Open AccessSynthetic residential load models for smart city energy management simulations
Author(s) -
Reis Fernando B.,
Tonkoski Reinaldo,
Hansen Timothy M.
Publication year - 2020
Publication title -
iet smart grid
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.612
H-Index - 11
ISSN - 2515-2947
DOI - 10.1049/iet-stg.2019.0296
Subject(s) - queueing theory , computer science , python (programming language) , scalability , demand response , load management , electricity , peak load , energy management , simulation , operations research , real time computing , automotive engineering , energy (signal processing) , database , engineering , computer network , operating system , statistics , mathematics , electrical engineering
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 characterise 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.