Evaluation of residential customer elasticity for incentive based demand response programs
A key component to understanding demand response programs design is elasticity, which reflects customer reaction to economic offers. In this work, customer elasticity for Incentive Based Demand Response (IBDR) programs is estimated using data from two nation wide surveys and integrated with a detail...
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| Autor*in: |
Asadinejad, Ailin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018transfer abstract |
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| Umfang: |
11 |
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| Übergeordnetes Werk: |
Enthalten in: MEALPY: An open-source library for latest meta-heuristic algorithms in Python - Van Thieu, Nguyen ELSEVIER, 2023, an international journal devoted to research and new applications in generation, transmission, distribution and utilization of electric power, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:158 ; year:2018 ; pages:26-36 ; extent:11 |
| Links: |
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| DOI / URN: |
10.1016/j.epsr.2017.12.017 |
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ELV042011051 |
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| 520 | |a A key component to understanding demand response programs design is elasticity, which reflects customer reaction to economic offers. In this work, customer elasticity for Incentive Based Demand Response (IBDR) programs is estimated using data from two nation wide surveys and integrated with a detailed residential load model. In addition, incentive based elasticity is calculated at the individual appliance level since this is more effective for operations than at an aggregate value for a feeder. The concept of appliance base elasticity is derived from various contributions of each appliance in the aggregate load signal and the necessity of use for the customer. Results show that the needed customer incentive for certain loads, such as, lighting and washing is less than HVAC, but since the HVAC energy share in total load is much higher generally, it has greater elasticity. Considering the important role of HVAC in the aggregate load signal, the elasticity is studied in more detail using estimates of different thermostat settings. Analysis shows that elasticity of HVAC decreases while average power increases. To disaggregate the load signal for each appliance, a constrained non-negative matrix factorization (CNMF) method is proposed. In addition, this method is used to decompose the HVAC signal to identify different thermostat settings. | ||
| 520 | |a A key component to understanding demand response programs design is elasticity, which reflects customer reaction to economic offers. In this work, customer elasticity for Incentive Based Demand Response (IBDR) programs is estimated using data from two nation wide surveys and integrated with a detailed residential load model. In addition, incentive based elasticity is calculated at the individual appliance level since this is more effective for operations than at an aggregate value for a feeder. The concept of appliance base elasticity is derived from various contributions of each appliance in the aggregate load signal and the necessity of use for the customer. Results show that the needed customer incentive for certain loads, such as, lighting and washing is less than HVAC, but since the HVAC energy share in total load is much higher generally, it has greater elasticity. Considering the important role of HVAC in the aggregate load signal, the elasticity is studied in more detail using estimates of different thermostat settings. Analysis shows that elasticity of HVAC decreases while average power increases. To disaggregate the load signal for each appliance, a constrained non-negative matrix factorization (CNMF) method is proposed. In addition, this method is used to decompose the HVAC signal to identify different thermostat settings. | ||
| 650 | 7 | |a Incentive base demand response |2 Elsevier | |
| 650 | 7 | |a Appliance elasticity |2 Elsevier | |
| 650 | 7 | |a Elasticity |2 Elsevier | |
| 650 | 7 | |a Residential load modeling |2 Elsevier | |
| 650 | 7 | |a Load disaggregation |2 Elsevier | |
| 700 | 1 | |a Rahimpour, Alireza |4 oth | |
| 700 | 1 | |a Tomsovic, Kevin |4 oth | |
| 700 | 1 | |a Qi, Hairong |4 oth | |
| 700 | 1 | |a Chen, Chien-fei |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Van Thieu, Nguyen ELSEVIER |t MEALPY: An open-source library for latest meta-heuristic algorithms in Python |d 2023 |d an international journal devoted to research and new applications in generation, transmission, distribution and utilization of electric power |g Amsterdam [u.a.] |w (DE-627)ELV009857966 |
