Meteorologically-Informed Spatial Planning of European PV Deployment to Reduce Multiday Generation Variability

Dirk Mühlemann*, Doris Folini, Stefan Pfenninger, Martin Wild, Jan Wohland

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
30 Downloads (Pure)

Abstract

Renewable generation variability over multiple days is a key challenge in decarbonizing the European power system. Weather regimes are one way to quantify this variability, but so far, their applications to energy research have focused on wind power generation in winter. However, the projected growth of solar photovoltaic (PV) capacity implies that its absolute variability across the continent will grow substantially. Here we combine weather regimes based on ERA5 reanalysis data with country-specific capacity factors to investigate multiday PV generation variability in Europe. With current installed capacity (131 GW), total PV production in Europe (52.3 GW) varies by 0.9 GW on average, with a maximum change of 3.0 GW, upon transition from one weather regime to another. Using projected PV capacity for 2050 (1.94 TW), variability would rise to 13.9 and 43.8 GW. We present optimized spatial distributions of capacity additions in three scenarios that substantially reduce variability by up to 40%. One of them ascertains a large local PV production, thereby minimizing the need for long-range power transmission while still reducing variability by about 30%, highlighting that optimized siting and local generation can be reconciled. Our results emphasize the value of leveraging climate information in decarbonizing power systems.
Original languageEnglish
Article numbere2022EF002673
JournalEarth's Future
Volume10
Issue number7
DOIs
Publication statusPublished - 2022

Keywords

  • Europe
  • photovoltaics
  • variability
  • weather regimes

Fingerprint

Dive into the research topics of 'Meteorologically-Informed Spatial Planning of European PV Deployment to Reduce Multiday Generation Variability'. Together they form a unique fingerprint.

Cite this