Interval optimal scheduling of integrated electricity and district heating systems considering dynamic characteristics of heating network
Coordinated operation of integrated electricity and district heating system (IEDHS) has great potential to enhance the flexibility of the power system to cope with the wind power curtailment. This study proposes an interval optimal scheduling algorithm for IEDHSs, considering the dynamic characteris...
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| Published in: | Energy systems integration Vol. 2; no. 3; pp. 179 - 186 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Tianjin
The Institution of Engineering and Technology
01.09.2020
John Wiley & Sons, Inc |
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| ISSN: | 2516-8401, 2516-8401 |
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| Abstract | Coordinated operation of integrated electricity and district heating system (IEDHS) has great potential to enhance the flexibility of the power system to cope with the wind power curtailment. This study proposes an interval optimal scheduling algorithm for IEDHSs, considering the dynamic characteristics of the heating network. The model of the district heating system with dynamic characteristics including transmission delay and heat losses, is established in detail, and the uncertainties of both wind power and electricity and heating loads are described with interval numbers. Then an interval optimal scheduling model of the IEDHS is formulated to minimise the IEDHS operation cost. The impacts of the transmission delay and heat losses of the heating network on the scheduling of the IEDHS are analysed. Case studies are performed on the PJM 5‐bus electricity system with a 6‐node district heating system and IEEE 39‐bus electricity system with a 12‐node district heating system to evaluate the effectiveness of the proposed model. The results demonstrate that the dynamic characteristics of the heating network can integrate more wind power and enlarge the width of the cost interval. |
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| AbstractList | Coordinated operation of integrated electricity and district heating system (IEDHS) has great potential to enhance the flexibility of the power system to cope with the wind power curtailment. This study proposes an interval optimal scheduling algorithm for IEDHSs, considering the dynamic characteristics of the heating network. The model of the district heating system with dynamic characteristics including transmission delay and heat losses, is established in detail, and the uncertainties of both wind power and electricity and heating loads are described with interval numbers. Then an interval optimal scheduling model of the IEDHS is formulated to minimise the IEDHS operation cost. The impacts of the transmission delay and heat losses of the heating network on the scheduling of the IEDHS are analysed. Case studies are performed on the PJM 5‐bus electricity system with a 6‐node district heating system and IEEE 39‐bus electricity system with a 12‐node district heating system to evaluate the effectiveness of the proposed model. The results demonstrate that the dynamic characteristics of the heating network can integrate more wind power and enlarge the width of the cost interval. |
| Author | Chen, Houhe Zhang, Rufeng Jiang, Tao Zhang, Ting Li, Xue Li, Guoqing |
| Author_xml | – sequence: 1 givenname: Houhe surname: Chen fullname: Chen, Houhe organization: Northeast Electric Power University – sequence: 2 givenname: Ting surname: Zhang fullname: Zhang, Ting organization: Northeast Electric Power University – sequence: 3 givenname: Rufeng surname: Zhang fullname: Zhang, Rufeng organization: Northeast Electric Power University – sequence: 4 givenname: Tao surname: Jiang fullname: Jiang, Tao email: tjiang@neepu.edu.cn organization: Northeast Electric Power University – sequence: 5 givenname: Xue surname: Li fullname: Li, Xue organization: Northeast Electric Power University – sequence: 6 givenname: Guoqing surname: Li fullname: Li, Guoqing organization: Northeast Electric Power University |
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| CitedBy_id | crossref_primary_10_1016_j_apenergy_2020_116124 crossref_primary_10_1016_j_epsr_2021_107683 crossref_primary_10_1016_j_ijepes_2021_107526 crossref_primary_10_1016_j_apenergy_2023_121038 crossref_primary_10_1016_j_energy_2022_124311 crossref_primary_10_1016_j_ijepes_2020_106522 crossref_primary_10_1016_j_seta_2021_101682 crossref_primary_10_1016_j_apenergy_2022_118586 crossref_primary_10_1016_j_applthermaleng_2022_119926 crossref_primary_10_1016_j_segan_2022_100618 |
| Cites_doi | 10.1016/j.solener.2019.03.034 10.1109/TSG.2015.2435011 10.1016/j.apenergy.2015.10.054 10.1109/TSTE.2017.2681108 10.1016/j.applthermaleng.2015.11.024 10.1109/TSTE.2014.2382122 10.1016/j.enbuild.2017.01.062 10.17775/CSEEJPES.2018.00030 10.1109/TSTE.2015.2500571 10.1016/j.apenergy.2017.05.004 10.1109/59.141794 10.1016/j.energy.2018.04.064 10.1109/TPWRS.2014.2356723 10.1109/TPWRS.2012.2191804 10.1016/j.apenergy.2016.08.139 10.1016/j.ejor.2008.12.019 10.1109/TSTE.2017.2718031 10.1016/j.apenergy.2018.09.068 10.1109/TPWRS.2012.2214405 10.1016/j.apenergy.2018.09.077 10.1049/iet-gtd.2009.0374 10.1016/j.renene.2018.06.068 10.1109/59.544642 10.1016/j.apenergy.2018.09.042 |
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| SubjectTerms | 12‐node district heating system 6‐node district heating system Algorithms Alternative energy sources Cooling Cost analysis District heating district heating systems Dynamic characteristics dynamic characteristics including transmission delay Efficiency Electricity Energy storage Heat heat losses Heating Heating load heating loads heating network Heating systems IEDHS operation cost IEEE 39‐bus electricity system Integrated approach integrated electricity interval optimal scheduling algorithm interval optimal scheduling model load flow optimisation PJM 5‐bus electricity system power markets power system Probability distribution Random variables Renewable resources Reserve requirements Scheduling Supply chains Water temperature Wind farms Wind power wind power curtailment wind power plants |
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| Title | Interval optimal scheduling of integrated electricity and district heating systems considering dynamic characteristics of heating network |
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