Optimal Resilience-Oriented Microgrid Formation Considering Failure Probability of Distribution Feeders
•Introducing a new methodology to determine the optimal number and configuration of MGs to improve the resiliency of the distribution network.•Studying the effect of post-restoration failure probability on MG formation strategy.•Suggesting a master/slave optimization algorithm to reduce the calculat...
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Veröffentlicht in: | Electric power systems research 2022-08, Vol.209, p.108012, Article 108012 |
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description | •Introducing a new methodology to determine the optimal number and configuration of MGs to improve the resiliency of the distribution network.•Studying the effect of post-restoration failure probability on MG formation strategy.•Suggesting a master/slave optimization algorithm to reduce the calculation time of MG formation problem.
After a natural disaster, there is an urgent need to supply critical loads such as hospitals as soon as possible. Microgrid (MG) formation is one of the quickest ways to achieve this goal. However, in MG formation studies, there is a trade-off between maximizing the amount of restored loads and minimizing their risk of interruption due to the following aftershocks. For the former objective, the minimum number of MGs should be formed, whereas, for the latter objective, the maximum number of MGs should be configured. This paper tackles this contradictory situation by considering the failure risk of distribution feeders in its proposed optimization framework. In this paper, at first, a novel objective function is proposed to model the impact of feeders’ failure probability on the survivability of MGs. Then, a two-stage master/slave optimization is presented to optimize the number and configuration of MGs. In this optimization framework, a heuristic algorithm will determine the open/close status of feeders, a graph search method will find the formed MGs, and finally, an optimal power flow study will be run to maximize the amount of supplied loads. This paper shows that the proposed methodology will result in an optimal solution, which establishes an appropriate balance between the amount of supplied loads and their risk of interruption. IEEE 33-bus test system is employed to investigate the effectiveness of the proposed methodology. |
doi_str_mv | 10.1016/j.epsr.2022.108012 |
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After a natural disaster, there is an urgent need to supply critical loads such as hospitals as soon as possible. Microgrid (MG) formation is one of the quickest ways to achieve this goal. However, in MG formation studies, there is a trade-off between maximizing the amount of restored loads and minimizing their risk of interruption due to the following aftershocks. For the former objective, the minimum number of MGs should be formed, whereas, for the latter objective, the maximum number of MGs should be configured. This paper tackles this contradictory situation by considering the failure risk of distribution feeders in its proposed optimization framework. In this paper, at first, a novel objective function is proposed to model the impact of feeders’ failure probability on the survivability of MGs. Then, a two-stage master/slave optimization is presented to optimize the number and configuration of MGs. In this optimization framework, a heuristic algorithm will determine the open/close status of feeders, a graph search method will find the formed MGs, and finally, an optimal power flow study will be run to maximize the amount of supplied loads. This paper shows that the proposed methodology will result in an optimal solution, which establishes an appropriate balance between the amount of supplied loads and their risk of interruption. IEEE 33-bus test system is employed to investigate the effectiveness of the proposed methodology.</description><identifier>ISSN: 0378-7796</identifier><identifier>EISSN: 1873-2046</identifier><identifier>DOI: 10.1016/j.epsr.2022.108012</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Algorithms ; Distributed generation ; Distribution system ; Failure ; Failure probability ; Feeders ; Heuristic ; Heuristic methods ; Microgrid ; Natural disasters ; Optimization ; Power flow ; Probability ; Resiliency ; Risk ; Risk assessment ; Risk factors ; Survivability</subject><ispartof>Electric power systems research, 2022-08, Vol.209, p.108012, Article 108012</ispartof><rights>2022 Elsevier B.V.</rights><rights>Copyright Elsevier Science Ltd. Aug 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c258t-da435c4e3aeb28c72054739293270e324cb13dea7c5cebd1f163373e219f373a3</citedby><cites>FETCH-LOGICAL-c258t-da435c4e3aeb28c72054739293270e324cb13dea7c5cebd1f163373e219f373a3</cites><orcidid>0000-0002-1178-5750 ; 0000-0001-9197-6591</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0378779622002383$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27903,27904,65309</link.rule.ids></links><search><creatorcontrib>Jahromi, Saeed Nikbakhsh</creatorcontrib><creatorcontrib>Hajipour, Ehsan</creatorcontrib><creatorcontrib>Ehsan, Mehdi</creatorcontrib><title>Optimal Resilience-Oriented Microgrid Formation Considering Failure Probability of Distribution Feeders</title><title>Electric power systems research</title><description>•Introducing a new methodology to determine the optimal number and configuration of MGs to improve the resiliency of the distribution network.•Studying the effect of post-restoration failure probability on MG formation strategy.•Suggesting a master/slave optimization algorithm to reduce the calculation time of MG formation problem.
