Self-Healing Resilient Distribution Systems Based on Sectionalization Into Microgrids
This paper proposes a novel comprehensive operation and self-healing strategy for a distribution system with both dispatchable and nondispatchable distributed generators (DGs). In the normal operation mode, the control objective of the system is to minimize the operation costs and maximize the reven...
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| Veröffentlicht in: | IEEE transactions on power systems 2015-11, Vol.30 (6), p.3139-3149 |
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| creator | Wang, Zhaoyu Wang, Jianhui |
| description | This paper proposes a novel comprehensive operation and self-healing strategy for a distribution system with both dispatchable and nondispatchable distributed generators (DGs). In the normal operation mode, the control objective of the system is to minimize the operation costs and maximize the revenues. A rolling-horizon optimization method is used to schedule the outputs of dispatchable DGs based on forecasts. In the self-healing mode, the on-outage portion of the distribution system will be optimally sectionalized into networked self-supplied microgrids (MGs) so as to provide reliable power supply to the maximum loads continuously. The outputs of the dispatchable DGs will be rescheduled accordingly too. In order to take into account the uncertainties of DG outputs and load consumptions, we formulate the problems as a stochastic program. A scenario reduction method is applied to achieve a tradeoff between the accuracy of the solution and the computational burden. A modified IEEE 123-node distribution system is used as a test system. The results of case studies demonstrate the effectiveness of the proposed methodology. |
| doi_str_mv | 10.1109/TPWRS.2015.2389753 |
| format | Article |
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(ANL), Argonne, IL (United States)</creatorcontrib><description>This paper proposes a novel comprehensive operation and self-healing strategy for a distribution system with both dispatchable and nondispatchable distributed generators (DGs). In the normal operation mode, the control objective of the system is to minimize the operation costs and maximize the revenues. A rolling-horizon optimization method is used to schedule the outputs of dispatchable DGs based on forecasts. In the self-healing mode, the on-outage portion of the distribution system will be optimally sectionalized into networked self-supplied microgrids (MGs) so as to provide reliable power supply to the maximum loads continuously. The outputs of the dispatchable DGs will be rescheduled accordingly too. In order to take into account the uncertainties of DG outputs and load consumptions, we formulate the problems as a stochastic program. A scenario reduction method is applied to achieve a tradeoff between the accuracy of the solution and the computational burden. A modified IEEE 123-node distribution system is used as a test system. The results of case studies demonstrate the effectiveness of the proposed methodology.</description><identifier>ISSN: 0885-8950</identifier><identifier>EISSN: 1558-0679</identifier><identifier>DOI: 10.1109/TPWRS.2015.2389753</identifier><identifier>CODEN: ITPSEG</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Case studies ; Consumption ; Control systems ; Distributed power generation ; Electric power grids ; Electric utilities ; Generators ; microgrid (MG) ; Microgrids ; Optimization ; Power distribution ; Power distribution faults ; Power system reliability ; Reduction ; Revenues ; Schedules ; Self-healing ; Stochastic Optimization ; Stochastic processes ; Uncertainty</subject><ispartof>IEEE transactions on power systems, 2015-11, Vol.30 (6), p.3139-3149</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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(ANL), Argonne, IL (United States)</creatorcontrib><title>Self-Healing Resilient Distribution Systems Based on Sectionalization Into Microgrids</title><title>IEEE transactions on power systems</title><addtitle>TPWRS</addtitle><description>This paper proposes a novel comprehensive operation and self-healing strategy for a distribution system with both dispatchable and nondispatchable distributed generators (DGs). In the normal operation mode, the control objective of the system is to minimize the operation costs and maximize the revenues. A rolling-horizon optimization method is used to schedule the outputs of dispatchable DGs based on forecasts. In the self-healing mode, the on-outage portion of the distribution system will be optimally sectionalized into networked self-supplied microgrids (MGs) so as to provide reliable power supply to the maximum loads continuously. The outputs of the dispatchable DGs will be rescheduled accordingly too. In order to take into account the uncertainties of DG outputs and load consumptions, we formulate the problems as a stochastic program. A scenario reduction method is applied to achieve a tradeoff between the accuracy of the solution and the computational burden. A modified IEEE 123-node distribution system is used as a test system. The results of case studies demonstrate the effectiveness of the proposed methodology.