Workforce Development for Future Smart Energy Systems

NSF CyberTraining Project: Data-Centric Security and Resilience of Cyber-Physical Energy Infrastructures (2021-2022)

NSF CyberTraining Project: Learning, Optimization and Communications for Next Generation Power Engineers (2019-2023)

Florida Atlantic University leads the workforce development for future smart energy systems and prepare future STEM scientists and engineers. This is in collaboration with the University of South Florida and the University of Wyoming.

Students and professionals are provided with mentored, hands-on training combining expertise across electrical engineering, communication, data science, and science and technology studies. Participants develop and refine the multi-disciplinary skillsets needed for the data-centric power and energy industry using unique, remotely connected smart grid cyberinfrastructure. Participants extend their academic research portfolios, strengthening their career competitiveness as future cyberinfrastructure professionals and users. The project thus serves the national interest, as stated by NSF's mission: to promote the progress of science and to secure the national defense.

Our training program includes the following key areas:

Artificial Intelligence and Data Analytics
Communication and Network Security
Sensor Networks and Internet-of-Things
Real-Time Learning and Microgrid Optimization
Multi-Level Decision-Making Process of Intelligent Systems

Smart Grid and Renewable Energy (Infographic description)

Internet of Energy (Infographic description)

YOUR FLORIDA EXPERIENCE

This paid, two-week program focuses on training and providing students and professionals with an exciting experience that will prepare participants for research or careers in engineering and science.
This program welcomes professionals (e.g., power engineer or software engineer working in a related industry) to apply.
Participants will typically work 9 a.m. to 5 p.m. each day. Participants are paid $900 along with a $252 meal allowance, per week.
Apartment-style dormitory housing will be provided to participants on FAU’s Boca Raton campus.
If COVID-19 restrictions/precautions do not allow for an in-person training then we will offer virtual/remote training and the participants will be offered the $900/week stipend (total $1,800).
Additional paid, project-based training opportunities are also available after the two-week training. See project descriptions below.

2021 TRAINING PROGRAMS

Two-Week Training (Preliminary Schedule):

