Our full detailed program is now here -- https://smartgridcomm.info
Tutorials schedule coming soon.
Title: Stochastic Models and Optimization Techniques for Efficient Integration of Electric Vehicles in Smart Grids
Muhammad Ismail, Assistant Professor, Department of Computer Science, College of Engineering, Tennessee Tech University, USA
I Safak Bayram, Lecturer/Assistant Professor at University of Strathclyde, Glasgow, UK. Research Assistant Professor at The University of New Mexico, USA
Abstract: The past decade has witnessed a growing interest in electric vehicles (EVs) from both academia and industry. Such an interest is driven by the environmental and economic advantages brought by EVs. A recent study has revealed that the annual operation cost of an EV in the U.S. is $485 on average, while it is $1,117 for a gasoline-fueled vehicle, which represents 57% reduction in annual expenses. Furthermore, recent studies have demonstrated that EVs can significantly reduce the carbon dioxide emissions as they reduce the dependence on fossil fuel. Due to the aforementioned advantages, a recent report has shown that the number of EVs on the U.S. roads has increased over the past decade from a couple of thousands in 2011 to $1.2 million vehicles in 2019. A similar trend has been also observed worldwide. To accommodate the charging demands of such EVs, charging facilities have been deployed across the parking lots at residential and commercial units and at work places. Furthermore, fast charging stations have been allocated to serve EVs traveling on the roads. To cope up with the exponential increase in the number of EVs, additional measures have been adopted including temporal and spatial coordination of EV charging and discharging requests. In order to carry out the aforementioned planning and operational goals, advanced stochastic models and optimization techniques must be employed in order to: (a) model the stochastic nature of arrival and departure of EV charging requests, (b) model regular loads and generation units in the power grid to efficiently balance the total supply and demand, (c) allocate EV charging stations in the most economic manner while accounting for spatial and temporal increase of EV charging demands, and (c) coordinate the charging requests of parked and mobile EVs in the most satisfactory manner. This tutorial will equip the researchers with theoretical background of stochastic models and optimization techniques needed for efficient integration of EVs in smart grids. These tools include: Markov processes, queue models, stochastic geometry, mixed-integer programming, heuristic optimization, and game theory. The application of these tools to design planning and operation algorithms for EV integration in smart grids will be covered. This include optimal static and dynamic allocation of charging stations, optimal design of number of chargers and waiting space in charging station, and temporal and spatial coordination of charging requests from parked and mobile EVs in gridto-vehicle (G2V), vehicle-to-grid (V2G), and vehicle-to-vehicle (V2V) scenarios. Furthermore, the tutorial will present datasets and simulators available for researchers and discuss their application scenarios.
Motivation: Over the last decade, smart grid literature focusing in the integration of EVs has evolved significantly and it is often challenging for graduate students and young researchers to develop the background needed to synthesize theory and practical execution. This is particularly the case for IEEE SmartGridComm audience as it is composed of researchers with diverse backgrounds and often not trained with power systems education and may lack necessary stochastic modelling and optimization background needed to carry out research in integration of EVs. This tutorial will put above said topics and solutions methods into perspective and present wide range of practical case studies and tools needed to address them.
- General motivation
- Overview of existing research efforts
- Trends for the future of EV integration in power grids
- Regulations and standards
- Part 1: Theoretical Background
- Overview of Probability with Matlab examples
- Stochastic models (random walk, pandemic, Poisson, etc.) with Matlab examples
- Queuing models (classical and spatial models) with Matlab examples
- Discrete-event simulations with Matlab examples
- Stochastic Geometry
- Optimization Techniques (Mixed-Integer Non-linear, Convex Optimization, Heuristic “Genetic” Optimization, etc.)
- Markov Decision Process (Constrained MDP)
- Game Theory (Oligopoly Game)
- Part 2: EV Planning Problems
- Static Allocation of EV Charging Stations (Application of Stochastic Models, Mixed-Integer Non-linear Programming, and Genetic Optimization)
- Dynamic Allocation of EV Charging Stations (Application of Stochastic Geometry, Queuing Theory, and Markov Decision Process)
- Design of Charging Station (Application of Queuing Theory and Optimization)
- Part 3: Coordination of EV Charging and Discharging
- Temporal Coordination of Parked EVs (Application of Stochastic Modelling and Mixed-Integer Non-linear Programming)
- Spatial and Temporal Coordination of Mobile EVs (Application of Game Theory and Convex Optimization)
- Part 4: Concluding Remarks: Public Datasets and Simulators
Assistant Professor, Department of Computer Science, College of Engineering, Tennessee Tech University, USA
Muhammad Ismail (Senior Member, IEEE) received the B.Sc. (Hons.) and M.Sc. degrees in Electrical Engineering (Electronics and Communications) from Ain Shams University, Cairo, Egypt, in 2007 and 2009, respectively, and the Ph.D. degree in Electrical and Computer Engineering from the University of Waterloo, Waterloo, ON, Canada, in 2013. He is an Assistant Professor with the Computer Science Department, Tennessee Tech University, USA. His research interests include wireless networks, smart grids, and cyberphysical security. He was a co-recipient of the Best Paper Awards in the IEEE ICC 2014, the IEEE Globecom 2014, the SGRE 2015, the Green 2016, and the IEEE Technical Committee on Green Communications and Networking (TCGCN) Best Paper Award at the IEEE ICC 2019. He was the Workshop Co-Chair of the IEEE Greencom 2018, the TPC Co-Chair of the IEEE VTC 2017 and 2016, the Publicity and Publication Co-Chair of CROWNCOM 2015, and the Web-Chair of the IEEE INFOCOM 2014. He is an IEEE Senior Member and an Associate Editor for the IEEE Internet-of-Things (IoT) Journal, IEEE Transactions on Green Communications and Networking, the IET Communications, and Elsevier PHYCOM. He was an Editorial Assistant of the IEEE Transactions on Vehicular Technology from 2011 to 2013.
