IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (IEEE SmartGridComm 2020)
Session Tutorial-1
Stochastic Models and Optimization Techniques for Efficient Integration of Electric Vehicles in Smart Grids
Conference
5:10 PM
—
5:40 PM UTC
Local
Nov 12 Thu, 9:10 AM
—
9:40 AM PST
Stochastic Models and Optimization Techniques for Efficient Integration of Electric Vehicles in Smart Grids
Muhammad Ismail (TNTech), I Safak Bayram (U. Strathclide)
1
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 grid-to-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.
Session Chair
Zoom Room Host(s): Shashini Desilva, Travis Hagan (Oregon State)
Session Tutorial-2
Power System Machine Learning Applications: From physics-informed learning for decision support to inference at the edge for control
Conference
5:40 PM
—
6:10 PM UTC
Local
Nov 12 Thu, 9:40 AM
—
10:10 AM PST
Power System Machine Learning Applications: From physics-informed learning for decision support to inference at the edge for control
Luigi Vanfretti (RPI), Tetiana Bogodorova (RPI)
1
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.
Session Chair
Zoom Room Host(s): Arka Sanka (UT Austin), Manish Singh (Virginia Tech)
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