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10.1016/j.epsr.2017.12.017 doi GBV00000000000141A.pica (DE-627)ELV042011051 (ELSEVIER)S0378-7796(17)30496-0 DE-627 ger DE-627 rakwb eng 620 620 DE-600 004 VZ 54.30 bkl Asadinejad, Ailin verfasserin aut Evaluation of residential customer elasticity for incentive based demand response programs 2018transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A key component to understanding demand response programs design is elasticity, which reflects customer reaction to economic offers. In this work, customer elasticity for Incentive Based Demand Response (IBDR) programs is estimated using data from two nation wide surveys and integrated with a detailed residential load model. In addition, incentive based elasticity is calculated at the individual appliance level since this is more effective for operations than at an aggregate value for a feeder. The concept of appliance base elasticity is derived from various contributions of each appliance in the aggregate load signal and the necessity of use for the customer. Results show that the needed customer incentive for certain loads, such as, lighting and washing is less than HVAC, but since the HVAC energy share in total load is much higher generally, it has greater elasticity. Considering the important role of HVAC in the aggregate load signal, the elasticity is studied in more detail using estimates of different thermostat settings. Analysis shows that elasticity of HVAC decreases while average power increases. To disaggregate the load signal for each appliance, a constrained non-negative matrix factorization (CNMF) method is proposed. In addition, this method is used to decompose the HVAC signal to identify different thermostat settings. A key component to understanding demand response programs design is elasticity, which reflects customer reaction to economic offers. In this work, customer elasticity for Incentive Based Demand Response (IBDR) programs is estimated using data from two nation wide surveys and integrated with a detailed residential load model. In addition, incentive based elasticity is calculated at the individual appliance level since this is more effective for operations than at an aggregate value for a feeder. The concept of appliance base elasticity is derived from various contributions of each appliance in the aggregate load signal and the necessity of use for the customer. Results show that the needed customer incentive for certain loads, such as, lighting and washing is less than HVAC, but since the HVAC energy share in total load is much higher generally, it has greater elasticity. Considering the important role of HVAC in the aggregate load signal, the elasticity is studied in more detail using estimates of different thermostat settings. Analysis shows that elasticity of HVAC decreases while average power increases. To disaggregate the load signal for each appliance, a constrained non-negative matrix factorization (CNMF) method is proposed. In addition, this method is used to decompose the HVAC signal to identify different thermostat settings. Incentive base demand response Elsevier Appliance elasticity Elsevier Elasticity Elsevier Residential load modeling Elsevier Load disaggregation Elsevier Rahimpour, Alireza oth Tomsovic, Kevin oth Qi, Hairong oth Chen, Chien-fei oth Enthalten in Elsevier Science Van Thieu, Nguyen ELSEVIER MEALPY: An open-source library for latest meta-heuristic algorithms in Python 2023 an international journal devoted to research and new applications in generation, transmission, distribution and utilization of electric power Amsterdam [u.a.] (DE-627)ELV009857966 volume:158 year:2018 pages:26-36 extent:11 https://doi.org/10.1016/j.epsr.2017.12.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 54.30 Systemarchitektur: Allgemeines Informatik VZ AR 158 2018 26-36 11 045F 620 |
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10.1016/j.epsr.2017.12.017 doi GBV00000000000141A.pica (DE-627)ELV042011051 (ELSEVIER)S0378-7796(17)30496-0 DE-627 ger DE-627 rakwb eng 620 620 DE-600 004 VZ 54.30 bkl Asadinejad, Ailin verfasserin aut Evaluation of residential customer elasticity for incentive based demand response programs 2018transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A key component to understanding demand response programs design is elasticity, which reflects customer reaction to economic offers. In this work, customer elasticity for Incentive Based Demand Response (IBDR) programs is estimated using data from two nation wide surveys and integrated with a detailed residential load model. In addition, incentive based elasticity is calculated at the individual appliance level since this is more effective for operations than at an aggregate value for a feeder. The concept of appliance base elasticity is derived from various contributions of each appliance in the aggregate load signal and the necessity of use for the customer. Results show that the needed customer incentive for certain loads, such as, lighting and washing is less than HVAC, but since