Days	Training Topics	Instructor
Monday	Morning: a short course that covers the background of electric power systems and the develoment of smart grid (e.g., renewable energy integration). Power load demand and renewable energy prediction will also be introduced. Afternoon: a hands-on project for renewable energy generation prediction using data-driven techniques. Off-the-shelf time series prediction techniques and AI technologies will be leveraged.	Dr. Yufei Tang
Tuesday	Morning: a tutorial on basic knowledge of communication and network security, common vulnerabilities, and security attacks. Afternoon: communication and security designs for smart grid systemss, including two-way communication architecture, security attack generation, security setting, as well as data collection.	Dr. Zhuo Lu
Wednesday	Morning: a short course on renewable energy generation, with a focus on modeling, conversion, and the impacts of this power on the grid. Afternoon: a hands-on laboratory where participants will write software to model different environmental conditions, such as turbulence levels and mean flow speeds, and turbine systems and then run the simulator using these numerical models.	Dr. James VanZwieten
Thursday	Morning: an intensive training section to introduce machine learning concepts, neural networks, and deep learning models. The training will help students build theoretical foundations and knowledge base that they need to cultivate their interests into hand-on skills. Afternoon: a hands-on data science training section to teach programming, experiment designs, and reporting skills (using Python) for object detection and text classification, as well as anomaly detection.	Dr. Hill Zhu
Friday	Morning: multiple industry representatives will be invited to provide short breakout courses. The session will conclude with a structured networking session, allowing students to actively discuss content and career paths with industry partners and recruiters. Afternoon: two tutorials will be provided. The first will provide students with an understanding of the social significance of securing IoT and energy systems, placing these technical systems into a broader social context. The second session will cover communication and inclusion in the cybersecurity workforce, drawing on a history of hacking.	Dr. Nathan Fisk
Monday	Morning: a short course on IoT that provides basic background in the history of distributed sensing and actuation, IoT architectures, and programming fundamentals. Afternoon: a hands-on laboratory focused on developing a complete IoT application. The laboratory will build upon the short-course, leveraging Raspberry Pi sensing kits and Telit’s cloud-based IoT platform to construct a basic monitoring system for smart home applications.	Dr. Jason Hallstrom
Tuesday	Morning: a short course on the introduction of microgrid energy systems. This course will have discussions on the key components and algorithms of microgrid and its applications to data center and other criticial infrastructures. Afternoon: a hands-on project on the microgrid energy mangement with AI methods. In the project, students will study how to apply learning methods for microgrid energy operation, bettery manegement and community load incentive designs.	Dr. Zhen Ni
Wednesday	Morning: a short course on wide-area monitoring system (WAMS) for power systems. This course will have discussions on the key measurement device in WAMS, namely the phasor measurement unit (PMU) and the various applications using the synchrophasor data provided by WAMS. Afternoon: a hands-on project on the power system state estimation by synchrophasor data. In the project, students will learn how to build the state estimation model for power systems with synchrophasor data and compare different algorithms for state estimation.	Dr. Dongliang Duan
Thursday	Morning: a short course on data-centric network science and graph signal processing with applications in security and reliability analysis of smart grids. This course will discuss a data-centric network-based modeling, visualization and analysis of energy data and measurement timeseries of smart grids. Afternoon: a hands-on network analytics project that will provide a practice on how the physical structure of the smart grid and the embedded structures and patterns in energy data and measurement timeseries can be modeled using networks and analyzed using network analytics techniques to address reliability and security challenges in smart grids.	Dr. Mahshid R. Naeini
Friday	Morning: a hands-on project on attack and defense design and analytics of the smart grid using deep learning. High-resolution spatial-temporal dataset will be collected from benchmark smart grid testbeds and provided to the students. Data samples will be collected in normal, faulty, and attacked scenarios based on accurate representation of real-world events through incorporating the cybersecurity module. Participants will be asked to use the attack detection algorithms developed previously for this probelm. Afternoon: Dr. Tang and Dr. Lu will review the training activities. All of the workshop participants will present one of their mini-projects to the team, followed by certification and award ceremony.	Dr. Yufei Tang and Dr. Zhuo Lu

2021 Training Outcomes: 29 participants have been virtually trained for two weeks in the summer of 2021. The participants include 18 graduates, 10 undergraduates and one high school student. The participants also include 16 women, 6 African Americans and 10 Hispanics.

List of the 29 participants: Jose Baca Bustillo; Muhammad Ahmed; Cleverson Viana; Yingqi Feng; Ishwarya Srikanth; Andrea Pena; Saad Saeed; Steven Kasakitis; Yairel Santos; Cedric Clement; Stephanie Langlois; Robert Blanchette; Danny Silvestre Suarez; Brunna Borges Dias; Virginia Romero; Jose Angel Sanchez; Sasha Fung; Arezoo Hasankhani; Shervin Firouzdehghan; Chetanna Iwuagwu; Rachel Kreynin; Md Jakir Hossain; Md Abul Hasnat; Ugonna Chikezie; Aline Schneider; Tanner Robart; Yahan Chang; Roxana Melendez; Kuan-Hsun Chou.

2022 TRAINING PROGRAMS

Two-Week Training:

