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Automated demand response applied to a set of commercial buildings


Please use this identifier to cite or link to this item: http://hdl.handle.net/1928/10305

Automated demand response applied to a set of commercial buildings

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dc.contributor.author Lincoln, Donald
dc.date.accessioned 2010-02-09T21:00:18Z
dc.date.available 2010-02-09T21:00:18Z
dc.date.issued 2010-02-09T21:00:18Z
dc.date.submitted December 2009
dc.identifier.uri http://hdl.handle.net/1928/10305
dc.description.abstract Commercial facility demand response refers to voluntary actions by customers that change their consumption of electric power in response to price signals, incentives, or directions from grid operators at times of high wholesale market prices or when electric system reliability is jeopardized. Energy management in a commercial facility can be segregated into two areas: energy efficiency and demand response. This dissertation assesses both in two commercial facilities: one designed and constructed prior to the development of demand response principles and the second designed and constructed with modern energy controls and energy efficient materials. The energy evaluation identified opportunities for energy conservation and strategies for demand response. Next this paper presents a fuzzy method for predicting a facility’s baseline load profile. The baseline load profile is the predicted energy use of a facility during a demand response event in the absence of any energy reduction. During a demand response event, building operators or their automated control systems make adjustments to building operations with the goal of reducing the building’s electric load during times of the electric system’s peak electric usage. The baseline load profile is key to assessing the actual peak load electric energy reduction from a demand response event. Some grid operators are considering compensating commercial facilities for the energy reduction they achieve during demand response events. In fact the Public Service Company of New Mexico, the electricity supplier to UNM, has a demand response program that would compensate in this manner. The method described here is based on fuzzy set theory and allows the inclusion of building occupancy in the calculation. Our method achieves greater accuracy than other methods currently in use. Third, this study developed strategies for minimizing occupant dissatisfaction during demand response events using fuzzy cognitive mapping. If occupant discomfort causes significant complaints to the facility operator or owner, they may direct the demand response event be discontinued and thus eliminate the electric power savings. Assessing and predicting this potential interruption of the demand response event is not readily evaluated with crisp analytical techniques. Thus we elected to assess this problem using fuzzy set theory as applied to cognitive maps. Our model focuses on the University of New Mexico (UNM) campus. Fourth, we developed the conceptual design and operation of a facility control system to manage demand response events for the campus of the University of New Mexico. This section presents the design principles, the demand response control system logic and operation, and the economic value based on the PNM Peak Saver Demand Response Program financial incentives. en_US
dc.language.iso en_US en_US
dc.subject Control System en_US
dc.subject Demand Response en_US
dc.subject Fuzzy Cognitive Maps en_US
dc.subject Fuzzy Logic en_US
dc.subject.lcsh Commercial buildings--Energy conservation--Automation.
dc.subject.lcsh Demand-side management (Electric utilities)
dc.subject.lcsh Electric power consumption--Mathematical models.
dc.subject.lcsh College buildings--Energy conservation--New Mexico--Albuquerque.
dc.subject.lcsh Fuzzy sets.
dc.title Automated demand response applied to a set of commercial buildings en_US
dc.type Dissertation en_US
dc.description.degree Mechanical Engineering en_US
dc.description.level Doctoral en_US
dc.description.department University of New Mexico. Dept. of Mechanical Engineering en_US
dc.description.advisor Mammoli, Andrea
dc.description.committee-member Graham, Edward Jr.
dc.description.committee-member Ross, Timothy
dc.description.committee-member Razani, Arsalan
dc.description.committee-member Vorobieff, Peter

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