After a natural disaster, there is an urgent need to supply critical loads such as hospitals as soon as possible. Microgrid (MG) formation is one of the quickest ways to achieve this goal. However, in MG formation studies, there is a trade-off between maximizing the amount of restored loads and minimizing their risk of interruption due to the following aftershocks. For the former objective, the minimum number of MGs should be formed, whereas, for the latter objective, the maximum number of MGs should be configured. This paper tackles this contradictory situation by considering the failure risk of distribution feeders in its proposed optimization framework. In this paper, at first, a novel objective function is proposed to model the impact of feeders’ failure probability on the survivability of MGs. Then, a two-stage master/slave optimization is presented to optimize the number and configuration of MGs. In this optimization framework, a heuristic algorithm will determine the open/close status of feeders, a graph search method will find the formed MGs, and finally, an optimal power flow study will be run to maximize the amount of supplied loads. This paper shows that the proposed methodology will result in an optimal solution, which establishes an appropriate balance between the amount of supplied loads and their risk of interruption. IEEE 33-bus test system is employed to investigate the effectiveness of the proposed methodology.</description><subject>Algorithms</subject><subject>Distributed generation</subject><subject>Distribution system</subject><subject>Failure</subject><subject>Failure probability</subject><subject>Feeders</subject><subject>Heuristic</subject><subject>Heuristic methods</subject><subject>Microgrid</subject><subject>Natural disasters</subject><subject>Optimization</subject><subject>Power flow</subject><subject>Probability</subject><subject>Resiliency</subject><subject>Risk</subject><subject>Risk assessment</subject><subject>Risk factors</subject><subject>Survivability</subject><issn>0378-7796</issn><issn>1873-2046</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LxDAURYMoOH78AVcB1x3z0TYtuJHRqjAyIroOafo6pHSamqTC_HszU9euLoR78t47CN1QsqSE5nfdEkbvlowwFh8KQtkJWtBC8ISRND9FC8JFkQhR5ufowvuOEJKXIlug7WYMZqd6_AHe9AYGDcnGxQzQ4Dejnd060-DKup0Kxg54ZQdvGnBm2OJKmX5ygN-drVUd8bDHtsWPxgdn6unYrwBi21-hs1b1Hq7_8hJ9VU-fq5dkvXl-XT2sE82yIiSNSnmmU-AKalZowUiWCl6ykjNBgLNU15Q3oITONNQNbWnOueDAaNnGVPwS3c7_js5-T-CD7OzkhjhSsrwoKGNpkccWm1vxPu8dtHJ00YLbS0rkQajs5EGoPAiVs9AI3c8QxP1_DDjp9VFYYxzoIBtr_sN_AWI8f-Y</recordid><startdate>202208</startdate><enddate>202208</enddate><creator>Jahromi, Saeed Nikbakhsh</creator><creator>Hajipour, Ehsan</creator><creator>Ehsan, Mehdi</creator><general>Elsevier B.V</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-1178-5750</orcidid><orcidid>https://orcid.org/0000-0001-9197-6591</orcidid></search><sort><creationdate>202208</creationdate><title>Optimal Resilience-Oriented Microgrid Formation Considering Failure Probability of Distribution Feeders</title><author>Jahromi, Saeed Nikbakhsh ; Hajipour, Ehsan ; Ehsan, Mehdi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c258t-da435c4e3aeb28c72054739293270e324cb13dea7c5cebd1f163373e219f373a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Distributed generation</topic><topic>Distribution system</topic><topic>Failure</topic><topic>Failure probability</topic><topic>Feeders</topic><topic>Heuristic</topic><topic>Heuristic methods</topic><topic>Microgrid</topic><topic>Natural disasters</topic><topic>Optimization</topic><topic>Power flow</topic><topic>Probability</topic><topic>Resiliency</topic><topic>Risk</topic><topic>Risk assessment</topic><topic>Risk factors</topic><topic>Survivability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jahromi, Saeed Nikbakhsh</creatorcontrib><creatorcontrib>Hajipour, Ehsan</creatorcontrib><creatorcontrib>Ehsan, Mehdi</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Electric power systems research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jahromi, Saeed Nikbakhsh</au><au>Hajipour, Ehsan</au><au>Ehsan, Mehdi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimal Resilience-Oriented Microgrid Formation Considering Failure Probability of Distribution Feeders</atitle><jtitle>Electric power systems research</jtitle><date>2022-08</date><risdate>2022</risdate><volume>209</volume><spage>108012</spage><pages>108012-</pages><artnum>108012</artnum><issn>0378-7796</issn><eissn>1873-2046</eissn><abstract>•Introducing a new methodology to determine the optimal number and configuration of MGs to improve the resiliency of the distribution network.•Studying the effect of post-restoration failure probability on MG formation strategy.•Suggesting a master/slave optimization algorithm to reduce the calculation time of MG formation problem.
After a natural disaster, there is an urgent need to supply critical loads such as hospitals as soon as possible. Microgrid (MG) formation is one of the quickest ways to achieve this goal. However, in MG formation studies, there is a trade-off between maximizing the amount of restored loads and minimizing their risk of interruption due to the following aftershocks. For the former objective, the minimum number of MGs should be formed, whereas, for the latter objective, the maximum number of MGs should be configured. This paper tackles this contradictory situation by considering the failure risk of distribution feeders in its proposed optimization framework. In this paper, at first, a novel objective function is proposed to model the impact of feeders’ failure probability on the survivability of MGs. Then, a two-stage master/slave optimization is presented to optimize the number and configuration of MGs. In this optimization framework, a heuristic algorithm will determine the open/close status of feeders, a graph search method will find the formed MGs, and finally, an optimal power flow study will be run to maximize the amount of supplied loads. This paper shows that the proposed methodology will result in an optimal solution, which establishes an appropriate balance between the amount of supplied loads and their risk of interruption. IEEE 33-bus test system is employed to investigate the effectiveness of the proposed methodology.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.epsr.2022.108012</doi><orcidid>https://orcid.org/0000-0002-1178-5750</orcidid><orcidid>https://orcid.org/0000-0001-9197-6591</orcidid></addata></record> |
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subjects | Algorithms Distributed generation Distribution system Failure Failure probability Feeders Heuristic Heuristic methods Microgrid Natural disasters Optimization Power flow Probability Resiliency Risk Risk assessment Risk factors Survivability |
title | Optimal Resilience-Oriented Microgrid Formation Considering Failure Probability of Distribution Feeders |
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