IEEE Systems, Man and Cybernetics Magazine - July 2021 - 18

discussed in the section " The Virtual Microgrid as a Cyberphysical
System, " all four stages of the energy server
require predictions about production and demand as well
as storage capabilities. Future research will focus on
developing predictive methods, as discussed by Kabir et al.
[43] and Bassamzadeh and Ghanem [44].
Acknowledgments
This article is partly supported by the Academy of Finland
via ee-IoT project 319009, the Framework for the Identification
of Rare Events via Machine Learning and IoT Networks
consortium CHIST-ERA/326270 (CHIST-ERA-17-BDSI-003),
and the EnergyNet Research Fellowship 321265/ 328869.
The authors would like to thank Chris Giotitsas, Vasilis
Kostakis, and Simon Pirani for their discussions about the
future of energy systems, which partly motivated this work.
About the Authors
Pedro H. J. Nardelli (pedro.nardelli@gmail.com) earned
his B.S. and M.Sc. degrees in electrical engineering from the
State University of Campinas, Brazil, in 2006 and 2008,
respectively. In 2013, he received his doctoral degree from
the University of Oulu, Finland, and the State University of
Campinas following a dual degree agreement. He is currently
an associate professor (tenure track) in the Internet of
Things (IoT) in energy systems at the Lappeenranta-Lahti
University of Technology (LUT University), Lappeenranta,
53850, Finland, and holds the position of Academy of Finland
Research Fellow with the project Building the Energy
Internet as a Large-Scale IoT-Based Cyberphysical System,
which manages the energy inventory of distribution grids as
discretized packets via machine-type communications (EnergyNet).
He leads the Cyber-Physical Systems Group at LUT
University and is project coordinator of the CHIST-ERA
European consortium Framework for the Identification of
Rare Events via Machine Learning and IoT Networks. He is
also a docent at the University of Oulu in communications
strategies and information processing in energy systems. His
research focuses on wireless communications, particularly
applied in industrial automation and energy systems. He
received a best paper award of the IEEE Power & Energy
Society Innovative Smart Grid Technologies Latin America
2019 in the track " Big Data and Internet of Things. " He is a
Senior Member of IEEE. More information can be obtained
at https://sites.google.com/view/nardelli/.
Hafiz Majid Hussain (majid.hussain@lut.fi) earned
his B.S. and M.S. degrees in electrical engineering from the
National University of Computer and Emerging Sciences
and the University of Engineering and Technology, Taxila,
Pakistan, in 2014 and 2017, respectively. He is the part of the
project Building the Energy Internet as a Large-Scale Internet-of-Things-Based
Cyberphysical System. Currently, he is
pursuing a Ph.D. degree in electrical engineering from the
Lappeenranta University of Technology in the Cyber-Physical
Systems Group, Lappeenranta, 53850, Finland. His
research interests include demand response applications,
18 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE July 2021
energy resource optimization in smart grids and the energy
Internet, and information security technologies. He is a Student
Member of IEEE. More information can be obtained at
https://sites.google.com/view/hafizmajidhussain/biography.
Arun Narayanan (arun.narayanan@lut.fi) earned his B.E.
degree in electrical engineering from Visvesvaraya National
Institute of Technology, Nagpur, India, and his M.Sc. degree in
energy technology from the Lappeenranta University of
Technology (LUT University), Finland, in 2002 and 2013,
respectively. He subsequently earned his Ph.D. degree from
the School of Energy Systems, LUT University. He is currently
a postdoctoral fellow with LUT University, Lappeenranta,
53850, Finland, in the Cyber-Physical Systems Group.
His research interests include renewable energy-based
smart microgrids, electricity markets, demand-side management,
energy management systems, and information and
communications technology. He focuses on applying optimization,
computational concepts, and artificial intelligence
techniques to renewable electrical energy problems. He is a
Member of IEEE.
Yongheng Yang (yang_yh@zju.edu.cn) received his
B.Eng. degree in electrical engineering and automation from
Northwestern Polytechnical University, Shaanxi, China, in
2009 and his Ph.D. degree in electrical engineering from Aalborg
University, Aalborg, Denmark, in 2014. He was a postgraduate
student with Southeast University, China, from 2009
to 2011. In 2013, he spent three months as a visiting scholar at
Texas A&M University, USA. Currently, he is a ZJU100 professor
at Zhejiang University, Hangzhou, 310027, China. Previously,
he was an associate professor with the Department of
Energy Technology, Aalborg University, where he also served
as the vice program leader for the research program on photovoltaic
systems. His current research is on the integration of
grid-friendly photovoltaic systems with an emphasis on power
electronics converter design, control, and reliability. Dr. Yang
was the chair of the IEEE Denmark Section. He serves as an
associate editor for several journals, including IEEE Transactions
on Industrial Electronics, IEEE Transactions on
Power Electronics, and publications of the IEEE Industry
Applications Society. He is a deputy editor of Institution of
Engineering and Technology (IET) Renewable Power Generation.
He received the 2018 IET Renewable Power Generation
Premium Award and was an outstanding reviewer for IEEE
Transactions on Power Electronics in 2018. He was included
in the world's top 2% of Scientists List conducted by Stanford
University in 2020. He is a Senior Member of IEEE.
References
[1] J. Rifkin, The Zero Marginal Cost Society: The Internet of Things, the Collaborative
Commons, and the Eclipse of Capitalism. New York: St. Martin's Press, 2014.
[2] A. Goldfarb and C. Tucker, " Digital economics, " J. Econ. Literat., vol. 57, no. 1, pp.
3-43, 2019. doi: 10.1257/jel.20171452.
[3] E. Ostrom, Governing the Commons: The Evolution of Institutions for Collective
Action. Cambridge, U.K.: Cambridge Univ. Press, 1990.
[4] H. Lo, S. Blumsack, P. Hines, and S. Meyn, " Electricity rates for the zero marginal
cost grid, " Electr. J., vol. 32, no. 3, pp. 39-43, 2019. doi: 10.1016/j.tej.2019.02.010.

https://sites.google.com/view/hafizmajidhussain/biography https://sites.google.com/view/nardelli/

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