Untangling global levelised cost of electricity based on multi-factor learning curve for renewable energy: Wind, solar, geothermal, hydropower and bioenergy

Renewable energy offers a less expensive source of electricity globally for the energy sector’s transformation towards a sustainable energy system. This paper untangles the driving mechanism behind the global renewable energy levelised cost of electricity (LCOE) development for seven promising renew...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yao, Yue [verfasserIn] Xu, Jin-Hua [verfasserIn] Sun, De-Qiang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Renewable energy Multi-factor learning curve (MFLC) Levelized cost of electricity (LCOE) Capacity factor

Übergeordnetes Werk:	Enthalten in: Journal of cleaner production - Amsterdam [u.a.] : Elsevier Science, 1993, 285
Übergeordnetes Werk:	volume:285

DOI / URN:	10.1016/j.jclepro.2020.124827

Katalog-ID:	ELV005371783

Internformat


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publishDate	2020
allfields	10.1016/j.jclepro.2020.124827 doi (DE-627)ELV005371783 (ELSEVIER)S0959-6526(20)34871-X DE-627 ger DE-627 rda eng 690 330 DE-600 43.35 bkl 85.35 bkl Yao, Yue verfasserin aut Untangling global levelised cost of electricity based on multi-factor learning curve for renewable energy: Wind, solar, geothermal, hydropower and bioenergy 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Renewable energy offers a less expensive source of electricity globally for the energy sector’s transformation towards a sustainable energy system. This paper untangles the driving mechanism behind the global renewable energy levelised cost of electricity (LCOE) development for seven promising renewable energy technologies from 2010 to 2018: onshore wind, offshore wind, solar photovoltaic, concentrating solar power (CSP), geothermal, hydropower and bioenergy. This research provides a comprehensive and repeatable version of multi-factor learning curve (MFLC) method based on a cost minimization approach, Cobb-Douglas function and engineering analysis to analyze factors affecting the renewable power generation cost. Capacity factors are highlighted as the indicators for natural resource volatility and technology progress. The modified MFLC models show that capacity factor effect, installed cost effect and learning effect are the main drivers of cost reduction. Rapidly declining wind and solar costs are driven by the competitive installed costs and upgraded technology in areas with excellent natural wind and solar resources. The irregular cost movements of geothermal, hydropower and bioenergy are heavily influenced by the site-specific characteristics of these projects, reflecting the high natural resource volatility and diversity in capital across regions. Renewable energy Multi-factor learning curve (MFLC) Levelized cost of electricity (LCOE) Capacity factor Xu, Jin-Hua verfasserin aut Sun, De-Qiang verfasserin aut Enthalten in Journal of cleaner production Amsterdam [u.a.] : Elsevier Science, 1993 285 Online-Ressource (DE-627)324655878 (DE-600)2029338-0 (DE-576)252613988 0959-6526 nnns volume:285 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 43.35 Umweltrichtlinien Umweltnormen 85.35 Fertigung AR 285
spelling	10.1016/j.jclepro.2020.124827 doi (DE-627)ELV005371783 (ELSEVIER)S0959-6526(20)34871-X DE-627 ger DE-627 rda eng 690 330 DE-600 43.35 bkl 85.35 bkl Yao, Yue verfasserin aut Untangling global levelised cost of electricity based on multi-factor learning curve for renewable energy: Wind, solar, geothermal, hydropower and bioenergy 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Renewable energy offers a less expensive source of electricity globally for the energy sector’s transformation towards a sustainable energy system. This paper untangles the driving mechanism behind the global renewable energy levelised cost of electricity (LCOE) development for seven promising renewable energy technologies from 2010 to 2018: onshore wind, offshore wind, solar photovoltaic, concentrating solar power (CSP), geothermal, hydropower and bioenergy. This research provides a comprehensive and repeatable version of multi-factor learning curve (MFLC) method based on a cost minimization approach, Cobb-Douglas function and engineering analysis to analyze factors affecting the renewable power generation cost. Capacity factors are highlighted as the indicators for natural resource volatility and technology progress. The modified MFLC models show that capacity factor effect, installed cost effect and learning effect are the main drivers of cost reduction. Rapidly declining wind and solar costs are driven by the competitive installed costs and upgraded