An Incentive-based Online Optimization Framework for Distribution Grids

doi:10.1109/TAC.2017.2760284

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

Article Details
Other Related Research

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:

Zhou, Xinyang ^[1] ; Dall'Anese, Emiliano ^[2] ; Chen, Lijun ^[3] ; Simonetto, Andrea ^[4]

Univ. of Colorado, Boulder, CO (United States). Interdisciplinary Telecommunication Program
National Renewable Energy Lab. (NREL), Golden, CO (United States). Power Systems Engineering Center
Univ. of Colorado, Boulder, CO (United States). Computer Science and Telecommunications
IBM Research Ireland, Mulhuddart (Ireland)

Publication Date:: 2017-10-09

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.

Copy to clipboard


                    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.

Copy to clipboard


                    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.

Copy to clipboard


                    
@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}

}

Copy to clipboard

Free Publicly Accessible Full Text
Accepted Manuscript (DOE)
Publisher's Version of Record
10.1109/TAC.2017.2760284
https://doi.org/10.1109/TAC.2017.2760284

Citation Metrics
Cited by: 1work
Citation information provided by
Web of Science

Save / Share this Record
Export Metadata
- Endnote
- RIS
- Excel
- CSV
- XML
Save to My Library
Send to Email

Send to Email

Email address:

Content:

Similar Records

Similar records in DOE PAGES and OSTI.GOV collections:

A Multiobjective Optimization Framework for Online Stochastic Optimal Control in Hybrid Electric Vehicles

Journal Article Malikopoulos, Andreas January 2015 - IEEE Transactions on Control Systems Technology

The increasing urgency to extract additional efficiency from hybrid propulsion systems has led to the development of advanced power management control algorithms. In this paper we address the problem of online optimization of the supervisory power management control in parallel hybrid electric vehicles (HEVs). We model HEV operation as a controlled Markov chain and we show that the control policy yielding the Pareto optimal solution minimizes online the long-run expected average cost per unit time criterion. The effectiveness of the proposed solution is validated through simulation and compared to the solution derived with dynamic programming using the average cost criterion.more »« less
Cited by 4Full Text Available
A Multiobjective Optimization Framework for Online Stochastic Optimal Control in Hybrid Electric Vehicles

Journal Article Malikopoulos, Andreas A. July 2015 - IEEE Transactions on Control Systems Technology

The increasing urgency to extract additional efficiency from hybrid propulsion systems has led to the development of advanced power management control algorithms. For this research, we address the problem of online optimization of the supervisory power management control in parallel hybrid electric vehicles (HEVs). We model HEV operation as a controlled Markov chain and show that the control policy yielding the Pareto optimal solution minimizes online the long-run expected average cost per unit time criterion. The effectiveness of the proposed solution is validated through simulation and compared with the solution derived with dynamic programming using the average cost criterion. Finally,more »« less
Cited by 4Full Text Available
Large-scale optimization-based non-negative computational framework for diffusion equations: Parallel implementation and performance studies

Journal Article Chang, Justin ; Karra, Satish ; Nakshatrala, Kalyana B. July 2016 - Journal of Scientific Computing

It is well-known that the standard Galerkin formulation, which is often the formulation of choice under the finite element method for solving self-adjoint diffusion equations, does not meet maximum principles and the non-negative constraint for anisotropic diffusion equations. Recently, optimization-based methodologies that satisfy maximum principles and the non-negative constraint for steady-state and transient diffusion-type equations have been proposed. To date, these methodologies have been tested only on small-scale academic problems. The purpose of this paper is to systematically study the performance of the non-negative methodology in the context of high performance computing (HPC). PETSc and TAO libraries are, respectively, usedmore »« less
Cited by 1Full Text Available
Sequential-Optimization-Based Framework for Robust Modeling and Design of Heterogeneous Catalytic Systems

Journal Article Rangarajan, Srinivas ; Maravelias, Christos T. ; Mavrikakis, Manos November 2017 - Journal of Physical Chemistry. C

Here, we present a general optimization-based framework for (i) ab initio and experimental data driven mechanistic modeling and (ii) optimal catalyst design of heterogeneous catalytic systems. Both cases are formulated as a nonlinear optimization problem that is subject to a mean-field microkinetic model and thermodynamic consistency requirements as constraints, for which we seek sparse solutions through a ridge (L ₂ regularization) penalty. The solution procedure involves an iterative sequence of forward simulation of the differential algebraic equations pertaining to the microkinetic model using a numerical tool capable of handling stiff systems, sensitivity calculations using linear algebra, and gradient-based nonlinear optimization.more »« less
Cited by 2Full Text Available
Data distribution service-based interoperability framework for smart grid testbed infrastructure

Journal Article Youssef, Tarek A. ; Elsayed, Ahmed T. ; Mohammed, Osama A. March 2016 - Energies (Basel)

This study presents the design and implementation of a communication and control infrastructure for smart grid operation. The proposed infrastructure enhances the reliability of the measurements and control network. The advantages of utilizing the data-centric over message-centric communication approach are discussed in the context of smart grid applications. The data distribution service (DDS) is used to implement a data-centric common data bus for the smart grid. This common data bus improves the communication reliability, enabling distributed control and smart load management. These enhancements are achieved by avoiding a single point of failure while enabling peer-to-peer communication and an automatic discoverymore »« less
Cited by 2Full Text Available

Access journal articles and accepted manuscripts resulting from DOE research funding

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

Access journal articles and accepted manuscripts
resulting from DOE research funding