Smart meter data-driven evaluation of operational demand response potential of residential air conditioning loads
Residential air conditioning loads (ACLs) are promising demand response (DR) resources with a certain flexibility and controllability that can enhance the operational flexibility and resource utilization of the power grid. The evaluation of DR potential is of great importance for estimating the powe...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Qi, Ning [verfasserIn] Cheng, Lin [verfasserIn] Xu, Helin [verfasserIn] Wu, Kuihua [verfasserIn] Li, XuLiang [verfasserIn] Wang, Yanshuo [verfasserIn] Liu, Rui [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Applied energy - Amsterdam [u.a.] : Elsevier Science, 1975, 279 |
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| Übergeordnetes Werk: |
volume:279 |
| DOI / URN: |
10.1016/j.apenergy.2020.115708 |
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| Katalog-ID: |
ELV00505608X |
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| 520 | |a Residential air conditioning loads (ACLs) are promising demand response (DR) resources with a certain flexibility and controllability that can enhance the operational flexibility and resource utilization of the power grid. The evaluation of DR potential is of great importance for estimating the power reduction, targeting appropriate DR customers, and obtaining constrained boundary for economic dispatch. To better reveal the multi-faceted factors and multi-uncertainties during DR, this paper present a novel definition and evaluation approach of operational DR potential from single customer to large-scale load centers. Aimed at resolving two main issues of evaluation: disaggregation of ACLs component and parameter estimation, an unsupervised load decomposition methodology considering load level difference and seasonal variation is proposed to disaggregate the whole-house energy consumption into ACLs and baseload components non-intrusively. Subsequently, based on the thermal dynamics model of ACLs, a segment analysis methodology is developed, including a constrained regression method for the static parameter estimation and a hybrid method for the dynamic parameter estimation. Data experiments based on ground truth data of residential customers in Austin, Texas, U.S., smart homes in Western Massachusetts and low voltage area in a developed city, Jiangsu province, China, validate the better performance of accuracy and robustness using the proposed methods. The proposed methods are further implemented to four application scenarios, including ACLs consumption behavior learning, operational DR potential analysis, customers targeting for different time-scale DR programs, and day ahead scheduling. These results also demonstrate that great difference in terms of ACLs usage patterns (a total of 19 patterns), DR potential (a maximum of 0.7 kW) results from different DR duration and operational conditions. The DR programs designers and load aggregators are suggested to consider the proposed 5 basic indicators to target customer and select participants in different DR scenarios. And the operational DR potential is more reliable and suitable to generate strategies for day ahead scheduling. | ||
| 650 | 4 | |a Demand response potential | |
| 650 | 4 | |a Air conditioning load | |
| 650 | 4 | |a Residential customer | |
| 650 | 4 | |a Load disaggregation | |
| 650 | 4 | |a Smart meter data | |
| 650 | 4 | |a Targeting strategy | |
| 700 | 1 | |a Cheng, Lin |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Helin |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Kuihua |e verfasserin |4 aut | |
| 700 | 1 | |a Li, XuLiang |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yanshuo |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Rui |e verfasserin |4 aut | |
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10.1016/j.apenergy.2020.115708 doi (DE-627)ELV00505608X (ELSEVIER)S0306-2619(20)31202-2 DE-627 ger DE-627 rda eng 620 DE-600 52.50 bkl Qi, Ning verfasserin aut Smart meter data-driven evaluation of operational demand response potential of residential air conditioning loads 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Residential air conditioning loads (ACLs) are promising demand response (DR) resources with a certain flexibility and controllability that can enhance the operational flexibility and resource utilization of the power grid. The evaluation of DR potential is of great importance for estimating the power reduction, targeting appropriate DR customers, and obtaining constrained boundary for economic dispatch. To better reveal the multi-faceted factors and multi-uncertainties during DR, this paper present a novel definition and evaluation approach of operational DR potential from single customer to large-scale load centers. Aimed at resolving two main issues of evaluation: disaggregation of ACLs component and parameter estimation, an unsupervised load decomposition methodology considering load level difference and seasonal variation is proposed to disaggregate the whole-house energy consumption into ACLs and baseload components non-intrusively. Subsequently, based on the thermal dynamics model of ACLs, a segment analysis methodology is developed, including a constrained regression method for the static parameter estimation and a hybrid method for the dynamic parameter estimation. Data experiments based on ground truth data of residential customers in Austin, Texas, U.S., smart homes in Western Massachusetts and low