technology in areas with excellent natural wind and solar resources. The irregular cost movements of geothermal, hydropower and bioenergy are heavily influenced by the site-specific characteristics of these projects, reflecting the high natural resource volatility and diversity in capital across regions. Renewable energy Multi-factor learning curve (MFLC) Levelized cost of electricity (LCOE) Capacity factor Xu, Jin-Hua verfasserin aut Sun, De-Qiang verfasserin aut Enthalten in Journal of cleaner production Amsterdam [u.a.] : Elsevier Science, 1993 285 Online-Ressource (DE-627)324655878 (DE-600)2029338-0 (DE-576)252613988 0959-6526 nnns volume:285 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 43.35 Umweltrichtlinien Umweltnormen 85.35 Fertigung AR 285
allfields_unstemmed	10.1016/j.jclepro.2020.124827 doi (DE-627)ELV005371783 (ELSEVIER)S0959-6526(20)34871-X DE-627 ger DE-627 rda eng 690 330 DE-600 43.35 bkl 85.35 bkl Yao, Yue verfasserin aut Untangling global levelised cost of electricity based on multi-factor learning curve for renewable energy: Wind, solar, geothermal, hydropower and bioenergy 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Renewable energy offers a less expensive source of electricity globally for the energy sector’s transformation towards a sustainable energy system. This paper untangles the driving mechanism behind the global renewable energy levelised cost of electricity (LCOE) development for seven promising renewable energy technologies from 2010 to 2018: onshore wind, offshore wind, solar photovoltaic, concentrating solar power (CSP), geothermal, hydropower and bioenergy. This research provides a comprehensive and repeatable version of multi-factor learning curve (MFLC) method based on a cost minimization approach, Cobb-Douglas function and engineering analysis to analyze factors affecting the renewable power generation cost. Capacity factors are highlighted as the indicators for natural resource volatility and technology progress. The modified MFLC models show that capacity factor effect, installed cost effect and learning effect are the main drivers of cost reduction. Rapidly declining wind and solar costs are driven by the competitive installed costs and upgraded technology in areas with excellent natural wind and solar resources. The irregular cost movements of geothermal, hydropower and bioenergy are heavily influenced by the site-specific characteristics of these projects, reflecting the high natural resource volatility and diversity in capital across regions. Renewable energy Multi-factor learning curve (MFLC) Levelized cost of electricity (LCOE) Capacity factor Xu, Jin-Hua verfasserin aut Sun, De-Qiang verfasserin aut Enthalten in Journal of cleaner production Amsterdam [u.a.] : Elsevier Science, 1993 285 Online-Ressource (DE-627)324655878 (DE-600)2029338-0 (DE-576)252613988 0959-6526 nnns volume:285 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 43.35 Umweltrichtlinien Umweltnormen 85.35 Fertigung AR 285
allfieldsGer	10.1016/j.jclepro.2020.124827 doi (DE-627)ELV005371783 (ELSEVIER)S0959-6526(20)34871-X DE-627 ger DE-627 rda eng 690 330 DE-600 43.35 bkl 85.35 bkl Yao, Yue verfasserin aut Untangling global levelised cost of electricity based on multi-factor learning curve for renewable energy: Wind, solar, geothermal, hydropower and bioenergy 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Renewable energy offers a less expensive source of electricity globally for the energy sector’s transformation towards a sustainable energy system. This paper untangles the driving mechanism behind the global renewable energy levelised cost of electricity (LCOE) development for seven promising renewable energy technologies from 2010 to 2018: onshore wind, offshore wind, solar photovoltaic, concentrating solar power (CSP), geothermal, hydropower and bioenergy. This research provides a comprehensive and repeatable version of multi-factor learning curve (MFLC) method based on a cost minimization approach, Cobb-Douglas function and engineering analysis to analyze factors affecting the renewable power generation cost. Capacity factors are highlighted as the indicators for natural resource volatility and technology progress. The modified MFLC models show that capacity factor effect, installed cost effect and learning effect are the main drivers of cost reduction. Rapidly declining wind and solar costs are driven by the competitive installed costs and upgraded technology in areas with excellent natural wind and solar resources. The irregular cost movements of geothermal, hydropower and bioenergy are heavily influenced by the site-specific characteristics of these projects, reflecting the high natural resource volatility and diversity in capital across regions. Renewable energy Multi-factor learning curve (MFLC) Levelized cost of electricity (LCOE) Capacity factor Xu, Jin-Hua verfasserin aut Sun, De-Qiang verfasserin aut Enthalten in Journal of cleaner production Amsterdam [u.a.] : Elsevier Science, 1993 285 Online-Ressource (DE-627)324655878 (DE-600)2029338-0 (DE-576)252613988 0959-6526 nnns volume:285 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 43.35 Umweltrichtlinien Umweltnormen 85.35 Fertigung AR 285
