Open AccessMeteorologically‐Informed Spatial Planning of European PV Deployment to Reduce Multiday Generation Variability
Author(s) -
Mühlemann Dirk,
Folini Doris,
Pfenninger Stefan,
Wild Martin,
Wohland Jan
Publication year - 2022
Publication title -
earth's future
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.641
H-Index - 39
ISSN - 2328-4277
DOI - 10.1029/2022ef002673
Subject(s) - environmental science , photovoltaic system , renewable energy , software deployment , spatial variability , electricity generation , climate change , solar power , nameplate capacity , meteorology , range (aeronautics) , power (physics) , computer science , geography , engineering , statistics , mathematics , ecology , physics , quantum mechanics , electrical engineering , biology , operating system , aerospace engineering
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.