voltage area in a developed city, Jiangsu province, China, validate the better performance of accuracy and robustness using the proposed methods. The proposed methods are further implemented to four application scenarios, including ACLs consumption behavior learning, operational DR potential analysis, customers targeting for different time-scale DR programs, and day ahead scheduling. These results also demonstrate that great difference in terms of ACLs usage patterns (a total of 19 patterns), DR potential (a maximum of 0.7 kW) results from different DR duration and operational conditions. The DR programs designers and load aggregators are suggested to consider the proposed 5 basic indicators to target customer and select participants in different DR scenarios. And the operational DR potential is more reliable and suitable to generate strategies for day ahead scheduling. Demand response potential Air conditioning load Residential customer Load disaggregation Smart meter data Targeting strategy Cheng, Lin verfasserin aut Xu, Helin verfasserin aut Wu, Kuihua verfasserin aut Li, XuLiang verfasserin aut Wang, Yanshuo verfasserin aut Liu, Rui verfasserin aut Enthalten in Applied energy Amsterdam [u.a.] : Elsevier Science, 1975 279 Online-Ressource (DE-627)320406709 (DE-600)2000772-3 (DE-576)256140251 1872-9118 nnns volume:279 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 279 |
| spelling |
10.1016/j.apenergy.2020.115708 doi (DE-627)ELV00505608X (ELSEVIER)S0306-2619(20)31202-2 DE-627 ger DE-627 rda eng 620 DE-600 52.50 bkl Qi, Ning verfasserin aut Smart meter data-driven evaluation of operational demand response potential of residential air conditioning loads 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Residential air conditioning loads (ACLs) are promising demand response (DR) resources with a certain flexibility and controllability that can enhance the operational flexibility and resource utilization of the power grid. The evaluation of DR potential is of great importance for estimating the power reduction, targeting appropriate DR customers, and obtaining constrained boundary for economic dispatch. To better reveal the multi-faceted factors and multi-uncertainties during DR, this paper present a novel definition and evaluation approach of operational DR potential from single customer to large-scale load centers. Aimed at resolving two main issues of evaluation: disaggregation of ACLs component and parameter estimation, an unsupervised load decomposition methodology considering load level difference and seasonal variation is proposed to disaggregate the whole-house energy consumption into ACLs and baseload components non-intrusively. Subsequently, based on the thermal dynamics model of ACLs, a segment analysis methodology is developed, including a constrained regression method for the static parameter estimation and a hybrid method for the dynamic parameter estimation. Data experiments based on ground truth data of residential customers in Austin, Texas, U.S., smart homes in Western Massachusetts and low voltage area in a developed city, Jiangsu province, China, validate the better performance of accuracy and robustness using the proposed methods. The proposed methods are further implemented to four application scenarios, including ACLs consumption behavior learning, operational DR potential analysis, customers targeting for different time-scale DR programs, and day ahead scheduling. These results also demonstrate that great difference in terms of ACLs usage patterns (a total of 19 patterns), DR potential (a maximum of 0.7 kW) results from different DR duration and operational conditions. The DR programs designers and load aggregators are suggested to consider the proposed 5 basic indicators to target customer and select participants in different DR scenarios. And the operational DR potential is more reliable and suitable to generate strategies for day ahead scheduling. Demand response potential Air conditioning load Residential customer Load disaggregation Smart meter data Targeting strategy Cheng, Lin verfasserin aut Xu, Helin verfasserin aut Wu, Kuihua verfasserin aut Li, XuLiang verfasserin aut Wang, Yanshuo verfasserin aut Liu, Rui verfasserin aut Enthalten in Applied energy Amsterdam [u.a.] : Elsevier Science, 1975 279 Online-Ressource (DE-627)320406709 (DE-600)2000772-3 (DE-576)256140251 1872-9118 nnns volume:279 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 279 |
| allfields_unstemmed |
10.1016/j.apenergy.2020.115708 doi (DE-627)ELV00505608X (ELSEVIER)S0306-2619(20)31202-2 DE-627 ger DE-627 rda eng 620 DE-600 52.50 bkl Qi, Ning verfasserin aut Smart meter data-driven evaluation of operational demand response potential of residential air conditioning loads 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Residential air conditioning loads (ACLs) are promising demand response (DR) resources with a certain flexibility and controllability that can enhance the operational flexibility and resource utilization of the power grid. The evaluation of DR potential is of great importance for estimating the power reduction, targeting appropriate DR customers, and obtaining constrained boundary for economic dispatch. To better reveal the multi-faceted factors and multi-uncertainties during DR, this paper present a novel definition and evaluation approach of operational DR potential from single customer to large-scale load centers. Aimed at resolving two main issues of evaluation: disaggregation of ACLs component and parameter estimation, an