Demand response scheduling under uncertainty: Chance–constrained framework and application to an air separation unit

Kelley, Morgan T.; Baldick, Ross; Baldea, Michael

doi:10.1002/aic.16273

Title: Demand response scheduling under uncertainty: Chance–constrained framework and application to an air separation unit

Abstract

Abstract Recent increases in renewable power generation challenge the operation of the power grid: generation rates fluctuate in time and are not synchronized with power demand fluctuations. Demand response (DR) consists of adjusting user electricity demand to match available power supply. Chemical plants are appealing candidates for DR programs; they offer large, concentrated loads that can be modulated via production scheduling. Price‐based DR is a common means of engaging industrial entities; its benefits increase significantly when a longer (typically, a few days) scheduling time horizon is considered. DR production scheduling comes with its own challenges, related to uncertainty in future (i.e., forecast) electricity prices and product demand. In this work, we provide a framework for DR production scheduling under uncertainty based on a chance‐constrained formulation that also accounts for the dynamics of the production facility. The ideas are illustrated with an air separation unit case study.

Authors:

Kelley, Morgan T. ^[1]; Baldick, Ross ^[1];

^[1]

Univ. of Texas at Austin, TX (United States)

Publication Date:: Sat May 16 00:00:00 EDT 2020

Research Org.:: Univ. of Texas at Austin, TX (United States)

Sponsoring Org.:: USDOE; National Science Foundation (NSF)

OSTI Identifier:: 1872911

Alternate Identifier(s):: OSTI ID: 1646957

Grant/Contract Number:: FG02-97ER25308; OE0000841; DE‐FG02‐97ER25308; DE‐OE0000841

Resource Type:: Accepted Manuscript

Journal Name:: AIChE Journal

Additional Journal Information:: Journal Volume: 66; Journal Issue: 9; Journal ID: ISSN 0001-1541

Publisher:: American Institute of Chemical Engineers

Country of Publication:: United States

Language:: English

Subject:: 24 POWER TRANSMISSION AND DISTRIBUTION; demand response; uncertainty; chance constraints; air separation unit

Citation Formats


                    Kelley, Morgan T., Baldick, Ross, and Baldea, Michael. Demand response scheduling under uncertainty: Chance–constrained framework and application to an air separation unit.  United States: N. p., 2020. 
Web.  doi:10.1002/aic.16273.

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                    Kelley, Morgan T., Baldick, Ross, & Baldea, Michael. Demand response scheduling under uncertainty: Chance–constrained framework and application to an air separation unit.  United States.  https://doi.org/10.1002/aic.16273

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                    Kelley, Morgan T., Baldick, Ross, and Baldea, Michael. Sat .  
"Demand response scheduling under uncertainty: Chance–constrained framework and application to an air separation unit".  United States.  https://doi.org/10.1002/aic.16273.  https://www.osti.gov/servlets/purl/1872911.

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@article{osti_1872911,

  title        = {Demand response scheduling under uncertainty: Chance–constrained framework and application to an air separation unit},

  author       = {Kelley, Morgan T. and Baldick, Ross and Baldea, Michael},

  abstractNote = {Abstract Recent increases in renewable power generation challenge the operation of the power grid: generation rates fluctuate in time and are not synchronized with power demand fluctuations. Demand response (DR) consists of adjusting user electricity demand to match available power supply. Chemical plants are appealing candidates for DR programs; they offer large, concentrated loads that can be modulated via production scheduling. Price‐based DR is a common means of engaging industrial entities; its benefits increase significantly when a longer (typically, a few days) scheduling time horizon is considered. DR production scheduling comes with its own challenges, related to uncertainty in future (i.e., forecast) electricity prices and product demand. In this work, we provide a framework for DR production scheduling under uncertainty based on a chance‐constrained formulation that also accounts for the dynamics of the production facility. The ideas are illustrated with an air separation unit case study.},

  doi          = {10.1002/aic.16273},

  journal      = {AIChE Journal},

  number       = 9,

  volume       = 66,

  place        = {United States},

  year         = {Sat May 16 00:00:00 EDT 2020},

  month        = {Sat May 16 00:00:00 EDT 2020}

}

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Similar Records

Title: Demand response scheduling under uncertainty: Chance–constrained framework and application to an air separation unit

Abstract

Citation Formats

An MILP framework for optimizing demand response operation of air separation units journal, July 2018

Integration of production planning and scheduling: Overview, challenges and opportunities journal, December 2009

Enterprise-wide optimization: A new frontier in process systems engineering journal, January 2005

Integrated production scheduling and process control: A systematic review journal, December 2014

Optimal Process Operations in Fast-Changing Electricity Markets: Framework for Scheduling with Low-Order Dynamic Models and an Air Separation Application journal, April 2016

Optimal scheduling of demand responsive industrial production with hybrid renewable energy systems journal, January 2017

Energy Pricing and Dispatch for Smart Grid Retailers Under Demand Response and Market Price Uncertainty journal, May 2015

