Residential Occupant-Dependent Appliance Power and Time-Of-Use Estimation for Grid Demand Response Applications

The electricity generation from renewable sources in the U.S. is projected to increase from 19% in 2019 to 38% in 2050. The major portion of this increase originates from highly variable energy generation sources, including solar and wind. This variability in electricity production, in combination w...

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Bibliographic Details
Published in:ASHRAE transactions 2021-01, Vol.127 (1), p.118-125
Main Authors: Kunwar, Niraj, Vanage, Soham, Cetin, Kristen
Format: Article
Language:English
Subjects:
Alternative energy sources
Appliances
Consumption
Datasets
Demand side management
Driers
Electric power demand
Electric power production
Electrical loads
Electricity generation
Energy management
Energy storage
Flexibility
Ovens
Renewable resources
Variability
Washers & dryers
Washing machines
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Summary:The electricity generation from renewable sources in the U.S. is projected to increase from 19% in 2019 to 38% in 2050. The major portion of this increase originates from highly variable energy generation sources, including solar and wind. This variability in electricity production, in combination with the variability of the load and system disturbances, demands higher flexibility in the grid, from the supply and/ or demand side. Demand-side management (DSM) will be a more economical and environmentally-friendly option to provideflexibility services, compared to non-renewable energy dependent generation plants which are commonly used today during peak use periods. The characterisation of different types of the load is essential to provide the quantification of potential flexibility of demand. This study uses a data-driven approach to model occupant-dependent residential appliance loads, based on several sources of high-resolution disaggregated energy data. The main components of this model for each end-use include, characterization of the load profile and duration of operation for a single load cycle, and the likelihood of use by the time of day. The uncertainties inherent in the appliance load curves are estimated from the different data sources, in an effort to make the model scalable to the regional level for grid-level modeling. End-use loads of inte[pi]st include coincidental loads from occupant-dependent loads including clothes washers and dryers, dishwashers and stove/ ovens. The resulting models will help to assess the capability of different types of loads to provide the flexibility services, such as capacity services, spinning reserves and/ or voltage regulation, to the grid.
ISSN:0001-2505