Applications of Artificial Intelligence in Planning and Operation of Smart Grids

Herausgegeben:Rahmani-Andebili, Mehdi

• Gebundenes Buch

Produktbeschreibung

Artificial intelligence (AI) is going to play a significant role in smart grid planning and operation, especially in solving its real-time problems, as it is fast, adaptive, robust, and less dependent on the system's accurate model and parameters. This collection covers research advancements in the application of AI in the planning and operation of smart grids. A global group of researchers and scholars present innovative approaches to AI-based smart grid planning and operation, cover the theoretical concepts and experimental results of the application of AI-based techniques, and apply these techniques to deal with smart grid issues. Applications of Artificial Intelligence in Planning and Operation of Smart Grids is an ideal resource for researchers on the theory and application of AI, practicing engineers working in electrical power engineering, and students in advanced graduate-level courses.

Produktdetails

• Power Systems
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-030-94521-3
• 1st ed. 2022
• Seitenzahl: 148
• Erscheinungstermin: 29. März 2022
• Englisch
• Abmessung: 241mm x 160mm x 14mm
• Gewicht: 395g
• ISBN-13: 9783030945213
• ISBN-10: 3030945219
• Artikelnr.: 63102167

Autorenporträt

Mehdi Rahmani-Andebili is an Assistant Professor in the Department of Engineering and Physics at the University of Central Oklahoma, OK, USA. Before that, he was also an Assistant Professor in the Electrical Engineering Department at Montana Technological University, MT, USA, and the Engineering Technology Department at State University of New York, Buffalo State, NY, USA, during 2019-2022. He received his first M.Sc. and Ph.D. degrees in Electrical Engineering (Power System) from Tarbiat Modares University and Clemson University in 2011 and 2016, respectively, and his second M.Sc. degree in Physics and Astronomy from the University of Alabama in Huntsville in 2019. Moreover, he was a Postdoctoral Fellow at Sharif University of Technology during 2016-2017. As a professor, he has taught many courses and labs, including Power System Analysis, DC and AC Electric Machines, Feedback Control Systems Analysis and Design, Renewable Distributed Generation and Storage, Industrial Electronics, Analog Electronics, Electrical Circuits and Devices, AC Electrical Circuits Analysis, DC Electrical Circuits Analysis, Essentials of Electrical Engineering Technology, and Algebra and Calculus-Based Physics. Dr. Rahmani-Andebili has more than 200 single-author and first-author publications, including journal papers, conference papers, textbooks, books, and book chapters. He is an IEEE Senior Member and the permanent reviewer of many credible journals. His research areas include Smart Grid, Power System Operation and Planning, Integration of Renewables and Energy Storages into Power System, Energy Scheduling and Demand-Side Management, Plug-in Electric Vehicles, Distributed Generation, and Advanced Optimization Techniques in Power System Studies.

Inhaltsangabe

A New Agent based Machine Learning Strategic Electricity Market Modeling Approach towards Efficient Smart Grid Operation.- Reinforcement learning techniques for MPPT control of PV system under climatic changes.- A Novel Three Stage Short-Term Photovoltaic Prediction Approach Based on Neighbourhood Component Analysis and ANN Optimized with PSO (NCA-PSO-ANN).- Applications of Artificial Intelligence in Short-Term and Long-Term Forecasting Techniques.

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A New Agent based Machine Learning Strategic Electricity Market Modeling Approach towards Efficient Smart Grid Operation.- Reinforcement learning techniques for MPPT control of PV system under climatic changes.- A Novel Three Stage Short-Term Photovoltaic Prediction Approach Based on Neighbourhood Component Analysis and ANN Optimized with PSO (NCA-PSO-ANN).- Applications of Artificial Intelligence in Short-Term and Long-Term Forecasting Techniques.