A day-ahead scheduling framework for thermostatically controlled loads with thermal inertia and thermal comfort model
This paper proposes a day-ahead dispatch framework of thermostatically controlled loads (TCLs) for system peak load reduction. The proposed day-ahead scheduling framework estimates the user’s indoor thermal comfort degree through the building thermal inertia modelling. Based on the thermal comfort e...
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| Published in | Journal of modern power systems and clean energy Vol. 7; no. 3; pp. 568 - 578 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Singapore
Springer Singapore
01.05.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) IEEE |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2196-5625 2196-5420 |
| DOI | 10.1007/s40565-018-0431-3 |
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| Abstract | This paper proposes a day-ahead dispatch framework of thermostatically controlled loads (TCLs) for system peak load reduction. The proposed day-ahead scheduling framework estimates the user’s indoor thermal comfort degree through the building thermal inertia modelling. Based on the thermal comfort estimation, a day-ahead TCL scheduling model is formulated, which consists of 3 stages: ① TCL aggregator estimate their maximal controllable TCL capacities at each scheduling time interval by solving a optimization model; ② the system operator performs the day-ahead system dispatch to determine the load shedding instruction for each aggregator; and ③ the TCL aggregators schedules the ON/OFF control actions of the TCL groups based on the instruction from the system operator. A heuristic based optimization method, history driven differential evolution (HDDE) algorithm, is employed to solve the day-ahead dispatch model of the TCL aggregator side. Simulations are conducted to validate the proposed model. |
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| AbstractList | This paper proposes a day-ahead dispatch framework of thermostatically controlled loads (TCLs) for system peak load reduction. The proposed day-ahead scheduling framework estimates the user’s indoor thermal comfort degree through the building thermal inertia modelling. Based on the thermal comfort estimation, a day-ahead TCL scheduling model is formulated, which consists of 3 stages: ① TCL aggregator estimate their maximal controllable TCL capacities at each scheduling time interval by solving a optimization model; ② the system operator performs the day-ahead system dispatch to determine the load shedding instruction for each aggregator; and ③ the TCL aggregators schedules the ON/OFF control actions of the TCL groups based on the instruction from the system operator. A heuristic based optimization method, history driven differential evolution (HDDE) algorithm, is employed to solve the day-ahead dispatch model of the TCL aggregator side. Simulations are conducted to validate the proposed model. |
| Author | Dong, Zhaoyang Luo, Fengji Ranzi, Gianluca Chen, Yingying Meng, Ke Wong, Kit Po |
| Author_xml | – sequence: 1 givenname: Yingying surname: Chen fullname: Chen, Yingying organization: Visionstream Australia Pty Ltd – sequence: 2 givenname: Fengji orcidid: 0000-0003-4041-6062 surname: Luo fullname: Luo, Fengji email: fengji.luo@sydney.edu.au organization: School of Civil Engineering, University of Sydney – sequence: 3 givenname: Zhaoyang surname: Dong fullname: Dong, Zhaoyang organization: School of Electrical Engineering and Telecommunications, University of New South Wales – sequence: 4 givenname: Ke surname: Meng fullname: Meng, Ke organization: School of Electrical Engineering and Telecommunications, University of New South Wales – sequence: 5 givenname: Gianluca surname: Ranzi fullname: Ranzi, Gianluca organization: School of Civil Engineering, University of Sydney – sequence: 6 givenname: Kit Po surname: Wong fullname: Wong, Kit Po organization: School of Electrical, Electronics, and Computer Engineering, University of Western Australia |
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| Cites_doi | 10.1109/59.119246 10.1016/j.enbuild.2010.10.023 10.1109/TPWRS.2012.2204074 10.1016/S0378-7788(02)00018-X 10.1109/JPROC.2010.2081652 10.1023/A:1008202821328 10.1109/TII.2011.2158841 10.1007/s40565-014-0075-x 10.1109/TPWRS.2008.2004732 10.1109/TII.2016.2515086 10.1287/mnsc.36.5.519 10.1049/iet-rpg.2015.0358 10.1016/j.ins.2014.02.039 10.1109/TPWRS.2014.2328865 10.1109/TSG.2014.2388233 10.1016/j.apenergy.2012.11.015 10.1080/23744731.2016.1188652 10.1109/TSG.2012.2212729 10.1016/j.enconman.2004.12.007 10.1109/TCST.2012.2204261 10.1109/SmartGridComm.2013.6687965 |
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| SubjectTerms | Computer simulation Demand response Demand side management Direct load control Electrical Machines and Networks Energy Energy Systems Evolutionary algorithms Heuristic methods Inertia Load shedding Optimization Peak load Power Electronics Renewable and Green Energy Schedules Scheduling Specific heat Stability Thermal comfort Thermal comfort model Thermostatically controlled load |
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| Title | A day-ahead scheduling framework for thermostatically controlled loads with thermal inertia and thermal comfort model |
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