Distributed Optimal Control of Large-Scale Wind Farm Clusters

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.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.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.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.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.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, EPSRC Chair Professor in power system engineering at the University of Manchester, UK, and an Assistant Professor at the University of Belgrade. Additionally, 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.

• Section I - Introduction1. Introduction to Large-Scale Wind Power IntegrationSection II - Optimal Active Power Control of Large-Scale Wind Farm Clusters2. Bi-Level Decentralized Active Power Control for Large-Scale Wind Farm Clusters3. Optimal Active Power Control Based on MPC for DFIG-based Wind Farm Equipped with Distributed Energy Storage Systems4. Hierarchical Active Power Control of DFIG-based Wind Farm with Distributed Energy Storage Systems based on Alternating Direction Method of Multipliers (ADMM)5 Hierarchical Optimal Control for Synthetic Inertial Response of Wind Farm Based on Alternating Direction Method of Multipliers (ADMM)Section III - Optimal Active and Reactive Power Control of Large-Scale Wind Farm Clusters6. Bi-Level Decentralized Active and Reactive Power Control for Large-Scale Wind Farm Cluster7. Two-Tier Combined Active and Reactive Power Control for VSC–HVDC Connected Large-Scale Wind Farm Cluster based on Alternating Direction Method of Multipliers (ADMM)8. Distributed Optimal Active and Reactive Power Control for Wind Farms Based on ADMM9. ADMM-based Distributed Active and Reactive Power Control for Regional AC Grids with Wind FarmsSection IV - Optimal Voltage Control of Large-Scale Wind Farm Clusters10. Distributed Voltage Control based on ADMM for Large Scale Wind Farm Cluster connected to VSC HVDC11. Distributed Optimal Voltage Control for VSC-HVDC Connected Large-Scale Wind Farm Cluster Based on Analytical Target Cascading Method12. Adaptive Droop-based Hierarchical Optimal Voltage Control Scheme for VSC-HVDC Connected Offshore Wind Farm13. Distributed Optimal Voltage Control Strategy for AC Grid with DC Connection and Offshore Wind Farms Based on Alternating Direction Method of Multipliers (ADMM)Section V - Fault Ride Through of Wind Farm Clusters14. Coordinated Droop Control and Adaptive Model Predictive Control for Enhancing HVRT and Post-Event Recovery of Large-Scale Wind Farms15. Hierarchical Event-Triggered MPC-Based Coordinated Control for HVRT and Voltage Restoration of Large-Scale Wind Farms16. Coordinated Voltage Support Control for Enhancing LVRT Capability of Large-Scale Wind Farms