Weerakorn Ongsakul, Ph.D
Energy Field of Study
School of Environment, Resources and Development
Asian Institute of Technology, Pathumthani 12120, Thailand
BIO
Dr. Weerakorn Ongsakul obtained B.Eng. (Electrical Eng.) in 1988 from Chulalongkorn University, Thailand; M.S. and Ph.D. (Electrical Eng.) from Texas A&M University, USA in 1991 and 1994, respectively. He is currently an Associate Professor of Energy and former Dean of School of Environment, Resources and Development, Asian Institute of Technology. His research interests are in power system operation, artificial intelligence applications in power system optimization, smart grid and micro grid. He has conducted projects sponsored by Sida, EC-ASEAN Energy Facility/ACE and EU-Thailand Economic Co-operation Small Project Facility, and projects sponsored by Energy Conservation and Promotion Fund and Electricity Generating Authority of Thailand (EGAT), Provincial Electricity Authority (PEA) with a combined funding of US$3.0 million. Based on his research work, he has published more than 200 international refereed journal articles and conference proceedings papers. He served as an Energy Specialist, Energy Standing Committee, Senate of Thailand during 2008-2011. He served as a consultant of Asian Development Bank Institute (ADBI) in 2011-2012. He has been serving as a Secretary General of the Greater Mekong Subregion Academic and Research Network (GMSARN) since 2006. He co-authored one book entitled Artificial Intelligence in Power System Optimization, published by CRC Press/Taylor & Francis in March 2013.
'Stochastic Optimal Energy and Risk Management in Microgrid Power Markets'
ABSTRACT
The development of self-sustainable microgrids on a restructured power sector platform, with increasing rate of demand growth, motivated the research on new techniques for economic and efficient operation and/or management of the micro-sources and loads. Even though the involvement of microgrid in power market is restricted in size, its energy is marketed to the main grid as well as in the local market through aggregators. The awareness of environmental concerns and volatile oil prices have encouraged the use of renewable energy sources (RES) and related technologies in microgrids. In consideration, it is required to enhance the performance of optimal energy management and scheduling in terms of technical and economic feasibility with reduced financial risk and increased robustness to microgrid volatilities. Suitable modifications are to be effected in the existing energy management techniques because of the increased penetration of uncertain sources like wind, solar and electric vehicle (EV) energy sources. Realistic models for EVs are to be proposed considering uncertainties in their trips, charging/discharging rate and number available in the parking lot. RES’s uncertainties can be modelled using interval or probabilistic nature of their statistical data. Thus, effects of uncertain nodal power injections due to RES, loads and EVs on optimizing microgrid benefits can be analyzed by evaluating the deviations of distributed energy resources (DER) dispatch, operational cost, node voltages etc. Reserve plays an important role in the compensation of uncertainties. That is, the optimal power schedule determined based on the load, wind, solar and EV power forecasts may be prone to possible actual uncertainties in real time. Sensitivity analysis methods can be used to estimate the perturbations in nodal power injections due to these uncertainties and the corresponding optimal spinning reserves to compensate the discrepancy between the measured and forecasted data. Remaining capacity of demand response, grid purchase, EVs and other non-renewable DERs can be utilized as reserves. But the cost incurred for reserve dispatch may increase in accordance with the uncertainties in day ahead schedules (long term schedules in general). Different levels of uncertainties pose distinctive levels of financial risks for the operator as well as the microgrid market participants. Therefore, it is necessary to consider both operational costs in the energy market as well as the reserve markets to properly hedge the financial risk of the operator while maximizing the overall benefits. Robust stochastic optimization techniques are to be used for obtaining a schedule with reduced financial risk and increased tolerance to uncertainties.
Dr. Lipo Wang
School of Electrical and Electronic Engineering
Nanyang Technological University
Singapore
https://www.ntu.edu.sg/home/elpwang
Email: [email protected]
BIO
Dr. Lipo Wang received the Bachelor degree from National University of Defense Technology (China) and PhD from Louisiana State University (USA). His research interest is intelligent techniques with applications to communications, image/video processing, biomedical engineering, and data mining. He is (co-)author of over 270 papers, of which more than 90 are in journals. He holds a U.S. patent in neural networks and a Chinese patent in VLSI. He has co-authored 2 monographs and (co-)edited 15 books. He was/will be keynote/panel speaker for 18 international conferences. He is/was Associate Editor/Editorial Board Member of 30 international journals, including 3 IEEE Transactions, and guest editor for 10 journal special issues. He is an AdCom member of the IEEE Computational Intelligence Society (CIS) for 2 terms and served as CIS Vice President for Technical Activities and Chair of Emergent Technologies Technical Committee. He is a member of the Board of Governors of the International Neural Network Society (2011-2016) and was an AdCom member of the IEEE Biometrics Council. He served as Chair of Education Committee, IEEE Engineering in Medicine and Biology Society (EMBS). He was President of the Asia-Pacific Neural Network Assembly (APNNA) and received the APNNA Excellent Service Award. He was founding Chair of both the EMBS Singapore Chapter and CIS Singapore Chapter. He serves/served as chair/committee members of over 200 international conferences.
ABSTRACT
'Natural Computation for Data Mining and Optimization '
This talk highlights some of our recent research results in data mining and optimization using intelligent techniques inspired from nature. Our techniques include novel feature selection algorithms, compact radial-basis-function (RBF) neural networks, class-dependent feature selection, and chaotic neural networks. We demonstrate our algorithms in data mining problems, such as content-based image retrieval, gene selection in microarray data and face recognition, as well as optimization problems, such as optimal channel assignment in mobile communications, optimal multicast routing, and image segmentation.
Athanasios V. Vasilakos
Lulea University of Technology, Sweden
BIO
Athanasios V. Vasilakos is recently Professor with the Lulea University of Technology. He served or is serving as an Editor for many technical journals, such as the IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT; IEEE TRANSACTIONS ON CLOUD COMPUTING, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,IEEE TRANSACTIONS ON CYBERNETICS; IEEE TRANSACTIONS ON NANOBIOSCIENCE; IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE;ACM Transactions on Autonomous and Adaptive Systems; the IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS. He is also General Chair of the European Alliances for Innovation(www.eai.eu).
ABSTRACT
'Big Data Analytics for Internet Security'
The age of big data is now coming. But the traditional data analytics may not be able to handle such large quantities of data. The question that arises now is, how to develop a high performance platform to efficiently analyze big data and how to design an appropriate mining algorithm to find the useful things from big data. To deeply discuss this issue, this talk begins with a brief introduction to data analytics, followed by the discussions of big data analytics in internet security.
