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Flexible Load Control for Enhancing Renewable Power System Operation de Yuanzheng Li

Flexible Load Control for Enhancing Renewable Power System Operation: Power Systems

Autor Yuanzheng Li, Yang Li, Zhigang Zeng
en Limba Engleză Hardback – 7 mar 2024

Nivelul de experiență necesar pentru parcurgerea acestui volum este unul avansat, fiind o resursă tehnică adresată inginerilor energetici, cercetătorilor și studenților la masterat sau doctorat care dețin deja baze solide în sistemele de putere și programare matematică. Lucrarea Flexible Load Control for Enhancing Renewable Power System Operation explorează soluții practice pentru una dintre cele mai mari provocări ale rețelelor moderne: variabilitatea producției regenerabile și complexitatea sarcinilor flexibile.

Observăm o organizare riguroasă a conținutului, structurată pe tipologii specifice de consumatori. Volumul debutează cu o perspectivă teoretică asupra controlului sarcinii, progresând rapid către aplicații critice, cum ar fi centrele de date (capitolele 2-4), unde se analizează integrarea stocării de hidrogen și optimizarea robustă. Cititorul care a aplicat deja ideile din Power System Flexibility de Ming Zhou va găsi aici o continuare necesară, trecând de la modelarea generală a flexibilității la algoritmi de control specifici pentru sarcini industriale și stații de încărcare a vehiculelor electrice (EV). Spre deosebire de Electric Vehicles and Renewable Generation, care se axează pe modele matematice de planificare sub incertitudine, volumul de față prioritizează controlul operațional coordonat și răspunsul cooperativ cerere-ofertă.

Suntem de părere că expertiza autorilor în optimizarea sistemelor de putere este evidentă prin modul în care integrează tehnologii emergente, precum microgridele bazate pe centre de date și sistemele de schimbare a bateriilor (battery swapping). Această lucrare completează temele abordate de Yuanzheng Li în Renewable Power System Optimization, mutând focusul de la optimizarea generală a sistemului către controlul granular al cererii ca pârghie de stabilitate în rețelele cu penetrare ridicată de energie verde.

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Specificații

ISBN-13: 9789819703111
ISBN-10: 9819703115
Pagini: 274
Ilustrații: XIX, 274 p. 109 illus., 106 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.64 kg
Ediția:2024
Editura: Springer Nature Singapore
Colecția Springer
Seria Power Systems

Locul publicării:Singapore, Singapore

De ce să citești această carte

Recomandăm această carte profesioniștilor care doresc să implementeze strategii de control în timp real pentru sarcini flexibile. Cititorul câștigă o înțelegere profundă a algoritmilor de optimizare robustă și a metodelor de coordonare între vehiculele electrice și rețea. Este un instrument esențial pentru tranziția către un sistem energetic cu emisii scăzute de carbon, oferind soluții concrete pentru integrarea eficientă a centrelor de date și a consumatorilor industriali mari în operarea rețelei.

Despre autor

Yuanzheng Li, Yang Li și Zhigang Zeng sunt cercetători recunoscuți în domeniul ingineriei electrice, cu o activitate axată pe rețele inteligente și sisteme de putere regenerabile. Yuanzheng Li a publicat anterior lucrări fundamentale precum Smart Power Distribution Network și Artificial Intelligence Enabled Computational Methods for Smart Grid Forecast and Dispatch, unde a explorat utilizarea inteligenței artificiale pentru prognoza și dispecerizarea energiei. Experiența lor colectivă în dezvoltarea de algoritmi pentru atenuarea incertitudinilor în rețelele electrice moderne se reflectă în rigoarea tehnică a acestui nou volum publicat în seria Power Systems la Springer.