allfieldsSound	10.1016/j.jclepro.2020.124827 doi (DE-627)ELV005371783 (ELSEVIER)S0959-6526(20)34871-X DE-627 ger DE-627 rda eng 690 330 DE-600 43.35 bkl 85.35 bkl Yao, Yue verfasserin aut Untangling global levelised cost of electricity based on multi-factor learning curve for renewable energy: Wind, solar, geothermal, hydropower and bioenergy 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Renewable energy offers a less expensive source of electricity globally for the energy sector’s transformation towards a sustainable energy system. This paper untangles the driving mechanism behind the global renewable energy levelised cost of electricity (LCOE) development for seven promising renewable energy technologies from 2010 to 2018: onshore wind, offshore wind, solar photovoltaic, concentrating solar power (CSP), geothermal, hydropower and bioenergy. This research provides a comprehensive and repeatable version of multi-factor learning curve (MFLC) method based on a cost minimization approach, Cobb-Douglas function and engineering analysis to analyze factors affecting the renewable power generation cost. Capacity factors are highlighted as the indicators for natural resource volatility and technology progress. The modified MFLC models show that capacity factor effect, installed cost effect and learning effect are the main drivers of cost reduction. Rapidly declining wind and solar costs are driven by the competitive installed costs and upgraded technology in areas with excellent natural wind and solar resources. The irregular cost movements of geothermal, hydropower and bioenergy are heavily influenced by the site-specific characteristics of these projects, reflecting the high natural resource volatility and diversity in capital across regions. Renewable energy Multi-factor learning curve (MFLC) Levelized cost of electricity (LCOE) Capacity factor Xu, Jin-Hua verfasserin aut Sun, De-Qiang verfasserin aut Enthalten in Journal of cleaner production Amsterdam [u.a.] : Elsevier Science, 1993 285 Online-Ressource (DE-627)324655878 (DE-600)2029338-0 (DE-576)252613988 0959-6526 nnns volume:285 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 43.35 Umweltrichtlinien Umweltnormen 85.35 Fertigung AR 285
language	English
source	Enthalten in Journal of cleaner production 285 volume:285
sourceStr	Enthalten in Journal of cleaner production 285 volume:285
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institution	findex.gbv.de
topic_facet	Renewable energy Multi-factor learning curve (MFLC) Levelized cost of electricity (LCOE) Capacity factor
dewey-raw	690
isfreeaccess_bool	false
container_title	Journal of cleaner production
authorswithroles_txt_mv	Yao, Yue @@aut@@ Xu, Jin-Hua @@aut@@ Sun, De-Qiang @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	324655878
dewey-sort	3690
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author	Yao, Yue
spellingShingle	Yao, Yue ddc 690 bkl 43.35 bkl 85.35 misc Renewable energy misc Multi-factor learning curve (MFLC) misc Levelized cost of electricity (LCOE) misc Capacity factor Untangling global levelised cost of electricity based on multi-factor learning curve for renewable energy: Wind, solar, geothermal, hydropower and bioenergy
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topic_title	690 330 DE-600 43.35 bkl 85.35 bkl Untangling global levelised cost of electricity based on multi-factor learning curve for renewable energy: Wind, solar, geothermal, hydropower and bioenergy Renewable energy Multi-factor learning curve (MFLC) Levelized cost of electricity (LCOE) Capacity factor
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title	Untangling global levelised cost of electricity based on multi-factor learning curve for renewable energy: Wind, solar, geothermal, hydropower and bioenergy
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title_full	Untangling global levelised cost of electricity based on multi-factor learning curve for renewable energy: Wind, solar, geothermal, hydropower and bioenergy
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title_sort	untangling global levelised cost of electricity based on multi-factor learning curve for renewable energy: wind, solar, geothermal, hydropower and bioenergy
