Estimating Aggregate Capacity of Connected DERs and Forecasting Feeder Power Flow With Limited Data Availability
By 2050, zero-carbon electric power systems will rely heavily on innumerable distributed energy resources (DERs), such as wind and solar. Accurate estimation of the aggregate connected DER capacity becomes pivotal in such a landscape. However, forecasting, power flow analysis, and optimization of fe...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Access Journal of Power and Energy |
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| Online Access: | https://ieeexplore.ieee.org/document/10555337/ |
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| author | Amir Reza Nikzad Amr Adel Mohamed Bala Venkatesh John Penaranda |
| author_facet | Amir Reza Nikzad Amr Adel Mohamed Bala Venkatesh John Penaranda |
| author_sort | Amir Reza Nikzad |
| collection | DOAJ |
| description | By 2050, zero-carbon electric power systems will rely heavily on innumerable distributed energy resources (DERs), such as wind and solar. Accurate estimation of the aggregate connected DER capacity becomes pivotal in such a landscape. However, forecasting, power flow analysis, and optimization of feeders for operational decision-making by individually modeling each of these numerous renewables in the absence of complete information are operationally challenging and technically impractical. In response, we introduce a method to accurately estimate the aggregate capacities of the connected DERs on distribution feeders and a near-term forecasting method. Our proposal comprises: 1) ovel deep learning-based architecture with a few convolutional neural network and long short-term memory (CNN-LSTM) modules to represent feeder connected aggregate models of DERs and loads and associated training algorithms; 2) method for estimating aggregate capacities of connected renewables and loads; and 3) method for short-term (hourly) high-resolution forecasting. This step of estimation of the aggregate capacities of connected DERs, is a sequel to solving feeder hosting capacity problem. The method is tested using a North American utility feeder data, achieving an average accuracy of 95.56% for forecasting aggregate load power, 93.70% for feeder flow predictions, and 97.53% for estimating the aggregate capacity of DERs. |
| format | Article |
| id | doaj-art-ae983992fe504bb0a2dae424227d356a |
| institution | Kabale University |
| issn | 2687-7910 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Access Journal of Power and Energy |
| spelling | doaj-art-ae983992fe504bb0a2dae424227d356a2025-01-21T00:03:05ZengIEEEIEEE Open Access Journal of Power and Energy2687-79102024-01-011126627910.1109/OAJPE.2024.341360610555337Estimating Aggregate Capacity of Connected DERs and Forecasting Feeder Power Flow With Limited Data AvailabilityAmir Reza Nikzad0https://orcid.org/0000-0002-2086-9278Amr Adel Mohamed1https://orcid.org/0000-0002-6637-4588Bala Venkatesh2https://orcid.org/0000-0002-0392-6269John Penaranda3Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, CanadaDepartment of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, CanadaDepartment of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, CanadaHydro One, Toronto, ON, CanadaBy 2050, zero-carbon electric power systems will rely heavily on innumerable distributed energy resources (DERs), such as wind and solar. Accurate estimation of the aggregate connected DER capacity becomes pivotal in such a landscape. However, forecasting, power flow analysis, and optimization of feeders for operational decision-making by individually modeling each of these numerous renewables in the absence of complete information are operationally challenging and technically impractical. In response, we introduce a method to accurately estimate the aggregate capacities of the connected DERs on distribution feeders and a near-term forecasting method. Our proposal comprises: 1) ovel deep learning-based architecture with a few convolutional neural network and long short-term memory (CNN-LSTM) modules to represent feeder connected aggregate models of DERs and loads and associated training algorithms; 2) method for estimating aggregate capacities of connected renewables and loads; and 3) method for short-term (hourly) high-resolution forecasting. This step of estimation of the aggregate capacities of connected DERs, is a sequel to solving feeder hosting capacity problem. The method is tested using a North American utility feeder data, achieving an average accuracy of 95.56% for forecasting aggregate load power, 93.70% for feeder flow predictions, and 97.53% for estimating the aggregate capacity of DERs.https://ieeexplore.ieee.org/document/10555337/Aggregate connected renewablesdeep neural network (DNN)distributed energy resources (DERs)estimationforecasting |
| spellingShingle | Amir Reza Nikzad Amr Adel Mohamed Bala Venkatesh John Penaranda Estimating Aggregate Capacity of Connected DERs and Forecasting Feeder Power Flow With Limited Data Availability IEEE Open Access Journal of Power and Energy Aggregate connected renewables deep neural network (DNN) distributed energy resources (DERs) estimation forecasting |
| title | Estimating Aggregate Capacity of Connected DERs and Forecasting Feeder Power Flow With Limited Data Availability |
| title_full | Estimating Aggregate Capacity of Connected DERs and Forecasting Feeder Power Flow With Limited Data Availability |
| title_fullStr | Estimating Aggregate Capacity of Connected DERs and Forecasting Feeder Power Flow With Limited Data Availability |
| title_full_unstemmed | Estimating Aggregate Capacity of Connected DERs and Forecasting Feeder Power Flow With Limited Data Availability |
| title_short | Estimating Aggregate Capacity of Connected DERs and Forecasting Feeder Power Flow With Limited Data Availability |
| title_sort | estimating aggregate capacity of connected ders and forecasting feeder power flow with limited data availability |
| topic | Aggregate connected renewables deep neural network (DNN) distributed energy resources (DERs) estimation forecasting |
| url | https://ieeexplore.ieee.org/document/10555337/ |
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