Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach
Uncertainty introduces significant complexity to the design process of distributed energy systems (DES) and introduces the risk of suboptimal decisions when the design is performed deterministically. Therefore, it is important that computational DES design models are able to account for the most rel...
Ausführliche Beschreibung
Autor*in: |
Mavromatidis, Georgios [verfasserIn] Orehounig, Kristina [verfasserIn] Carmeliet, Jan [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2018 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Applied energy - Amsterdam [u.a.] : Elsevier Science, 1975, 222, Seite 932-950 |
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Übergeordnetes Werk: |
volume:222 ; pages:932-950 |
DOI / URN: |
10.1016/j.apenergy.2018.04.019 |
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Katalog-ID: |
ELV001917927 |
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520 | |a Uncertainty introduces significant complexity to the design process of distributed energy systems (DES) and introduces the risk of suboptimal decisions when the design is performed deterministically. Therefore, it is important that computational DES design models are able to account for the most relevant uncertainty sources when identifying optimal DES configurations. In this paper, a model for optimal DES design under uncertainty is presented and is formulated as a Two-stage Stochastic Mixed-Integer Linear Program. As uncertain parameters, energy carrier prices and emission factors, building heating and electricity demands, and incoming solar radiation patterns are considered and probabilistic scenarios are used to describe their uncertainty. The model seeks to make cost-optimal DES design decisions (technology selection and sizing) before these uncertain parameters are known, while it also identifies the optimal operation of the selected DES configuration for multiple uncertain scenarios. Moreover, two strategies for emission reduction are employed that set CO2 limits either to the system’s average emissions under uncertainty (‘neutral’ strategy) or individually to the system’s emissions for every possible uncertainty outcome to ensure a more robust emission performance (‘aggressive’ strategy). | ||
650 | 4 | |a Distributed energy systems | |
650 | 4 | |a Uncertainty | |
650 | 4 | |a Multi-objective optimisation | |
650 | 4 | |a Two-stage stochastic programming | |
650 | 4 | |a Scenario generation | |
650 | 4 | |a Scenario reduction | |
700 | 1 | |a Orehounig, Kristina |e verfasserin |4 aut | |
700 | 1 | |a Carmeliet, Jan |e verfasserin |4 aut | |
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10.1016/j.apenergy.2018.04.019 doi (DE-627)ELV001917927 (ELSEVIER)S0306-2619(18)30558-0 DE-627 ger DE-627 rda eng 620 DE-600 52.50 bkl Mavromatidis, Georgios verfasserin (orcid)0000-0003-0227-4518 aut Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Uncertainty introduces significant complexity to the design process of distributed energy systems (DES) and introduces the risk of suboptimal decisions when the design is performed deterministically. Therefore, it is important that computational DES design models are able to account for the most relevant uncertainty sources when identifying optimal DES configurations. In this paper, a model for optimal DES design under uncertainty is presented and is formulated as a Two-stage Stochastic Mixed-Integer Linear Program. As uncertain parameters, energy carrier prices and emission factors, building heating and electricity demands, and incoming solar radiation patterns are considered and probabilistic scenarios are used to describe their uncertainty. The model seeks to make cost-optimal DES design decisions (technology selection and sizing) before these uncertain parameters are known, while it also identifies the optimal operation of the selected DES configuration for multiple uncertain scenarios. Moreover, two strategies for emission reduction are employed that set CO2 limits either to the system’s average emissions under uncertainty (‘neutral’ strategy) or individually to the system’s emissions for every possible uncertainty outcome to ensure a more robust emission performance (‘aggressive’ strategy). Distributed energy systems Uncertainty Multi-objective optimisation Two-stage stochastic programming Scenario generation Scenario reduction Orehounig, Kristina verfasserin aut Carmeliet, Jan verfasserin aut Enthalten in Applied energy Amsterdam [u.a.] : Elsevier Science, 1975 222, Seite 932-950 Online-Ressource (DE-627)320406709 (DE-600)2000772-3 (DE-576)256140251 1872-9118 nnns volume:222 pages:932-950 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_2006 GBV_ILN_2008 GBV_ILN_2010 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_2088 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_2470 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_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.50 Energietechnik: Allgemeines AR 222 932-950 |
