IEEE Power and Energy Society
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1 IEEE Power and Energy Society Working Group Data - Driven Modeling, Monitoring, and Control in Power Distribution Networks Nanpeng Yu, Associate Professor Department of Electrical and Computer Engineering University of California, Riverside Email: [email protected] Website: https://intra.ece.ucr.edu/~nyu/
Transcript of IEEE Power and Energy Society
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IEEE Power and Energy Society
Working Group
Data-Driven Modeling, Monitoring, and Control in Power Distribution Networks
Nanpeng Yu, Associate Professor
Department of Electrical and Computer Engineering
University of California, Riverside
Email: [email protected]
Website: https://intra.ece.ucr.edu/~nyu/
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Meeting Agenda
Scope of Working Group
Activities from Last Year
Planned Activities of the Working Group
Task Groups for Key Machine Learning Use Case/Applications
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Scope of the Working GroupGoal: Bring together engineers, data-scientists, system operatorsfrom academia and industry
Objective: Improve the modeling, monitoring, control, and planning of power distribution systems with data-driven methods.
Key message: Synergistically combine merits of state-of-the-art machine learning techniques with model-based approaches.
Data-driven analytics
Power distribution system, Distributed energy resources, End-use customers
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Applications of Machine Learning in Distribution SystemSpatio-temporal Forecasting
Electric Load / DERs – Short-Term / Long-TermAnomaly DetectionElectricity Theft, Unauthorized Solar
Interconnection
Equipment Monitoring
Predictive Maintenance
Online Diagnosis
System MonitoringState Estimation & Visualization
Network Topology &Parameter IdentificationTransformer-to-meter, Phase connectivity, Impedance estimation
Customer Behavior AnalysisCustomer segmentation, nonintrusive load monitoring, demand response
Distribution System ControlsDeep Reinforcement Learning
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Activities from Last YearOnline Short Course: Machine Learning and Big Data Analytics in Smart Grid
Applications of Machine Learning in Distribution Systems
Applications of Machine Learning for End-use Customers
Applications of Machine Learning in Transmission Systems
Applications of Machine Learning in Electricity Market
Instructors: Nanpeng Yu (UCR)
Offerings: Nov 2020, March 2021, June 2021
Attendees: ~100 attendees from electric utility industry and academia.
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Activities from Last YearDevelopment of a new course: Introduction to Reinforcement Learning with Applications in Power Systems
Graduate level course
Course Modules
Markov Decision Process
Temporal-Difference Learning
Policy Gradient Methods
State-of-the-art RL Algorithms and Advanced Topics in RL.
Reinforcement Learning-based Control in Power Systems
Course material will be shared with affiliate universities of the DOE GREAT project.
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Annual Working Group Meeting
Time: 08/07/2020
Zoom Meeting
30 Attendees from Universities, Software Vendors, National Laboratories, Electric Utilities attended the WG meeting
Formed Task Groups to work on key use cases and applications
Organize quarterly meetings for Task Groups
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Tasks Groups for Applications/Use CaseFormed Four Task Groups
Data-Driven Control
Topology Identification
Anomaly Detection
Net-load Forecasting
Each Task Group is Working on a White Paper
Updates from the Data-Driven Control Task Group
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2021 PES GM Panel SessionsPanel Sessions
Panel Session 1: Reinforcement Learning in Power Distribution Systems: Theory, Algorithms, and Applications. (Live, Monday July 26, 6-8 pm)
Panel Session 2: Physics-informed ML for Power Systems. (Live, Monday 1-3 pm)
Panel Session 3: Big Data Analysis of Synchrophasor Data: Academic Track (Live, Tuesday July 27, 10 am – 12 pm)
Panel Session 4: Big Data Analysis of Synchrophasor Data: Industry Track (Live, Tuesday July 27 3-5 pm)
Panel Session 5: Distribution System Operations in the Age of Big Data. (Pre-recorded)
Panel Session 6: Distribution-level Optimal Power Flow with Fast DER Control. (Pre-recorded)
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Planned ActivitiesDevelop, coordinate, and sponsor panel sessions, workshops, and tutorials related to the scope of the WG.
Create knowledge base in the area of machine learning/big data analytics in power distribution systems.
Prepare white papers, magazine article, and technical reports/publications on various applications/use cases of machine learning in distribution system
Develop a website with learning materials for students, scholars and industry practitioners (course syllabus, lecture slides, papers, self-study material, sample codes, implementation experience)
Testbed for machine learning applications/competition
