Demand Response Alert Service Based on Appliance Modeling
Demand response has been widely developed during recent years to increase efficiency and decrease the cost in the electric power sector by shifting energy use, smoothening the load curve, and thus ensuring benefits for all participating parties. This paper introduces a Demand Response Alert Service...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/10/2953 |
| _version_ | 1797533413630017536 |
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| author | Ioanna-M. Chatzigeorgiou Christos Diou Kyriakos C. Chatzidimitriou Georgios T. Andreou |
| author_facet | Ioanna-M. Chatzigeorgiou Christos Diou Kyriakos C. Chatzidimitriou Georgios T. Andreou |
| author_sort | Ioanna-M. Chatzigeorgiou |
| collection | DOAJ |
| description | Demand response has been widely developed during recent years to increase efficiency and decrease the cost in the electric power sector by shifting energy use, smoothening the load curve, and thus ensuring benefits for all participating parties. This paper introduces a Demand Response Alert Service (DRAS) that can optimize the interaction between the energy industry parties and end users by sending the minimum number of relatable alerts to satisfy the transformation of the load curve. The service creates appliance models for certain deferrable appliances based on past-usage measurements and prioritizes households according to the probability of the use of their appliances. Several variations of the appliance model are examined with respect to the probabilistic association of appliance usage on different days. The service is evaluated for a peak-shaving scenario when either one or more appliances per household are involved. The results demonstrate a significant improvement compared to a random selection of end users, thus promising increased participation and engagement. Indicatively, in terms of the Area Under the Curve (AUC) index, the proposed method achieves, in all the studied scenarios, an improvement ranging between 41.33% and 64.64% compared to the baseline scenario. In terms of the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>F</mi><mn>1</mn></msub></mrow></semantics></math></inline-formula> score index, the respective improvement reaches up to 221.05%. |
| first_indexed | 2024-03-10T11:14:16Z |
| format | Article |
| id | doaj.art-744da2cffbe54f25a3116923cb9695fa |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T11:14:16Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-744da2cffbe54f25a3116923cb9695fa2023-11-21T20:33:08ZengMDPI AGEnergies1996-10732021-05-011410295310.3390/en14102953Demand Response Alert Service Based on Appliance ModelingIoanna-M. Chatzigeorgiou0Christos Diou1Kyriakos C. Chatzidimitriou2Georgios T. Andreou3School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceDepartment of Informatics and Telematics, Harokopio University of Athens, 17778 Athens, GreeceSchool of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceSchool of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceDemand response has been widely developed during recent years to increase efficiency and decrease the cost in the electric power sector by shifting energy use, smoothening the load curve, and thus ensuring benefits for all participating parties. This paper introduces a Demand Response Alert Service (DRAS) that can optimize the interaction between the energy industry parties and end users by sending the minimum number of relatable alerts to satisfy the transformation of the load curve. The service creates appliance models for certain deferrable appliances based on past-usage measurements and prioritizes households according to the probability of the use of their appliances. Several variations of the appliance model are examined with respect to the probabilistic association of appliance usage on different days. The service is evaluated for a peak-shaving scenario when either one or more appliances per household are involved. The results demonstrate a significant improvement compared to a random selection of end users, thus promising increased participation and engagement. Indicatively, in terms of the Area Under the Curve (AUC) index, the proposed method achieves, in all the studied scenarios, an improvement ranging between 41.33% and 64.64% compared to the baseline scenario. In terms of the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>F</mi><mn>1</mn></msub></mrow></semantics></math></inline-formula> score index, the respective improvement reaches up to 221.05%.https://www.mdpi.com/1996-1073/14/10/2953data analyticsartificial intelligence applied to power systemsdemand side managementflexibilitydemand responseappliance modeling |
| spellingShingle | Ioanna-M. Chatzigeorgiou Christos Diou Kyriakos C. Chatzidimitriou Georgios T. Andreou Demand Response Alert Service Based on Appliance Modeling Energies data analytics artificial intelligence applied to power systems demand side management flexibility demand response appliance modeling |
| title | Demand Response Alert Service Based on Appliance Modeling |
| title_full | Demand Response Alert Service Based on Appliance Modeling |
| title_fullStr | Demand Response Alert Service Based on Appliance Modeling |
| title_full_unstemmed | Demand Response Alert Service Based on Appliance Modeling |
| title_short | Demand Response Alert Service Based on Appliance Modeling |
| title_sort | demand response alert service based on appliance modeling |
| topic | data analytics artificial intelligence applied to power systems demand side management flexibility demand response appliance modeling |
| url | https://www.mdpi.com/1996-1073/14/10/2953 |
| work_keys_str_mv | AT ioannamchatzigeorgiou demandresponsealertservicebasedonappliancemodeling AT christosdiou demandresponsealertservicebasedonappliancemodeling AT kyriakoscchatzidimitriou demandresponsealertservicebasedonappliancemodeling AT georgiostandreou demandresponsealertservicebasedonappliancemodeling |