skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

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

Abstract

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:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability (OE)
OSTI Identifier:
1409162
Report Number(s):
NREL/JA-5D00-68133
Journal ID: ISSN 0018-9286
Grant/Contract Number:
AC36-08GO28308
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
IEEE Transactions on Automatic Control
Additional Journal Information:
Journal Name: IEEE Transactions on Automatic Control; Journal ID: ISSN 0018-9286
Publisher:
IEEE
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

Citation Formats

Zhou, Xinyang, Dall'Anese, Emiliano, Chen, Lijun, and Simonetto, Andrea. An Incentive-based Online Optimization Framework for Distribution Grids. United States: N. p., 2017. 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. Mon . "An Incentive-based Online Optimization Framework for Distribution Grids". United States. doi:10.1109/TAC.2017.2760284.
@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 = ,
volume = ,
place = {United States},
year = {Mon Oct 09 00:00:00 EDT 2017},
month = {Mon Oct 09 00:00:00 EDT 2017}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on October 9, 2018
Publisher's Version of Record

Save / Share: