Work Description

Title: Inferring the behavior of distributed energy resources from incomplete measurements (project outputs) Open Access Deposited

http://creativecommons.org/licenses/by/4.0/
Attribute Value
Methodology
  • 1. Online learning 2. Kalman filtering 3. Optimal control (MPC and LQR) 4. Aggregate modeling
Description
  • This is the code that resulted from NSF grant ECCS-1508943, "Inferring the behavior of distributed energy resources from incomplete measurements." The project focused on developing control, estimation, and modeling methods for residential demand response and electric distribution networks. The talks, papers, and poster in Deep Blue:  http://hdl.handle.net/2027.42/149480
Creator
Depositor
  • gsledv@umich.edu
Contact information
Discipline
Funding agency
  • National Science Foundation (NSF)
ORSP grant number
  • ECCS-1508943
Keyword
Citations to related material
  • Ledva, Gregory S., Laura Balzano, and Johanna L. Mathieu. "Inferring the behavior of distributed energy resources with online learning." 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2015. https://doi.org/10.1109/ALLERTON.2015.7447003
  • Ledva, Gregory S., and Johanna L. Mathieu. "A linear approach to manage input delays while supplying frequency regulation using residential loads." 2017 American Control Conference (ACC). IEEE, 2017. https://doi.org/10.23919/ACC.2017.7963041
  • Ledva, Gregory S., Laura Balzano, and Johanna L. Mathieu. "Exploring Connections Between a Multiple Model Kalman Filter and Dynamic Fixed Share with Applications to Demand Response." 2018 IEEE Conference on Control Technology and Applications (CCTA). IEEE, 2018. https://doi.org/10.1109/CCTA.2018.8511493
  • Ledva, Gregory S., et al. "Disaggregating Load by Type from Distribution System Measurements in Real Time." Energy Markets and Responsive Grids. Springer, New York, NY, 2018. 413-437. https://doi.org/10.1007/978-1-4939-7822-9_17
  • Ledva, Gregory S., Sarah Peterson, and Johanna L. Mathieu. "Benchmarking of Aggregate Residential Load Models Used for Demand Response." 2018 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2018. https://doi.org/10.1109/PESGM.2018.8585847
  • Ledva, Gregory S., et al. "Managing communication delays and model error in demand response for frequency regulation." IEEE Transactions on Power Systems 33.2 (2018): 1299-1308. https://doi.org/10.1109/TPWRS.2017.2725834
  • Ledva, Gregory S., Laura Balzano, and Johanna L. Mathieu. "Real-time energy disaggregation of a distribution feeder's demand using online learning." IEEE Transactions on Power Systems 33.5 (2018): 4730-4740. https://doi.org/10.1109/TPWRS.2018.2800535
  • Talks, papers, and poster in Deep Blue: http://hdl.handle.net/2027.42/149480
Related items in Deep Blue
Resource type
Curation notes
  • Code file will be available after the paper has been published.
Last modified
  • 04/02/2020
Published
  • 06/19/2019
Language
DOI
  • https://doi.org/10.7302/xtsr-jx10
License
To Cite this Work:
Ledva, G., Zhe, D., Peterson, S., Balzano, L., Mathieu, J. (2019). Inferring the behavior of distributed energy resources from incomplete measurements (project outputs) [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/xtsr-jx10

Relationships

Files (Count: 2; Size: 2.51 GB)

Research Overview:
The aim of this project was to incorporate online learning with dynamics into inference algorithms within an electric power system, e.g., for residential demand response or real-time inference in distribution networks.
The research was conducted at the University of Michigan in Ann Arbor, MI, USA under NSF grant ECCS-1508943, "Inferring the behavior of distributed energy resources from incomplete measurements", between August 1, 2015 and July 31, 2019.

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Methods:

The methods used in this work fall into the following categories:
1) Aggregate modeling of demand-responsive loads
2) Optimal control using MPC and LQR
3) Estimation using Kalman filtering and online learning

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File Inventory:

Files in Deep Blue Data (https://doi.org/10.7302/xtsr-jx10):
- "2014_EPCN_code.zip" contains the code generated from this grant and an additional readme to explain its contents further

Files in Deep Blue ( http://hdl.handle.net/2027.42/149480)
- "paper" contains pdfs of the papers generated from this grant

- "poster" contains a pdf of a posted generated from this grant

- "talk" contains pdfs of any talks given based on the work from this grant

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Use and Access:

-All code was developed using MATLAB 2014a.

-Raw data from the Pecan Street Dataport ( https://www.pecanstreet.org/dataport/ ) and pertaining to commercial
buildings in CA were excluded from the code as per the license or agreement with the data
providers.

- Please cite the relevant paper and this dataset (see below for citation) if the code is used for future work.
Relevant papers are listed in the "code" readme for each directory of code.

- Additional details about the code can be found in the readme file within the "code" directory

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License:
Attribution 4.0 International (CC BY 4.0)

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Suggested Citation:

G.S. Ledva, L. Balzano, and J.L. Mathieu,
“Inferring the behavior of distributed energy resources from incomplete measurements (project outputs),”
Mlibrary Deep Blue Data, 2019.

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(end)

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