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Title: Evaluating demand response opportunities for power systems resilience using MILP and MINLP Formulations

Abstract

While peak shaving is commonly used to reduce power costs, chemical process facilities that can reduce power consumption on demand during emergencies (e.g., extreme weather events) bring additional value through improved resilience. For process facilities to effectively negotiate demand response (DR) contracts and make investment decisions regarding flexibility, they need to quantify their additional value to the grid. We present a grid–centric mixed–integer stochastic programming framework to determine the value of DR for improving grid resilience in place of capital investments that can be cost prohibitive for system operators. We formulate problems using both a linear approximation and a nonlinear alternating current power flow model. Our numerical results with both models demonstrate that DR can be used to reduce the capital investment necessary for resilience, increasing the value that chemical process facilities bring through DR. Furthermore, the linearized model often underestimates the amount of DR needed in our case studies.

Authors:
ORCiD logo [1];  [2];  [2];  [1]
  1. Purdue Univ., West Lafayette, IN (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Laboratories, Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1492800
Alternate Identifier(s):
OSTI ID: 1490559
Report Number(s):
SAND-2019-0430J
Journal ID: ISSN 0001-1541; 671568
Grant/Contract Number:  
AC04-94AL85000
Resource Type:
Accepted Manuscript
Journal Name:
AIChE Journal
Additional Journal Information:
Journal Name: AIChE Journal; Journal ID: ISSN 0001-1541
Publisher:
American Institute of Chemical Engineers
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

Bynum, Michael Lee, Castillo, Anya, Watson, Jean -Paul, and Laird, Carl Damon. Evaluating demand response opportunities for power systems resilience using MILP and MINLP Formulations. United States: N. p., 2018. Web. doi:10.1002/aic.16508.
Bynum, Michael Lee, Castillo, Anya, Watson, Jean -Paul, & Laird, Carl Damon. Evaluating demand response opportunities for power systems resilience using MILP and MINLP Formulations. United States. doi:10.1002/aic.16508.
Bynum, Michael Lee, Castillo, Anya, Watson, Jean -Paul, and Laird, Carl Damon. Sat . "Evaluating demand response opportunities for power systems resilience using MILP and MINLP Formulations". United States. doi:10.1002/aic.16508.
@article{osti_1492800,
title = {Evaluating demand response opportunities for power systems resilience using MILP and MINLP Formulations},
author = {Bynum, Michael Lee and Castillo, Anya and Watson, Jean -Paul and Laird, Carl Damon},
abstractNote = {While peak shaving is commonly used to reduce power costs, chemical process facilities that can reduce power consumption on demand during emergencies (e.g., extreme weather events) bring additional value through improved resilience. For process facilities to effectively negotiate demand response (DR) contracts and make investment decisions regarding flexibility, they need to quantify their additional value to the grid. We present a grid–centric mixed–integer stochastic programming framework to determine the value of DR for improving grid resilience in place of capital investments that can be cost prohibitive for system operators. We formulate problems using both a linear approximation and a nonlinear alternating current power flow model. Our numerical results with both models demonstrate that DR can be used to reduce the capital investment necessary for resilience, increasing the value that chemical process facilities bring through DR. Furthermore, the linearized model often underestimates the amount of DR needed in our case studies.},
doi = {10.1002/aic.16508},
journal = {AIChE Journal},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {12}
}

Journal Article:
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This content will become publicly available on December 29, 2019
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