the HVAC energy share in total load is much higher generally, it has greater elasticity. Considering the important role of HVAC in the aggregate load signal, the elasticity is studied in more detail using estimates of different thermostat settings. Analysis shows that elasticity of HVAC decreases while average power increases. To disaggregate the load signal for each appliance, a constrained non-negative matrix factorization (CNMF) method is proposed. In addition, this method is used to decompose the HVAC signal to identify different thermostat settings. A key component to understanding demand response programs design is elasticity, which reflects customer reaction to economic offers. In this work, customer elasticity for Incentive Based Demand Response (IBDR) programs is estimated using data from two nation wide surveys and integrated with a detailed residential load model. In addition, incentive based elasticity is calculated at the individual appliance level since this is more effective for operations than at an aggregate value for a feeder. The concept of appliance base elasticity is derived from various contributions of each appliance in the aggregate load signal and the necessity of use for the customer. Results show that the needed customer incentive for certain loads, such as, lighting and washing is less than HVAC, but since the HVAC energy share in total load is much higher generally, it has greater elasticity. Considering the important role of HVAC in the aggregate load signal, the elasticity is studied in more detail using estimates of different thermostat settings. Analysis shows that elasticity of HVAC decreases while average power increases. To disaggregate the load signal for each appliance, a constrained non-negative matrix factorization (CNMF) method is proposed. In addition, this method is used to decompose the HVAC signal to identify different thermostat settings. Incentive base demand response Elsevier Appliance elasticity Elsevier Elasticity Elsevier Residential load modeling Elsevier Load disaggregation Elsevier Rahimpour, Alireza oth Tomsovic, Kevin oth Qi, Hairong oth Chen, Chien-fei oth Enthalten in Elsevier Science Van Thieu, Nguyen ELSEVIER MEALPY: An open-source library for latest meta-heuristic algorithms in Python 2023 an international journal devoted to research and new applications in generation, transmission, distribution and utilization of electric power Amsterdam [u.a.] (DE-627)ELV009857966 volume:158 year:2018 pages:26-36 extent:11 https://doi.org/10.1016/j.epsr.2017.12.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 54.30 Systemarchitektur: Allgemeines Informatik VZ AR 158 2018 26-36 11 045F 620 |
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10.1016/j.epsr.2017.12.017 doi GBV00000000000141A.pica (DE-627)ELV042011051 (ELSEVIER)S0378-7796(17)30496-0 DE-627 ger DE-627 rakwb eng 620 620 DE-600 004 VZ 54.30 bkl Asadinejad, Ailin verfasserin aut Evaluation of residential customer elasticity for incentive based demand response programs 2018transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A key component to understanding demand response programs design is elasticity, which reflects customer reaction to economic offers. In this work, customer elasticity for Incentive Based Demand Response (IBDR) programs is estimated using data from two nation wide surveys and integrated with a detailed residential load model. In addition, incentive based elasticity is calculated at the individual appliance level since this is more effective for operations than at an aggregate value for a feeder. The concept of appliance base elasticity is derived from various contributions of each appliance in the aggregate load signal and the necessity of use for the customer. Results show that the needed customer incentive for certain loads, such as, lighting and washing is less than HVAC, but since the HVAC energy share in total load is much higher generally, it has greater elasticity. Considering the important role of HVAC in the aggregate load signal, the elasticity is studied in more detail using estimates of different thermostat settings. Analysis shows that elasticity of HVAC decreases while average power increases. To disaggregate the load signal for each appliance, a constrained non-negative matrix factorization (CNMF) method is proposed. In addition, this method is used to decompose the HVAC signal to identify different thermostat settings. A key component to understanding demand response programs design is elasticity, which reflects customer reaction to economic offers. In this work, customer elasticity for Incentive Based Demand Response (IBDR) programs is estimated using data from two nation wide surveys and integrated with a detailed residential load model. In addition, incentive based elasticity is calculated at the individual appliance level since this is more effective for operations than at an aggregate value for a feeder. The