Transactive control of industrial heating–ventilation–air-conditioning units in cold-storage warehouses for demand response
This paper establishes a transactive coordination model of heating–ventilation–air-conditioning (HVAC) loads for demand response purpose. The thermal inertia of industrial thermostatically controlled loads may provide verities of ancillary service in the power system by adjusting their temperature s...
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| Published in | Sustainable Energy, Grids and Networks Vol. 18; p. 100201 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.06.2019
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| Abstract | This paper establishes a transactive coordination model of heating–ventilation–air-conditioning (HVAC) loads for demand response purpose. The thermal inertia of industrial thermostatically controlled loads may provide verities of ancillary service in the power system by adjusting their temperature set-points. The HVAC aggregator bids temperature set-points to minimize opportunity cost subject to crucial operational constraints. The cost due to changing temperature set-point from reference is captured by a piece-wise linear function. A dual decomposition based distributed optimization framework is used to optimize the coolant flow rate in the facility for a given temperature set-point. By optimizing temperature set-point and coolant flow, the HVAC system can modify the electrical power consumption at peak hours. The effectiveness of the model is verified by simulating several plausible cases. It is found that overall energy consumption cost significantly reduces; drop the electricity prices while having little effect on the operation of the cold-storage plants. |
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| AbstractList | This paper establishes a transactive coordination model of heating–ventilation–air-conditioning (HVAC) loads for demand response purpose. The thermal inertia of industrial thermostatically controlled loads may provide verities of ancillary service in the power system by adjusting their temperature set-points. The HVAC aggregator bids temperature set-points to minimize opportunity cost subject to crucial operational constraints. The cost due to changing temperature set-point from reference is captured by a piece-wise linear function. A dual decomposition based distributed optimization framework is used to optimize the coolant flow rate in the facility for a given temperature set-point. By optimizing temperature set-point and coolant flow, the HVAC system can modify the electrical power consumption at peak hours. The effectiveness of the model is verified by simulating several plausible cases. It is found that overall energy consumption cost significantly reduces; drop the electricity prices while having little effect on the operation of the cold-storage plants. |
| ArticleNumber | 100201 |
| Author | Mohammad, Nur Rahman, Ataur |
| Author_xml | – sequence: 1 givenname: Nur surname: Mohammad fullname: Mohammad, Nur email: nur.mohammad@cuet.ac.bd – sequence: 2 givenname: Ataur surname: Rahman fullname: Rahman, Ataur |
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| CitedBy_id | crossref_primary_10_1080_14786451_2019_1649263 crossref_primary_10_1016_j_rser_2023_113161 crossref_primary_10_1016_j_renene_2023_119637 crossref_primary_10_1016_j_rser_2021_110904 crossref_primary_10_1109_TII_2022_3149941 crossref_primary_10_1016_j_apenergy_2022_119521 crossref_primary_10_1109_OJIA_2022_3178235 crossref_primary_10_2174_2352096512666191111120050 crossref_primary_10_3390_en16031514 crossref_primary_10_3390_en13112704 crossref_primary_10_3390_en14196261 crossref_primary_10_3390_en14082154 |
| Cites_doi | 10.1109/TPWRS.2002.804956 10.1287/opre.1070.0416 10.1109/TSG.2014.2377241 10.1016/j.segan.2018.09.007 10.1109/TIE.2016.2599479 10.1109/TPWRS.2002.800900 10.1016/j.segan.2015.03.003 10.1109/TSG.2014.2298840 10.1049/iet-gtd.2018.0110 10.1016/S0378-7796(01)00154-7 10.1109/TSG.2013.2268664 10.1109/TSG.2013.2258410 10.1016/j.segan.2017.08.001 10.1016/j.segan.2019.100190 10.1109/TSG.2014.2367494 10.3390/en11123296 10.1109/TSTE.2013.2293772 10.1109/TSG.2013.2257894 10.1109/TSG.2013.2264970 10.1109/TPWRS.2016.2559485 10.1016/j.segan.2015.06.001 10.1016/j.segan.2018.08.002 |
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| Keywords | Cold-storage warehouse Electricity market Thermostatically controlled load Aggregators Demand response Chiller industry Temperature set point |
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| SubjectTerms | Aggregators Chiller industry Cold-storage warehouse Demand response Electricity market Temperature set point Thermostatically controlled load |
| Title | Transactive control of industrial heating–ventilation–air-conditioning units in cold-storage warehouses for demand response |
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