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 i...

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Bibliographic Details
Published in:AIChE journal Vol. 61; no. 11; pp. 3756 - 3769
Main Authors: Tong, Chudong, Palazoglu, Ahmet, El-Farra, Nael H., Yan, Xuefeng
Format: Journal Article
Language:English
Published: New York Blackwell Publishing Ltd 01.11.2015
American Institute of Chemical Engineers
Subjects:
Alternative energy sources
demand response
Dynamical systems
Dynamics
Electricity
Electricity pricing
Energy demand
Energy management
Energy sources
Formulations
Management
Nonlinear programming
Optimization
Production scheduling
Renewable energy
Renewable resources
Resource availability
scheduling and control
Simulation
ISSN:0001-1541, 1547-5905
Online Access:Get full text
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Summary: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
Bibliography:China Scholarship Council (CSC), the US National Science Foundation - No. CBET-1438456
istex:73152EAAEDC173E44BB65B103CDC49F8F773625D
National Natural Science Foundation of China - No. 21176073
ark:/67375/WNG-LC63QT0L-S
ArticleID:AIC15033
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ISSN:0001-1541
1547-5905
DOI:10.1002/aic.15033