Days	Training Topics	Instructor
May 31, Tuesday	Morning: program overview, followed by a short course on electric power systems and smart grids (e.g., renewable energy integration). Afternoon: a hands-on project on attack and defense design and analytics of the smart grid using deep learning. High-resolution spatial-temporal datasets will be collected from benchmark smart grids. Data samples will be in normal, faulty, and attacked scenarios based on accurate representation of real-world events.	Dr. Yufei Tang
June 1, Wednesday	Morning: a tutorial on basic knowledge of communication and network security, common vulnerabilities, and security attacks. Afternoon: communication and security designs for smart grid systems, including two-way communication architecture, attack generation, security setting, and data collection.	Dr. Zhuo Lu
June 2, Thursday	Morning: a short course on renewable energy generation, with a focus on modeling and conversion. Afternoon: a hands-on project of marine renewable energy numerical simulation to model different environmental conditions, such as turbulence levels and mean flow speeds, and turbine systems and then run the simulator using these numerical models.	Dr. James VanZwieten
June 3, Friday	Morning: an intensive training section to introduce machine learning concepts, neural networks, and deep learning models. The training will help students build theoretical foundations and knowledge base that they need to cultivate their interests into hands-on skills. Afternoon: a hands-on data science training section to teach programming, experiment designs, and reporting skills (using Python) for object detection and text classification, as well as anomaly detection.	Dr. Hill Zhu
June 6, Monday	Morning: a short course on IoT that provides a basic background in the history of distributed sensing and actuation, IoT architectures, and programming fundamentals. Afternoon: a hands-on laboratory focused on developing a complete IoT application. The laboratory will build upon the short course, leveraging Raspberry Pi sensing kits and Telit's cloud-based IoT platform to construct a basic monitoring system.	Dr. Jason Hallstrom
June 7, Tuesday	Morning: a short course on the introduction of microgrid energy systems. This course will have discussions on the key components and algorithms of microgrids and their applications to data centers and other critical infrastructures. Afternoon: a hands-on project on microgrid energy management with AI methods. In the project, students will study how to apply reinforcement learning methods for microgrid energy operation, battery management, and community load incentive designs.	Dr. Zhen Ni
June 8, Wednesday	Morning: a short course on wide-area monitoring systems (WAMS) for power systems. This course will have discussions on the key measurement device in WAMS, namely the phasor measurement unit and the various applications using the synchrophasor data provided by WAMS. Afternoon: a hands-on project on the power system state estimation by synchrophasor data. In the project, students will learn how to build the state estimation model for power systems with synchrophasor data and compare different algorithms for state estimation.	Dr. Dongliang Duan
June 9, Thursday;	Morning: a short course on network science and graph signal processing with applications in smart grids, such as security and reliability analysis of smart grids. Afternoon: two hands-on network analytics projects for smart grid reliability and security. The projects will provide practice on how the physical structure of the smart grid and the embedded structures and patterns in energy data and measurement time series can be modeled using networks and analyzed using network analytics techniques to address reliability and security challenges in smart grids.	Dr. Mahshid R. Naeini
June 10, Friday	Morning: industry representatives will be invited to provide short breakout courses, followed by a short course and discussion of the social significance of securing IoT and energy systems.	Dr. Nathan Fisk

Project-based Training:

We plan to provide two additional longer-term research projects for the Summer/Fall of 2022. Participants are encouraged to take the two-week training to build the basic skills. Certain amount of incentives are also provided if participants are selected on the additional projects. The projects are described below.

Community Microgrid Energy Management
Led by Zhen Ni, Ph.D.
In-Person or Remote project
Learn more

Machine Learning Techniques to Detect GPS Spoofing Attacks
Led by Dongliang Duan, Ph.D.
In-Person or Remote project
Learn more

HOW TO APPLY

Submit the following information in one email to Sasha Fung (sfung2017@fau.edu).

One-page statement of interest that describes your motivations, expectations and long-term objectives
Two-page CV (including your personal details, e.g., demographic information, if possible)
Complete transcript

DATES

Application Window Opens: Feb. 1, 2022
Application Deadline: The application deadline has been extended and there are quite a few slots open (almost first come first serve for university students!).
Notification Date: On or before April 30, 2022
Two-week Training: 05/31/2022 – 06/10/2022 (subject to change)
Project-based Training Start Date: 06/15/2022 (subject to change)

ELIGIBILITY

Senior undergraduate, graduate, and professionals in science or engineering.
Minorities and women are strongly encouraged to apply.
Selections will be based on a combination of research interests, academic qualifications, and faculty recommendations.

CONTACT

For additional information contact Dr. Yufei Tang at 561-297-4781 or Dr. Zhen Ni at 561-297-0035.

To print the NSF CyberTraining Flyer, click here.

FUNDING

This program is funded by the National Science Foundation grants OAC 2017597/2017194 & 1949921/1923983.

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