Dynamic multi agent-based management and load frequency control of PV/Fuel cell/ wind turbine/ CHP in autonomous microgrid system
This work proposes an adaptive Multi-Input and Single-Output fuzzy controller which is designed in a supervisory manner for a multi-agent system. This paper mainly aims to control the frequency oscillation of each agent and minimize the production cost of the whole interconnected system. In the prop...
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Veröffentlicht in: | Energy (Oxford) 2019-04, Vol.173, p.554-568 |
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creator | Yu, Dongmin Zhu, Haoming Han, Wenqi Holburn, Daniel |
description | This work proposes an adaptive Multi-Input and Single-Output fuzzy controller which is designed in a supervisory manner for a multi-agent system. This paper mainly aims to control the frequency oscillation of each agent and minimize the production cost of the whole interconnected system. In the proposed controller, the load frequency control and management controller loops are integrated into the designing phase. In many parallel works, designing a load frequency controller and managing unit have been carried out separately which leads to disturbance in both outputs. In the present work, the disruption is eliminated by the implementation of the proposed controlling method. To improve the performance of the proposed controller, the key parameters of the controller are tuned using the modified particle swarm optimization algorithm. The designed control system namely “distributed control method” is applied for several independent units (agents). The controller parameters are tuned in a supervisory manner for the considered multi-agent system, where each agent is connected to the adjacent agents. A wide range of operation points is regarded in the designing phase to adjust the parameters in a way that the controller can effectively control the system in the whole of this range.
•Designing multi-agent based MISO fuzzy controller for management of microgrid DGs.•Regarding production cost and LFC in micro-grid as objective function.•Choosing a proper method for optimization with analysis of different methods. |
doi_str_mv | 10.1016/j.energy.2019.02.094 |
format | Article |
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•Designing multi-agent based MISO fuzzy controller for management of microgrid DGs.•Regarding production cost and LFC in micro-grid as objective function.•Choosing a proper method for optimization with analysis of different methods.</description><identifier>ISSN: 0360-5442</identifier><identifier>EISSN: 1873-6785</identifier><identifier>DOI: 10.1016/j.energy.2019.02.094</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Adaptive systems ; Algorithms ; Control systems design ; Controllers ; Disruption ; Distributed control ; Distributed generation ; Frequency control ; Fuel cells ; Fuel technology ; Fuzzy control ; Fuzzy systems ; Microgrid management ; MISO fuzzy controller ; MPSO ; Multi agent system ; Multiagent systems ; Optimal controller ; Parameter modification ; Particle swarm optimization ; Performance enhancement ; Photovoltaic cells ; Production costs ; Solar cells ; Turbines ; Wind power ; Wind turbines</subject><ispartof>Energy (Oxford), 2019-04, Vol.173, p.554-568</ispartof><rights>2019 Elsevier Ltd</rights><rights>Copyright Elsevier BV Apr 15, 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c439t-eb20e41c96381b1cda4c95b8b0d8d43a79dab8f77b7c367245ddd6d3b4b1fdcc3</citedby><cites>FETCH-LOGICAL-c439t-eb20e41c96381b1cda4c95b8b0d8d43a79dab8f77b7c367245ddd6d3b4b1fdcc3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.energy.2019.02.094$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Yu, Dongmin</creatorcontrib><creatorcontrib>Zhu, Haoming</creatorcontrib><creatorcontrib>Han, Wenqi</creatorcontrib><creatorcontrib>Holburn, Daniel</creatorcontrib><title>Dynamic multi agent-based management and load frequency control of PV/Fuel cell/ wind turbine/ CHP in autonomous microgrid system</title><title>Energy (Oxford)</title><description>This work proposes an adaptive Multi-Input and Single-Output fuzzy controller which is designed in a supervisory manner for a multi-agent system. This paper mainly aims to control the frequency oscillation of each agent and minimize the production cost of the whole interconnected system. In the proposed controller, the load frequency control and management controller loops are integrated into the designing phase. In many parallel works, designing a load frequency controller and managing unit have been carried out separately which leads to disturbance in both outputs. In the present work, the disruption is eliminated by the implementation of the proposed controlling method. To improve the performance of the proposed controller, the key parameters of the controller are tuned using the modified particle swarm optimization algorithm. The designed control system namely “distributed control method” is applied for several independent units (agents). The controller parameters are tuned in a supervisory manner for the considered multi-agent system, where each agent is connected to the adjacent agents. A wide range of operation points is regarded in the designing phase to adjust the parameters in a way that the controller can effectively control the system in the whole of this range.