spelling |
10.1016/j.apenergy.2018.04.019 doi (DE-627)ELV001917927 (ELSEVIER)S0306-2619(18)30558-0 DE-627 ger DE-627 rda eng 620 DE-600 52.50 bkl Mavromatidis, Georgios verfasserin (orcid)0000-0003-0227-4518 aut Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Uncertainty introduces significant complexity to the design process of distributed energy systems (DES) and introduces the risk of suboptimal decisions when the design is performed deterministically. Therefore, it is important that computational DES design models are able to account for the most relevant uncertainty sources when identifying optimal DES configurations. In this paper, a model for optimal DES design under uncertainty is presented and is formulated as a Two-stage Stochastic Mixed-Integer Linear Program. As uncertain parameters, energy carrier prices and emission factors, building heating and electricity demands, and incoming solar radiation patterns are considered and probabilistic scenarios are used to describe their uncertainty. The model seeks to make cost-optimal DES design decisions (technology selection and sizing) before these uncertain parameters are known, while it also identifies the optimal operation of the selected DES configuration for multiple uncertain scenarios. Moreover, two strategies for emission reduction are employed that set CO2 limits either to the system’s average emissions under uncertainty (‘neutral’ strategy) or individually to the system’s emissions for every possible uncertainty outcome to ensure a more robust emission performance (‘aggressive’ strategy). Distributed energy systems Uncertainty Multi-objective optimisation Two-stage stochastic programming Scenario generation Scenario reduction Orehounig, Kristina verfasserin aut Carmeliet, Jan verfasserin aut Enthalten in Applied energy Amsterdam [u.a.] : Elsevier Science, 1975 222, Seite 932-950 Online-Ressource (DE-627)320406709 (DE-600)2000772-3 (DE-576)256140251 1872-9118 nnns volume:222 pages:932-950 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_2006 GBV_ILN_2008 GBV_ILN_2010 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_2088 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_2470 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_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.50 Energietechnik: Allgemeines AR 222 932-950 |
allfields_unstemmed |
10.1016/j.apenergy.2018.04.019 doi (DE-627)ELV001917927 (ELSEVIER)S0306-2619(18)30558-0 DE-627 ger DE-627 rda eng 620 DE-600 52.50 bkl Mavromatidis, Georgios verfasserin (orcid)0000-0003-0227-4518 aut Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Uncertainty introduces significant complexity to the design process of distributed energy systems (DES) and introduces the risk of suboptimal decisions when the design is performed deterministically. Therefore, it is important that computational DES design models are able to account for the most relevant uncertainty sources when identifying optimal DES configurations. In this paper, a model for optimal DES design under uncertainty is presented and is formulated as a Two-stage Stochastic Mixed-Integer Linear Program. As uncertain parameters, energy carrier prices and emission factors, building heating and electricity demands, and incoming solar radiation patterns are considered and probabilistic scenarios are used to describe their uncertainty. The model seeks to make cost-optimal DES design decisions (technology selection and sizing) before these uncertain parameters are known, while it also identifies the optimal operation of the selected DES configuration for multiple uncertain scenarios. Moreover, two strategies for emission reduction are employed that set CO2 limits either to the system’s average emissions under uncertainty (‘neutral’ strategy) or individually to the system’s emissions for every possible uncertainty outcome to ensure a more robust emission performance (‘aggressive’ strategy). Distributed energy systems Uncertainty Multi-objective optimisation Two-stage stochastic programming Scenario generation Scenario reduction Orehounig, Kristina verfasserin aut Carmeliet, Jan verfasserin aut Enthalten in Applied energy Amsterdam [u.a.] : Elsevier Science, 1975 222, Seite 932-950 Online-Ressource (DE-627)320406709 (DE-600)2000772-3 (DE-576)256140251 1872-9118 nnns volume:222 pages:932-950 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_2006 GBV_ILN_2008 GBV_ILN_2010 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_2088 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_2470 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_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.50 Energietechnik: Allgemeines AR 222 932-950 |
allfieldsGer |
