Distributed Optimal Control of Large-Scale Wind Farm Clusters

Optimal Active and Reactive Power Control, and Fault Ride Through

1st Edition - March 29, 2025
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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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 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.

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

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, UK

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, China

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, China

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, China

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, China

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, UK

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Distributed Optimal Control of Large-Scale Wind Farm Clusters

Optimal Active and Reactive Power Control, and Fault Ride Through

Purchase options

Innovate. Sustain. Transform.

Institutional subscription on ScienceDirect

Qiuwei Wu

Sheng Huang

Juan Wei

Pengda Wang

Canbing Li

Vladimir Terzija

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