A GBDT‐SOA approach for the system modelling of optimal energy management in grid‐connected micro‐grid system
Summary In this manuscript, a hybrid approach for energy management in grid connected MG system is proposed. The grid connected MG system has photovoltaic (PV), wind turbine (WT), micro turbine (MT), battery. The proposed hybrid approach is the consolidation of both the Gradient Boosting Decision Tr...
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| Veröffentlicht in: | International journal of energy research 2021-04, Vol.45 (5), p.6765-6783 |
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| creator | Arumugam, Prakash Kuppan, Vasudevan |
| description | Summary
In this manuscript, a hybrid approach for energy management in grid connected MG system is proposed. The grid connected MG system has photovoltaic (PV), wind turbine (WT), micro turbine (MT), battery. The proposed hybrid approach is the consolidation of both the Gradient Boosting Decision Trees (GBDT) and Sandpiper Optimization Algorithm (SOA) and this way the proposed technique is named as GBDT‐SOA. Here, at grid connected micro‐grid configuration the required load demand is ever monitored by the GBDT approach. The perfect combination of the MG is optimized by SOA considering the predicted load requirement. The fuel cost including grid power hourly power variation, operation and maintenance cost of the grid connected micro‐grid system is defined as the objective of the proposed technique. The constraints are power demand, renewable energy sources, state of charge of storage elements. Batteries have been used as an energy source, to stabilize and allow the renewable power system units to maintain running in a steady, stable output power. At that point, the proposed model is executed in MATLAB/Simulink work site and the performance is analyzed with existing techniques, such as BFO, SOA and SSA. The efficiency of the sources like photovoltaic, wind turbine, micro turbine, and battery using proposed technique is 95.9375%, 92.113%, 94.387% and 93.7560%. |
| doi_str_mv | 10.1002/er.6270 |
| format | Article |
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In this manuscript, a hybrid approach for energy management in grid connected MG system is proposed. The grid connected MG system has photovoltaic (PV), wind turbine (WT), micro turbine (MT), battery. The proposed hybrid approach is the consolidation of both the Gradient Boosting Decision Trees (GBDT) and Sandpiper Optimization Algorithm (SOA) and this way the proposed technique is named as GBDT‐SOA. Here, at grid connected micro‐grid configuration the required load demand is ever monitored by the GBDT approach. The perfect combination of the MG is optimized by SOA considering the predicted load requirement. The fuel cost including grid power hourly power variation, operation and maintenance cost of the grid connected micro‐grid system is defined as the objective of the proposed technique. The constraints are power demand, renewable energy sources, state of charge of storage elements. Batteries have been used as an energy source, to stabilize and allow the renewable power system units to maintain running in a steady, stable output power. At that point, the proposed model is executed in MATLAB/Simulink work site and the performance is analyzed with existing techniques, such as BFO, SOA and SSA. The efficiency of the sources like photovoltaic, wind turbine, micro turbine, and battery using proposed technique is 95.9375%, 92.113%, 94.387% and 93.7560%.</description><identifier>ISSN: 0363-907X</identifier><identifier>EISSN: 1099-114X</identifier><identifier>DOI: 10.1002/er.6270</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Inc</publisher><subject>Algorithms ; Alternative energy sources ; Batteries ; Decision trees ; Energy & Fuels ; Energy charge ; Energy management ; energy management system ; Energy resources ; Environmental management ; gradient boosting decision trees ; grid connected MG system ; Maintenance costs ; Nuclear Science & Technology ; Optimization ; Photovoltaic cells ; Photovoltaics ; predicted load demand ; Renewable energy ; Renewable energy sources ; Renewable resources ; Resource management ; sandpiper optimization algorithm ; Science & Technology ; Storage batteries ; Technology ; Turbine engines ; Turbines ; Wind power ; Wind turbines</subject><ispartof>International journal of energy research, 2021-04, Vol.45 (5), p.6765-6783</ispartof><rights>2020 John Wiley & Sons Ltd</rights><rights>2021 