unsupervised load decomposition methodology considering load level difference and seasonal variation is proposed to disaggregate the whole-house energy consumption into ACLs and baseload components non-intrusively. Subsequently, based on the thermal dynamics model of ACLs, a segment analysis methodology is developed, including a constrained regression method for the static parameter estimation and a hybrid method for the dynamic parameter estimation. Data experiments based on ground truth data of residential customers in Austin, Texas, U.S., smart homes in Western Massachusetts and low voltage area in a developed city, Jiangsu province, China, validate the better performance of accuracy and robustness using the proposed methods. The proposed methods are further implemented to four application scenarios, including ACLs consumption behavior learning, operational DR potential analysis, customers targeting for different time-scale DR programs, and day ahead scheduling. These results also demonstrate that great difference in terms of ACLs usage patterns (a total of 19 patterns), DR potential (a maximum of 0.7 kW) results from different DR duration and operational conditions. The DR programs designers and load aggregators are suggested to consider the proposed 5 basic indicators to target customer and select participants in different DR scenarios. And the operational DR potential is more reliable and suitable to generate strategies for day ahead scheduling. Demand response potential Air conditioning load Residential customer Load disaggregation Smart meter data Targeting strategy Cheng, Lin verfasserin aut Xu, Helin verfasserin aut Wu, Kuihua verfasserin aut Li, XuLiang verfasserin aut Wang, Yanshuo verfasserin aut Liu, Rui verfasserin aut Enthalten in Applied energy Amsterdam [u.a.] : Elsevier Science, 1975 279 Online-Ressource (DE-627)320406709 (DE-600)2000772-3 (DE-576)256140251 1872-9118 nnns volume:279 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 279 |
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10.1016/j.apenergy.2020.115708 doi (DE-627)ELV00505608X (ELSEVIER)S0306-2619(20)31202-2 DE-627 ger DE-627 rda eng 620 DE-600 52.50 bkl Qi, Ning verfasserin aut Smart meter data-driven evaluation of operational demand response potential of residential air conditioning loads 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Residential air conditioning loads (ACLs) are promising demand response (DR) resources with a certain flexibility and controllability that can enhance the operational flexibility and resource utilization of the power grid. The evaluation of DR potential is of great importance for estimating the power reduction, targeting appropriate DR customers, and obtaining constrained boundary for economic dispatch. To better reveal the multi-faceted factors and multi-uncertainties during DR, this paper present a novel definition and evaluation approach of operational DR potential from single customer to large-scale load centers. Aimed at resolving two main issues of evaluation: disaggregation of ACLs component and parameter estimation, an unsupervised load decomposition methodology considering load level difference and seasonal variation is proposed to disaggregate the whole-house energy consumption into ACLs and baseload components non-intrusively. Subsequently, based on the thermal dynamics model of ACLs, a segment analysis methodology is developed, including a constrained regression method for the static parameter estimation and a hybrid method for the dynamic parameter estimation. Data experiments based on ground truth data of residential customers in Austin, Texas, U.S., smart homes in Western Massachusetts and low voltage area in a developed city, Jiangsu province, China, validate the better performance of accuracy and robustness using the proposed methods. The proposed methods are further implemented to four application scenarios, including ACLs consumption behavior learning, operational DR potential analysis, customers targeting for different time-scale DR programs, and day ahead scheduling. These results also demonstrate that great difference in terms of ACLs usage patterns (a total of 19 patterns), DR potential (a maximum of 0.7 kW) results from different DR duration and operational conditions. The DR programs designers and load aggregators are suggested to consider the proposed 5 basic indicators to target customer and select participants in different DR scenarios. And the operational DR potential is more reliable and suitable to generate strategies for day ahead scheduling. Demand response potential Air conditioning load Residential customer Load disaggregation Smart meter data Targeting strategy Cheng, Lin verfasserin aut Xu, Helin verfasserin aut Wu, Kuihua verfasserin aut Li, XuLiang verfasserin aut Wang, Yanshuo verfasserin aut Liu, Rui verfasserin aut Enthalten in Applied energy Amsterdam [u.a.] : Elsevier Science, 1975 279 Online-Ressource (DE-627)320406709 (DE-600)2000772-3 (DE-576)256140251 1872-9118 nnns volume:279 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 279 |
