Synthetic residential load models for smart city energy management simulations. Issue 3 (19th May 2020)

• Record Type: Journal Article
• Authors: dos Reis, Fernando B.; Tonkoski, Reinaldo; Hansen, Timothy M.
• Is Part Of: IET smart grid. Volume 3:Issue 3(2020)
• Page Start: 342
• Page End: 354
• Publication Date: 2020-05-19
• Subjects: energy management systems -- queueing theory -- power engineering computing -- demand side management -- load forecasting -- power system simulation -- smart power grids; synthetic residential load models -- smart city energy management simulations -- residential electricity customers -- coordinated manner -- system‐wide electric load changes -- reduce congestion -- peak demand -- demand‐side management -- power system simulation studies -- home energy management -- large‐scale residential demand response -- system load -- individual residential customers -- individual electric assets -- time‐varying queueing models -- customer loads -- known city‐sized load curve -- presented residential queueing load models -- residential load data -- model parameters; B8110B Power system management, operation and economics -- B8110D Power system planning and layout -- C7410B Power engineering computing; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2019.0296
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : 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.