Control architecture
- 12.2.1 WF topology
- 12.3 Wind farm model with droop control
- 12.3.1 WFVSC and WT model
- 12.3.2 Wind farm model
- 12.3.3 MPC-based droop control optimization problem formulation
- 12.4 Optimization problem with droop control
- 12.4.1 Constraint
- 12.4.2 Stability analysis
- 12.4.3 ADMM-based hierarchical solution
- 12.5 Case study
- 12.5.1 Test system
- 12.5.2 Control performance
- 12.6 Discussion
- 12.7 Conclusion
- References
- Chapter Thirteen Distributed optimal voltage control strategy for AC grid with DC connection and offshore wind farms based on ADMM
- Abstract
- 13.1 Introduction
- 13.2 Control strategy architecture
- 13.2.1 System configuration
- 13.2.2 Strategy concept
- 13.3 AC grid with DC connection optimization model
- 13.3.1 The MPC-based OPF of AC grid with DC connection formulation
- 13.3.2 Convex relaxation of AC-DC OPF within MPC
- 13.4 Offshore wind farm control
- 13.4.1 Modeling of offshore wind farm
- 13.4.2 MPC-based offshore wind farm control
- 13.5 ADMM formulation for the whole system
- 13.6 Case study
- 13.6.1 Test system
- 13.6.2 Control performance
- 13.7 Conclusion
- References
- Part Five: Fault ride through of wind farm clusters
- Chapter Fourteen Coordinated droop control and adaptive model predictive control for enhancing HVRT and postevent recovery of large-scale wind farm
- Abstract
- 14.1 Introduction
- 14.2 Problem statement of HVRT and postevent recovery processes
- 14.3 Control architecture and WF modeling
- 14.3.1 Control concept
- 14.3.2 Modeling of WTs and WF
- 14.4 Proposed optimal combined control strategy
- 14.4.1 Optimal droop coefficient calculation
- 14.4.2 Optimal droop control during HVRT
- 14.4.3 AMVRC during postevent recovery period
- 14.5 Case study
- 14.5.1 Test system
- 14.5.2 Control performance under different schemes
- 14.5.3 Control performance under subsequent HVRT and LVRT accidents
- 14.6 Conclusion
- References
- Chapter Fifteen Hierarchical event-triggered MPC-based coordinated control for HVRT and voltage restoration of large-scale wind farm
- Abstract
- 15.1 Introduction
- 15.2 Control architecture
- 15.3 WF modeling
- 15.3.1 GSC model
- 15.3.2 RSC model
- 15.3.3 WT model
- 15.3.4 Wind farm model
- 15.4 Hierarchical event-triggered MPC-based coordinated voltage control strategy
- 15.4.1 MPC-based voltage recovery control
- 15.4.2 ADMM-based hierarchical solution
- 15.4.3 Event triggering mechanism
- 15.5 Case study
- 15.5.1 Test system
- 15.5.2 Control performance with different control strategies
- 15.5.3 Control performance under LVRT and subsequent HVRT events
- 15.6 Conclusion
- References
- Chapter Sixteen Coordinated voltage support control for enhancing LVRT capability of large-scale wind farm
- Abstract
- 16.1 Introduction
- 16.2 Problem description and mechanism analysis
- 16.2.1 Problem description
- 16.2.2 Mechanism analysis
- 16.2.3 Feasible region of D-PMSG operation
- 16.3 RSC-CHOPPER-GSC integrated model
- 16.4 MPC-based LVRT control scheme
- 16.5 Case study
- 16.5.1 Operational feasible region of the current
- 16.5.2 Control performance with different control strategies
- 16.6 Conclusion
- References
- Appendix 1 Introduction of model predictive control
- Appendix 2 Introduction of distributed control
- Index
- Edition: 1
- Published: March 29, 2025
- Imprint: Academic Press
- No. of pages: 446
- Language: English
- Paperback ISBN: 9780443292347
- eBook ISBN: 9780443292354
QW
Qiuwei Wu
Qiuwei Wu received the PhD degree in Electrical Engineering from Nanyang Technological University, Singapore, in 2009. He is a professor with the School of Electronics, Electrical Engineering, and Computer Science (EEECS), Queen’s University Belfast, the UK. His research interests are distributed optimal operation and control of low carbon power and energy systems, including distributed optimal control of wind power, optimal operation of active distribution networks, and optimal operation and planning of integrated energy systems.
Affiliations and expertise
Professor, School of Electronics, Electrical Engineering, and Computer Science, Queen’s University Belfast, UKSH
Sheng Huang
Sheng Huang received the M.S. and Ph.D. degrees in electrical engineering from the College of Electrical and Information Engineering, Hunan University, Changsha, China, in 2012 and 2016, respectively. Since 2023, he has been a Professor with College of Electrical and Information Engineering, Hunan University. His research interests include renewable energy generation, modeling and integration study of wind power, and permanent magnet synchronous motor systems.
Affiliations and expertise
Professor, College of Electrical and Information Engineering, Hunan University, ChinaJW
Juan Wei
Juan Wei is currently a Postdoc with the College of Electrical and Information Engineering, Hunan University, China. She received her B.S. and M.S. degrees in electrical engineering from the North China Electric Power University, Beijing, China, in 2011 and 2014, and obtained her PhD degree in electrical engineering from Hunan University, China, in 2022. Her research interests include wind power modeling and control, decentralized/distributed voltage control and optimal operation of integrated wind power systems, and high-voltage ride-through and post-event recovery control of large-scale wind farms. Dr. Wei is an Editor of Hunan Electric Power.
Affiliations and expertise
College of Electrical and Information Engineering, Hunan University, ChinaPW
Pengda Wang
Pengda Wang is a Research Associate with College of Electrical and Information Engineering, Hunan University, China. He obtained his PhD degree in Electrical Engineering from the Technical University of Denmark, Denmark, in 2022. His research interests lie in coordinated control of wind power and combined AC/DC grid, including distributed wind power modelling and control, voltage control and active/reactive power control of large-scale wind farms and combined AC/DC grid.
Affiliations and expertise
Research Associate, College of Electrical and Information Engineering, Hunan University, ChinaCL
Canbing Li
Canbing Li is currently a Professor with the School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China. He received his B.E. and Ph.D. degrees from Tsinghua University, Beijing, China, in 2001 and 2006, respectively, both in electrical engineering. His research interests include power systems, smart grid, renewable energy with an emphasis on large-scale power system dispatch, economic and secure operation of power systems, energy efficiency and energy saving in smart grid, electric demand management of data centers, vehicle-to-grid technologies.
Affiliations and expertise
Professor, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaVT
Vladimir Terzija
Vladimir Terzija is a Full Professor at the School of Engineering, Newcastle University, in the United Kingdom. He was previously a Full Professor and the Head of Laboratory of Modern Energy Systems with Skoltech, Russian Federation (2021-23), EPSRC Chair Professor in power system engineering at the University of Manchester, UK (2006-20), and an Assistant Professor at the University of Belgrade (1997-99). From 2000 to 2006, he was a Senior Specialist for switchgear and distribution automation with ABB, in Germany. His research interests include smart grid applications, wide-area monitoring, protection and control, multi-energy systems, switchgear and transient processes, ICT, data analytics, and digital signal processing applications in power systems. Prof. Terzija was the recipient of the prestigious Alexander von Humboldt Fellowship, and is the Editor-in-Chief of the International Journal of Electrical Power and Energy Systems.
Affiliations and expertise
Professor, School of Engineering, Newcastle University, UKRead Distributed Optimal Control of Large-Scale Wind Farm Clusters on ScienceDirect