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10.1016/j.apenergy.2020.115708 doi (DE-627)ELV00505608X (ELSEVIER)S0306-2619(20)31202-2 DE-627 ger DE-627 rda eng 620 DE-600 52.50 bkl Qi, Ning verfasserin aut Smart meter data-driven evaluation of operational demand response potential of residential air conditioning loads 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Residential air conditioning loads (ACLs) are promising demand response (DR) resources with a certain flexibility and controllability that can enhance the operational flexibility and resource utilization of the power grid. The evaluation of DR potential is of great importance for estimating the power reduction, targeting appropriate DR customers, and obtaining constrained boundary for economic dispatch. To better reveal the multi-faceted factors and multi-uncertainties during DR, this paper present a novel definition and evaluation approach of operational DR potential from single customer to large-scale load centers. Aimed at resolving two main issues of evaluation: disaggregation of ACLs component and parameter estimation, an unsupervised load decomposition methodology considering load level difference and seasonal variation is proposed to disaggregate the whole-house energy consumption into ACLs and baseload components non-intrusively. Subsequently, based on the thermal dynamics model of ACLs, a segment analysis methodology is developed, including a constrained regression method for the static parameter estimation and a hybrid method for the dynamic parameter estimation. Data experiments based on ground truth data of residential customers in Austin, Texas, U.S., smart homes in Western Massachusetts and low voltage area in a developed city, Jiangsu province, China, validate the better performance of accuracy and robustness using the proposed methods. The proposed methods are further implemented to four application scenarios, including ACLs consumption behavior learning, operational DR potential analysis, customers targeting for different time-scale DR programs, and day ahead scheduling. These results also demonstrate that great difference in terms of ACLs usage patterns (a total of 19 patterns), DR potential (a maximum of 0.7 kW) results from different DR duration and operational conditions. The DR programs designers and load aggregators are suggested to consider the proposed 5 basic indicators to target customer and select participants in different DR scenarios. And the operational DR potential is more reliable and suitable to generate strategies for day ahead scheduling. Demand response potential Air conditioning load Residential customer Load disaggregation Smart meter data Targeting strategy Cheng, Lin verfasserin aut Xu, Helin verfasserin aut Wu, Kuihua verfasserin aut Li, XuLiang verfasserin aut Wang, Yanshuo verfasserin aut Liu, Rui verfasserin aut Enthalten in Applied energy Amsterdam [u.a.] : Elsevier Science, 1975 279 Online-Ressource (DE-627)320406709 (DE-600)2000772-3 (DE-576)256140251 1872-9118 nnns volume:279 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 279 |
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Qi, Ning @@aut@@ Cheng, Lin @@aut@@ Xu, Helin @@aut@@ Wu, Kuihua @@aut@@ Li, XuLiang @@aut@@ Wang, Yanshuo @@aut@@ Liu, Rui @@aut@@ |
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Qi, Ning |
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620 DE-600 52.50 bkl Smart meter data-driven evaluation of operational demand response potential of residential air conditioning loads Demand response potential Air conditioning load Residential customer Load disaggregation Smart meter data Targeting strategy |
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ddc 620 bkl 52.50 misc Demand response potential misc Air conditioning load misc Residential customer misc Load disaggregation misc Smart meter data misc Targeting strategy |
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ddc 620 bkl 52.50 misc Demand response potential misc Air conditioning load misc Residential customer misc Load disaggregation misc Smart meter data misc Targeting strategy |
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Smart meter data-driven evaluation of operational demand response potential of residential air conditioning loads |
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Qi, Ning Cheng, Lin Xu, Helin Wu, Kuihua Li, XuLiang Wang, Yanshuo Liu, Rui |
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smart meter data-driven evaluation of operational demand response potential of residential air conditioning loads |
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Smart meter data-driven evaluation of operational demand response potential of residential air conditioning loads |
| abstract |
Residential air conditioning loads (ACLs) are promising demand response (DR) resources with a certain flexibility and controllability that can enhance the operational flexibility and resource utilization of the power grid. The evaluation of DR potential is of great importance for estimating the power reduction, targeting appropriate DR customers, and obtaining constrained boundary for economic dispatch. To better reveal the multi-faceted factors and multi-uncertainties during DR, this paper present a novel definition and evaluation approach of operational DR potential from single customer to large-scale load centers. Aimed at resolving two main issues of evaluation: disaggregation of ACLs component and parameter estimation, an unsupervised load decomposition methodology considering load level difference and seasonal variation is proposed to disaggregate the whole-house energy consumption into ACLs and baseload components non-intrusively. Subsequently, based on the thermal dynamics model of ACLs, a segment analysis methodology is developed, including a constrained regression method for the static parameter estimation and a hybrid method for the dynamic parameter estimation. Data experiments