Energy demand management for process systems through production scheduling and control

Demand response (DR) is an integral part of the Smart Grid paradigm, and has become the focus of growing research, development, and deployment in residential, commercial and industrial systems over the last few years. In process systems, energy demand management through production scheduling is an increasingly important tool that has the potential to provide significant economic and operational benefits by promoting the responsiveness of the process operation and its interactions with the utility providers. However, the dynamic behavior of the underlying process, especially during process transitions, is seldom taken into account as part of the DR problem formulation. Furthermore, the incorporation of energy constraints related to electricity pricing and energy resource availability presents an additional challenge. The goal of this study is to present a novel optimization formulation for energy demand management in process systems that accounts explicitly for transition behaviors and costs, subject to time-sensitive electricity prices and uncertainties in renewable energy resources. The proposed formulation brings together production scheduling and closed-loop control, and is realized through a real-time or receding-horizon optimization framework depending on the underlying operational scenarios. The dynamic formulation is cast as a mixed-integer nonlinear programming problem based on a proposed discretization approach, and its merits are demonstrated using a simulated continuous stirred tank reactor where the energy required is assumed to be roughly proportional to the material flow. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3756–3769, 2015

[1]  L. Biegler,et al.  Dynamic Optimization in the Design and Scheduling of Multiproduct Batch Plants , 1996 .

[2]  Paul I. Barton,et al.  Mixed-integer dynamic optimization I: problem formulation , 1999 .

[3]  Francis J. Doyle,et al.  Control‐relevant scheduling of polymer grade transitions , 2002 .

[4]  Ignacio E. Grossmann,et al.  Simultaneous Cyclic Scheduling and Control of a Multiproduct CSTR Reactor , 2006 .

[5]  J. Butcher.  Numerical Methods for Ordinary Differential Equations: Butcher/Numerical Methods , 2005 .

[6]  Rüdiger Franke,et al.  Production campaign planning including grade transition sequencing and dynamic optimization , 2005, Comput. Chem. Eng..

[7]  L. Biegler,et al.  A robust and efficient mixed-integer non-linear dynamic optimization approach for simultaneous design and control , 2005 .

[8]  Pawel Pawlewski,et al.  Multiagent Approach for Supply Chain Integration by Distributed Production Planning, Scheduling and Control System , 2008, DCAI.

[9]  Ignacio E. Grossmann,et al.  Lagrangean heuristic for the scheduling and control of polymerization reactors , 2008 .

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

[11]  Farrokh Albuyeh,et al.  Grid of the future , 2009, IEEE Power and Energy Magazine.

[12]  W. Short,et al.  Evaluating the Impacts of Real-Time Pricing on the Usage of Wind Generation , 2009, IEEE Transactions on Power Systems.

[13]  Iiro Harjunkoski,et al.  Integration of scheduling and control - Theory or practice? , 2009, Comput. Chem. Eng..

[14]  Ignacio E. Grossmann,et al.  Simultaneous scheduling and control of multiproduct continuous parallel lines , 2010 .

[15]  Carlos Rubio-Maya,et al.  Design optimization of a polygeneration plant fuelled by natural gas and renewable energy sources , 2011 .

[16]  Lu Zhang,et al.  Optimal sizing study of hybrid wind/PV/diesel power generation unit , 2011 .

[17]  Frieder Borggrefe,et al.  The potential of demand-side management in energy-intensive industries for electricity markets in Germany , 2011 .

[18]  Hyung-Geun Kwag,et al.  Optimal combined scheduling of generation and demand response with demand resource constraints , 2012 .

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

[20]  Lorenz T. Biegler,et al.  Integrated scheduling and dynamic optimization of batch processes using state equipment networks , 2012 .

[21]  Marianthi G. Ierapetritou,et al.  Integration of Scheduling and Control with Closed Loop Implementation , 2012 .

[22]  Dolf Gielen,et al.  The potential for renewable energy in industrial applications , 2012 .

[23]  Abdulkerim Karabiber,et al.  A user-mode distributed energy management architecture for smart grid applications , 2012 .

[24]  J. Aghaei,et al.  Demand response in smart electricity grids equipped with renewable energy sources: A review , 2013 .

[25]  Wei Qiao,et al.  Short-term solar power prediction using a support vector machine , 2013 .

[26]  E. Lima,et al.  Modeling and performance monitoring of multivariate multimodal processes , 2013 .

[27]  Martin Rudberg,et al.  Strategic perspectives on energy management: A case study in the process industry , 2013 .

[28]  Benjamin P. Omell,et al.  IGCC Power Plant Dispatch Using Infinite-Horizon Economic Model Predictive Control , 2013 .

[29]  Y. Wang,et al.  Time-of-use based electricity demand response for sustainable manufacturing systems , 2013 .

[30]  Jan Kleissl,et al.  A high-resolution, cloud-assimilating numerical weather prediction model for solar irradiance forecasting , 2013 .

[31]  Ahmet Palazoglu,et al.  An economic receding horizon optimization approach for energy management in the chlor-alkali process with hybrid renewable energy generation , 2014 .

[32]  Christos T. Maravelias,et al.  Economic model predictive control for inventory management in supply chains , 2014, Comput. Chem. Eng..

[33]  M. Ierapetritou,et al.  Integration of scheduling and control for batch processes using multi‐parametric model predictive control , 2014 .

[34]  Michael Baldea,et al.  Integrated production scheduling and process control: A systematic review , 2014, Comput. Chem. Eng..

[35]  Marianthi G. Ierapetritou,et al.  An integrated framework for scheduling and control using fast model predictive control , 2015 .

[36]  Michael Baldea,et al.  A time scale-bridging approach for integrating production scheduling and process control , 2015, Comput. Chem. Eng..

[37]  Ahmet Palazoglu,et al.  Operational optimization and demand response of hybrid renewable energy systems , 2015 .