Synthetic residential load models for smart city energy management simulations
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-02-01
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| Series: | IET Smart Grid |
| Subjects: | |
| Online Access: | https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2019.0296 |
| _version_ | 1818642334802247680 |
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| author | Fernando B. dos Reis Reinaldo Tonkoski Reinaldo Tonkoski Timothy M. Hansen |
| author_facet | Fernando B. dos Reis Reinaldo Tonkoski Reinaldo Tonkoski Timothy M. Hansen |
| author_sort | Fernando B. dos Reis |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T23:41:25Z |
| format | Article |
| id | doaj.art-34a7927857f64ddea862372f052004bb |
| institution | Directory Open Access Journal |
| issn | 2515-2947 |
| language | English |
| last_indexed | 2024-12-16T23:41:25Z |
| publishDate | 2020-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Smart Grid |
| spelling | doaj.art-34a7927857f64ddea862372f052004bb2022-12-21T22:11:36ZengWileyIET Smart Grid2515-29472020-02-0110.1049/iet-stg.2019.0296IET-STG.2019.0296Synthetic residential load models for smart city energy management simulationsFernando B. dos Reis0Reinaldo Tonkoski1Reinaldo Tonkoski2Timothy M. Hansen3South Dakota State UniversitySouth Dakota State UniversitySouth Dakota State UniversitySouth Dakota State UniversityThe 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.https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2019.0296energy management systemsqueueing theorypower engineering computingdemand side managementload forecastingpower system simulationsmart power gridssynthetic residential load modelssmart city energy management simulationsresidential electricity customerscoordinated mannersystem-wide electric load changesreduce congestionpeak demanddemand-side managementpower system simulation studieshome energy managementlarge-scale residential demand responsesystem loadindividual residential customersindividual electric assetstime-varying queueing modelscustomer loadsknown city-sized load curvepresented residential queueing load modelsresidential load datamodel parameters |
| spellingShingle | Fernando B. dos Reis Reinaldo Tonkoski Reinaldo Tonkoski Timothy M. Hansen Synthetic residential load models for smart city energy management simulations IET Smart Grid 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 |
| title | Synthetic residential load models for smart city energy management simulations |
| title_full | Synthetic residential load models for smart city energy management simulations |
| title_fullStr | Synthetic residential load models for smart city energy management simulations |
| title_full_unstemmed | Synthetic residential load models for smart city energy management simulations |
| title_short | Synthetic residential load models for smart city energy management simulations |
| title_sort | synthetic residential load models for smart city energy management simulations |
| topic | 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 |
| url | https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2019.0296 |
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