concept of appliance base elasticity is derived from various contributions of each appliance in the aggregate load signal and the necessity of use for the customer. Results show that the needed customer incentive for certain loads, such as, lighting and washing is less than HVAC, but since the HVAC energy share in total load is much higher generally, it has greater elasticity. Considering the important role of HVAC in the aggregate load signal, the elasticity is studied in more detail using estimates of different thermostat settings. Analysis shows that elasticity of HVAC decreases while average power increases. To disaggregate the load signal for each appliance, a constrained non-negative matrix factorization (CNMF) method is proposed. In addition, this method is used to decompose the HVAC signal to identify different thermostat settings. Incentive base demand response Elsevier Appliance elasticity Elsevier Elasticity Elsevier Residential load modeling Elsevier Load disaggregation Elsevier Rahimpour, Alireza oth Tomsovic, Kevin oth Qi, Hairong oth Chen, Chien-fei oth Enthalten in Elsevier Science Van Thieu, Nguyen ELSEVIER MEALPY: An open-source library for latest meta-heuristic algorithms in Python 2023 an international journal devoted to research and new applications in generation, transmission, distribution and utilization of electric power Amsterdam [u.a.] (DE-627)ELV009857966 volume:158 year:2018 pages:26-36 extent:11 https://doi.org/10.1016/j.epsr.2017.12.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 54.30 Systemarchitektur: Allgemeines Informatik VZ AR 158 2018 26-36 11 045F 620 |
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10.1016/j.epsr.2017.12.017 doi GBV00000000000141A.pica (DE-627)ELV042011051 (ELSEVIER)S0378-7796(17)30496-0 DE-627 ger DE-627 rakwb eng 620 620 DE-600 004 VZ 54.30 bkl Asadinejad, Ailin verfasserin aut Evaluation of residential customer elasticity for incentive based demand response programs 2018transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A key component to understanding demand response programs design is elasticity, which reflects customer reaction to economic offers. In this work, customer elasticity for Incentive Based Demand Response (IBDR) programs is estimated using data from two nation wide surveys and integrated with a detailed residential load model. In addition, incentive based elasticity is calculated at the individual appliance level since this is more effective for operations than at an aggregate value for a feeder. The concept of appliance base elasticity is derived from various contributions of each appliance in the aggregate load signal and the necessity of use for the customer. Results show that the needed customer incentive for certain loads, such as, lighting and washing is less than HVAC, but since the HVAC energy share in total load is much higher generally, it has greater elasticity. Considering the important role of HVAC in the aggregate load signal, the elasticity is studied in more detail using estimates of different thermostat settings. Analysis shows that elasticity of HVAC decreases while average power increases. To disaggregate the load signal for each appliance, a constrained non-negative matrix factorization (CNMF) method is proposed. In addition, this method is used to decompose the HVAC signal to identify different thermostat settings. A key component to understanding demand response programs design is elasticity, which reflects customer reaction to economic offers. In this work, customer elasticity for Incentive Based Demand Response (IBDR) programs is estimated using data from two nation wide surveys and integrated with a detailed residential load model. In addition, incentive based elasticity is calculated at the individual appliance level since this is more effective for operations than at an aggregate value for a feeder. The concept of appliance base elasticity is derived from various contributions of each appliance in the aggregate load signal and the necessity of use for the customer. Results show that the needed customer incentive for certain loads, such as, lighting and washing is less than HVAC, but since the HVAC energy share in total load is much higher generally, it has greater elasticity. Considering the important role of HVAC in the aggregate load signal, the elasticity is studied in more detail using estimates of different thermostat settings. Analysis shows that elasticity of HVAC decreases while average power increases. To disaggregate the load signal for each appliance, a constrained non-negative matrix factorization (CNMF) method is proposed. In addition, this method is used to decompose the HVAC signal to identify different thermostat settings. Incentive base demand response Elsevier Appliance elasticity Elsevier Elasticity Elsevier Residential load modeling Elsevier Load disaggregation Elsevier Rahimpour, Alireza oth Tomsovic, Kevin oth Qi, Hairong oth Chen, Chien-fei oth Enthalten in Elsevier Science Van Thieu, Nguyen ELSEVIER MEALPY: An open-source library for latest meta-heuristic algorithms in Python 2023 an international journal devoted to research and new applications in generation, transmission, distribution and utilization of electric power Amsterdam [u.a.] (DE-627)ELV009857966 volume:158 year:2018 pages:26-36 extent:11 https://doi.org/10.1016/j.epsr.2017.12.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 54.30 Systemarchitektur: Allgemeines Informatik VZ AR 158 2018 26-36 11 045F 620 |
