Distributed PV generation estimation using multi‐rate and event‐driven Kalman kriging filter. Issue 4 (23rd June 2020)

• Record Type: Journal Article
• Authors: Alam, SM Shafiul; Florita, Anthony R.; Hodge, Bri‐Mathias
• Is Part Of: IET smart grid. Volume 3:Issue 4(2020)
• Page Start: 538
• Page End: 546
• Publication Date: 2020-06-23
• Subjects: 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; B0240Z Other topics in statistics -- B8250 Solar power stations and photovoltaic power systems -- B8120K Distributed power generation; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2018.0246
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : 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.