Online Stochastic Optimization of Networked Distributed Energy Resources

Zhou, Xinyang; Dall'Anese, Emiliano; Chen, Lijun

doi:10.1109/TAC.2019.2927925

Title: Online Stochastic Optimization of Networked Distributed Energy Resources

Abstract

Our work investigates distributed control and incentive mechanisms to coordinate distributed energy resources (DERs) with both continuous and discrete decision variables as well as device dynamics in distribution grids. We formulate a multi-period social welfare maximization problem, and based on its convex relaxation propose a distributed stochastic dual gradient algorithm for managing DERs. Moreover, we extend it to an online realtime setting with time-varying operating conditions, asynchronous updates by devices, and feedback being leveraged to account for nonlinear power flows as well as reduce communication overhead. The resulting algorithm provides a general online stochastic optimization algorithm for coordinating networked DERs with discrete power setpoints and dynamics to meet operational and economic objectives and constraints. We characterize the convergence of the algorithm analytically and evaluate its performance numerically.

Authors:

Zhou, Xinyang ^[1]; Dall'Anese, Emiliano ^[2]; Chen, Lijun ^[2]

National Renewable Energy Lab. (NREL), Golden, CO (United States)
Univ. of Colorado, Boulder, CO (United States)

Publication Date:: Thu Jul 11 00:00:00 EDT 2019

Research Org.:: National Renewable Energy Lab. (NREL), Golden, CO (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

OSTI Identifier:: 1574288

Report Number(s):: NREL/JA-5D00-74054
Journal ID: ISSN 0018-9286; MainId:19823;UUID:9f53de84-5c82-e911-9c21-ac162d87dfe5;MainAdminID:8300

Grant/Contract Number:: AC36-08GO28308

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Automatic Control

Additional Journal Information:: Journal Volume: 65; Journal Issue: 6; Journal ID: ISSN 0018-9286

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 24 POWER TRANSMISSION AND DISTRIBUTION; discrete decision variables; real-time pricing; asynchronous updates; stochastic dual algorithm; distribution grids; time-varying optimization

Citation Formats


                    Zhou, Xinyang, Dall'Anese, Emiliano, and Chen, Lijun. Online Stochastic Optimization of Networked Distributed Energy Resources.  United States: N. p., 2019. 
Web.  doi:10.1109/TAC.2019.2927925.

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                    Zhou, Xinyang, Dall'Anese, Emiliano, & Chen, Lijun. Online Stochastic Optimization of Networked Distributed Energy Resources.  United States.  https://doi.org/10.1109/TAC.2019.2927925

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                    Zhou, Xinyang, Dall'Anese, Emiliano, and Chen, Lijun. Thu .  
"Online Stochastic Optimization of Networked Distributed Energy Resources".  United States.  https://doi.org/10.1109/TAC.2019.2927925.  https://www.osti.gov/servlets/purl/1574288.

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@article{osti_1574288,

  title        = {Online Stochastic Optimization of Networked Distributed Energy Resources},

  author       = {Zhou, Xinyang and Dall'Anese, Emiliano and Chen, Lijun},

  abstractNote = {Our work investigates distributed control and incentive mechanisms to coordinate distributed energy resources (DERs) with both continuous and discrete decision variables as well as device dynamics in distribution grids. We formulate a multi-period social welfare maximization problem, and based on its convex relaxation propose a distributed stochastic dual gradient algorithm for managing DERs. Moreover, we extend it to an online realtime setting with time-varying operating conditions, asynchronous updates by devices, and feedback being leveraged to account for nonlinear power flows as well as reduce communication overhead. The resulting algorithm provides a general online stochastic optimization algorithm for coordinating networked DERs with discrete power setpoints and dynamics to meet operational and economic objectives and constraints. We characterize the convergence of the algorithm analytically and evaluate its performance numerically.},

  doi          = {10.1109/TAC.2019.2927925},

  journal      = {IEEE Transactions on Automatic Control},

  number       = 6,

  volume       = 65,

  place        = {United States},

  year         = {Thu Jul 11 00:00:00 EDT 2019},

  month        = {Thu Jul 11 00:00:00 EDT 2019}

}

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Journal Article:

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https://doi.org/10.1109/TAC.2019.2927925

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Cited by: 13 works

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Figures / Tables:

Table I: Notation. For notational simplicity, we apply bold symbols with superscript $t$ to represent the stacked variables within the w timeslots starting from time $t$, e.g., $z$^t = [($z$^$t$) ^Τ, . . . , ($z$^$t+w$) ^Τ] ^Τ. Similarly, functions denoted with a bold letter are vector-valued function over $w$more »

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