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Building Energy Systems as Behind-the-Meter Resources for Grid Services: Intelligent load control and transactive control and coordination

Journal Article · · IEEE Electrification Magazine

To mitigate the impacts of climate change, significant reductions in emissions from all sectors of the economy are needed. The electricity generation sector has embarked on an ambitious plan to include renewable generation as part of its decarbonization efforts, and many cities and states are mandating all-electric buildings. While renewable resources will reduce emissions, they are not dispatchable, they vary temporally, and their generation is uncertain. Under these conditions, traditional approaches to managing grid reliability, where supply follows demand, will not be efficient and may not be cost-effective. Further, there is a more efficient alternative for balancing the supply–demand imbalance and for absorbing variability and uncertainty of renewable energy using distributed energy resources (DERs) as opposed to reserve generation. Because buildings consume more than 75% of total U.S. annual electricity consumption, behind-the-meter (BTM) DERs have a load flexibility of 77 GW of power and 90 GWh of virtual energy storage capacity nationwide (Kalsi, 2017). Therefore, some portion of the supply–demand imbalance can be met by these DERs at a lower cost compared to business-as-usual solutions.

Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office; USDOE Office of Electricity (OE); Washington State Department of Commerce, Clean Energy Fund
Grant/Contract Number:
AC05-76RL01830
OSTI ID:
1909796
Report Number(s):
PNNL-SA-173372
Journal Information:
IEEE Electrification Magazine, Journal Name: IEEE Electrification Magazine Journal Issue: 4 Vol. 10; ISSN 2325-5897
Publisher:
IEEE XploreCopyright Statement
Country of Publication:
United States
Language:
English

References (1)

Development and validation of an intelligent load control algorithm journal January 2017