Study on Auto-DR and pre-cooling of commercial buildings with thermal mass in California

Yin, Rongxin; Xu, Peng; Piette, Mary Ann; Kiliccote, Sila

doi:10.1016/j.enbuild.2010.01.008

Cited by 144 publications

(66 citation statements)

References 17 publications

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“…Market platforms, a regulatory environment that rewards smart consumer behavior, and automation infrastructure are successfully presented in field trials [14], but still need improvement and a broader application in other countries. Several demonstration projects that are currently starting show that there is an active research community in this field, and that industry is already implementing their results.…”

Section: Discussionmentioning

confidence: 99%

“…One modern system for automated demand response, OpenADR [13], [14], is developed by the leading research group on DR, the Demand Response Research Center (DRRC) at the Lawrence Berkeley National Laboratory. OpenADR is an open specification and an open-source reference implementation of a distributed, client-server oriented DR infrastructure with a publisher-subscriber model.…”

Section: Demand Responsementioning

confidence: 99%

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Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads

Pálenský

Dietrich

2011

IEEE Trans. Ind. Inf.

2,459

1,065

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Abstract-Energy management means to optimize one of the most complex and important technical creations that we know: the energy system. While there is plenty of experience in optimizing energy generation and distribution, it is the demand side that receives increasing attention by research and industry. Demand Side Management (DSM) is a portfolio of measures to improve the energy system at the side of consumption. It ranges from improving energy efficiency by using better materials, over smart energy tariffs with incentives for certain consumption patterns, up to sophisticated real-time control of distributed energy resources. This paper gives an overview and a taxonomy for DSM, analyzes the various types of DSM, and gives an outlook on the latest demonstration projects in this domain.Index Terms-Building automation, demand response, demand side management (DSM), energy efficiency, energy management, IEC 61850, load management, peak shaving, smart grids.

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Section: Discussionmentioning

confidence: 99%

Section: Demand Responsementioning

confidence: 99%

Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads

Pálenský

Dietrich

2011

IEEE Trans. Ind. Inf.

2,459

1,065

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“…Initial research has identified resetting zone temperatures as a promising technique to modify cooling loads on hot summer days . Previous research has included evaluating different HVAC systems as well as examining the use of thermal mass to pre-cool buildings (Yin et al 2010). DR strategies for lighting systems vary based on the type of controls (Rubinstein and Kiliccote 2007).…”

Section: Background and Previous Researchmentioning

confidence: 99%

Field demonstration of automated demand response for both winter and summer events in large buildings in the Pacific Northwest

2013

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There are growing strains on the electric grid as cooling peaks grow and equipment ages. Increased penetration of renewables on the grid is also straining electricity supply systems and the need for flexible demand is growing. This paper summarizes results of a series of field test of automated demand response systems in large buildings in the Pacific Northwest. The objective of the research was twofold. One objective was to evaluate the use demand response automation technologies. A second objective was to evaluate control strategies that could change the electric load shape in both winter and summer conditions. Winter conditions focused on cold winter mornings, a time when the electric grid is often stressed. The summer test evaluated DR strategies in the afternoon. We found that we could automate both winter and summer control strategies with the open automated demand response communication standard. The buildings were able to provide significant demand response in both winter and summer events.

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“…Reference [34] shows the potential for reducing peak electrical demand in moderate-sized commercial buildings by modifying heating, ventilation and air conditioning (HVAC) system control. Peak-load reduction strategy modeling studies by Yin et al [35] developed and calibrated simulation models of 11 commercial buildings for evaluating the effect of different thermostat control strategies.…”

Section: Demand Response Quick Assessment Toolmentioning

confidence: 99%

“…The Demand Response Quick Assessment Tool (DRQAT) is another tool for this application, and is used to develop models for simulating various DR strategies to estimate the demand shedding and shifting potential [35]. DRQAT is based on the modeling engine and capabilities of EnergyPlus [36].…”

Section: Demand Response Quick Assessment Toolmentioning

confidence: 99%

Distributed energy systems integration and demand optimization for autonomous operations and electric grid transactions

et al. 2016

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Distributed power systems in the U.S. and globally are evolving to provide reliable and clean energy to consumers. In California, existing regulations require significant increases in renewable generation, as well as identification of customer-side distributed energy resources (DER) controls, communication technologies, and standards for interconnection with the electric grid systems. As DER deployment expands, customer-side DER control and optimization will be critical for system flexibility and demand response (DR) participation, which improves the economic viability of DER systems. Current DER systems integration and communication challenges include leveraging the existing DER and DR technology and systems infrastructure, and enabling optimized cost, energy and carbon choices for customers to deploy interoperable grid transactions and renewable energy systems at scale. This paper presents a cost-effective solution to these challenges by exploring communication technologies and information models for DER system integration and interoperability. This system uses open standards and optimization models for resource planning based on dynamic-pricing notifications and autonomous operations within various domains of the smart grid energy system. It identifies architectures and customer engagement strategies in dynamic DR pricing transactions to generate feedback information models for load flexibility, load profiles, and participation schedules. The models are tested at a real site in California-Fort Hunter Liggett (FHL). The results for FHL show that the model fits within the existing and new DR business models and networked systems for transactive energy concepts. Integrated energy systems, communication networks, and modeling tools that coordinate supply-side networks and DER will enable electric grid system operators to use DER for grid transactions in an integrated system.

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Study on Auto-DR and pre-cooling of commercial buildings with thermal mass in California

Cited by 144 publications

References 17 publications

Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads

Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads

Field demonstration of automated demand response for both winter and summer events in large buildings in the Pacific Northwest

Distributed energy systems integration and demand optimization for autonomous operations and electric grid transactions

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