10.1016/j.apenergy.2018.04.019 doi (DE-627)ELV001917927 (ELSEVIER)S0306-2619(18)30558-0 DE-627 ger DE-627 rda eng 620 DE-600 52.50 bkl Mavromatidis, Georgios verfasserin (orcid)0000-0003-0227-4518 aut Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Uncertainty introduces significant complexity to the design process of distributed energy systems (DES) and introduces the risk of suboptimal decisions when the design is performed deterministically. Therefore, it is important that computational DES design models are able to account for the most relevant uncertainty sources when identifying optimal DES configurations. In this paper, a model for optimal DES design under uncertainty is presented and is formulated as a Two-stage Stochastic Mixed-Integer Linear Program. As uncertain parameters, energy carrier prices and emission factors, building heating and electricity demands, and incoming solar radiation patterns are considered and probabilistic scenarios are used to describe their uncertainty. The model seeks to make cost-optimal DES design decisions (technology selection and sizing) before these uncertain parameters are known, while it also identifies the optimal operation of the selected DES configuration for multiple uncertain scenarios. Moreover, two strategies for emission reduction are employed that set CO2 limits either to the system’s average emissions under uncertainty (‘neutral’ strategy) or individually to the system’s emissions for every possible uncertainty outcome to ensure a more robust emission performance (‘aggressive’ strategy). Distributed energy systems Uncertainty Multi-objective optimisation Two-stage stochastic programming Scenario generation Scenario reduction Orehounig, Kristina verfasserin aut Carmeliet, Jan verfasserin aut Enthalten in Applied energy Amsterdam [u.a.] : Elsevier Science, 1975 222, Seite 932-950 Online-Ressource (DE-627)320406709 (DE-600)2000772-3 (DE-576)256140251 1872-9118 nnns volume:222 pages:932-950 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_2006 GBV_ILN_2008 GBV_ILN_2010 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_2088 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_2470 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_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.50 Energietechnik: Allgemeines AR 222 932-950 |
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10.1016/j.apenergy.2018.04.019 doi (DE-627)ELV001917927 (ELSEVIER)S0306-2619(18)30558-0 DE-627 ger DE-627 rda eng 620 DE-600 52.50 bkl Mavromatidis, Georgios verfasserin (orcid)0000-0003-0227-4518 aut Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Uncertainty introduces significant complexity to the design process of distributed energy systems (DES) and introduces the risk of suboptimal decisions when the design is performed deterministically. Therefore, it is important that computational DES design models are able to account for the most relevant uncertainty sources when identifying optimal DES configurations. In this paper, a model for optimal DES design under uncertainty is presented and is formulated as a Two-stage Stochastic Mixed-Integer Linear Program. As uncertain parameters, energy carrier prices and emission factors, building heating and electricity demands, and incoming solar radiation patterns are considered and probabilistic scenarios are used to describe their uncertainty. The model seeks to make cost-optimal DES design decisions (technology selection and sizing) before these uncertain parameters are known, while it also identifies the optimal operation of the selected DES configuration for multiple uncertain scenarios. Moreover, two strategies for emission reduction are employed that set CO2 limits either to the system’s average emissions under uncertainty (‘neutral’ strategy) or individually to the system’s emissions for every possible uncertainty outcome to ensure a more robust emission performance (‘aggressive’ strategy). Distributed energy systems Uncertainty Multi-objective optimisation Two-stage stochastic programming Scenario generation Scenario reduction Orehounig, Kristina verfasserin aut Carmeliet, Jan verfasserin aut Enthalten in Applied energy Amsterdam [u.a.] : Elsevier Science, 1975 222, Seite 932-950 Online-Ressource (DE-627)320406709 (DE-600)2000772-3 (DE-576)256140251 1872-9118 nnns volume:222 pages:932-950 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_2006 GBV_ILN_2008 GBV_ILN_2010 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_2088 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_2470 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_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 52.50 Energietechnik: Allgemeines AR 222 932-950 |
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Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach |
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Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach |
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Mavromatidis, Georgios |
