FUZZ-IEEE 2021

Special Session on "Fuzzy Systems for Renewable Energy and Smart Grid"

FUZZ-IEEE 2021: Special Session on "Fuzzy Systems in Renewable Energy and Smart Grid"

Aim and Scope

The main aim of this session is to provide a forum for researchers covering the whole range of fuzzy systems applications to Smart Grid systems and renewable power generation and use.

Smart Grid technology employ information, communication, and automation technology to deploy an integrated power grid with smart power generation, transmission, distribution and the integration of renewable energy sources.

Owing to the relatively higher investment cost of renewable power generation systems, it is important to operate the systems near their maximum power output point, especially for the wind and solar PV generation systems. In addition, since the wind and solar PV power resources are intermittent, accurate predictions and modeling of wind speed and solar insolation are necessary. Moreover, Smart Grid integrated with smart meters, EV charging stations and home (building) energy management system are the key enabling factor toward the Smart City concept.

As a result, effective uses of computational intelligence techniques such as fuzzy systems for the controlling and modeling of renewable power generation in a smart-grid system turns out to be very crucial for successful operations of the systems.

The session continues the series of special sessions on the topic organized by some of the organizers of this session in past conferences (FUZZ-IEEE 2011, WCCI 2012, FUZZ-IEEE 2013, WCCI 2014, WCCI 2016, FUZZ-IEEE 2017, WCCI 2018, FUZZ-IEEE 2019) and is supported by the IEEE CIS Task Force on “Fuzzy Systems in Renewable Energy and Smart Grid”.

Topics

The topics include but are not limited to:

  • Fuzzy modeling of renewable power generation systems.

  • Fuzzy control of renewable power generation systems.

  • Prediction of renewable energy using fuzzy and neuro-fuzzy systems.

  • Hybrid systems of computational intelligence techniques in Smart Grid and renewable power generation systems.

  • Neuro-Fuzzy system for oil and gas integration with renewable sources.

  • Fuzzy energy management systems.

  • Fuzzy distribution systems automation.

  • Fuzzy power quality, protection and reliability analysis of power system.

  • Fuzzy Logic application for Demand-Response and Smart Buildings.

  • Fuzzy Logic application for Smart Grid and Smart Cities.

  • Novel applications in electric energy market.

  • Fuzzy observer-based fault diagnosis and Fault Tolerant Control of Renewable Energy Systems (RES)

  • Fuzzy modeling and control of distributed virtual power plants

  • Stability Analysis of RES connected to the grid based on T-S fuzzy systems

Important dates

  • Paper Submission Deadline: 10 February 2021

  • Paper Acceptance Notification Date: March 22, 2021

  • Final Paper Submission Deadline: April 12, 2021

Paper submission

  1. Please refer to authors' instructions and guidelines available here: https://attend.ieee.org/fuzzieee-2021/instructions-for-authors/

  2. Please follow the FUZZ-IEEE 2021 Submission (https://ieee-cis.org/conferences/fuzzieee2021/upload.php).

  3. Please specify that “Main research topic*” of your paper is the Special Session "Fuzzy System for Renewable Energy and Smart Grid".

All papers accepted and presented at will be included in the conference proceedings published by IEEE Explore.

Organizing Committe

Prof. Marco Mussetta, Dept. of Energy, Politecnico di Milano, Italy, marco.mussetta@polimi.it

Marco Mussetta (S'03-M'08-SM'17) is an Associate Professor of Electrical Engineering in Politecnico di Milano, Italy. He was born in 1979. He received the M.S. degree in mechanical engineering and the Ph.D in electrical engineering from the Politecnico di Milano, Milan, Italy, in 2003 and 2007, respectively.

In March 2011 he joined the Department of Energy, Politecnico di Milano, as an assistant professor.

His research activities include global evolutionary optimization techniques applied to reflectarray antennas design, printed antennas, FSS, wireless sensor networks, and modeling and optimization of renewable energy systems by means of advanced soft computing techniques.

Since 2001, Dr. Mussetta coauthored more than 150 publications on Scopus-indexed journals and international conferences. He serves as a Reviewer for IEEE Transactions on Evolutionary Computation, IEEE Transaction on Neural Networks, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Industrial Electronics, IEEE Transactions on Magnetics, IEEE Transactions on Electromagnetic Compatibility, IEEE Antennas and Wireless Propagation Letters.

Prof. Mussetta is Senior Member of IEEE, CIS, PES, IES. He is also Chair of the IEEE CIS Task Force on "Fuzzy Systems in Renewable Energy and Smart Grid".