based on ground truth data of residential customers in Austin, Texas, U.S., smart homes in Western Massachusetts and low voltage area in a developed city, Jiangsu province, China, validate the better performance of accuracy and robustness using the proposed methods. The proposed methods are further implemented to four application scenarios, including ACLs consumption behavior learning, operational DR potential analysis, customers targeting for different time-scale DR programs, and day ahead scheduling. These results also demonstrate that great difference in terms of ACLs usage patterns (a total of 19 patterns), DR potential (a maximum of 0.7 kW) results from different DR duration and operational conditions. The DR programs designers and load aggregators are suggested to consider the proposed 5 basic indicators to target customer and select participants in different DR scenarios. And the operational DR potential is more reliable and suitable to generate strategies for day ahead scheduling. |
| abstractGer |
Residential air conditioning loads (ACLs) are promising demand response (DR) resources with a certain flexibility and controllability that can enhance the operational flexibility and resource utilization of the power grid. The evaluation of DR potential is of great importance for estimating the power reduction, targeting appropriate DR customers, and obtaining constrained boundary for economic dispatch. To better reveal the multi-faceted factors and multi-uncertainties during DR, this paper present a novel definition and evaluation approach of operational DR potential from single customer to large-scale load centers. Aimed at resolving two main issues of evaluation: disaggregation of ACLs component and parameter estimation, an unsupervised load decomposition methodology considering load level difference and seasonal variation is proposed to disaggregate the whole-house energy consumption into ACLs and baseload components non-intrusively. Subsequently, based on the thermal dynamics model of ACLs, a segment analysis methodology is developed, including a constrained regression method for the static parameter estimation and a hybrid method for the dynamic parameter estimation. Data experiments based on ground truth data of residential customers in Austin, Texas, U.S., smart homes in Western Massachusetts and low voltage area in a developed city, Jiangsu province, China, validate the better performance of accuracy and robustness using the proposed methods. The proposed methods are further implemented to four application scenarios, including ACLs consumption behavior learning, operational DR potential analysis, customers targeting for different time-scale DR programs, and day ahead scheduling. These results also demonstrate that great difference in terms of ACLs usage patterns (a total of 19 patterns), DR potential (a maximum of 0.7 kW) results from different DR duration and operational conditions. The DR programs designers and load aggregators are suggested to consider the proposed 5 basic indicators to target customer and select participants in different DR scenarios. And the operational DR potential is more reliable and suitable to generate strategies for day ahead scheduling. |
| abstract_unstemmed |
Residential air conditioning loads (ACLs) are promising demand response (DR) resources with a certain flexibility and controllability that can enhance the operational flexibility and resource utilization of the power grid. The evaluation of DR potential is of great importance for estimating the power reduction, targeting appropriate DR customers, and obtaining constrained boundary for economic dispatch. To better reveal the multi-faceted factors and multi-uncertainties during DR, this paper present a novel definition and evaluation approach of operational DR potential from single customer to large-scale load centers. Aimed at resolving two main issues of evaluation: disaggregation of ACLs component and parameter estimation, an unsupervised load decomposition methodology considering load level difference and seasonal variation is proposed to disaggregate the whole-house energy consumption into ACLs and baseload components non-intrusively. Subsequently, based on the thermal dynamics model of ACLs, a segment analysis methodology is developed, including a constrained regression method for the static parameter estimation and a hybrid method for the dynamic parameter estimation. Data experiments based on ground truth data of residential customers in Austin, Texas, U.S., smart homes in Western Massachusetts and low voltage area in a developed city, Jiangsu province, China, validate the better performance of accuracy and robustness using the proposed methods. The proposed methods are further implemented to four application scenarios, including ACLs consumption behavior learning, operational DR potential analysis, customers targeting for different time-scale DR programs, and day ahead scheduling. These results also demonstrate that great difference in terms of ACLs usage patterns (a total of 19 patterns), DR potential (a maximum of 0.7 kW) results from different DR duration and operational conditions. The DR programs designers and load aggregators are suggested to consider the proposed 5 basic indicators to target customer and select participants in different DR scenarios. And the operational DR potential is more reliable and suitable to generate strategies for day ahead scheduling. |
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| score |
7.3993254 |