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10.1016/j.epsr.2017.12.017 doi GBV00000000000141A.pica (DE-627)ELV042011051 (ELSEVIER)S0378-7796(17)30496-0 DE-627 ger DE-627 rakwb eng 620 620 DE-600 004 VZ 54.30 bkl Asadinejad, Ailin verfasserin aut Evaluation of residential customer elasticity for incentive based demand response programs 2018transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A key component to understanding demand response programs design is elasticity, which reflects customer reaction to economic offers. In this work, customer elasticity for Incentive Based Demand Response (IBDR) programs is estimated using data from two nation wide surveys and integrated with a detailed residential load model. In addition, incentive based elasticity is calculated at the individual appliance level since this is more effective for operations than at an aggregate value for a feeder. The concept of appliance base elasticity is derived from various contributions of each appliance in the aggregate load signal and the necessity of use for the customer. Results show that the needed customer incentive for certain loads, such as, lighting and washing is less than HVAC, but since the HVAC energy share in total load is much higher generally, it has greater elasticity. Considering the important role of HVAC in the aggregate load signal, the elasticity is studied in more detail using estimates of different thermostat settings. Analysis shows that elasticity of HVAC decreases while average power increases. To disaggregate the load signal for each appliance, a constrained non-negative matrix factorization (CNMF) method is proposed. In addition, this method is used to decompose the HVAC signal to identify different thermostat settings. A key component to understanding demand response programs design is elasticity, which reflects customer reaction to economic offers. In this work, customer elasticity for Incentive Based Demand Response (IBDR) programs is estimated using data from two nation wide surveys and integrated with a detailed residential load model. In addition, incentive based elasticity is calculated at the individual appliance level since this is more effective for operations than at an aggregate value for a feeder. The concept of appliance base elasticity is derived from various contributions of each appliance in the aggregate load signal and the necessity of use for the customer. Results show that the needed customer incentive for certain loads, such as, lighting and washing is less than HVAC, but since the HVAC energy share in total load is much higher generally, it has greater elasticity. Considering the important role of HVAC in the aggregate load signal, the elasticity is studied in more detail using estimates of different thermostat settings. Analysis shows that elasticity of HVAC decreases while average power increases. To disaggregate the load signal for each appliance, a constrained non-negative matrix factorization (CNMF) method is proposed. In addition, this method is used to decompose the HVAC signal to identify different thermostat settings. Incentive base demand response Elsevier Appliance elasticity Elsevier Elasticity Elsevier Residential load modeling Elsevier Load disaggregation Elsevier Rahimpour, Alireza oth Tomsovic, Kevin oth Qi, Hairong oth Chen, Chien-fei oth Enthalten in Elsevier Science Van Thieu, Nguyen ELSEVIER MEALPY: An open-source library for latest meta-heuristic algorithms in Python 2023 an international journal devoted to research and new applications in generation, transmission, distribution and utilization of electric power Amsterdam [u.a.] (DE-627)ELV009857966 volume:158 year:2018 pages:26-36 extent:11 https://doi.org/10.1016/j.epsr.2017.12.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 54.30 Systemarchitektur: Allgemeines Informatik VZ AR 158 2018 26-36 11 045F 620 |
