Panayiotis (Panos) Moutis, PhD, is Assistant Professor at the Dept. of Electrical Engineering at the City College (CCNY) of the University of New York (CUNY). Panos studies how Artificial Intelligence and Machine Learning enable the pragmatic-data-driven cost optimization, stability control and asset planning of electrical grids with high shares of renewables. He has worked with the grid operator of Portugal, REN, the moonshot factory of Google, X, the grid operator of New York, NYISO, the engineering advisory provider Arup, UK, and the wind developer Vestas. He has been special faculty (2018-23) and postdoctoral research associate (2016-18) at Carnegie Mellon University, Marie Curie Research Fellow (with Depsys), Switzerland (2018-20), and Arup fellow (with the University of Greenwich), UK (2014). Between 2008-15, as part of Prof. Nikos Hatziargyriou’s research group he contributed to over a dozen R&D projects funded by the European Commission. Panos received both his MSc (2007) and his PhD (2015) degrees in Electrical and Computer Engineering at the National Technical University of Athens, Greece, and has published more than 30 papers and contributed to 4 book chapters. He has over 10 years of experience in the renewable energy and energy efficiency industries, and is an advisor to energy tech start-ups. He is Chair of the IEEE-USA Energy Policy Committee, senior editor of IEEE & IET journals, member of IEEE standard working groups, 2023-24 Young Professionals’ representative at the IEEE Publications Services & Products Board, leads the Power & Energy Community at the Climate Change AI initiative, co-leads the Distribution Task Team at the North American Synchro-Phasor Initiative, and is a senior IEEE member. Personal Website for more information: https://panay1ot1s.com/

• 2015: PhD, School of Electrical & Computer Engineering, National Technical University of Athens Greece
• 2007: MSc, School of Electrical & Computer Engineering, National Technical University of Athens Greece

• Electric Power Engineering - EE 35700
• Economics in Electricity Markets - EE G3304

Renewable Energy, Power Systems, Smart Grid, Control Systems, Machine Learning, Artificial Intelligence, Optimization

1. Moutis P, Thakar P. Stochasticity agnostic solution to the AC optimal power flow by recursive bound tightening with top‐down heuristically inducted binary decision trees. IET Generation, Transmission & Distribution. 2023
2. Moutis P, Sriram U. PMU-driven non-preemptive disconnection of overhead lines at the approach or break-out of forest fires. IEEE Transactions on Power Systems. 2022 
3. Moutis P, Alizadeh-Mousavi O. Digital twin of distribution power transformer for real-time monitoring of medium voltage from low voltage measurements. IEEE Transactions on Power Delivery. 2020
4. He G, Chen Q, Moutis P, Kar S, Whitacre JF. An intertemporal decision framework for electrochemical energy storage management. Nature Energy. 2018 
5. Moutis P, Georgilakis PS, Hatziargyriou ND. Voltage regulation support along a distribution line by a virtual power plant based on a center of mass load modeling. IEEE Transactions on Smart Grid. 2016
6. Moutis P, Hatziargyriou ND. Decision trees-aided active power reduction of a virtual power plant for power system over-frequency mitigation. IEEE Transactions on Industrial Informatics. 2014 
7. Moutis P, Papathanassiou SA, Hatziargyriou ND. Improved load-frequency control contribution of variable speed variable pitch wind generators. Renewable Energy. 2012