An MILP framework for optimizing demand response operation of air separation units

Kelley, Morgan T.; Pattison, Richard C.; Baldick, Ross; Baldea, Michael

doi:10.1016/j.apenergy.2017.12.127

An MILP framework for optimizing demand response operation of air separation units

Journal Article · Wed May 09 00:00:00 EDT 2018 · Applied Energy

DOI:https://doi.org/10.1016/j.apenergy.2017.12.127· OSTI ID:1605934

Kelley, Morgan T. ^[1]; Pattison, Richard C. ^[2]; ^[2]; ^[2]

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

Renewable electricity generation and consumer demand are desynchronized in time, posing a challenge for grid operators. Industrial demand response (DR), has emerged as a strong candidate for mitigating demand variability. In this paper, we demonstrate the application of DR to an air separation unit (ASU), a chemical process that consumes significant amounts of electricity. We develop a novel optimal production scheduling framework that accounts for day-ahead electricity prices to modulate the grid load presented by the plant. Our framework accounts for the dynamics of the plant using a novel dynamic modeling framework. Further, we present a new decomposition scheme that allows us to solve the corresponding optimization problem in a matter of minutes. Extensive simulation results show significant reductions in operating costs (that benefit the plant) and reductions in peak power demand (which benefits the grid).

View Accepted Manuscript (DOE)

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

Sponsoring Organization:: USDOE Office of Electricity Delivery and Energy Reliability (OE); National Science Foundation (NSF)

Grant/Contract Number:: OE0000841

OSTI ID:: 1605934

Alternate ID(s):: OSTI ID: 1872912
OSTI ID: 1692214
OSTI ID: 23066513

Journal Information:: Applied Energy, Journal Name: Applied Energy Journal Issue: C Vol. 222; ISSN 0306-2619

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (36)

