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Title: Managing time-substitutable electricity usage using dynamic controls

Abstract

A predictive-control approach allows an electricity provider to monitor and proactively manage peak and off-peak residential intra-day electricity usage in an emerging smart energy grid using time-dependent dynamic pricing incentives. The daily load is modeled as time-shifted, but cost-differentiated and substitutable, copies of the continuously-consumed electricity resource, and a consumer-choice prediction model is constructed to forecast the corresponding intra-day shares of total daily load according to this model. This is embedded within an optimization framework for managing the daily electricity usage. A series of transformations are employed, including the reformulation-linearization technique (RLT) to obtain a Mixed-Integer Programming (MIP) model representation of the resulting nonlinear optimization problem. In addition, various regulatory and pricing constraints are incorporated in conjunction with the specified profit and capacity utilization objectives.

Inventors:
; ; ; ;
Publication Date:
Research Org.:
INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1343280
Patent Number(s):
9,563,924
Application Number:
13/918,312
Assignee:
INTERNATIONAL BUSINESS MACHINES CORPORATION NETL
DOE Contract Number:
OE0000190
Resource Type:
Patent
Resource Relation:
Patent File Date: 2013 Jun 14
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION

Citation Formats

Ghosh, Soumyadip, Hosking, Jonathan R., Natarajan, Ramesh, Subramaniam, Shivaram, and Zhang, Xiaoxuan. Managing time-substitutable electricity usage using dynamic controls. United States: N. p., 2017. Web.
Ghosh, Soumyadip, Hosking, Jonathan R., Natarajan, Ramesh, Subramaniam, Shivaram, & Zhang, Xiaoxuan. Managing time-substitutable electricity usage using dynamic controls. United States.
Ghosh, Soumyadip, Hosking, Jonathan R., Natarajan, Ramesh, Subramaniam, Shivaram, and Zhang, Xiaoxuan. Tue . "Managing time-substitutable electricity usage using dynamic controls". United States. doi:. https://www.osti.gov/servlets/purl/1343280.
@article{osti_1343280,
title = {Managing time-substitutable electricity usage using dynamic controls},
author = {Ghosh, Soumyadip and Hosking, Jonathan R. and Natarajan, Ramesh and Subramaniam, Shivaram and Zhang, Xiaoxuan},
abstractNote = {A predictive-control approach allows an electricity provider to monitor and proactively manage peak and off-peak residential intra-day electricity usage in an emerging smart energy grid using time-dependent dynamic pricing incentives. The daily load is modeled as time-shifted, but cost-differentiated and substitutable, copies of the continuously-consumed electricity resource, and a consumer-choice prediction model is constructed to forecast the corresponding intra-day shares of total daily load according to this model. This is embedded within an optimization framework for managing the daily electricity usage. A series of transformations are employed, including the reformulation-linearization technique (RLT) to obtain a Mixed-Integer Programming (MIP) model representation of the resulting nonlinear optimization problem. In addition, various regulatory and pricing constraints are incorporated in conjunction with the specified profit and capacity utilization objectives.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Feb 07 00:00:00 EST 2017},
month = {Tue Feb 07 00:00:00 EST 2017}
}

Patent:

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  • A predictive-control approach allows an electricity provider to monitor and proactively manage peak and off-peak residential intra-day electricity usage in an emerging smart energy grid using time-dependent dynamic pricing incentives. The daily load is modeled as time-shifted, but cost-differentiated and substitutable, copies of the continuously-consumed electricity resource, and a consumer-choice prediction model is constructed to forecast the corresponding intra-day shares of total daily load according to this model. This is embedded within an optimization framework for managing the daily electricity usage. A series of transformations are employed, including the reformulation-linearization technique (RLT) to obtain a Mixed-Integer Programming (MIP) model representationmore » of the resulting nonlinear optimization problem. In addition, various regulatory and pricing constraints are incorporated in conjunction with the specified profit and capacity utilization objectives.« less
  • Dynamic time expansion or compression of a small amplitude input signal generated with an initial scale is performed using a nonlinear waveguide. A nonlinear waveguide having a variable refractive index is connected to a bias voltage source having a bias signal amplitude that is large relative to the input signal to vary the reflective index and concomitant speed of propagation of the nonlinear waveguide and an electrical circuit for applying the small amplitude signal and the large amplitude bias signal simultaneously to the nonlinear waveguide. The large amplitude bias signal with the input signal alters the speed of propagation ofmore » the small-amplitude signal with time in the nonlinear waveguide to expand or contract the initial time scale of the small-amplitude input signal.« less
  • Dynamic time expansion or compression of a small-amplitude input signal generated with an initial scale is performed using a nonlinear waveguide. A nonlinear waveguide having a variable refractive index is connected to a bias voltage source having a bias signal amplitude that is large relative to the input signal to vary the reflective index and concomitant speed of propagation of the nonlinear waveguide and an electrical circuit for applying the small-amplitude signal and the large amplitude bias signal simultaneously to the nonlinear waveguide. The large amplitude bias signal with the input signal alters the speed of propagation of the small-amplitudemore » signal with time in the nonlinear waveguide to expand or contract the initial time scale of the small-amplitude input signal.« less
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