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Title: Assessing the system value of optimal load shifting

Abstract

We analyze a competitive electricity market, where consumers exhibit optimal load shifting behavior to maximize utility and producers/suppliers maximize their profit under supply capacity constraints. The associated computationally tractable formulation can be used to inform market design or policy analysis in the context of increasing availability of the smart grid technologies that enable optimal load shifting. Through analytic and numeric assessment of the model, we assess the equilibrium value of optimal electricity load shifting, including how the value changes as more electricity consumers adopt associated technologies. For our illustrative numerical case, derived from the Current Trends scenario of the ERCOT Long Term System Assessment, the average energy arbitrage value per ERCOT customer of optimal load shifting technologies is estimated to be $3 for the 2031 scenario year. We assess the sensitivity of this result to the flexibility of load, along with its relationship to the deployment of renewables. Finally, the model presented can also be a starting point for designing system operation infrastructure that communicates with the devices that schedule loads in response to price signals.

Authors:
ORCiD logo [1];  [1];  [2]
  1. Stanford Univ., Stanford, CA (United States)
  2. Electric Power Research Institute (EPRI), Palo Alto, CA (United States)
Publication Date:
Research Org.:
Energy Modeling Forum at Stanford Univ., Stanford, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1373112
Grant/Contract Number:  
SC0005171
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Smart Grid
Additional Journal Information:
Journal Volume: PP; Journal Issue: 99 (Early Access Article); Journal ID: ISSN 1949-3053
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 24 POWER TRANSMISSION AND DISTRIBUTION

Citation Formats

Merrick, James, Ye, Yinyu, and Entriken, Bob. Assessing the system value of optimal load shifting. United States: N. p., 2017. Web. doi:10.1109/TSG.2017.2699921.
Merrick, James, Ye, Yinyu, & Entriken, Bob. Assessing the system value of optimal load shifting. United States. https://doi.org/10.1109/TSG.2017.2699921
Merrick, James, Ye, Yinyu, and Entriken, Bob. Sun . "Assessing the system value of optimal load shifting". United States. https://doi.org/10.1109/TSG.2017.2699921. https://www.osti.gov/servlets/purl/1373112.
@article{osti_1373112,
title = {Assessing the system value of optimal load shifting},
author = {Merrick, James and Ye, Yinyu and Entriken, Bob},
abstractNote = {We analyze a competitive electricity market, where consumers exhibit optimal load shifting behavior to maximize utility and producers/suppliers maximize their profit under supply capacity constraints. The associated computationally tractable formulation can be used to inform market design or policy analysis in the context of increasing availability of the smart grid technologies that enable optimal load shifting. Through analytic and numeric assessment of the model, we assess the equilibrium value of optimal electricity load shifting, including how the value changes as more electricity consumers adopt associated technologies. For our illustrative numerical case, derived from the Current Trends scenario of the ERCOT Long Term System Assessment, the average energy arbitrage value per ERCOT customer of optimal load shifting technologies is estimated to be $3 for the 2031 scenario year. We assess the sensitivity of this result to the flexibility of load, along with its relationship to the deployment of renewables. Finally, the model presented can also be a starting point for designing system operation infrastructure that communicates with the devices that schedule loads in response to price signals.},
doi = {10.1109/TSG.2017.2699921},
journal = {IEEE Transactions on Smart Grid},
number = 99 (Early Access Article),
volume = PP,
place = {United States},
year = {Sun Apr 30 00:00:00 EDT 2017},
month = {Sun Apr 30 00:00:00 EDT 2017}
}

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