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Special Session

Special Session Introduction

Abstract: The proportion of renewable power generation in the power system is gradually increasing. Accurate renewable power forecast can weaken the impact of its strong volatility and high uncertainty on the economic operation of power system. The traditional single static forecast model is no longer suitable for the current complex and changeable forecast scenarios. Multiple information integration, dynamic model combination, prediction error correction and other methods can further improve the accuracy of power prediction. This Special Session aims to improve the dynamic characteristics and accuracy of renewable power forecast.
Technical Outline of the Session: With the large-scale renewable energy integration, the power forecasting errors are becoming larger and larger, which will significantly affect the operational security and economy of power grids. Therefore, it is necessary to improve the accuracy of renewable energy power forecasting, i.e., this special session. The Special Session is about renewable energy power forecasting, which is relevant to the topic "Renewable Energy Integration" of this conference. Excellent renewable energy power forecasting results are the important foundation of renewable energy integration.

Special Session Organizer

Bo Wang is currently a Professorate Senior Engineer and Director with Electric Power Meteorological Simulation and Application Technology Research Department, Renewable Energy Research Center, China Electric Power Research Institute, Beijing, China. He is the member of IEC SC8A and "Power Satellite Remote Sensing (Meteorology)" Standards Working Group. He won the second prize of the National Science and Technology Progress Award in 2013, the first prize of the Beijing Science and Technology Progress Award in 2015, and first prize of the China Electrotechnical Society Science and Technology Progress Award in 2022, respectively. His research interests are mainly renewable generation forecasting and power meteorology.

Jie Shi is the member of IEEE. She received the B.S. degree in building environment and equipment engineering from Shandong Jianzhu University, Jinan, China, in 2007, and the M.S. and Ph.D. degrees in thermal engineering from North China Electric Power University, Beijing, China, in 2009 and 2013, respectively. She is currently a Faculty Member with the University of Jinan, Jinan. From 2010 to 2012, she was a Visiting Student at the University of Texas at Arlington, Arlington, TX, USA. Her research interests include wind-energy storage system optimization and control, wind power output prediction, and applications of artificial neural networks and support vector machines to wind power output.

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Paper Submission Important Dates

Submission Deadline
April 30, 2023
Notification Deadline
May 30, 2023

Please enter IEEE I&CPS Asia 2023 online submission system and choose 'SS009: Renewable Power Forecast Accuracy Improvement Technology and Its application' to submit papers.

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