title_auth	Untangling global levelised cost of electricity based on multi-factor learning curve for renewable energy: Wind, solar, geothermal, hydropower and bioenergy
abstract	Renewable energy offers a less expensive source of electricity globally for the energy sector’s transformation towards a sustainable energy system. This paper untangles the driving mechanism behind the global renewable energy levelised cost of electricity (LCOE) development for seven promising renewable energy technologies from 2010 to 2018: onshore wind, offshore wind, solar photovoltaic, concentrating solar power (CSP), geothermal, hydropower and bioenergy. This research provides a comprehensive and repeatable version of multi-factor learning curve (MFLC) method based on a cost minimization approach, Cobb-Douglas function and engineering analysis to analyze factors affecting the renewable power generation cost. Capacity factors are highlighted as the indicators for natural resource volatility and technology progress. The modified MFLC models show that capacity factor effect, installed cost effect and learning effect are the main drivers of cost reduction. Rapidly declining wind and solar costs are driven by the competitive installed costs and upgraded technology in areas with excellent natural wind and solar resources. The irregular cost movements of geothermal, hydropower and bioenergy are heavily influenced by the site-specific characteristics of these projects, reflecting the high natural resource volatility and diversity in capital across regions.
abstractGer	Renewable energy offers a less expensive source of electricity globally for the energy sector’s transformation towards a sustainable energy system. This paper untangles the driving mechanism behind the global renewable energy levelised cost of electricity (LCOE) development for seven promising renewable energy technologies from 2010 to 2018: onshore wind, offshore wind, solar photovoltaic, concentrating solar power (CSP), geothermal, hydropower and bioenergy. This research provides a comprehensive and repeatable version of multi-factor learning curve (MFLC) method based on a cost minimization approach, Cobb-Douglas function and engineering analysis to analyze factors affecting the renewable power generation cost. Capacity factors are highlighted as the indicators for natural resource volatility and technology progress. The modified MFLC models show that capacity factor effect, installed cost effect and learning effect are the main drivers of cost reduction. Rapidly declining wind and solar costs are driven by the competitive installed costs and upgraded technology in areas with excellent natural wind and solar resources. The irregular cost movements of geothermal, hydropower and bioenergy are heavily influenced by the site-specific characteristics of these projects, reflecting the high natural resource volatility and diversity in capital across regions.
abstract_unstemmed	Renewable energy offers a less expensive source of electricity globally for the energy sector’s transformation towards a sustainable energy system. This paper untangles the driving mechanism behind the global renewable energy levelised cost of electricity (LCOE) development for seven promising renewable energy technologies from 2010 to 2018: onshore wind, offshore wind, solar photovoltaic, concentrating solar power (CSP), geothermal, hydropower and bioenergy. This research provides a comprehensive and repeatable version of multi-factor learning curve (MFLC) method based on a cost minimization approach, Cobb-Douglas function and engineering analysis to analyze factors affecting the renewable power generation cost. Capacity factors are highlighted as the indicators for natural resource volatility and technology progress. The modified MFLC models show that capacity factor effect, installed cost effect and learning effect are the main drivers of cost reduction. Rapidly declining wind and solar costs are driven by the competitive installed costs and upgraded technology in areas with excellent natural wind and solar resources. The irregular cost movements of geothermal, hydropower and bioenergy are heavily influenced by the site-specific characteristics of these projects, reflecting the high natural resource volatility and diversity in capital across regions.
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title_short	Untangling global levelised cost of electricity based on multi-factor learning curve for renewable energy: Wind, solar, geothermal, hydropower and bioenergy
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up_date	2024-07-06T17:44:47.149Z
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