Nonlinear System Identification Billings, Stephen A. John Wiley & Sons, Ltd. https://doi.org/10.1002/9781118535561	book	January 2013
Enterprise-wide optimization: A new frontier in process systems engineering Grossmann, Ignacio AIChE Journal, Vol. 51, Issue 7 https://doi.org/10.1002/aic.10617	journal	January 2005
Air separation with cryogenic energy storage: Optimal scheduling considering electric energy and reserve markets Zhang, Qi; Grossmann, Ignacio E.; Heuberger, Clara F. AIChE Journal, Vol. 61, Issue 5 https://doi.org/10.1002/aic.14730	journal	February 2015
Integrated production scheduling and model predictive control of continuous processes Baldea, Michael; Du, Juan; Park, Jungup AIChE Journal, Vol. 61, Issue 12 https://doi.org/10.1002/aic.14951	journal	August 2015
Preemptive dynamic operation of cryogenic air separation units Cao, Yanan; Swartz, Christopher L. E.; Flores‐Cerrillo, Jesus AIChE Journal, Vol. 63, Issue 9 https://doi.org/10.1002/aic.15753	journal	May 2017
Lagrangean relaxation Guignard, Monique Top, Vol. 11, Issue 2 https://doi.org/10.1007/BF02579036	journal	December 2003
Vapour-liquid equilibrium N2ArO2 for lower argon concentrations Harmens, A. Cryogenics, Vol. 10, Issue 5 https://doi.org/10.1016/0011-2275(70)90010-X	journal	October 1970
The potential of demand-side management in energy-intensive industries for electricity markets in Germany Paulus, Moritz; Borggrefe, Frieder Applied Energy, Vol. 88, Issue 2 https://doi.org/10.1016/j.apenergy.2010.03.017	journal	February 2011
A cryogenic air separation process based on self-heat recuperation for oxy-combustion plants Fu, Qian; Kansha, Yasuki; Song, Chunfeng Applied Energy, Vol. 162 https://doi.org/10.1016/j.apenergy.2015.03.039	journal	January 2016
Potential for improving the energy efficiency of cryogenic air separation unit (ASU) using binary heat recovery cycles Aneke, Mathew; Wang, Meihong Applied Thermal Engineering, Vol. 81 https://doi.org/10.1016/j.applthermaleng.2015.02.034	journal	April 2015
Air separation control technology Vinson, David R. Computers & Chemical Engineering, Vol. 30, Issue 10-12 https://doi.org/10.1016/j.compchemeng.2006.05.038	journal	September 2006
Coordinator MPC for maximizing plant throughput Aske, Elvira Marie B.; Strand, Stig; Skogestad, Sigurd Computers & Chemical Engineering, Vol. 32, Issue 1-2 https://doi.org/10.1016/j.compchemeng.2007.05.012	journal	January 2008
Modeling, dynamics and control of process networks with high energy throughput Baldea, Michael; Daoutidis, Prodromos Computers & Chemical Engineering, Vol. 32, Issue 9 https://doi.org/10.1016/j.compchemeng.2008.02.012	journal	September 2008
Integration of production planning and scheduling: Overview, challenges and opportunities Maravelias, Christos T.; Sung, Charles Computers & Chemical Engineering, Vol. 33, Issue 12 https://doi.org/10.1016/j.compchemeng.2009.06.007	journal	December 2009
Optimal design of cryogenic air separation columns under uncertainty Zhu, Yu; Legg, Sean; Laird, Carl D. Computers & Chemical Engineering, Vol. 34, Issue 9 https://doi.org/10.1016/j.compchemeng.2010.02.007	journal	September 2010
Optimal production planning under time-sensitive electricity prices for continuous power-intensive processes Mitra, Sumit; Grossmann, Ignacio E.; Pinto, Jose M. Computers & Chemical Engineering, Vol. 38 https://doi.org/10.1016/j.compchemeng.2011.09.019	journal	March 2012
Integrated production scheduling and process control: A systematic review Baldea, Michael; Harjunkoski, Iiro Computers & Chemical Engineering, Vol. 71 https://doi.org/10.1016/j.compchemeng.2014.09.002	journal	December 2014
A time scale-bridging approach for integrating production scheduling and process control Du, Juan; Park, Jungup; Harjunkoski, Iiro Computers & Chemical Engineering, Vol. 79 https://doi.org/10.1016/j.compchemeng.2015.04.026	journal	August 2015
Practical optimization for cost reduction of a liquefier in an industrial air separation plant Cao, Yanan; Flores-Cerrillo, Jesus; Swartz, Christopher L. E. Computers & Chemical Engineering, Vol. 99 https://doi.org/10.1016/j.compchemeng.2016.12.011	journal	April 2017
Integrated real-time production scheduling of a multiple cryogenic air separation unit and compressor plant Adamson, Richard; Hobbs, Martin; Silcock, Andy Computers & Chemical Engineering, Vol. 104 https://doi.org/10.1016/j.compchemeng.2017.04.001	journal	September 2017
An efficient MILP framework for integrating nonlinear process dynamics and control in optimal production scheduling calculations Kelley, Morgan T.; Pattison, Richard C.; Baldick, Ross Computers & Chemical Engineering, Vol. 110 https://doi.org/10.1016/j.compchemeng.2017.11.021	journal	February 2018
Operations planning with real time pricing of a primary input Karwan, Mark H.; Keblis, Matthew F. Computers & Operations Research, Vol. 34, Issue 3 https://doi.org/10.1016/j.cor.2005.05.014	journal	March 2007
Using exergy analysis to reduce power consumption in air separation units for oxy-combustion processes Fu, Chao; Gundersen, Truls Energy, Vol. 44, Issue 1 https://doi.org/10.1016/j.energy.2012.01.065	journal	August 2012
Advanced step nonlinear model predictive control for air separation units Huang, Rui; Zavala, Victor M.; Biegler, Lorenz T. Journal of Process Control, Vol. 19, Issue 4 https://doi.org/10.1016/j.jprocont.2008.07.006	journal	April 2009
Integrating scheduling and control for economic MPC of buildings with energy storage Touretzky, Cara R.; Baldea, Michael Journal of Process Control, Vol. 24, Issue 8 https://doi.org/10.1016/j.jprocont.2014.04.015	journal	August 2014
Optimization-based assessment of design limitations to air separation plant agility in demand response scenarios Cao, Yanan; Swartz, Christopher L. E.; Baldea, Michael Journal of Process Control, Vol. 33 https://doi.org/10.1016/j.jprocont.2015.05.002	journal	September 2015
A novel cryogenic air separation process based on self-heat recuperation Kansha, Yasuki; Kishimoto, Akira; Nakagawa, Tsuguhiko Separation and Purification Technology, Vol. 77, Issue 3 https://doi.org/10.1016/j.seppur.2011.01.012	journal	March 2011
Optimal scheduling of multiple sets of air separation units with frequent load-change operation Zhou, Danyan; Zhou, Kai; Zhu, Lingyu Separation and Purification Technology, Vol. 172 https://doi.org/10.1016/j.seppur.2016.08.009	journal	January 2017
Optimal Process Operations in Fast-Changing Electricity Markets: Framework for Scheduling with Low-Order Dynamic Models and an Air Separation Application Pattison, Richard C.; Touretzky, Cara R.; Johansson, Ted Industrial & Engineering Chemistry Research, Vol. 55, Issue 16 https://doi.org/10.1021/acs.iecr.5b03499	journal	April 2016
Selection of process control structure based on linear dynamic economics Narraway, Lawrence T.; Perkins, John D. Industrial & Engineering Chemistry Research, Vol. 32, Issue 11 https://doi.org/10.1021/ie00023a035	journal	November 1993
Cost Minimization in an Energy-Intensive Plant Using Mathematical Programming Approaches Ierapetritou, M. G.; Wu, D.; Vin, J. Industrial & Engineering Chemistry Research, Vol. 41, Issue 21 https://doi.org/10.1021/ie011012b	journal	October 2002
Economic Incentive for Intermittent Operation of Air Separation Plants with Variable Power Costs Miller, Jason; Luyben, William L.; Blouin, Stephane Industrial & Engineering Chemistry Research, Vol. 47, Issue 4 https://doi.org/10.1021/ie070593n	journal	February 2008
Dynamics and Control of Process Networks with Large Energy Recycle Jogwar, Sujit S.; Baldea, Michael; Daoutidis, Prodromos Industrial & Engineering Chemistry Research, Vol. 48, Issue 13 https://doi.org/10.1021/ie801050b	journal	July 2009
An Applications Oriented Guide to Lagrangian Relaxation Fisher, Marshall L. Interfaces, Vol. 15, Issue 2 https://doi.org/10.1287/inte.15.2.10	journal	April 1985
The Lagrangian Relaxation Method for Solving Integer Programming Problems Fisher, Marshall L. Management Science, Vol. 50, Issue 12_supplement https://doi.org/10.1287/mnsc.1040.0263	journal	December 2004
A Tutorial on Dual Decomposition and Lagrangian Relaxation for Inference in Natural Language Processing Rush, A. M.; Collins, M. J. Journal of Artificial Intelligence Research, Vol. 45 https://doi.org/10.1613/jair.3680	journal	September 2012

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