Prof. Faa-Jeng Lin, Dept. of Electrical Engineering, National Central University, Taiwan, linfj@ee.ncu.edu.tw

Faa-Jeng Lin (M’93, SM’99, F’17) is currently Chair Professor with the Department of Electrical Engineering, National Central University, Taoyuan, Taiwan. His research interests include intelligent (fuzzy systems, neural networks and evolutionary computation) control theories, nonlinear (adaptive and sliding-mode) control theories, control theory applications, AC motor servo drives, ultrasonic motor drives, renewable energy systems, power electronics, and microgrid. He has published more than 210 SCI journal papers including 97 IEEE Trans. papers and 133 conference papers and 15 patents in the areas of intelligent control, nonlinear control, motor drives, renewable energy systems and mechatronics. Several of these referred papers helped to establish research areas such as fuzzy neural network control of motor drives and intelligent motion control systems. He was the Chair, Power Engineering Division, National Science Council, Taiwan, 2007 to 2009. Moreover, he served closely to IEEE activities such as Fuzzy Systems Technical Committee of CIS, Chair of IE/PEL Taipei Chapter, Director and Officer of Taipei Section, and Chair of CIS Taipei Chapter. Furthermore, Prof. Lin was the Chair and principle investigator of the National Energy Project - Smart Grid Focus Center in Taiwan from 2010 to 2019. This project aims to integrate Taiwan's R&D resources in Smart Grid and renewable energy resources in an effective manner, to formulate overall development strategies and implementation approaches, to achieve the vision of enhancing energy security, to reduce greenhouse gas emissions and to support the development of power industries in Taiwan. Professor Lin has received the Outstanding Research Award from National Science Council (NSC), in 2004, 2010 and 2013. This award is the highest honor bestowed in academia of Taiwan. He also received the Outstanding Professor of Engineering Award, the Chinese Institute of Engineers, Taiwan, 2016. Furthermore, he is a Fellow of the Institution of Engineering and Technology (IET) since 2007 and a Fellow of IEEE since 2017.

Prof. Horst Schulte, University of Applied Sciences, Berlin (HTW), Germany, schulte@htw-berlin.de

Horst Schulte received the diploma degree in Electrical Engineering from TU Berlin and the Ph.D. degree in Control Engineering from University Kassel (Germany). He joined the Bosch Group in 2005 where he worked in R&D projects in the field of modeling, optimization and advanced control of actuators, power systems and drive trains. Since November 2009, he has been a full Professor at the University of Applied Sciences HTW Berlin. His research interests include nonlinear controller and observer design with Takagi-Sugeno (TS), LPV and sliding-mode techniques, robust control system design, active fault-tolerant control (FTC) system design with applications to nominal and fault tolerant control of wind turbines and wind farms, the optimal control of air-conditioning systems, and the modeling and control of power converters for sustainable power systems. He is the author of more than

134 scientific publications including international journal papers, book chapters, patents, and conference papers. Prof. Schulte is one of European Advanced Control and Diagnosis (EACD) steering committee members and a member of IFAC TC 6.4 Fault Detection, Supervision & Safety of Technical Processes and of IFAC TC 7.1 Automotive Control. He is an associate editor of the Journal ISA Transactions, editor of the Journal Intelligent & Robotic Systems, a board member of the Journal of Applied Mathematics and Computer Science, and in the management board of Federation of German Wind power and other Renewable Energies (FGW e.V.).

Prof. Francesco Grimaccia. Dept. of Energy, Politecnico di Milano, Italy, francesco.grimaccia@polimi.it

Francesco Grimaccia (SM’17) was born in 1979, Master Degree in Mechanical Engineering from Politecnico di Milano University in 2003, PhD in Electrical Engineering (Cum Laude) in 2007 from the same institution. Since 2001 is active in theoretical and experimental research, dedicated to the study and development of innovative optimization methods in the evolutionary computation field for engineering applications. In 2004 he received the Young Scientist Award for the article "Genetical Swarm Optimization: A New Hybrid Evolutionary Algorithm For Electromagnetics". In 2005 he started a collaboration with the University of Queensland (Brisbane, Australia) on the subject of WSN for monitoring applications on the Barrier Reef. In the European Seventh Framework Programme he has participated in several activities and research projects concerning in particular topics of ICT, UAVs and Energy. In 2016 he received the Best Paper Award at IEEE PES for an article on UAV technologies in O&M operations in PV plant monitoring and control. He has published over 120 publications in journals, conference proceedings and book contributions on the topics of RES integration, forecasting tools and computational intelligence techniques. He is a Senior Member of the IEEE society, CIS, SPIE and Vice-President of the Italian electrical society AEIT. He is now professor at Department of Energy of Politecnico di Milano University.

Session Schedule

  • TBD