Cost optimization of PV-Diesel Systems in Nanogrid Using L J Cuckoo Search and its Application in Mobile Towers

Abstract Nano-grids are a replication of large electricity grids resembling their model but only on a smaller scale to locations that are not accessible easily and the connectivity of grid is not possible. Generally, these are used in remote hilly regions where there can be a renewable source of ene...
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Autor*in:	Murugesan, C. [verfasserIn] Marimuthu, C. N. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Nano-grid Photo voltaic (PV) array Diesel generator (DG) Renewable energy Cuckoo search (CS) and Luus and Jaakola (LJ)

Übergeordnetes Werk:	Enthalten in: Mobile networks and applications - New York, NY : ACM, 1996, 24(2018), 2 vom: 09. Mai, Seite 340-349
Übergeordnetes Werk:	volume:24 ; year:2018 ; number:2 ; day:09 ; month:05 ; pages:340-349

Links:	Volltext

DOI / URN:	10.1007/s11036-018-1046-7

Katalog-ID:	SPR015937119

Internformat


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520			\|a Abstract Nano-grids are a replication of large electricity grids resembling their model but only on a smaller scale to locations that are not accessible easily and the connectivity of grid is not possible. Generally, these are used in remote hilly regions where there can be a renewable source of energy. For the purpose of stand-alone nano-grid, an algorithm which is simple to determine the required Photo Voltaic (PV) panel, a Diesel Generator (DG) along with battery models are used. To utilize these effectively, investigation of the algorithm has been done so as to calculate the capacity of the generator unit whose power system is dependable along with reduced cost. Many methods have been used which can be iterative with artificial modes of intelligence which are reported in order to design energy system which is hybrid and renewable and technically and economically optimal. Cuckoo Search (CS) is a robust algorithm for optimal sizing which takes its inspiration from the breeding behavior of cuckoo. Region size determination is incorporated through the Luus and Jaakola (LJ) optimizing technique, which helps in improving the computational efficiency. Convergence speed and reliability of CS and LJ are compared in achieving the global optimum.
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700	1		\|a Marimuthu, C. N. \|e verfasserin \|4 aut
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author_variant	c m cm c n m cn cnm
matchkey_str	article:15728153:2018----::otpiiainfvislytminngiuigjukoerhnis
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publishDate	2018
allfields	10.1007/s11036-018-1046-7 doi (DE-627)SPR015937119 (SPR)s11036-018-1046-7-e DE-627 ger DE-627 rakwb eng 004 ASE 54.32 bkl Murugesan, C. verfasserin aut Cost optimization of PV-Diesel Systems in Nanogrid Using L J Cuckoo Search and its Application in Mobile Towers 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Nano-grids are a replication of large electricity grids resembling their model but only on a smaller scale to locations that are not accessible easily and the connectivity of grid is not possible. Generally, these are used in remote hilly regions where there can be a renewable source of energy. For the purpose of stand-alone nano-grid, an algorithm which is simple to determine the required Photo Voltaic (PV) panel, a Diesel Generator (DG) along with battery models are used. To utilize these effectively, investigation of the algorithm has been done so as to calculate the capacity of the generator unit whose power system is dependable along with reduced cost. Many methods have been used which can be iterative with artificial modes of intelligence which are reported in order to design energy system which is hybrid and renewable and technically and economically optimal. Cuckoo Search (CS) is a robust algorithm for optimal