Comparing Spatio-Temporal Models for Aggregate PV Power Nowcasting

Ruan, Guoping, Chen, Xiaoyang, Du, Yang, Lim, Eng Gee, Fang, Lurui, and Yan, Ke (2022) Comparing Spatio-Temporal Models for Aggregate PV Power Nowcasting. In: Proceedings of the 11th International Conference on Innovative Smart Grid Technologies - Asia, ISGT-Asia 2022. pp. 580-584. From: ISGT Asia 2022: IEEE PES Innovative Smart Grid Technologies, 1-5 November 2022, Singapore.

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The photovoltaic (PV) power fluctuations caused by passing clouds have become a major concern for grid operators. Consequently, utilities are requiring proper treatments to limit the intermittent PV generation. On this point, solar nowcasting provides a remedy by enabling the transition from reactive control to proactive, which often offers remarkable reliability to PV systems. Sensor networks that utilize spatio-temporal models are considered promising for solar nowcasting. However, current studies on sensor network nowcasting have dedicated much to point nowcasts, where the geographic smoothing effect that occurs in aggregate PV systems is generally left out. In this context, this paper presents a comparing study on spatio-temporal models for aggregate PV power nowcasting. Through empirical studies, the forecast skill of spatio-temporal models is found to decrease for a larger PV aggregation. In addition, the spatio-temporal regression shows competitive performance in various scenarios, yielding a priority for practical use.

Item ID: 77650
Item Type: Conference Item (Research - E1)
ISBN: 9798350399660
Keywords: Grid integration, Photovoltaic power, Solar forecasting, Spatio-temporal models
Copyright Information: © 2022 IEEE.
Date Deposited: 28 Feb 2023 23:07
FoR Codes: 40 ENGINEERING > 4008 Electrical engineering > 400808 Photovoltaic power systems @ 100%
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