Optimization Under Uncertainty of Thermal Storage-Based Flexible Demand Response With Quantification of Residential Users’ Discomfort journal, September 2015

Real-Time Demand Response Model journal, December 2010

Operational scheduling of microgrids via parametric programming journal, October 2016

A rolling horizon approach for optimal management of microgrids under stochastic uncertainty journal, March 2018

Energy management and load shaping for commercial microgrids coupled with flexible building environment control journal, April 2018

Dynamics and Control of Process Networks with Large Energy Recycle journal, July 2009

Integrating scheduling and control for economic MPC of buildings with energy storage journal, August 2014

A time scale-bridging approach for integrating production scheduling and process control journal, August 2015

An efficient MILP framework for integrating nonlinear process dynamics and control in optimal production scheduling calculations journal, February 2018

The Elements of Statistical Learning book, January 2009

Chance-Constrained Programming journal, October 1959

Chance Constrained Programming with Joint Constraints journal, December 1965

Scheduling of batch processes with operational uncertainties journal, January 1996

Multiperiod Planning and Scheduling of Multiproduct Batch Plants under Demand Uncertainty journal, November 1997

Simultaneous production planning and scheduling in multiproduct batch plants journal, April 1990

Chance-Constrained Day-Ahead Scheduling in Stochastic Power System Operation journal, July 2014

Chance Constrained Reserve Scheduling Using Uncertain Controllable Loads Part I: Formulation and Scenario-Based Analysis journal, March 2019

Chance Constrained Reserve Scheduling Using Uncertain Controllable Loads Part II: Analytical Reformulation journal, March 2019

Moving horizon closed-loop production scheduling using dynamic process models journal, July 2016

Dynamic models and fault diagnosis‐based triggers for closed‐loop scheduling journal, December 2016

An empirical study of moving horizon closed-loop demand response scheduling journal, August 2020

Chance constrained programming approach to process optimization under uncertainty journal, January 2008

Optimization of conditional value-at-risk journal, January 2000

An adjustable robust optimization approach to scheduling of continuous industrial processes providing interruptible load journal, March 2016

Air separation with cryogenic energy storage: Optimal scheduling considering electric energy and reserve markets journal, February 2015

A flexible air separation process: 2. Optimal operation using economic model predictive control journal, July 2019

Optimal Dynamic Operation of a High-Purity Air Separation Plant under Varying Market Conditions journal, September 2016

An MILP framework for optimizing demand response operation of air separation units
journal, July 2018

Integration of production planning and scheduling: Overview, challenges and opportunities
journal, December 2009

Enterprise-wide optimization: A new frontier in process systems engineering
journal, January 2005

Integrated production scheduling and process control: A systematic review
journal, December 2014

Optimal Process Operations in Fast-Changing Electricity Markets: Framework for Scheduling with Low-Order Dynamic Models and an Air Separation Application
journal, April 2016

Optimal scheduling of demand responsive industrial production with hybrid renewable energy systems
journal, January 2017

Energy Pricing and Dispatch for Smart Grid Retailers Under Demand Response and Market Price Uncertainty
journal, May 2015

Optimization Under Uncertainty of Thermal Storage-Based Flexible Demand Response With Quantification of Residential Users’ Discomfort
journal, September 2015

Real-Time Demand Response Model
journal, December 2010

Operational scheduling of microgrids via parametric programming
journal, October 2016

A rolling horizon approach for optimal management of microgrids under stochastic uncertainty
journal, March 2018

Energy management and load shaping for commercial microgrids coupled with flexible building environment control
journal, April 2018

Dynamics and Control of Process Networks with Large Energy Recycle
journal, July 2009

Integrating scheduling and control for economic MPC of buildings with energy storage
journal, August 2014

A time scale-bridging approach for integrating production scheduling and process control
journal, August 2015

An efficient MILP framework for integrating nonlinear process dynamics and control in optimal production scheduling calculations
journal, February 2018

The Elements of Statistical Learning
book, January 2009

Chance-Constrained Programming
journal, October 1959

Chance Constrained Programming with Joint Constraints
journal, December 1965

Scheduling of batch processes with operational uncertainties
journal, January 1996

Multiperiod Planning and Scheduling of Multiproduct Batch Plants under Demand Uncertainty
journal, November 1997

Simultaneous production planning and scheduling in multiproduct batch plants
journal, April 1990

Chance-Constrained Day-Ahead Scheduling in Stochastic Power System Operation
journal, July 2014

Chance Constrained Reserve Scheduling Using Uncertain Controllable Loads Part I: Formulation and Scenario-Based Analysis
journal, March 2019

Chance Constrained Reserve Scheduling Using Uncertain Controllable Loads Part II: Analytical Reformulation
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An empirical study of moving horizon closed-loop demand response scheduling
journal, August 2020

Chance constrained programming approach to process optimization under uncertainty
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Optimization of conditional value-at-risk
journal, January 2000

An adjustable robust optimization approach to scheduling of continuous industrial processes providing interruptible load
journal, March 2016

Air separation with cryogenic energy storage: Optimal scheduling considering electric energy and reserve markets
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