sizing which takes its inspiration from the breeding behavior of cuckoo. Region size determination is incorporated through the Luus and Jaakola (LJ) optimizing technique, which helps in improving the computational efficiency. Convergence speed and reliability of CS and LJ are compared in achieving the global optimum. Nano-grid (dpeaa)DE-He213 Photo voltaic (PV) array (dpeaa)DE-He213 Diesel generator (DG) (dpeaa)DE-He213 Renewable energy (dpeaa)DE-He213 Cuckoo search (CS) and Luus and Jaakola (LJ) (dpeaa)DE-He213 Marimuthu, C. N. verfasserin aut Enthalten in Mobile networks and applications New York, NY : ACM, 1996 24(2018), 2 vom: 09. Mai, Seite 340-349 (DE-627)318370794 (DE-600)2000637-8 1572-8153 nnns volume:24 year:2018 number:2 day:09 month:05 pages:340-349 https://dx.doi.org/10.1007/s11036-018-1046-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.32 ASE AR 24 2018 2 09 05 340-349
spelling	10.1007/s11036-018-1046-7 doi (DE-627)SPR015937119 (SPR)s11036-018-1046-7-e DE-627 ger DE-627 rakwb eng 004 ASE 54.32 bkl Murugesan, C. verfasserin aut Cost optimization of PV-Diesel Systems in Nanogrid Using L J Cuckoo Search and its Application in Mobile Towers 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Nano-grids are a replication of large electricity grids resembling their model but only on a smaller scale to locations that are not accessible easily and the connectivity of grid is not possible. Generally, these are used in remote hilly regions where there can be a renewable source of energy. For the purpose of stand-alone nano-grid, an algorithm which is simple to determine the required Photo Voltaic (PV) panel, a Diesel Generator (DG) along with battery models are used. To utilize these effectively, investigation of the algorithm has been done so as to calculate the capacity of the generator unit whose power system is dependable along with reduced cost. Many methods have been used which can be iterative with artificial modes of intelligence which are reported in order to design energy system which is hybrid and renewable and technically and economically optimal. Cuckoo Search (CS) is a robust algorithm for optimal sizing which takes its inspiration from the breeding behavior of cuckoo. Region size determination is incorporated through the Luus and Jaakola (LJ) optimizing technique, which helps in improving the computational efficiency. Convergence speed and reliability of CS and LJ are compared in achieving the global optimum. Nano-grid (dpeaa)DE-He213 Photo voltaic (PV) array (dpeaa)DE-He213 Diesel generator (DG) (dpeaa)DE-He213 Renewable energy (dpeaa)DE-He213 Cuckoo search (CS) and Luus and Jaakola (LJ) (dpeaa)DE-He213 Marimuthu, C. N. verfasserin aut Enthalten in Mobile networks and applications New York, NY : ACM, 1996 24(2018), 2 vom: 09. Mai, Seite 340-349 (DE-627)318370794 (DE-600)2000637-8 1572-8153 nnns volume:24 year:2018 number:2 day:09 month:05 pages:340-349 https://dx.doi.org/10.1007/s11036-018-1046-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.32 ASE AR 24 2018 2 09 05 340-349
allfields_unstemmed	10.1007/s11036-018-1046-7 doi (DE-627)SPR015937119 (SPR)s11036-018-1046-7-e DE-627 ger DE-627 rakwb eng 004 ASE 54.32 bkl Murugesan, C. verfasserin aut Cost optimization of PV-Diesel Systems in Nanogrid Using L J Cuckoo Search and its Application in Mobile Towers 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Nano-grids are a replication of large electricity grids resembling their model but only on a smaller scale to locations that are not accessible easily and the connectivity of grid is not possible. Generally, these are used in remote hilly regions where there can be a renewable source of energy. For the purpose of stand-alone nano-grid, an algorithm which is simple to determine the required Photo Voltaic (PV) panel, a Diesel Generator (DG) along with battery models are used. To utilize these effectively, investigation of the algorithm