I Safak Bayram
Lecturer/Assistant Professor at University of Strathclyde, Glasgow, UK. Research Assistant Professor at The University of New Mexico
I Safak Bayram is a Lecturer (Chancellor's Fellow) at the Department of Electronic and Electrical Engineering at the University of Strathclyde. Between 2015 and 2019 he was an Assistant Professor(joint) at the Division of Sustainability at College of Science and Engineering and a Staff Scientist at Qatar Environment and Energy Research Institute both at Hamad Bin Khalifa University. He received a B.S. degree in Electrical and Electronics Engineering from Dokuz Eylul University, Izmir, Turkey in 2007, the M.S. degree in Telecommunications from the University of Pittsburgh in August 2010, and the Ph.D. degree in Electrical and Computer Engineering from North Carolina State University, in 2014. He received the Best Paper Award at the Third IEEE International Conference on Smart Grid Communications and at the First IEEE Workshop on Renewable Energy and Smart Grid in March 2015. His research interests include a variety of interdisciplinary problems arising in societal infrastructures such as power grids and transportation networks.
Title: Accelerating AI on the Grid: A Hands On Tutorial on PMU Data Analysis
Abstract: This tutorial will prepare attendees to analyze PMU (synchrophasor) data for research and practical applications. The tutorial will provide hands-on experience with state-of-the-art tools for digesting and visualizing high-frequency time series data, and for exploring novel applications.
PMU data give empirical evidence of physical phenomena that happen on time scales unobservable to conventional sensor networks. Effective use of PMU measurement data can unlock novel opportunities for using Artificial Intelligence (AI) to extract insights into the condition of the grid. Making these insights accessible to decision makers in real-time has already begun to radically change best practices in grid operations, maintenance, and planning.
This tutorial will provide attendees with the context and skills they need to leverage AI methods to begin using PMU and other high frequency data in their own work. The course will begin by teaching fundamental concepts from power systems engineering, and their relation to PMU measurement data. This talk will provide context necessary for both newcomers and domain experts to begin analyzing and interpreting PMU data. The course will go on to describe the data analytics program at Dominion, where streamlined access to PMU data has unlocked unexpected opportunities to improve decision-making processes related to grid operations, maintenance, and planning. Finally, we describe how companies that are successful at leveraging data and AI have radically changed the way they do business. We discuss examples from other sectors, such as Amazon and Google, and will share an outlook for similar transitions in the energy sector
Attendees will gain hands-on experience working with PMU data and state-of-the-art computational tools designed to facilitate the analysis and interpretation of big data. The hands-on portion of the session will use the National Infrastructure for AI on the Grid (NI4AI) powered by PingThings’ PredictiveGrid TM Platform and will provide real-time support for participants to gain API access to PMU and other data that are publicly hosted on the platform. Talks will include an interactive exercise for participants to familiarize themselves with the visualization capabilities of the platform, and to access the data on their personal computers using the Python API. Talks will include live coding demonstrations of two use cases for PMU data, to examine voltage sag events and explore the relationship between solar generation and voltage or frequency on the grid. Participants are requested to bring personal computers that they may follow along and replicate analytics on their own devices
Motivation: There is growing interest among researchers and utilities to apply AI to extract insights from PMU and other sensor data. Early successes have demonstrated that these data can enable decision-makers to take more effective and more timely actions. Many of these insights were either time-consuming or impossible to access using conventional sensor data. Work to date provides strong evidence that continued advancements in the collection and interpretation of sensor data will fundamentally change the nature of decisions that are made in grid operations and planning. Familiarity with PMU measurement data and tools that can streamline workflows for analysts will become increasingly valuable. These skills are relevant both to researchers seeking to develop new analytic algorithms, and to industry practitioners seeking to improve decision-making processes within their organizations. The tutorial will also familiarize participants with NI4AI, an ARPA-E funded project that makes high-frequency sensor data readily accessible to students, academics and professionals. The platform provides users with access to anonymized sensor data, and can facilitate selected data sharing between academic institutions or with industry partners. Users can easily and efficiently query the data through an API that supports Python, Julia, and other programming languages. Through hands-on experience, participants will be able to more effectively leverage these state-of-the-art tools for grid data analysis in their own work.
