Synthetic residential load models for smart city energy management simulations

Reis, Fernando B.; Tonkoski, Reinaldo; Hansen, Timothy M.

doi:10.1049/iet-stg.2019.0296

Title: Synthetic residential load models for smart city energy management simulations

Journal Article · Tue May 19 00:00:00 EDT 2020 · IET Smart Grid

DOI:https://doi.org/10.1049/iet-stg.2019.0296· OSTI ID:1870512

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South Dakota State Univ., Brookings, SD (United States)

The ability to control tens of thousands of residential electricity customers in a coordinated manner has the potential to enact system-wide electric load changes, such as reduce congestion and peak demand, among other benefits. To quantify the potential benefits of demand-side management and other power system simulation studies (e.g. home energy management, large-scale residential demand response), synthetic load datasets that accurately characterize the system load are required. This study designs a combined top-down and bottom-up approach for modelling individual residential customers and their individual electric assets, each possessing their own characteristics, using time-varying queueing models. The aggregation of all customer loads created by the queueing models represents a known city-sized load curve to be used in simulation studies. The three presented residential queueing load models use only publicly available data. An open-source Python tool to allow researchers to generate residential load data for their studies is also provided. The simulation results presented consider the ComEd region (utility company from Chicago, IL) and demonstrate the characteristics of the three proposed residential queueing load models, the impact of the choice of model parameters, and scalability performance of the Python tool.

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Research Organization:: South Dakota State Univ., Brookings, SD (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF)

Grant/Contract Number:: SC0020281; CNS-1726946

OSTI ID:: 1870512

Journal Information:: IET Smart Grid, Vol. 3, Issue 3; ISSN 2515-2947

Publisher:: Institution of Engineering and Technology (IET)Copyright Statement

Country of Publication:: United States

Language:: English

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