has been done so as to calculate the capacity of the generator unit whose power system is dependable along with reduced cost. Many methods have been used which can be iterative with artificial modes of intelligence which are reported in order to design energy system which is hybrid and renewable and technically and economically optimal. Cuckoo Search (CS) is a robust algorithm for optimal sizing which takes its inspiration from the breeding behavior of cuckoo. Region size determination is incorporated through the Luus and Jaakola (LJ) optimizing technique, which helps in improving the computational efficiency. Convergence speed and reliability of CS and LJ are compared in achieving the global optimum. Nano-grid (dpeaa)DE-He213 Photo voltaic (PV) array (dpeaa)DE-He213 Diesel generator (DG) (dpeaa)DE-He213 Renewable energy (dpeaa)DE-He213 Cuckoo search (CS) and Luus and Jaakola (LJ) (dpeaa)DE-He213 Marimuthu, C. N. verfasserin aut Enthalten in Mobile networks and applications New York, NY : ACM, 1996 24(2018), 2 vom: 09. Mai, Seite 340-349 (DE-627)318370794 (DE-600)2000637-8 1572-8153 nnns volume:24 year:2018 number:2 day:09 month:05 pages:340-349 https://dx.doi.org/10.1007/s11036-018-1046-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.32 ASE AR 24 2018 2 09 05 340-349
allfieldsGer	10.1007/s11036-018-1046-7 doi (DE-627)SPR015937119 (SPR)s11036-018-1046-7-e DE-627 ger DE-627 rakwb eng 004 ASE 54.32 bkl Murugesan, C. verfasserin aut Cost optimization of PV-Diesel Systems in Nanogrid Using L J Cuckoo Search and its Application in Mobile Towers 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Nano-grids are a replication of large electricity grids resembling their model but only on a smaller scale to locations that are not accessible easily and the connectivity of grid is not possible. Generally, these are used in remote hilly regions where there can be a renewable source of energy. For the purpose of stand-alone nano-grid, an algorithm which is simple to determine the required Photo Voltaic (PV) panel, a Diesel Generator (DG) along with battery models are used. To utilize these effectively, investigation of the algorithm has been done so as to calculate the capacity of the generator unit whose power system is dependable along with reduced cost. Many methods have been used which can be iterative with artificial modes of intelligence which are reported in order to design energy system which is hybrid and renewable and technically and economically optimal. Cuckoo Search (CS) is a robust algorithm for optimal sizing which takes its inspiration from the breeding behavior of cuckoo. Region size determination is incorporated through the Luus and Jaakola (LJ) optimizing technique, which helps in improving the computational efficiency. Convergence speed and reliability of CS and LJ are compared in achieving the global optimum. Nano-grid (dpeaa)DE-He213 Photo voltaic (PV) array (dpeaa)DE-He213 Diesel generator (DG) (dpeaa)DE-He213 Renewable energy (dpeaa)DE-He213 Cuckoo search (CS) and Luus and Jaakola (LJ) (dpeaa)DE-He213 Marimuthu, C. N. verfasserin aut Enthalten in Mobile networks and applications New York, NY : ACM, 1996 24(2018), 2 vom: 09. Mai, Seite 340-349 (DE-627)318370794 (DE-600)2000637-8 1572-8153 nnns volume:24 year:2018 number:2 day:09 month:05 pages:340-349 https://dx.doi.org/10.1007/s11036-018-1046-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.32 ASE AR 24 2018 2 09 05 340-349
