A Real-World Test Distribution System With Appliance-Level Load Data for Demand Response and Transactive Energy Studies

Research on demand response and transactive energy systems often require granular, appliance-level demand data. However, there is no existing test system with such appliance-level data with proper temporospatial diversity in a realistic distribution system. This paper develops a 240-node real distri...

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Main Authors: Fernando Bereta Dos Reis, Reinaldo Tonkoski, Bishnu P. Bhattarai, Timothy M. Hansen
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9604915/
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author Fernando Bereta Dos Reis
Reinaldo Tonkoski
Bishnu P. Bhattarai
Timothy M. Hansen
author_facet Fernando Bereta Dos Reis
Reinaldo Tonkoski
Bishnu P. Bhattarai
Timothy M. Hansen
author_sort Fernando Bereta Dos Reis
collection DOAJ
description Research on demand response and transactive energy systems often require granular, appliance-level demand data. However, there is no existing test system with such appliance-level data with proper temporospatial diversity in a realistic distribution system. This paper develops a 240-node real distribution test system with appliance-level demand data for responsive loads. The residential appliance-level demand data are derived from smart meters connected to 1,120 homes in a real distribution system from Iowa State, hereafter called Midwest 240-Node test distribution system. A queueing load model was used to derive the appliance-level data from the smart meter data. The Midwest 240-Node test distribution system provides granular appliance-level information for all homes in the distribution system (i.e., individual appliances that constitute the home load), and the aggregate of all customer load emulates the actual smart meter data. The performance of the Midwest 240-Node test distribution system is evaluated by comparing the aggregated appliance-level demand with the actual measured smart meter data from the utility. The one-year appliance data has a mean absolute percentage error of 2.58% compared to the measured smart meter data. The test system is modeled in OpenDSS and GridLAB-D and is openly available to researchers to enable demand response and transactive energy studies with active end-users.
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spelling doaj.art-473fdc574c6e403eb621f9afb4a6aedc2022-12-22T04:04:41ZengIEEEIEEE Access2169-35362021-01-01914950614951910.1109/ACCESS.2021.31209239604915A Real-World Test Distribution System With Appliance-Level Load Data for Demand Response and Transactive Energy StudiesFernando Bereta Dos Reis0https://orcid.org/0000-0001-6890-9585Reinaldo Tonkoski1https://orcid.org/0000-0003-3057-1085Bishnu P. Bhattarai2Timothy M. Hansen3https://orcid.org/0000-0001-8096-1255Electricity Infrastructure and Buildings Division, Pacific Northwest National Laboratory, Richland, WA, USADepartment of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD, USAElectricity Infrastructure and Buildings Division, Pacific Northwest National Laboratory, Richland, WA, USADepartment of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD, USAResearch on demand response and transactive energy systems often require granular, appliance-level demand data. However, there is no existing test system with such appliance-level data with proper temporospatial diversity in a realistic distribution system. This paper develops a 240-node real distribution test system with appliance-level demand data for responsive loads. The residential appliance-level demand data are derived from smart meters connected to 1,120 homes in a real distribution system from Iowa State, hereafter called Midwest 240-Node test distribution system. A queueing load model was used to derive the appliance-level data from the smart meter data. The Midwest 240-Node test distribution system provides granular appliance-level information for all homes in the distribution system (i.e., individual appliances that constitute the home load), and the aggregate of all customer load emulates the actual smart meter data. The performance of the Midwest 240-Node test distribution system is evaluated by comparing the aggregated appliance-level demand with the actual measured smart meter data from the utility. The one-year appliance data has a mean absolute percentage error of 2.58% compared to the measured smart meter data. The test system is modeled in OpenDSS and GridLAB-D and is openly available to researchers to enable demand response and transactive energy studies with active end-users.https://ieeexplore.ieee.org/document/9604915/Demand responsedistributed energy managementhome energy managementqueueing load modelsynthetic distribution test systemtransactive energy
spellingShingle Fernando Bereta Dos Reis
Reinaldo Tonkoski
Bishnu P. Bhattarai
Timothy M. Hansen
A Real-World Test Distribution System With Appliance-Level Load Data for Demand Response and Transactive Energy Studies
IEEE Access
Demand response
distributed energy management
home energy management
queueing load model
synthetic distribution test system
transactive energy
title A Real-World Test Distribution System With Appliance-Level Load Data for Demand Response and Transactive Energy Studies
title_full A Real-World Test Distribution System With Appliance-Level Load Data for Demand Response and Transactive Energy Studies
title_fullStr A Real-World Test Distribution System With Appliance-Level Load Data for Demand Response and Transactive Energy Studies
title_full_unstemmed A Real-World Test Distribution System With Appliance-Level Load Data for Demand Response and Transactive Energy Studies
title_short A Real-World Test Distribution System With Appliance-Level Load Data for Demand Response and Transactive Energy Studies
title_sort real world test distribution system with appliance level load data for demand response and transactive energy studies
topic Demand response
distributed energy management
home energy management
queueing load model
synthetic distribution test system
transactive energy
url https://ieeexplore.ieee.org/document/9604915/
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