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Title: An Incentive-based Online Optimization Framework for Distribution Grids

This article formulates a time-varying social-welfare maximization problem for distribution grids with distributed energy resources (DERs) and develops online distributed algorithms to identify (and track) its solutions. In the considered setting, network operator and DER-owners pursue given operational and economic objectives, while concurrently ensuring that voltages are within prescribed limits. The proposed algorithm affords an online implementation to enable tracking of the solutions in the presence of time-varying operational conditions and changing optimization objectives. It involves a strategy where the network operator collects voltage measurements throughout the feeder to build incentive signals for the DER-owners in real time; DERs then adjust the generated/consumed powers in order to avoid the violation of the voltage constraints while maximizing given objectives. Stability of the proposed schemes is analytically established and numerically corroborated.
Authors:
 [1] ;  [2] ;  [3] ;  [4]
  1. Univ. of Colorado, Boulder, CO (United States). Interdisciplinary Telecommunication Program
  2. National Renewable Energy Lab. (NREL), Golden, CO (United States). Power Systems Engineering Center
  3. Univ. of Colorado, Boulder, CO (United States). Computer Science and Telecommunications
  4. IBM Research Ireland, Mulhuddart (Ireland)
Publication Date:
Report Number(s):
NREL/JA-5D00-68133
Journal ID: ISSN 0018-9286
Grant/Contract Number:
AC36-08GO28308
Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Automatic Control
Additional Journal Information:
Journal Volume: 63; Journal Issue: 7; Journal ID: ISSN 0018-9286
Publisher:
IEEE
Research Org:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org:
USDOE Office of Electricity Delivery and Energy Reliability (OE)
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; voltage regulation; real-time pricing; social welfare maximization; distribution networks; time-varying optimization
OSTI Identifier:
1409162

Zhou, Xinyang, Dall'Anese, Emiliano, Chen, Lijun, and Simonetto, Andrea. An Incentive-based Online Optimization Framework for Distribution Grids. United States: N. p., Web. doi:10.1109/TAC.2017.2760284.
Zhou, Xinyang, Dall'Anese, Emiliano, Chen, Lijun, & Simonetto, Andrea. An Incentive-based Online Optimization Framework for Distribution Grids. United States. doi:10.1109/TAC.2017.2760284.
Zhou, Xinyang, Dall'Anese, Emiliano, Chen, Lijun, and Simonetto, Andrea. 2017. "An Incentive-based Online Optimization Framework for Distribution Grids". United States. doi:10.1109/TAC.2017.2760284. https://www.osti.gov/servlets/purl/1409162.
@article{osti_1409162,
title = {An Incentive-based Online Optimization Framework for Distribution Grids},
author = {Zhou, Xinyang and Dall'Anese, Emiliano and Chen, Lijun and Simonetto, Andrea},
abstractNote = {This article formulates a time-varying social-welfare maximization problem for distribution grids with distributed energy resources (DERs) and develops online distributed algorithms to identify (and track) its solutions. In the considered setting, network operator and DER-owners pursue given operational and economic objectives, while concurrently ensuring that voltages are within prescribed limits. The proposed algorithm affords an online implementation to enable tracking of the solutions in the presence of time-varying operational conditions and changing optimization objectives. It involves a strategy where the network operator collects voltage measurements throughout the feeder to build incentive signals for the DER-owners in real time; DERs then adjust the generated/consumed powers in order to avoid the violation of the voltage constraints while maximizing given objectives. Stability of the proposed schemes is analytically established and numerically corroborated.},
doi = {10.1109/TAC.2017.2760284},
journal = {IEEE Transactions on Automatic Control},
number = 7,
volume = 63,
place = {United States},
year = {2017},
month = {10}
}