allfieldsSound	10.1007/s11036-018-1046-7 doi (DE-627)SPR015937119 (SPR)s11036-018-1046-7-e DE-627 ger DE-627 rakwb eng 004 ASE 54.32 bkl Murugesan, C. verfasserin aut Cost optimization of PV-Diesel Systems in Nanogrid Using L J Cuckoo Search and its Application in Mobile Towers 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Nano-grids are a replication of large electricity grids resembling their model but only on a smaller scale to locations that are not accessible easily and the connectivity of grid is not possible. Generally, these are used in remote hilly regions where there can be a renewable source of energy. For the purpose of stand-alone nano-grid, an algorithm which is simple to determine the required Photo Voltaic (PV) panel, a Diesel Generator (DG) along with battery models are used. To utilize these effectively, investigation of the algorithm has been done so as to calculate the capacity of the generator unit whose power system is dependable along with reduced cost. Many methods have been used which can be iterative with artificial modes of intelligence which are reported in order to design energy system which is hybrid and renewable and technically and economically optimal. Cuckoo Search (CS) is a robust algorithm for optimal sizing which takes its inspiration from the breeding behavior of cuckoo. Region size determination is incorporated through the Luus and Jaakola (LJ) optimizing technique, which helps in improving the computational efficiency. Convergence speed and reliability of CS and LJ are compared in achieving the global optimum. Nano-grid (dpeaa)DE-He213 Photo voltaic (PV) array (dpeaa)DE-He213 Diesel generator (DG) (dpeaa)DE-He213 Renewable energy (dpeaa)DE-He213 Cuckoo search (CS) and Luus and Jaakola (LJ) (dpeaa)DE-He213 Marimuthu, C. N. verfasserin aut Enthalten in Mobile networks and applications New York, NY : ACM, 1996 24(2018), 2 vom: 09. Mai, Seite 340-349 (DE-627)318370794 (DE-600)2000637-8 1572-8153 nnns volume:24 year:2018 number:2 day:09 month:05 pages:340-349 https://dx.doi.org/10.1007/s11036-018-1046-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.32 ASE AR 24 2018 2 09 05 340-349
language	English
source	Enthalten in Mobile networks and applications 24(2018), 2 vom: 09. Mai, Seite 340-349 volume:24 year:2018 number:2 day:09 month:05 pages:340-349
sourceStr	Enthalten in Mobile networks and applications 24(2018), 2 vom: 09. Mai, Seite 340-349 volume:24 year:2018 number:2 day:09 month:05 pages:340-349
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Nano-grid Photo voltaic (PV) array Diesel generator (DG) Renewable energy Cuckoo search (CS) and Luus and Jaakola (LJ)
dewey-raw	004
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container_title	Mobile networks and applications
authorswithroles_txt_mv	Murugesan, C. @@aut@@ Marimuthu, C. N. @@aut@@
publishDateDaySort_date	2018-05-09T00:00:00Z
hierarchy_top_id	318370794
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language_de	englisch
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Generally, these are used in remote hilly regions where there can be a renewable source of energy. For the purpose of stand-alone nano-grid, an algorithm which is simple to determine the required Photo Voltaic (PV) panel, a Diesel Generator (DG) along with battery models are used. To utilize these effectively, investigation of the algorithm has been done so as to calculate the capacity of the generator unit whose power system is dependable along with reduced cost. Many methods have been used which can be iterative with artificial modes of intelligence which are reported in order to design energy system which is hybrid and renewable and technically and economically optimal. Cuckoo Search (CS) is a robust algorithm for optimal sizing which takes its inspiration from the breeding behavior of cuckoo. Region size determination is incorporated through the Luus and Jaakola (LJ) optimizing technique, which helps in improving the computational efficiency. 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author	Murugesan, C.
spellingShingle	Murugesan, C. ddc 004 bkl 54.32 misc Nano-grid misc Photo voltaic (PV) array misc Diesel generator (DG) misc Renewable energy misc Cuckoo search (CS) and Luus and Jaakola (LJ) Cost optimization of PV-Diesel Systems in Nanogrid Using L J Cuckoo Search and its Application in Mobile Towers
authorStr	Murugesan, C.