•Designing multi-agent based MISO fuzzy controller for management of microgrid DGs.•Regarding production cost and LFC in micro-grid as objective function.•Choosing a proper method for optimization with analysis of different methods.</description><subject>Adaptive systems</subject><subject>Algorithms</subject><subject>Control systems design</subject><subject>Controllers</subject><subject>Disruption</subject><subject>Distributed control</subject><subject>Distributed generation</subject><subject>Frequency control</subject><subject>Fuel cells</subject><subject>Fuel technology</subject><subject>Fuzzy control</subject><subject>Fuzzy systems</subject><subject>Microgrid management</subject><subject>MISO fuzzy controller</subject><subject>MPSO</subject><subject>Multi agent system</subject><subject>Multiagent systems</subject><subject>Optimal controller</subject><subject>Parameter modification</subject><subject>Particle swarm optimization</subject><subject>Performance enhancement</subject><subject>Photovoltaic cells</subject><subject>Production costs</subject><subject>Solar cells</subject><subject>Turbines</subject><subject>Wind power</subject><subject>Wind turbines</subject><issn>0360-5442</issn><issn>1873-6785</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kMtqHDEQRUVwIGMnf5CFIOvu0asf2gTCOI4NBnvhZCv0qB40dEuOpHboZf48MpO1V0UVt-6tOgh9pqSlhPb7UwsB0nFrGaGyJawlUrxDOzoOvOmHsbtAO8J70nRCsA_oMucTIaQbpdyhv9db0Iu3eFnn4rE-QiiN0RkcXnSo7VIHWAeH56gdnhL8XiHYDdsYSoozjhN-_LW_WWHGFuZ5j__4Ki5rMj7AHh9uH7EPWK8lhrjENeMaluIxeYfzlgssH9H7Sc8ZPv2vV-jnzfenw21z__Dj7vDtvrGCy9KAYQQEtbLnIzXUOi2s7MxoiBud4HqQTptxGgYzWN4PTHTOud5xIwydnLX8Cn05-z6nWH_IRZ3imkKNVIzRUfaMk6GqxFlVj8w5waSek1902hQl6hW2OqkzbPUKWxGmKuy69vW8BvWDFw9JZesrJ3A-gS3KRf-2wT8hJ4yf</recordid><startdate>20190415</startdate><enddate>20190415</enddate><creator>Yu, Dongmin</creator><creator>Zhu, Haoming</creator><creator>Han, Wenqi</creator><creator>Holburn, Daniel</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7ST</scope><scope>7TB</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope><scope>SOI</scope></search><sort><creationdate>20190415</creationdate><title>Dynamic multi agent-based management and load frequency control of PV/Fuel cell/ wind turbine/ CHP in autonomous microgrid system</title><author>Yu, Dongmin ; Zhu, Haoming ; Han, Wenqi ; Holburn, Daniel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c439t-eb20e41c96381b1cda4c95b8b0d8d43a79dab8f77b7c367245ddd6d3b4b1fdcc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adaptive systems</topic><topic>Algorithms</topic><topic>Control systems design</topic><topic>Controllers</topic><topic>Disruption</topic><topic>Distributed control</topic><topic>Distributed generation</topic><topic>Frequency control</topic><topic>Fuel cells</topic><topic>Fuel technology</topic><topic>Fuzzy control</topic><topic>Fuzzy systems</topic><topic>Microgrid management</topic><topic>MISO fuzzy controller</topic><topic>MPSO</topic><topic>Multi agent system</topic><topic>Multiagent systems</topic><topic>Optimal controller</topic><topic>Parameter modification</topic><topic>Particle swarm optimization</topic><topic>Performance enhancement</topic><topic>Photovoltaic cells</topic><topic>Production costs</topic><topic>Solar cells</topic><topic>Turbines</topic><topic>Wind power</topic><topic>Wind turbines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yu, Dongmin</creatorcontrib><creatorcontrib>Zhu, Haoming</creatorcontrib><creatorcontrib>Han, Wenqi</creatorcontrib><creatorcontrib>Holburn, Daniel</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Environment Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Environment Abstracts</collection><jtitle>Energy (Oxford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yu, Dongmin</au><au>Zhu, Haoming</au><au>Han, Wenqi</au><au>Holburn, Daniel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dynamic multi agent-based management and load frequency control of PV/Fuel cell/ wind turbine/ CHP in autonomous microgrid system</atitle><jtitle>Energy (Oxford)</jtitle><date>2019-04-15</date><risdate>2019</risdate><volume>173</volume><spage>554</spage><epage>568</epage><pages>554-568</pages><issn>0360-5442</issn><eissn>1873-6785</eissn><abstract>This work proposes an adaptive Multi-Input and Single-Output fuzzy controller which is designed in a supervisory manner for a multi-agent system. This paper mainly aims to control the frequency oscillation of each agent and minimize the production cost of the whole interconnected system. In the proposed controller, the load frequency control and management controller loops are integrated into the designing phase. In many parallel works, designing a load frequency controller and managing unit have been carried out separately which leads to disturbance in both outputs. In the present work, the disruption is eliminated by the implementation of the proposed controlling method. To improve the performance of the proposed controller, the key parameters of the controller are tuned using the modified particle swarm optimization algorithm. The designed control system namely “distributed control method” is applied for several independent units (agents). The controller parameters are tuned in a supervisory manner for the considered multi-agent system, where each agent is connected to the adjacent agents. A wide range of operation points is regarded in the designing phase to adjust the parameters in a way that the controller can effectively control the system in the whole of this range.
•Designing multi-agent based MISO fuzzy controller for management of microgrid DGs.•Regarding production cost and LFC in micro-grid as objective function.•Choosing a proper method for optimization with analysis of different methods.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.energy.2019.02.094</doi><tpages>15</tpages></addata></record> |
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source | ScienceDirect Journals (5 years ago - present) |
subjects | Adaptive systems Algorithms Control systems design Controllers Disruption Distributed control Distributed generation Frequency control Fuel cells Fuel technology Fuzzy control Fuzzy systems Microgrid management MISO fuzzy controller MPSO Multi agent system Multiagent systems Optimal controller Parameter modification Particle swarm optimization Performance enhancement Photovoltaic cells Production costs Solar cells Turbines Wind power Wind turbines |
title | Dynamic multi agent-based management and load frequency control of PV/Fuel cell/ wind turbine/ CHP in autonomous microgrid system |
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