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>11</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000599580400001</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c3610-e3fdd8ecd36116663656577efc2a80b2785f990e1b04843c76f495b9813c2273</citedby><cites>FETCH-LOGICAL-c3610-e3fdd8ecd36116663656577efc2a80b2785f990e1b04843c76f495b9813c2273</cites><orcidid>0000-0002-4656-6822</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fer.6270$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fer.6270$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>315,781,785,1418,27929,27930,39263,45579,45580</link.rule.ids></links><search><creatorcontrib>Arumugam, Prakash</creatorcontrib><creatorcontrib>Kuppan, Vasudevan</creatorcontrib><title>A GBDT‐SOA approach for the system modelling of optimal energy management in grid‐connected micro‐grid system</title><title>International journal of energy research</title><addtitle>INT J ENERG RES</addtitle><description>Summary
In this manuscript, a hybrid approach for energy management in grid connected MG system is proposed. The grid connected MG system has photovoltaic (PV), wind turbine (WT), micro turbine (MT), battery. The proposed hybrid approach is the consolidation of both the Gradient Boosting Decision Trees (GBDT) and Sandpiper Optimization Algorithm (SOA) and this way the proposed technique is named as GBDT‐SOA. Here, at grid connected micro‐grid configuration the required load demand is ever monitored by the GBDT approach. The perfect combination of the MG is optimized by SOA considering the predicted load requirement. The fuel cost including grid power hourly power variation, operation and maintenance cost of the grid connected micro‐grid system is defined as the objective of the proposed technique. The constraints are power demand, renewable energy sources, state of charge of storage elements. Batteries have been used as an energy source, to stabilize and allow the renewable power system units to maintain running in a steady, stable output power. At that point, the proposed model is executed in MATLAB/Simulink work site and the performance is analyzed with existing techniques, such as BFO, SOA and SSA. The efficiency of the sources like photovoltaic, wind turbine, micro turbine, and battery using proposed technique is 95.9375%, 92.113%, 94.387% and 93.7560%.</description><subject>Algorithms</subject><subject>Alternative energy sources</subject><subject>Batteries</subject><subject>Decision trees</subject><subject>Energy & Fuels</subject><subject>Energy charge</subject><subject>Energy management</subject><subject>energy management system</subject><subject>Energy resources</subject><subject>Environmental management</subject><subject>gradient boosting decision trees</subject><subject>grid connected MG system</subject><subject>Maintenance costs</subject><subject>Nuclear Science & Technology</subject><subject>Optimization</subject><subject>Photovoltaic cells</subject><subject>Photovoltaics</subject><subject>predicted load demand</subject><subject>Renewable energy</subject><subject>Renewable energy sources</subject><subject>Renewable resources</subject><subject>Resource management</subject><subject>sandpiper optimization algorithm</subject><subject>Science & Technology</subject><subject>Storage batteries</subject><subject>Technology</subject><subject>Turbine engines</subject><subject>Turbines</subject><subject>Wind power</subject><subject>Wind turbines</subject><issn>0363-907X</issn><issn>1099-114X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>HGBXW</sourceid><recordid>eNqNkMtKAzEUhoMoWC_4CgEXLmT0ZC6ZybLWKwiCdtHdMM2c1MhMUpMU6c5H8Bl9ElNbxI3gKpfznf_85yfkiMEZA0jP0Z3xtIQtMmAgRMJYPtkmA8h4lggoJ7tkz_sXgFhj5YD4Ib25uBx_vn88PQxpM58728hnqqyj4RmpX_qAPe1ti12nzYxaRe086L7pKBp0syXtG9PMsEcTqDZ05nQbxaQ1BmXAlvZaOht_VoWN3AHZUU3n8XBz7pPx9dV4dJvcP9zcjYb3icw4gwQz1bYVyja-GOc84wUvyhKVTJsKpmlZFUoIQDaFvMozWXKVi2IqKpbJNC2zfXK8lo07vS7Qh_rFLpyJE-u0AJEWQvAVdbKmok3vHap67uJ6blkzqFeB1ujqVaCRrNbkG06t8lKjkfhDx0SjYFFBHm_ARjo0QVszsgsTYuvp_1t_0brD5V9-6qvHb1tfQiuZIQ</recordid><startdate>202104</startdate><enddate>202104</enddate><creator>Arumugam, Prakash</creator><creator>Kuppan, Vasudevan</creator><general>John Wiley & Sons, Inc</general><general>Wiley</general><general>Hindawi Limited</general><scope>BLEPL</scope><scope>DTL</scope><scope>HGBXW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7ST</scope><scope>7TB</scope><scope>7TN</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>F28</scope><scope>FR3</scope><scope>H96</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0002-4656-6822</orcidid></search><sort><creationdate>202104</creationdate><title>A