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topic_title	004 ASE 54.32 bkl Cost optimization of PV-Diesel Systems in Nanogrid Using L J Cuckoo Search and its Application in Mobile Towers Nano-grid (dpeaa)DE-He213 Photo voltaic (PV) array (dpeaa)DE-He213 Diesel generator (DG) (dpeaa)DE-He213 Renewable energy (dpeaa)DE-He213 Cuckoo search (CS) and Luus and Jaakola (LJ) (dpeaa)DE-He213
topic	ddc 004 bkl 54.32 misc Nano-grid misc Photo voltaic (PV) array misc Diesel generator (DG) misc Renewable energy misc Cuckoo search (CS) and Luus and Jaakola (LJ)
topic_unstemmed	ddc 004 bkl 54.32 misc Nano-grid misc Photo voltaic (PV) array misc Diesel generator (DG) misc Renewable energy misc Cuckoo search (CS) and Luus and Jaakola (LJ)
topic_browse	ddc 004 bkl 54.32 misc Nano-grid misc Photo voltaic (PV) array misc Diesel generator (DG) misc Renewable energy misc Cuckoo search (CS) and Luus and Jaakola (LJ)
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title	Cost optimization of PV-Diesel Systems in Nanogrid Using L J Cuckoo Search and its Application in Mobile Towers
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title_full	Cost optimization of PV-Diesel Systems in Nanogrid Using L J Cuckoo Search and its Application in Mobile Towers
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title_sort	cost optimization of pv-diesel systems in nanogrid using l j cuckoo search and its application in mobile towers
title_auth	Cost optimization of PV-Diesel Systems in Nanogrid Using L J Cuckoo Search and its Application in Mobile Towers
abstract	Abstract Nano-grids are a replication of large electricity grids resembling their model but only on a smaller scale to locations that are not accessible easily and the connectivity of grid is not possible. Generally, these are used in remote hilly regions where there can be a renewable source of energy. For the purpose of stand-alone nano-grid, an algorithm which is simple to determine the required Photo Voltaic (PV) panel, a Diesel Generator (DG) along with battery models are used. To utilize these effectively, investigation of the algorithm has been done so as to calculate the capacity of the generator unit whose power system is dependable along with reduced cost. Many methods have been used which can be iterative with artificial modes of intelligence which are reported in order to design energy system which is hybrid and renewable and technically and economically optimal. Cuckoo Search (CS) is a robust algorithm for optimal sizing which takes its inspiration from the breeding behavior of cuckoo. Region size determination is incorporated through the Luus and Jaakola (LJ) optimizing technique, which helps in improving the computational efficiency. Convergence speed and reliability of CS and LJ are compared in achieving the global optimum.
abstractGer	Abstract Nano-grids are a replication of large electricity grids resembling their model but only on a smaller scale to locations that are not accessible easily and the connectivity of grid is not possible. Generally, these are used in remote hilly regions where there can be a renewable source of energy. For the purpose of stand-alone nano-grid, an algorithm which is simple to determine the required Photo Voltaic (PV) panel, a Diesel Generator (DG) along with battery models are used. To utilize these effectively, investigation of the algorithm has been done so as to calculate the capacity of the generator unit whose power system is dependable along with reduced cost. Many methods have been used which can be iterative with artificial modes of intelligence which are reported in order to design energy system which is hybrid and renewable and technically and economically optimal. Cuckoo Search (CS) is a robust algorithm for optimal sizing which takes its inspiration from the breeding behavior of cuckoo. Region size determination is incorporated through the Luus and Jaakola (LJ) optimizing technique, which helps in improving the computational efficiency. Convergence speed and reliability of CS and LJ are compared in achieving the global optimum.
abstract_unstemmed	Abstract Nano-grids are a replication of large electricity grids resembling their model but only on a smaller scale to locations that are not accessible easily and the connectivity of grid is not possible. Generally, these are used in remote hilly regions where there can be a renewable source of energy. For the purpose of stand-alone nano-grid, an algorithm which is simple to determine the required Photo Voltaic (PV) panel, a Diesel Generator (DG) along with battery models are used. To utilize these effectively, investigation of the algorithm has been done so as to calculate the capacity of the generator unit whose power system is dependable along with reduced cost. Many methods have been used which can be iterative with artificial modes of intelligence which are reported in order to design energy system which is hybrid and renewable and technically and economically optimal. Cuckoo Search (CS) is a robust algorithm for optimal sizing which takes its inspiration from the breeding behavior of cuckoo. Region size determination is incorporated through the Luus and Jaakola (LJ) optimizing technique, which helps in improving the computational efficiency. Convergence speed and reliability of CS and LJ are compared in achieving the global optimum.
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container_issue	2
title_short	Cost optimization of PV-Diesel Systems in Nanogrid Using L J Cuckoo Search and its Application in Mobile Towers
url	https://dx.doi.org/10.1007/s11036-018-1046-7
remote_bool	true
author2	Marimuthu, C. N.
author2Str	Marimuthu, C. N.
ppnlink	318370794
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isOA_txt	false
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doi_str	10.1007/s11036-018-1046-7
up_date	2024-07-03T19:40:18.911Z
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