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Title: Preventing conflicts among bid curves used with transactive controllers in a market-based resource allocation system

Abstract

Disclosed herein are representative embodiments of methods, apparatus, and systems for distributing a resource (such as electricity) using a resource allocation system. One of the disclosed embodiments is a method for operating a transactive thermostatic controller configured to submit bids to a market-based resource allocation system. According to the exemplary method, a first bid curve is determined, the first bid curve indicating a first set of bid prices for corresponding temperatures and being associated with a cooling mode of operation for a heating and cooling system. A second bid curve is also determined, the second bid curve indicating a second set of bid prices for corresponding temperatures and being associated with a heating mode of operation for a heating and cooling system. In this embodiment, the first bid curve, the second bid curve, or both the first bid curve and the second bid curve are modified to prevent overlap of any portion of the first bid curve and the second bid curve.

Inventors:
; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1346022
Patent Number(s):
9,589,297
Application Number:
13/096,770
Assignee:
Battelle Memorial Institute PNNL
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Patent
Resource Relation:
Patent File Date: 2011 Apr 28
Country of Publication:
United States
Language:
English
Subject:
29 ENERGY PLANNING, POLICY, AND ECONOMY

Citation Formats

Fuller, Jason C., Chassin, David P., Pratt, Robert G., Hauer, Matthew, and Tuffner, Francis K. Preventing conflicts among bid curves used with transactive controllers in a market-based resource allocation system. United States: N. p., 2017. Web.
Fuller, Jason C., Chassin, David P., Pratt, Robert G., Hauer, Matthew, & Tuffner, Francis K. Preventing conflicts among bid curves used with transactive controllers in a market-based resource allocation system. United States.
Fuller, Jason C., Chassin, David P., Pratt, Robert G., Hauer, Matthew, and Tuffner, Francis K. Tue . "Preventing conflicts among bid curves used with transactive controllers in a market-based resource allocation system". United States. doi:. https://www.osti.gov/servlets/purl/1346022.
@article{osti_1346022,
title = {Preventing conflicts among bid curves used with transactive controllers in a market-based resource allocation system},
author = {Fuller, Jason C. and Chassin, David P. and Pratt, Robert G. and Hauer, Matthew and Tuffner, Francis K.},
abstractNote = {Disclosed herein are representative embodiments of methods, apparatus, and systems for distributing a resource (such as electricity) using a resource allocation system. One of the disclosed embodiments is a method for operating a transactive thermostatic controller configured to submit bids to a market-based resource allocation system. According to the exemplary method, a first bid curve is determined, the first bid curve indicating a first set of bid prices for corresponding temperatures and being associated with a cooling mode of operation for a heating and cooling system. A second bid curve is also determined, the second bid curve indicating a second set of bid prices for corresponding temperatures and being associated with a heating mode of operation for a heating and cooling system. In this embodiment, the first bid curve, the second bid curve, or both the first bid curve and the second bid curve are modified to prevent overlap of any portion of the first bid curve and the second bid curve.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Mar 07 00:00:00 EST 2017},
month = {Tue Mar 07 00:00:00 EST 2017}
}

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Works referenced in this record:

Market Clearing Price Forecasting in Deregulated Electricity Markets Using Adaptively Trained Neural Networks
book, January 2006

  • Georgilakis, Pavlos S.; Antoniou, Grigoris; Potamias, George
  • Advances in Artificial Intelligence
  • DOI: 10.1007/11752912_8