Journal article · Ahead of Print article
On the simulation of aggregated solar PV forecast errors
The uncertainty arising from high levels of solar photovoltaic (PV) penetration can have a substantial impact on power system operation. Therefore, there is a need to develop models capable of representing PV generation in a rigorous manner. This paper introduces a novel transformation-based methodology to generate stochastic solar area power forecast scenarios; easy to apply to new locations.
We present a simulation study comparing day-ahead solar forecast errors covering regions with different geographical sizes, total installed capacities and climatic characteristics. The results show that our model can capture the spatio-temporal properties and match the long-term statistical properties of actual data.
Hence, it can be used to characterize the PV input uncertainty in power system studies.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2018 |
| Pages: | 1889-1898 |
| ISSN: | 19493037 and 19493029 |
| Types: | Journal article and Ahead of Print article |
| DOI: | 10.1109/TSTE.2018.2818727 |
| ORCIDs: | Koivisto, Matti Juhani , Cutululis, Nicolaos Antonio , Sorensen, Poul and 0000-0003-0978-7711 |
Autoregressive processes Forecasting Power system simulation Power systems Predictive models Production SDG 7 - Affordable and Clean Energy Solar power generation Stochastic processes Time series analysis Uncertainty