Descriere scurtă

This book addresses the pressing challenges faced by renewable power system operation (RPSO) due to the increasing penetration of renewable energy and flexible load. These challenges can be divided into two categories. Firstly, the inherent uncertainties associated with renewable energy sources pose significant difficulties in RPSO. Secondly, the presence of various types of flexible load, along with their complex constraint relationships, adds to the operational complexities. Recognizing the growing emphasis on the economic and low-carbon aspects of RPSO, this book focuses on the key issues of flexible load control. It mainly consists of following categories: (1) The control of data centers, a booming flexible load, to enhance RPSO through renewable energy integration and advanced robust multi-objective optimization. (2) The introduction of flexible industrial load control, employing effective demand-supply cooperative responding strategies for RPSO. (3) The exploration of electricvehicle flexible charging load control and centralized electric vehicle charging system control in the context of RPSO. The book also covers the emerging field of flexible integrated load control for renewable energy-based comprehensive energy system operation. Aimed at researchers, engineers, and graduate students in electrical engineering and computer science, this book provides a valuable resource for understanding and implementing flexible load control in the context of RPSO.

Cuprins

Chapter 1. Overview of flexible load control.- Chapter 2. Data center flexible load control for renewable energy integration.- Chapter 3. Data center load control based microgrid operation via robust multi-objective optimization.- Chapter 4. Collaborative control of data center and hydrogen storage system for renewable energy absorption.- Chapter 5. Flexible industrial load control for renewable power system operation.- Chapter 6. A demand-supply cooperative responding control of industrial load and renewable power system.- Chapter 7. Electric vehicle flexible charging load control for renewable power system operation.- Chapter 8. Battery swapping control for centralized electric vehicle charging system with photovoltaic.- Chapter 9. Coordinated Operation Between Electric Vehicle Charging Stations and Distribution Power Network Considering Energy and Reserve.- Chapter 10. Flexible integrated load control based comprehensive energy system operation.- Chapter 11. Data-driven distributionally robust scheduling of community comprehensive energy systems considering integrated load control.- Chapter 12. Concluding remarks.

Notă biografică

Dr. Yuanzheng Li is currently an associate professor in the School of Artificial Intelligence and Automation at Huazhong University of Science and Technology. His main research fields are artificial intelligence and its application in smart grid, deep learning, reinforcement learning, big data analysis, operation research optimization, etc. He has investigated 2 projects of the National Natural Science Foundation of China, 2 Science and Technology projects of the State Grid of China Corporation, and participated in the National Key Basic Research and Development Plan (973 Plan). Among them, the research results were selected as excellent achievements of the State Key Laboratory of Renewable Energy Power System and research innovation points of the 973 program. He has published more than 50 journal papers, including 16 first-author IEEE Transactions. Currently, he is the associate editor of IEEE Transactions on Intelligent Vehicles and the editorial board of IET Renewable Power Generation.
Dr. Yang Li is a professor at the Northeast Electric Power University, China. He received his Ph.D. degree in Electrical Engineering from North China Electric Power University (NCEPU), China, in 2014. He is a professor at the School of Electrical Engineering, Northeast Electric Power University, Jilin, China. From Jan. 2017 to Feb. 2019, he was a postdoc with Argonne National Laboratory, Lemont, USA. He has published more than 50 academic papers, including 6 ESI highly cited papers. His research interests include power systems, integrated energy systems, and renewable energy. He serves as an associate editor for the journals of IEEE Transactions on Industry Applications, IET Renewable Power Generation and Scientific Reports.
Dr. Zeng Zhigang is the dean of School of Artificial Intelligence and Automation at Huazhong University of Science and Technology, National Science Fund for Distinguished Young Scholars, distinguished professor of Changjiang Scholars of the Ministry of Education, and director of Key Laboratory of Image Information Processing and Intelligent Control of the Ministry of Education. In June 2003, he received a doctor's degree in system analysis and integration from Huazhong University of Science and Technology. He has been engaged in postdoctoral research at the Chinese University of Hong Kong and the University of Science and Technology of China. He has also visited the University of Western Sydney in Australia and the Qatar branch of Texas A&M University in the USA. He is mainly engaged in modeling, analysis, control, and applications of complex systems. Professor Zeng has published more than 100 academic papers included in SCI.

Caracteristici

Combines mathematics and electrical expertise toward the flexible load control and renewable power system Introduces the credible and efficient optimization technology for enhancing renewable power system operation Investigates math and AI-based flexible load control to enhance economic efficiency and reliability