</description><subject>Case studies</subject><subject>Consumption</subject><subject>Control systems</subject><subject>Distributed power generation</subject><subject>Electric power grids</subject><subject>Electric utilities</subject><subject>Generators</subject><subject>microgrid (MG)</subject><subject>Microgrids</subject><subject>Optimization</subject><subject>Power distribution</subject><subject>Power distribution faults</subject><subject>Power system reliability</subject><subject>Reduction</subject><subject>Revenues</subject><subject>Schedules</subject><subject>Self-healing</subject><subject>Stochastic Optimization</subject><subject>Stochastic processes</subject><subject>Uncertainty</subject><issn>0885-8950</issn><issn>1558-0679</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkU9P4zAQxa0VK23p8gWWSwQXLiljO67tI_9BYrWIgvZoOc4YjNIEYvdQPj1OizhwGmnm92bm6RHyh8KMUtDHD3f_7xczBlTMGFdaCv6DTKgQqoS51DtkAkqJUmkBv8hujC8AMM-DCXlcYOvLa7Rt6J6Ke4yhDdil4jzENIR6lULfFYt1TLiMxamN2BRjA904yKJ3uyFuutQXf4Mb-qchNPE3-eltG3Hvs07J4-XFw9l1efvv6ubs5LZ0oqpSiTWrauQMrZtzrGyFijmqdOOlqBtHqbe1VrrWzFsHnjGUAKLx2krpGTA-JQfbvX1MwUQXErpn13dd_s9QxqWmkKGjLfQ69G8rjMksQ3TYtrbDfhUNlTxvVZRXGT38hr70qyH7HClgUopqc5VtqWw3xgG9eR3C0g5rQ8GMcZhNHGaMw3zGkUX7W1FAxC-BBCorofgH2cSG_A</recordid><startdate>20151101</startdate><enddate>20151101</enddate><creator>Wang, Zhaoyu</creator><creator>Wang, Jianhui</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope><scope>F28</scope><scope>OTOTI</scope></search><sort><creationdate>20151101</creationdate><title>Self-Healing Resilient Distribution Systems Based on Sectionalization Into Microgrids</title><author>Wang, Zhaoyu ; Wang, Jianhui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c544t-eb24be32eac63e4a4e82c189df75bdc11fab989b92fac0f22e7005df9a77f2023</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Case studies</topic><topic>Consumption</topic><topic>Control systems</topic><topic>Distributed power generation</topic><topic>Electric power grids</topic><topic>Electric utilities</topic><topic>Generators</topic><topic>microgrid (MG)</topic><topic>Microgrids</topic><topic>Optimization</topic><topic>Power distribution</topic><topic>Power distribution faults</topic><topic>Power system reliability</topic><topic>Reduction</topic><topic>Revenues</topic><topic>Schedules</topic><topic>Self-healing</topic><topic>Stochastic Optimization</topic><topic>Stochastic processes</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Zhaoyu</creatorcontrib><creatorcontrib>Wang, Jianhui</creatorcontrib><creatorcontrib>Argonne National Lab. 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(ANL), Argonne, IL (United States)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Self-Healing Resilient Distribution Systems Based on Sectionalization Into Microgrids</atitle><jtitle>IEEE transactions on power systems</jtitle><stitle>TPWRS</stitle><date>2015-11-01</date><risdate>2015</risdate><volume>30</volume><issue>6</issue><spage>3139</spage><epage>3149</epage><pages>3139-3149</pages><issn>0885-8950</issn><eissn>1558-0679</eissn><coden>ITPSEG</coden><abstract>This paper proposes a novel comprehensive operation and self-healing strategy for a distribution system with both dispatchable and nondispatchable distributed generators (DGs). In the normal operation mode, the control objective of the system is to minimize the operation costs and maximize the revenues. A rolling-horizon optimization method is used to schedule the outputs of dispatchable DGs based on forecasts. In the self-healing mode, the on-outage portion of the distribution system will be optimally sectionalized into networked self-supplied microgrids (MGs) so as to provide reliable power supply to the maximum loads continuously. The outputs of the dispatchable DGs will be rescheduled accordingly too. In order to take into account the uncertainties of DG outputs and load consumptions, we formulate the problems as a stochastic program. A scenario reduction method is applied to achieve a tradeoff between the accuracy of the solution and the computational burden. A modified IEEE 123-node distribution system is used as a test system. The results of case studies demonstrate the effectiveness of the proposed methodology.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TPWRS.2015.2389753</doi><tpages>11</tpages></addata></record> |
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| subjects | Case studies Consumption Control systems Distributed power generation Electric power grids Electric utilities Generators microgrid (MG) Microgrids Optimization Power distribution Power distribution faults Power system reliability Reduction Revenues Schedules Self-healing Stochastic Optimization Stochastic processes Uncertainty |
| title | Self-Healing Resilient Distribution Systems Based on Sectionalization Into Microgrids |
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