1. Introduction to PMU data and applications [45 min]
1.1. Time-series data in the 21st century grid: what are we looking at, and why?
1.2. How data analytics are changing the way utilities operate
2. Live coding demonstration and hands-on exercises using PMU data [90 min]
2.1. National Infrastructure for AI on the Grid (NI4AI)
2.2. Exploring voltage sag events
2.3. Analyzing frequency volatility from phasor angle measurements
3. Future outlook for data analytics on the grid [45 min]
3.1. Accelerating artificial intelligence on the grid: Opportunities and outlook
3.2. Closing Remarks
Alexandra von Meier, University of California, Berkeley: Topic 1.1
Kevin Jones, Dominion Energy: Topic 1.2
Laurel Dunn, University of California, Berkeley: Topic 2.1
Mohini Bariya, University of California, Berkeley: Topic 2.2
Miles Rusch, University of California, Berkeley: Topic 2.3
Sean Murphy, PingThings: Topic 3
Title: Power System Machine Learning Applications: From physics-informed learning for decision support to inference at the edge for control
Abstract: Electrical power networks are facing unique challenges in their operation and control. With the increasing penetration of variable & intermittent renewable energy sources and limited transmission capabilities, grid operations and control is becoming evermore complex. However, the transition into the “digital utility” is brining unprecedented opportunities to leverage measurement data with conventional analysis methods, that when combined together, can help in achieving the goals for a “green” energy transition. In this context, Artificial Intelligence and Machine Learning are emerging as a cohort of theory, methods and technologies that if applied properly to solve power system problems, may have an invaluable contributions to solve existing and future grid challenges.
This tutorial provides insights from a team of power system specialists on the development of Machine Learning-based for power system applications using both measurements and physics-informed simulation. The scope of the presentation is on how to frame to power system problems and apply ML existing methods and technologies, and not on ML itself.
The tutorial is divided in three parts. First, an overview on today’s hierarchical power system operation and control is given, identifying a few of the potential areas where ML can be be of substantial benefit to power system operations for decision making at the control center to inference at edge devices in control/protection.
Second, on-going research in the development of a “recommender system” for operator decision support will be presented. Such type of predictive application cannot rely on measurement data alone, it has to be complemented with physics-informed models and simulation. In other words, this is a case where both measurement-based and simulation-based ML analytics need to be combined. Hence, this part of the presentation makes a strong emphasis on the careful design of simulation models, algorithms for automated simulation scenario design and software pipelines for automated generation of simulation data. Many challenges were faced when building a toolchain that makes this possible. We illustrate the challenges faced to adopt not only ML methods, but the computing software environments and hardware required in ML workflows so to be able to scale for realistic use cases.
Finally, we illustrate the first results obtained using our proposed approach for classification of power system stability using both traditional data science methods and Deep Learning. Finally, on-going research in the development of “edge” applications in power systems will be presented. The use case is the detection of undesirable sub-synchronous control interactions between the power grid and wind turbines for potential use in control and protection at the “edge”, i.e at the wind-farm location, which would require a ML-based apparatus capable to provide reliable predictions in real-time. We illustrate the challenge of having a reduced measurement data set to train such detection algorithm, and how simulation helps to solve this problem. Furthermore, we illustrate the performance of the developed ML-based solution on three different hardware platforms.
Luigi Vanfretti is an Associate Professor at Rensselaer Polytechnic Institute, Troy, NY. He was previously an Associate Professor at KTH Royal Institute of Technology, and a Special Advisor at the Norwegian transmission operator at Statnett SF. His work at Statnett SF focused on synchrophasor data transfer, communications and PMU applications. His research experience spans several areas of electrical engineering including: cyber-physical modeling and simulation; real-time hardware-in-the-loop simulation; PMU/Synchrophasor data computer systems, applications and analytics in transmission and distribution systems; AI/Machine Learning and system identification applications to power systems.
Since joining RPI, Prof. Vanfretti’s research has leveraged this experience and results by collaborating with industry in the United States in different research projects through state and federal grants. Related to this work, results from collaborations with the New York Power Authority in the projects “Deep Learning Computer System for Grid Operations Study,” and “Model Translations for Smart Grid Applications Study,” funded by the New York State Energy Research Development Agency under Projects No. No. 137951 and 137940, and the Gridx - The Autonomous Digital Grid project funded by the Center-of-Excellence for NEOM Research of KAUST (Saudi Arabia) will be presented.
Dr. Tetiana Bogodorova is a Research Scientist at RPI. She is serving as a “Deep Learning Computer System for Grid Operations Study,” project lead and a key contributor in development of deep learning algorithms and recommender systems for power system analysis. She was previously an Associate Professor at the Ukranian Catholic University, Lviv, Ukraine, with the primary duty of teaching machine learning, reinforcement learning and principles of data processing, robotics in the Computer Science Bachelor’s and MSc program. She was also a Data Scientist at ABM Cloud, developing artificial intelligence algorithms and machine learning models for retail inventory management enhancement.
Her areas of research expertise are modeling, data science and artificial intelligence for power systems, system identification and control. Her research experience includes development of Bayesian algorithms for parameter and uncertainty identification for power systems, reinforcement learning application for voltage-based thermostatically controlled loads to provide ancillary services, deep learning application for power system stability assessment.