A reactive-iterative optimization algorithm for scheduling of air separation units under uncertainty in electricity prices

Abstract The high energy demand in power-intensive processes and the possibility of reducing the energy bills by an optimal scheduling are the motivation for incorporating energy consideration in the production scheduling of air separation plants. Optimization opportunities exist at different time scales for day-ahead scheduling decisions and real-time decisions regarding all fluctuations in electricity prices. Consequently, this paper presents a reactive-iterative optimization approach, integrating the rolling horizon (RH) concept into an iterative solution algorithm, for optimizing production decisions when an industry participates in both the day-ahead electricity market and the spot electricity market. A novel discrete-time MILP formulation is used as a basis of the proposal, which allows adjusting production rates to electricity prices varying hourly or faster. Several scenarios from a real-life air separation industrial plant are solved to show interesting trade-offs between the predictive approach and the reactive-iterative strategy.

[1]  Carl D. Laird,et al.  Optimal operation of cryogenic air separation systems with demand uncertainty and contractual obligations , 2011 .

[2]  Morgan T. Kelley,et al.  An empirical study of moving horizon closed-loop demand response scheduling , 2020 .

[3]  David R. Vinson,et al.  Air separation control technology , 2006, Comput. Chem. Eng..

[4]  Michael Baldea,et al.  Optimal Process Operations in Fast-Changing Electricity Markets: Framework for Scheduling with Low-Order Dynamic Models and an Air Separation Application , 2016 .

[5]  Ignacio E. Grossmann,et al.  Planning and Scheduling for Industrial Demand Side Management: Advances and Challenges , 2016 .

[6]  Qi Zhang,et al.  Expanding scope and computational challenges in process scheduling , 2018, Comput. Chem. Eng..

[7]  Christos T. Maravelias,et al.  General framework and modeling approach classification for chemical production scheduling , 2012 .

[8]  I. Grossmann,et al.  New continuous-time scheduling formulation for continuous plants under variable electricity cost , 2009 .

[9]  Ignacio E. Grossmann,et al.  Optimal multi-scale capacity planning for power-intensive continuous processes under time-sensitive electricity prices and demand uncertainty. Part I: Modeling , 2014, Comput. Chem. Eng..

[10]  A. Isaksson,et al.  Scheduling and energy - Industrial challenges and opportunities , 2015, Comput. Chem. Eng..

[11]  Mark H. Karwan,et al.  Operations planning with real time pricing of a primary input , 2007, Comput. Oper. Res..

[12]  Morgan T. Kelley,et al.  An efficient MILP framework for integrating nonlinear process dynamics and control in optimal production scheduling calculations , 2018, Comput. Chem. Eng..

[13]  Jaime Cerdá,et al.  State-of-the-art review of optimization methods for short-term scheduling of batch processes , 2006, Comput. Chem. Eng..

[14]  Christopher L.E. Swartz,et al.  Optimization-based assessment of design limitations to air separation plant agility in demand response scenarios , 2015 .

[15]  Alexander Mitsos,et al.  Economic Nonlinear Model Predictive Control for Flexible Operation of Air Separation Units , 2018 .

[16]  Nur I. Zulkafli,et al.  A rolling horizon stochastic programming approach for the integrated planning of production and utility systems , 2018 .

[17]  Marianthi G. Ierapetritou,et al.  From process control to supply chain management: An overview of integrated decision making strategies , 2017, Comput. Chem. Eng..

[18]  Morgan T. Kelley,et al.  Demand Response Operation of Electricity-Intensive Chemical Processes for Reduced Greenhouse Gas Emissions: Application to an Air Separation Unit , 2018, ACS Sustainable Chemistry & Engineering.

[19]  Morgan T. Kelley,et al.  Demand response scheduling under uncertainty: Chance‐constrained framework and application to an air separation unit , 2020 .

[20]  Christos T. Maravelias,et al.  Framework for studying online production scheduling under endogenous uncertainty , 2020, Comput. Chem. Eng..

[21]  Ignacio E. Grossmann,et al.  Novel MILP Scheduling Model for Power-Intensive Processes under Time-Sensitive Electricity Prices , 2018 .

[22]  Qi Zhang,et al.  A discrete-time scheduling model for continuous power-intensive process networks with various power contracts , 2016, Comput. Chem. Eng..

[23]  Michael C. Georgiadis,et al.  Optimization-Based Scheduling for the Process Industries: From Theory to Real-Life Industrial Applications , 2019, Processes.

[24]  Ajit Gopalakrishnan,et al.  On improving the online performance of production scheduling: Application to air separation units , 2018, Comput. Chem. Eng..

[25]  Michael Baldea,et al.  Moving horizon closed‐loop production scheduling using dynamic process models , 2017 .

[26]  Ignacio E. Grossmann,et al.  Optimal production planning under time-sensitive electricity prices for continuous power-intensive processes , 2012, Comput. Chem. Eng..

[27]  M. Ierapetritou,et al.  Cost Minimization in an Energy-Intensive Plant Using Mathematical Programming Approaches , 2002 .

[28]  Ignacio E. Grossmann,et al.  Optimization of steel production scheduling with complex time-sensitive electricity cost , 2015, Comput. Chem. Eng..

[29]  Antonio Espuña Camarasa,et al.  A rolling horizon stochastic programming framework for the energy supply and demand management in microgrids , 2015 .

[30]  Alexander Mitsos,et al.  A flexible air separation process: 2. Optimal operation using economic model predictive control , 2019, AIChE Journal.

[31]  Efstratios N. Pistikopoulos,et al.  Reactive Scheduling by a Multiparametric Programming Rolling Horizon Framework: A Case of a Network of Combined Heat and Power Units , 2014 .

[32]  Iiro Harjunkoski,et al.  Integration of production scheduling and energy-cost optimization using Mean Value Cross Decomposition , 2019, Comput. Chem. Eng..

[33]  Lazaros G. Papageorgiou,et al.  A rolling horizon approach for optimal management of microgrids under stochastic uncertainty , 2017 .

[34]  Pedro M. Castro,et al.  Scope for industrial applications of production scheduling models and solution methods , 2014, Comput. Chem. Eng..

[35]  Ignacio E. Grossmann,et al.  Enterprise-wide optimization for industrial demand side management: Fundamentals, advances, and perspectives , 2016 .

[36]  Michael Baldea,et al.  The integration of scheduling and control: Top-down vs. bottom-up , 2020 .

[37]  Michael Baldea,et al.  Optimal demand response scheduling of an industrial air separation unit using data-driven dynamic models , 2019, Comput. Chem. Eng..

[38]  Marianthi G. Ierapetritou,et al.  Optimal operation and control of intensified processes — challenges and opportunities , 2019 .

[39]  Michael Baldea,et al.  A simulation-based optimization framework for integrating scheduling and model predictive control, and its application to air separation units , 2018, Comput. Chem. Eng..

[40]  Carl D. Laird,et al.  A multiperiod nonlinear programming approach for operation of air separation plants with variable power pricing , 2011 .

[41]  Morgan T. Kelley,et al.  An MILP framework for optimizing demand response operation of air separation units , 2018, Applied Energy.