Behavioral and Population Data-Driven Distribution System Load Modeling

Bromley-Dulfano, Issac; Zhu, Xiangqi; Mather, Barry

doi:10.1109/PVSC48317.2022.9938823

Behavioral and Population Data-Driven Distribution System Load Modeling

Conference · 14 November 2022

DOI:https://doi.org/10.1109/PVSC48317.2022.9938823· OSTI ID:1909901

Bromley-Dulfano, Issac; Zhu, Xiangqi;

Distribution system residential load modeling and analysis for different geographic areas within a utility or an independent system operator territory are critical for enabling small-scale, aggregated distributed energy resources to participate in grid services under Federal Energy Regulatory Commission Order No. 2222 [1]. In this study, we develop a methodology of modeling residential load profiles in different geographic areas with a focus on human behavior impact. First, we construct a behavior-based load profile model leveraging state-of-the-art appliance models. We simulate human activity and occupancy using Markov chain Monte Carlo methods calibrated with the American Time Use Survey data set. Second, we link our model with cleaned Current Population Survey data from the U.S. Census Bureau. Finally, we populate two sets of 500 households using California and Texas census data, respectively, to perform an initial analysis of the load in different geographic areas with various group features (e.g., different income levels). To distinguish the effect of population behavior differences on aggregated load, we simulate load profiles for both sets assuming fixed physical household parameters and weather data. Analysis shows that average daily load profiles vary significantly by income and income dependency varies by locality.

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Research Organization:: National Renewable Energy Lab. (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Science Undergraduate Laboratory Internships (SULI) Program; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Manufacturing Office

DOE Contract Number:: AC36-08GO28308

OSTI ID:: 1909901

Report Number(s):: NREL/CP-6A40-81349; MainId:82122; UUID:1345afb6-8449-41c0-9fdf-a739703fde51; MainAdminID:68486

Resource Relation:: Conference: Presented at the 2022 IEEE 49th Photovoltaics Specialists Conference (PVSC), 5-10 June 2022, Philadelphia, Pennsylvania

Country of Publication:: United States

Language:: English

References (24)

