Distributed PV generation estimation using multi-rate and event-driven Kalman kriging filter
The ever-growing penetration of cost-effective photovoltaic (PV) panels within the distribution grid requires a robust and efficient method for PV system monitoring. Especially, the geographical proximity of PV panels can play an important role in lowering the dimension of measurements required for...
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doaj-ce0234f4a4b9458eb7f913c826fcd3652021-04-02T13:26:56ZengWileyIET Smart Grid2515-29472020-03-0110.1049/iet-stg.2018.0246IET-STG.2018.0246Distributed PV generation estimation using multi-rate and event-driven Kalman kriging filterSM Shafiul Alam0Anthony R. Florita1Anthony R. Florita2Bri-Mathias Hodge3Power Systems Engineering Center, National Renewable Energy LaboratoryPower Systems Engineering Center, National Renewable Energy LaboratoryPower Systems Engineering Center, National Renewable Energy LaboratoryPower Systems Engineering Center, National Renewable Energy LaboratoryThe ever-growing penetration of cost-effective photovoltaic (PV) panels within the distribution grid requires a robust and efficient method for PV system monitoring. Especially, the geographical proximity of PV panels can play an important role in lowering the dimension of measurements required for full system observability. Furthermore, the direct impact of variable cloud formation and uncertain propagation necessitates the development and validation of a spatiotemporal model. Accordingly, this study presents the modelling and validation of the spatiotemporal variability of solar power indices at 1 minute resolution for the scale of a residential neighbourhood. The spatiotemporal model is then applied to a Multi-Rate and Event-DRIven Kalman Kriging (MREDRIKK) filter to dynamically estimate behind-the-meter PV generation. The Kriging step exploits spatial correlations to estimate PV power output at locations from where measurements are unobserved. The multi-rate feature of the MREDRIKK filter enables the sampling of measurements at a rate much lower than the temporal dynamics of the associated states. A comprehensive study is undertaken to investigate the effect of multi-rate and event-driven measurement updates on the performance of the MREDRIKK filter. In addition, the superior performance of MREDRIKK filter is represented as compared to the persistence method irrespective of the observation size.https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2018.0246statistical analysissolar powerkalman filtersphotovoltaic power systemsdistributed power generationtime 1.0 minevent-driven kalman kriging filterpv panelsgeographical proximitypv system monitoringrobust methoddistribution gridcost-effective photovoltaic panelsevent-driven kalman kriging filterdistributed pv generation estimationevent-driven measurement updatesmredrikk filtermultirate featurepv power outputkriging stepbehind-the-meter pv generationsolar power indicesspatiotemporal variabilitymodelling validationspatiotemporal modeluncertain propagationvariable cloud formation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
SM Shafiul Alam Anthony R. Florita Anthony R. Florita Bri-Mathias Hodge |
spellingShingle |
SM Shafiul Alam Anthony R. Florita Anthony R. Florita Bri-Mathias Hodge Distributed PV generation estimation using multi-rate and event-driven Kalman kriging filter IET Smart Grid statistical analysis solar power kalman filters photovoltaic power systems distributed power generation time 1.0 min event-driven kalman kriging filter pv panels geographical proximity pv system monitoring robust method distribution grid cost-effective photovoltaic panels event-driven kalman kriging filter distributed pv generation estimation event-driven measurement updates mredrikk filter multirate feature pv power output kriging step behind-the-meter pv generation solar power indices spatiotemporal variability modelling validation spatiotemporal model uncertain propagation variable cloud formation |
author_facet |
SM Shafiul Alam Anthony R. Florita Anthony R. Florita Bri-Mathias Hodge |
author_sort |
SM Shafiul Alam |
title |
Distributed PV generation estimation using multi-rate and event-driven Kalman kriging filter |
title_short |
Distributed PV generation estimation using multi-rate and event-driven Kalman kriging filter |
title_full |
Distributed PV generation estimation using multi-rate and event-driven Kalman kriging filter |
title_fullStr |
Distributed PV generation estimation using multi-rate and event-driven Kalman kriging filter |
title_full_unstemmed |
Distributed PV generation estimation using multi-rate and event-driven Kalman kriging filter |
title_sort |
distributed pv generation estimation using multi-rate and event-driven kalman kriging filter |
publisher |
Wiley |
series |
IET Smart Grid |
issn |
2515-2947 |
publishDate |
2020-03-01 |
description |
The ever-growing penetration of cost-effective photovoltaic (PV) panels within the distribution grid requires a robust and efficient method for PV system monitoring. Especially, the geographical proximity of PV panels can play an important role in lowering the dimension of measurements required for full system observability. Furthermore, the direct impact of variable cloud formation and uncertain propagation necessitates the development and validation of a spatiotemporal model. Accordingly, this study presents the modelling and validation of the spatiotemporal variability of solar power indices at 1 minute resolution for the scale of a residential neighbourhood. The spatiotemporal model is then applied to a Multi-Rate and Event-DRIven Kalman Kriging (MREDRIKK) filter to dynamically estimate behind-the-meter PV generation. The Kriging step exploits spatial correlations to estimate PV power output at locations from where measurements are unobserved. The multi-rate feature of the MREDRIKK filter enables the sampling of measurements at a rate much lower than the temporal dynamics of the associated states. A comprehensive study is undertaken to investigate the effect of multi-rate and event-driven measurement updates on the performance of the MREDRIKK filter. In addition, the superior performance of MREDRIKK filter is represented as compared to the persistence method irrespective of the observation size. |
topic |
statistical analysis solar power kalman filters photovoltaic power systems distributed power generation time 1.0 min event-driven kalman kriging filter pv panels geographical proximity pv system monitoring robust method distribution grid cost-effective photovoltaic panels event-driven kalman kriging filter distributed pv generation estimation event-driven measurement updates mredrikk filter multirate feature pv power output kriging step behind-the-meter pv generation solar power indices spatiotemporal variability modelling validation spatiotemporal model uncertain propagation variable cloud formation |
url |
https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2018.0246 |
work_keys_str_mv |
AT smshafiulalam distributedpvgenerationestimationusingmultirateandeventdrivenkalmankrigingfilter AT anthonyrflorita distributedpvgenerationestimationusingmultirateandeventdrivenkalmankrigingfilter AT anthonyrflorita distributedpvgenerationestimationusingmultirateandeventdrivenkalmankrigingfilter AT brimathiashodge distributedpvgenerationestimationusingmultirateandeventdrivenkalmankrigingfilter |
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1721565065658761216 |