GBDT‐SOA approach for the system modelling of optimal energy management in grid‐connected micro‐grid system</title><author>Arumugam, Prakash ; Kuppan, Vasudevan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3610-e3fdd8ecd36116663656577efc2a80b2785f990e1b04843c76f495b9813c2273</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Alternative energy sources</topic><topic>Batteries</topic><topic>Decision trees</topic><topic>Energy & Fuels</topic><topic>Energy charge</topic><topic>Energy management</topic><topic>energy management system</topic><topic>Energy resources</topic><topic>Environmental management</topic><topic>gradient boosting decision trees</topic><topic>grid connected MG system</topic><topic>Maintenance costs</topic><topic>Nuclear Science & Technology</topic><topic>Optimization</topic><topic>Photovoltaic cells</topic><topic>Photovoltaics</topic><topic>predicted load demand</topic><topic>Renewable energy</topic><topic>Renewable energy sources</topic><topic>Renewable resources</topic><topic>Resource management</topic><topic>sandpiper optimization algorithm</topic><topic>Science & Technology</topic><topic>Storage batteries</topic><topic>Technology</topic><topic>Turbine engines</topic><topic>Turbines</topic><topic>Wind power</topic><topic>Wind turbines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Arumugam, Prakash</creatorcontrib><creatorcontrib>Kuppan, Vasudevan</creatorcontrib><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Environment Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Environment Abstracts</collection><jtitle>International journal of energy research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Arumugam, Prakash</au><au>Kuppan, Vasudevan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A GBDT‐SOA approach for the system modelling of optimal energy management in grid‐connected micro‐grid system</atitle><jtitle>International journal of energy research</jtitle><stitle>INT J ENERG RES</stitle><date>2021-04</date><risdate>2021</risdate><volume>45</volume><issue>5</issue><spage>6765</spage><epage>6783</epage><pages>6765-6783</pages><issn>0363-907X</issn><eissn>1099-114X</eissn><abstract>Summary
In this manuscript, a hybrid approach for energy management in grid connected MG system is proposed. The grid connected MG system has photovoltaic (PV), wind turbine (WT), micro turbine (MT), battery. The proposed hybrid approach is the consolidation of both the Gradient Boosting Decision Trees (GBDT) and Sandpiper Optimization Algorithm (SOA) and this way the proposed technique is named as GBDT‐SOA. Here, at grid connected micro‐grid configuration the required load demand is ever monitored by the GBDT approach. The perfect combination of the MG is optimized by SOA considering the predicted load requirement. The fuel cost including grid power hourly power variation, operation and maintenance cost of the grid connected micro‐grid system is defined as the objective of the proposed technique. The constraints are power demand, renewable energy sources, state of charge of storage elements. Batteries have been used as an energy source, to stabilize and allow the renewable power system units to maintain running in a steady, stable output power. At that point, the proposed model is executed in MATLAB/Simulink work site and the performance is analyzed with existing techniques, such as BFO, SOA and SSA. The efficiency of the sources like photovoltaic, wind turbine, micro turbine, and battery using proposed technique is 95.9375%, 92.113%, 94.387% and 93.7560%.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1002/er.6270</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0002-4656-6822</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Alternative energy sources Batteries Decision trees Energy & Fuels Energy charge Energy management energy management system Energy resources Environmental management gradient boosting decision trees grid connected MG system Maintenance costs Nuclear Science & Technology Optimization Photovoltaic cells Photovoltaics predicted load demand Renewable energy Renewable energy sources Renewable resources Resource management sandpiper optimization algorithm Science & Technology Storage batteries Technology Turbine engines Turbines Wind power Wind turbines |
| title | A GBDT‐SOA approach for the system modelling of optimal energy management in grid‐connected micro‐grid system |
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