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Applied energy |
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Mavromatidis, Georgios Orehounig, Kristina Carmeliet, Jan |
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Mavromatidis, Georgios |
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10.1016/j.apenergy.2018.04.019 |
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design of distributed energy systems under uncertainty: a two-stage stochastic programming approach |
title_auth |
Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach |
abstract |
Uncertainty introduces significant complexity to the design process of distributed energy systems (DES) and introduces the risk of suboptimal decisions when the design is performed deterministically. Therefore, it is important that computational DES design models are able to account for the most relevant uncertainty sources when identifying optimal DES configurations. In this paper, a model for optimal DES design under uncertainty is presented and is formulated as a Two-stage Stochastic Mixed-Integer Linear Program. As uncertain parameters, energy carrier prices and emission factors, building heating and electricity demands, and incoming solar radiation patterns are considered and probabilistic scenarios are used to describe their uncertainty. The model seeks to make cost-optimal DES design decisions (technology selection and sizing) before these uncertain parameters are known, while it also identifies the optimal operation of the selected DES configuration for multiple uncertain scenarios. Moreover, two strategies for emission reduction are employed that set CO2 limits either to the system’s average emissions under uncertainty (‘neutral’ strategy) or individually to the system’s emissions for every possible uncertainty outcome to ensure a more robust emission performance (‘aggressive’ strategy). |
abstractGer |
Uncertainty introduces significant complexity to the design process of distributed energy systems (DES) and introduces the risk of suboptimal decisions when the design is performed deterministically. Therefore, it is important that computational DES design models are able to account for the most relevant uncertainty sources when identifying optimal DES configurations. In this paper, a model for optimal DES design under uncertainty is presented and is formulated as a Two-stage Stochastic Mixed-Integer Linear Program. As uncertain parameters, energy carrier prices and emission factors, building heating and electricity demands, and incoming solar radiation patterns are considered and probabilistic scenarios are used to describe their uncertainty. The model seeks to make cost-optimal DES design decisions (technology selection and sizing) before these uncertain parameters are known, while it also identifies the optimal operation of the selected DES configuration for multiple uncertain scenarios. Moreover, two strategies for emission reduction are employed that set CO2 limits either to the system’s average emissions under uncertainty (‘neutral’ strategy) or individually to the system’s emissions for every possible uncertainty outcome to ensure a more robust emission performance (‘aggressive’ strategy). |
abstract_unstemmed |
Uncertainty introduces significant complexity to the design process of distributed energy systems (DES) and introduces the risk of suboptimal decisions when the design is performed deterministically. Therefore, it is important that computational DES design models are able to account for the most relevant uncertainty sources when identifying optimal DES configurations. In this paper, a model for optimal DES design under uncertainty is presented and is formulated as a Two-stage Stochastic Mixed-Integer Linear Program. As uncertain parameters, energy carrier prices and emission factors, building heating and electricity demands, and incoming solar radiation patterns are considered and probabilistic scenarios are used to describe their uncertainty. The model seeks to make cost-optimal DES design decisions (technology selection and sizing) before these uncertain parameters are known, while it also identifies the optimal operation of the selected DES configuration for multiple uncertain scenarios. Moreover, two strategies for emission reduction are employed that set CO2 limits either to the system’s average emissions under uncertainty (‘neutral’ strategy) or individually to the system’s emissions for every possible uncertainty outcome to ensure a more robust emission performance (‘aggressive’ strategy). |
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title_short |
Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach |
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author2 |
Orehounig, Kristina Carmeliet, Jan |
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up_date |
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