Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems
Considering that most of the photovoltaic (PV) data are behind-the-meter (BTM), there is a great challenge to implement effective demand response projects and make a precise customer baseline (CBL) prediction. To solve the problem, this paper proposes a data-driven PV output power estimation approac...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Forecasting |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-9394/2/4/25 |
| _version_ | 1827703007073009664 |
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| author | Keda Pan Changhong Xie Chun Sing Lai Dongxiao Wang Loi Lei Lai |
| author_facet | Keda Pan Changhong Xie Chun Sing Lai Dongxiao Wang Loi Lei Lai |
| author_sort | Keda Pan |
| collection | DOAJ |
| description | Considering that most of the photovoltaic (PV) data are behind-the-meter (BTM), there is a great challenge to implement effective demand response projects and make a precise customer baseline (CBL) prediction. To solve the problem, this paper proposes a data-driven PV output power estimation approach using only net load data, temperature data, and solar irradiation data. We first obtain the relationship between delta actual load and delta temperature by calculating the delta net load from matching the net load of irradiation for an approximate day with the least squares method. Then we match and make a difference of the net load with similar electricity consumption behavior to establish the relationship between delta PV output power and delta irradiation. Finally, we get the PV output power and implement PV-load decoupling by modifying the relationship between delta PV and delta irradiation. The case studies verify the effectiveness of the approach and it provides an important reference to perform PV-load decoupling and CBL prediction in a residential distribution network with BTM PV systems. |
| first_indexed | 2024-03-10T15:09:26Z |
| format | Article |
| id | doaj.art-d2e81443e78e47dbab190e0660fcd181 |
| institution | Directory Open Access Journal |
| issn | 2571-9394 |
| language | English |
| last_indexed | 2024-03-10T15:09:26Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forecasting |
| spelling | doaj.art-d2e81443e78e47dbab190e0660fcd1812023-11-20T19:31:55ZengMDPI AGForecasting2571-93942020-11-012447048710.3390/forecast2040025Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter SystemsKeda Pan0Changhong Xie1Chun Sing Lai2Dongxiao Wang3Loi Lei Lai4Department of Control Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaDepartment of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaDepartment of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaDepartment of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaDepartment of Control Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaConsidering that most of the photovoltaic (PV) data are behind-the-meter (BTM), there is a great challenge to implement effective demand response projects and make a precise customer baseline (CBL) prediction. To solve the problem, this paper proposes a data-driven PV output power estimation approach using only net load data, temperature data, and solar irradiation data. We first obtain the relationship between delta actual load and delta temperature by calculating the delta net load from matching the net load of irradiation for an approximate day with the least squares method. Then we match and make a difference of the net load with similar electricity consumption behavior to establish the relationship between delta PV output power and delta irradiation. Finally, we get the PV output power and implement PV-load decoupling by modifying the relationship between delta PV and delta irradiation. The case studies verify the effectiveness of the approach and it provides an important reference to perform PV-load decoupling and CBL prediction in a residential distribution network with BTM PV systems.https://www.mdpi.com/2571-9394/2/4/25PV output power estimationPV-load decouplingbehind-the-meter PVbaseline prediction |
| spellingShingle | Keda Pan Changhong Xie Chun Sing Lai Dongxiao Wang Loi Lei Lai Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems Forecasting PV output power estimation PV-load decoupling behind-the-meter PV baseline prediction |
| title | Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems |
| title_full | Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems |
| title_fullStr | Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems |
| title_full_unstemmed | Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems |
| title_short | Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems |
| title_sort | photovoltaic output power estimation and baseline prediction approach for a residential distribution network with behind the meter systems |
| topic | PV output power estimation PV-load decoupling behind-the-meter PV baseline prediction |
| url | https://www.mdpi.com/2571-9394/2/4/25 |
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