Stochastic model for lighting's electricity consumption in the residential sector. Impact of energy saving actions Palacios-Garcia, E. J.; Chen, A.; Santiago, I. Energy and Buildings, Vol. 89 https://doi.org/10.1016/j.enbuild.2014.12.028	journal	February 2015
A high-resolution stochastic model of domestic activity patterns and electricity demand Widén, Joakim; Wäckelgård, Ewa Applied Energy, Vol. 87, Issue 6 https://doi.org/10.1016/j.apenergy.2009.11.006	journal	June 2010
A highly resolved modeling technique to simulate residential power demand Muratori, Matteo; Roberts, Matthew C.; Sioshansi, Ramteen Applied Energy, Vol. 107, p. 465-473 https://doi.org/10.1016/j.apenergy.2013.02.057	journal	July 2013
Residential high-resolution electricity demand optimization with a cooperative PSO algorithm Zhu, Jiawei; Liao, Qiang; Lin, Yishuai Energy Reports, Vol. 7 https://doi.org/10.1016/j.egyr.2021.02.031	journal	April 2021
A behavior-centered framework for real-time control and load-shedding using aggregated residential energy resources in distribution microgrids Mammoli, Andrea; Robinson, Matthew; Ayon, Victor Energy and Buildings, Vol. 198 https://doi.org/10.1016/j.enbuild.2019.06.021	journal	September 2019
Domestic Energy Consumption Modeling per Physical Characteristics and Behavioral Factors Ge, Shaoyun; Li, Jifeng; Liu, Hong Energy Procedia, Vol. 158 https://doi.org/10.1016/j.egypro.2019.01.399	journal	February 2019
Models of domestic occupancy, activities and energy use based on time-use data: deterministic and stochastic approaches with application to various building-related simulations Widén, Joakim; Molin, Andreas; Ellegård, Kajsa Journal of Building Performance Simulation, Vol. 5, Issue 1 https://doi.org/10.1080/19401493.2010.532569	journal	January 2012
Bottom-up Markov Chain Monte Carlo approach for scenario based residential load modelling with publicly available data Nijhuis, M.; Gibescu, M.; Cobben, J. F. G. Energy and Buildings, Vol. 112 https://doi.org/10.1016/j.enbuild.2015.12.004	journal	January 2016
Residential Load Modeling of Price-Based Demand Response for Network Impact Studies McKenna, Killian; Keane, Andrew IEEE Transactions on Smart Grid, Vol. 7, Issue 5 https://doi.org/10.1109/TSG.2015.2437451	journal	September 2016
Development of a Monte Carlo based aggregate model for residential electric water heater loads Dolan, P. S.; Nehrir, M. H.; Gerez, V. Electric Power Systems Research, Vol. 36, Issue 1 https://doi.org/10.1016/0378-7796(95)01011-4	journal	January 1996
Load modeling techniques in distribution networks: a review Mohammed, Musa; Abdulkarim, Abubakar; Abubakar, Adamu Sa’idu Journal of Applied Materials and Technology, Vol. 1, Issue 2 https://doi.org/10.31258/Jamt.1.2.63-70	journal	March 2020
An Evaluation of the HVAC Load Potential for Providing Load Balancing Service Lu, Ning IEEE Transactions on Smart Grid, Vol. 3, Issue 3 https://doi.org/10.1109/TSG.2012.2183649	journal	September 2012
A model of residential energy use Hirst, Eric SIMULATION, Vol. 30, Issue 3 https://doi.org/10.1177/003754977803000301	journal	March 1978
Domestic lighting: A high-resolution energy demand model Richardson, Ian; Thomson, Murray; Infield, David Energy and Buildings, Vol. 41, Issue 7 https://doi.org/10.1016/j.enbuild.2009.02.010	journal	July 2009
Development of Low-Voltage Load Models for the Residential Load Sector Collin, Adam J.; Tsagarakis, George; Kiprakis, Aristides E. IEEE Transactions on Power Systems, Vol. 29, Issue 5 https://doi.org/10.1109/TPWRS.2014.2301949	journal	September 2014
Data-Driven Distribution System Load Modeling for Quasi-Static Time-Series Simulation Zhu, Xiangqi; Mather, Barry IEEE Transactions on Smart Grid, Vol. 11, Issue 2 https://doi.org/10.1109/TSG.2019.2940084	journal	March 2020
Implementation of advanced functionalities for Distribution Management Systems: Load forecasting and modeling through Artificial Neural Networks ensembles Saviozzi, M.; Massucco, S.; Silvestro, F. Electric Power Systems Research, Vol. 167 https://doi.org/10.1016/j.epsr.2018.10.036	journal	February 2019
Domestic electricity use: A high-resolution energy demand model Richardson, Ian; Thomson, Murray; Infield, David Energy and Buildings, Vol. 42, Issue 10 https://doi.org/10.1016/j.enbuild.2010.05.023	journal	October 2010
A high-resolution domestic building occupancy model for energy demand simulations Richardson, Ian; Thomson, Murray; Infield, David Energy and Buildings, Vol. 40, Issue 8 https://doi.org/10.1016/j.enbuild.2008.02.006	journal	January 2008
Modeling energy consumption in residential buildings: A bottom-up analysis based on occupant behavior pattern clustering and stochastic simulation Diao, Longquan; Sun, Yongjun; Chen, Zejun Energy and Buildings, Vol. 147 https://doi.org/10.1016/j.enbuild.2017.04.072	journal	July 2017
A bottom-up stochastic model to predict building occupants' time-dependent activities Wilke, Urs; Haldi, Frédéric; Scartezzini, Jean-Louis Building and Environment, Vol. 60 https://doi.org/10.1016/j.buildenv.2012.10.021	journal	February 2013
A high spatial resolution residential energy model based on American Time Use Survey data and the bootstrap sampling method Chiou, Yun-Shang; Carley, Kathleen M.; Davidson, Cliff I. Energy and Buildings, Vol. 43, Issue 12 https://doi.org/10.1016/j.enbuild.2011.09.020	journal	December 2011
A multi-scale adaptive model of residential energy demand Farzan, Farbod; Jafari, Mohsen A.; Gong, Jie Applied Energy, Vol. 150 https://doi.org/10.1016/j.apenergy.2015.04.008	journal	July 2015
Data Watch The American Time Use Survey Hamermesh, Daniel S.; Frazis, Harley; Stewart, Jay Journal of Economic Perspectives, Vol. 19, Issue 1 https://doi.org/10.1257/0895330053148029	journal	February 2005

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ENERGY PLANNING, POLICY, AND ECONOMY,POWER TRANSMISSION AND DISTRIBUTION
American Time Use Survey
behavior-based load profile modeling
Markov Chain Monte Carlo
U.S. Census

Behavioral and Population Data-Driven Distribution System Load Modeling

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