Location and solar system parameter extraction from power measurement time series
Abstract Photovoltaic (PV) systems are considered an important pillar in the energy transition because they are usually located near the consumers. In order to provide accurate PV system models, e.g. for microgrid simulation or hybrid-physical forecast models, it is of high importance to know the un...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2021-09-01
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| Series: | Energy Informatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42162-021-00176-2 |
| _version_ | 1819240104037711872 |
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| author | Philipp Danner Hermann de Meer |
| author_facet | Philipp Danner Hermann de Meer |
| author_sort | Philipp Danner |
| collection | DOAJ |
| description | Abstract Photovoltaic (PV) systems are considered an important pillar in the energy transition because they are usually located near the consumers. In order to provide accurate PV system models, e.g. for microgrid simulation or hybrid-physical forecast models, it is of high importance to know the underlying PV system parameters, such as location, panel orientation and peak power. In most open PV generation databases, these parameters are missing or are inaccurate.In this paper, we present a framework based on particle swarm optimisation and the PVWatts model to estimate PV system parameters using only power feed-in measurements and satellite-based ERA5 climate reanalysis data. Our sensitivity analysis points out the most relevant PV system parameters, which are panel and inverter peak power, panel orientation, system location and a small but not negligible influence of ambient temperature and albedo. The detailed evaluation on one exemplary PV system shows an acceptable accuracy in panel azimuth and tilt for the use in microgrid PV system simulation. The extracted location has less than 25 km of positioning error in the best case, which is more than satisfying with respect to the underlying data resolution of the ERA5 dataset. Similar results are observed for 10 systems in Europe and the USA. |
| first_indexed | 2024-12-23T14:02:42Z |
| format | Article |
| id | doaj.art-7f427894d8c342d1a918c9ee4698354c |
| institution | Directory Open Access Journal |
| issn | 2520-8942 |
| language | English |
| last_indexed | 2024-12-23T14:02:42Z |
| publishDate | 2021-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj.art-7f427894d8c342d1a918c9ee4698354c2022-12-21T17:44:16ZengSpringerOpenEnergy Informatics2520-89422021-09-014S312010.1186/s42162-021-00176-2Location and solar system parameter extraction from power measurement time seriesPhilipp Danner0Hermann de Meer1University of PassauUniversity of PassauAbstract Photovoltaic (PV) systems are considered an important pillar in the energy transition because they are usually located near the consumers. In order to provide accurate PV system models, e.g. for microgrid simulation or hybrid-physical forecast models, it is of high importance to know the underlying PV system parameters, such as location, panel orientation and peak power. In most open PV generation databases, these parameters are missing or are inaccurate.In this paper, we present a framework based on particle swarm optimisation and the PVWatts model to estimate PV system parameters using only power feed-in measurements and satellite-based ERA5 climate reanalysis data. Our sensitivity analysis points out the most relevant PV system parameters, which are panel and inverter peak power, panel orientation, system location and a small but not negligible influence of ambient temperature and albedo. The detailed evaluation on one exemplary PV system shows an acceptable accuracy in panel azimuth and tilt for the use in microgrid PV system simulation. The extracted location has less than 25 km of positioning error in the best case, which is more than satisfying with respect to the underlying data resolution of the ERA5 dataset. Similar results are observed for 10 systems in Europe and the USA.https://doi.org/10.1186/s42162-021-00176-2PV modelParameter estimationParticle swarm optimization |
| spellingShingle | Philipp Danner Hermann de Meer Location and solar system parameter extraction from power measurement time series Energy Informatics PV model Parameter estimation Particle swarm optimization |
| title | Location and solar system parameter extraction from power measurement time series |
| title_full | Location and solar system parameter extraction from power measurement time series |
| title_fullStr | Location and solar system parameter extraction from power measurement time series |
| title_full_unstemmed | Location and solar system parameter extraction from power measurement time series |
| title_short | Location and solar system parameter extraction from power measurement time series |
| title_sort | location and solar system parameter extraction from power measurement time series |
| topic | PV model Parameter estimation Particle swarm optimization |
| url | https://doi.org/10.1186/s42162-021-00176-2 |
| work_keys_str_mv | AT philippdanner locationandsolarsystemparameterextractionfrompowermeasurementtimeseries AT hermanndemeer locationandsolarsystemparameterextractionfrompowermeasurementtimeseries |