ISGT NA 2022 Tutorials
Tutorial 1 (Half Day) – Sunday 24 April, 8:00 AM – 12:00 PM
Mathematical Optimization in Active Power Distribution Systems
$195- Early Bird, $240 – Regular
Tutorial 2 (Full Day) – Sunday 24 April, 8:00 AM – 5:00 PM
Net Load Forecasting and Microgrid Decisioning
$295 – Early Bird, $395 – Regular
Registration – https://isgt.ieeepesreg.com/isgt2022/
Mathematical Optimization in Active Power Distribution Systems (Half Day)
Description: The proliferation of distributed energy resources and the deployment of advanced sensing and control technologies in electric power distribution systems calls for coordinated management of the grid’s resources. This has led to a growing interest in academia and industry alike on optimization methods for the large-scale unbalanced power distribution systems for improved operational efficiency and resilience. The current fast-paced research in this domain is driven by the challenging mathematical problem of three-phase optimal power flow (OPF). This tutorial aims to introduce the state-of-the-art optimization methods applied to unbalanced power distribution systems and their use cases for distribution systems applications. We will start with a discussion on the different models for formulating the distribution OPF problem and discuss their trade-offs. Next, we will describe the approach to formulate two advanced distribution- level applications to improve resilience and sustainability as an optimization problem. The applications are (1) Photo-Voltaic (PV) control considering grid sustainability and (2) Solutions for Tomorrow’s Grid Reconfiguration and Restoration. The application cases will include a hands-on tutorial on modeling the optimization problem using OpenDSS and Matlab/Python for the IEEE test systems.
Target Audience: Graduate students (masters and Ph.D.), Early researchers at National Labs, ADMS vendors, entry-level utility engineers.
- Get familiar with the fundamentals of distribution-level optimal power flow methods, unique challenges and differences compared to the bulk grid, and related aspects of computational complexity due to mutual coupling, unbalanced loading conditions, and control of legacy devices.
- Learn how to cast distribution system applications as an optimization problem and methods to relax or approximate the formulation to achieve a tractable model. The use cases will be demonstrated for two problem objectives, loss minimization and conservation voltage reduction (CVR).
- Learn about two advanced distribution level applications modeled as optimization problems: Resilience enhancement using PVs and DG-assisted feeder restoration.
Anamika Dubey, Washington State University
Sumit Paudyal, Florida International University,
Sukumar Kamalasadan, The University of North Carolina at Charlotte
Net Load Forecasting and Microgrid Decisioning (Full Day)
Description: Rooftop solar and other distributed generation and renewable energy technologies are becoming more and more cost effective. Add the proliferation of safer and cheaper next generation batteries, and microgrid operators and industrial and commercial producer/consumers can really participate in a more democratized grid.
This tutorial discusses some of these technologies and how they combine good load and generation forecasts and allow the grid operators and prosumers to maximize their benefits from analytics applications on their facilities.
This tutorial will cover forecasting, long-term planning and short-term optimization with considerations for grid resilience and sustainability.
Target Audience: Power system operators and planners, microgrid operators, industrial and commercial facility engineers, and electricity forecasters will benefit from knowing how to forecast their loads and how much they can gain from sustainability planning and dynamically responding to variations in the electric distribution system.
This is intended to be a basic to intermediate-level course. Any coverage of advanced technologies will only be at these levels.
At the end of this tutorial the attendee will be able to do load and solar generation forecasts, calculate net loads and perform optimization of own generation and battery capacity plans and operations schedules.
Arnie de Castro, SAS Institute