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In this work, customer elasticity for Incentive Based Demand Response (IBDR) programs is estimated using data from two nation wide surveys and integrated with a detailed residential load model. In addition, incentive based elasticity is calculated at the individual appliance level since this is more effective for operations than at an aggregate value for a feeder. The concept of appliance base elasticity is derived from various contributions of each appliance in the aggregate load signal and the necessity of use for the customer. Results show that the needed customer incentive for certain loads, such as, lighting and washing is less than HVAC, but since the HVAC energy share in total load is much higher generally, it has greater elasticity. Considering the important role of HVAC in the aggregate load signal, the elasticity is studied in more detail using estimates of different thermostat settings. Analysis shows that elasticity of HVAC decreases while average power increases. 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Evaluation of residential customer elasticity for incentive based demand response programs |
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A key component to understanding demand response programs design is elasticity, which reflects customer reaction to economic offers. In this work, customer elasticity for Incentive Based Demand Response (IBDR) programs is estimated using data from two nation wide surveys and integrated with a detailed residential load model. In addition, incentive based elasticity is calculated at the individual appliance level since this is more effective for operations than at an aggregate value for a feeder. The concept of appliance base elasticity is derived from various contributions of each appliance in the aggregate load signal and the necessity of use for the customer. Results show that the needed customer incentive for certain loads, such as, lighting and washing is less than HVAC, but since the HVAC energy share in total load is much higher generally, it has greater elasticity. Considering the important role of HVAC in the aggregate load signal, the elasticity is studied in more detail using estimates of different thermostat settings. Analysis shows that elasticity of HVAC decreases while average power increases. To disaggregate the load signal for each appliance, a constrained non-negative matrix factorization (CNMF) method is proposed. In addition, this method is used to decompose the HVAC signal to identify different thermostat settings. |
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A key component to understanding demand response programs design is elasticity, which reflects customer reaction to economic offers. In this work, customer elasticity for Incentive Based Demand Response (IBDR) programs is estimated using data from two nation wide surveys and integrated with a detailed residential load model. In addition, incentive based elasticity is calculated at the individual appliance level since this is more effective for operations than at an aggregate value for a feeder. The concept of appliance base elasticity is derived from various contributions of each appliance in the aggregate load signal and the necessity of use for the customer. Results show that the needed customer incentive for certain loads, such as, lighting and washing is less than HVAC, but since the HVAC energy share in total load is much higher generally, it has greater elasticity. Considering the important role of HVAC in the aggregate load signal, the elasticity is studied in more detail using estimates of different thermostat settings. Analysis shows that elasticity of HVAC decreases while average power increases. To disaggregate the load signal for each appliance, a constrained non-negative matrix factorization (CNMF) method is proposed. In addition, this method is used to decompose the HVAC signal to identify different thermostat settings. |
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A key component to understanding demand response programs design is elasticity, which reflects customer reaction to economic offers. In this work, customer elasticity for Incentive Based Demand Response (IBDR) programs is estimated using data from two nation wide surveys and integrated with a detailed residential load model. In addition, incentive based elasticity is calculated at the individual appliance level since this is more effective for operations than at an aggregate value for a feeder. The concept of appliance base elasticity is derived from various contributions of each appliance in the aggregate load signal and the necessity of use for the customer. Results show that the needed customer incentive for certain loads, such as, lighting and washing is less than HVAC, but since the HVAC energy share in total load is much higher generally, it has greater elasticity. Considering the important role of HVAC in the aggregate load signal, the elasticity is studied in more detail using estimates of different thermostat settings. Analysis shows that elasticity of HVAC decreases while average power increases. To disaggregate the load signal for each appliance, a constrained non-negative matrix factorization (CNMF) method is proposed. In addition, this method is used to decompose the HVAC signal to identify different thermostat settings. |
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