Tutorial T3 (Full day)
Computational Intelligence in Power System Applications
- Kalyanmoy Deb, Michigan State University, email@example.com
- Marco Mussetta, Politecnico di Milano, firstname.lastname@example.org
- Emanuele Ogliari, Politecnico di Milano, email@example.com
Module A: Machine Learning for power forecasting
The variability of renewable energy represents a huge challenge in the integrated electricity systems: power production forecasts can help reducing the amount of operating reserves needed for the system, finally reducing the balancing costs. While physical predition methods strongly rely on the accuracy of the weather forecast, Artificial Neural Networks are based on the learning process of the underlying models and are commonly referred to as a “data-driven” or “black box” approaches. In fact, they need historical data that, after being collected, are used to infer a general trend and behavior in order to predict future output of the power plant. Hybrid methods, consisting in any combination of the physical-based approach and Machine Learning can guarantees the highest level of accuracy when adopted to the power forecast of RES.
Module B: Evolutionary Multi-Criterion Optimization with Case Studies on Power Dispatch Problem Solving
Evolutionary optimization methods, proposed in early sixties and used in practice since eighties, are population-based algorithms which are easily customizable to suit different problem-solving tasks. Evolutionary multi-criterion optimization (EMO) algorithms, proposed since early nineties, revolutionized the solution of problems having multiple conflicting objectives. Starting with two and three-objective problems, EMO researchers have devised algorithms for solving up to 15-objective problems and applied to many engineering and practical problems. In this tutorial, we shall present a step by step account of the growth of EMO field by describing the principles of multi-criterion optimization, some key algorithms, and recent advances in the field. Case studies on power dispatch problem for single and multiple criteria aspects and its static and dynamic versions will be presented.
See Tutorials program page.
See Registration page.
|09:00-10:30||Module A: Machine Learning techniques for power forecasting (Marco Mussetta)|
|10:45-12:15||Module A: Hybrid methods for power forecasting (Emanuele Ogliari)|
|12:15-12:30||Questions and Answers|
|13:30-15:00||Module B: Evolutionary Multi-Criterion Optimization with Case Studies on Power Dispatch Problem Solving – part 1 (Kalyanmoy Deb)|
|15:15-16:45||Module B: Evolutionary Multi-Criterion Optimization with Case Studies on Power Dispatch Problem Solving – part 2 (Kalyanmoy Deb)|
|16:45-17:00||Questions and Answers|
Kalyanmoy Deb is Koenig Endowed Chair Professor at Department of Electrical and Computer Engineering in Michigan State University, USA.
Prof. Deb’s research interests are in evolutionary optimization and their application in multi-criterion optimization, modeling, and machine learning.
He received a number of prestigious awards, including Infosys Prize, EC Pioneer award, TWAS Prize in Engineering Sciences, CajAstur Mamdani Prize, Edgeworth-Pareto award, Bhatnagar Prize in Engineering Sciences, and Bessel Research award from Germany.
He is fellow of IEEE and ASME.
He has published over 505 research papers with Google Scholar citation of over 122,000 with h-index 112.
He is in the editorial board on 18 major international journals.
More information about his research contribution can be found from http://www.egr.msu.edu/~kdeb and his COIN lab website at MSU: http://www.coin-laboratory.com.
Marco Mussetta is an Associate Professor of Electrical Engineering in Politecnico di Milano, Italy.
His research activities include global evolutionary optimization techniques and modeling of renewable energy systems by means of advanced soft computing techniques.
Since 2001, Prof. Mussetta coauthored about 200 publications on WoS/Scopus-indexed journals and proceedings of international conferences. He serves as a Reviewer for several IEEE Transactions.
He is a Senior Member of IEEE, PES, CIS, IES, a member of the IEEE CIS Fuzzy Systems Technical Committee and the Chair of the IEEE CIS Task Force on “Fuzzy Systems in Renewable Energy and Smart Grid”.
Emanuele Ogliari received the Ph.D. degree in electrical engineering from the Politecnico di Milano, Italy, in 2016.
He has been working on photovoltaics power plant design and their optimization since 2010 and RES expected power by means of computational intelligence techniques since 2012.
He is currently an Assistant Professor with the Department of Energy, Politecnico di Milano, Italy.