Impact of Shading on Energy Management Strategies
DOI:
https://doi.org/10.52825/pv-symposium.v2i.2656Keywords:
Energy Management Strategies, Forecasting, Machine Learning, Energy Storage SystemsAbstract
The rapid expansion of photovoltaics (PV) requires intelligent operational strategies for energy storage systems. These strategies help manage PV surpluses during peak generation periods, reducing grid stress while optimising storage system performance. By minimising long periods of high state of charge and maintaining efficient operating conditions, such strategies also help to extend the lifetime of storage systems. However, effective implementation requires accurate forecasting of both PV generation and load demand. This study investigates the impact of forecast errors caused by shading, in particular by stationary objects, on storage system operation. An adaptive forecasting approach that dynamically adapts to shading conditions was developed and compared to a non-adaptive method. The analysis, based on real PV generation and load data over ten days, showed that the adaptive approach reduced grid consumption by 24% and the time spent at high state of charge by 29%. These results highlight the potential of adaptive prediction models to improve the efficiency and durability of storage systems.
Downloads
References
M.J. Mayer, G. Gróf, “Extensive comparison of physical models for photovoltaic power forecasting,” Energy, vol. 283, p. 116239, 2021.
D. Masa-Bote, M. Castillo-Cagigal, E. Matallanas, E. Caamaño Martín, A. Gutiérrez, F. Monasterio-Huelín, J. Jiménez-Leube, “Improving photovoltaics grid integration through short time forecasting and self-consumption,” Applied Energy, vol. 125, pp. 103-110, 2014.
S. Meisenbacher et al., “AutoPV: An Automated Approach to PV Forecasting,” Solar Energy, vol. 210, pp. 252-263, 2020.
J. Weniger, J. Bergner, V. Quaschning, “Integration of PV Power and Load Forecasts into the Operation of Residential PV Battery Systems,” 2014.
P. Keil et al., “Calendar Aging of Lithium-Ion Batteries I. Impact of the Graphite Anode on Capacity Fade,” Journal of the Electrochemical Society, vol. 163, no. 9, pp. A1872–A1880, 2016.
J. Barry, J. Thomas, “Online and Offline PV Power Forecasts for Optimal Control of Storage Systems,” 33rd European Photovoltaic Solar Energy Conference and Exhibition, 2017.
Palaniswamy, Lakshimi Narayanan & Munzke, Nina & Kupper, Christian & Hiller, Marc. (2023). Optimized Energy Management of a Solar and Wind Equipped Student Residence with Innovative Hybrid Energy Storage and Power to Heat Solutions. 10.2991/978-94-6463-156-2_24.
L. Strobel, B. Schwarz, N. Munzke, and M. Hiller, “Optimized energy management of a photovoltaic-heat pump sector coupling system with electrical and thermal energy storages in an office building,” 8th International Hybrid Power Plants & Systems Workshop (HYB 2024), Azores, Portugal, 2024.
A. Stein, A. S. Starosta, B. Schwarz, N. Munzke, and M. Hiller, "Comparison of Small EV Charging Station’s Load Forecasts and its Impact on the Operational Costs," Proceedings of the International Conference on Smart Energy Systems and Technologies (SEST 2023), Muğla, Turkey, September 2023, 6 pages. DOI: 10.1109/SEST57387.2023.10257538
Wei Jiang, Zhongkai Yi, Li Wang, Hanwei Zhang, Jihai Zhang, Fangquan Lin, and Cheng Yang. 2023. A Stochastic Online Forecast-and-Optimize Framework for Real-Time Energy Dispatch in Virtual Power Plants under Uncertainty. DOI: 10.1145/3583780.3614653
D. Masa-Bote, M. Castillo-Cagigal, E. Matallanas, A. Gutiérrez, F. Monasterio-Huelin, and J. Jiménez-Leube, "Improving photovoltaics grid integration through short time forecasting and self-consumption," Applied Energy, vol. 125, pp. 103–113, 2014.
O. Gandhi, W. Zhang, D. S. Kumar, C. D. Rodríguez-Gallegos, G. M. Yagli, D. Yang, T. Reindl, and D. Srinivasan, "The value of solar forecasts and the cost of their errors: A review," Renewable and Sustainable Energy Reviews, vol. 189, p. 113915, 2024. DOI: 10.1016/j.rser.2023.113915.
A. Einfalt, A. Schuster, C. Leitinger, D. Tiefgraber, M. Litzlbauer, S. Ghaemi, D. Wertz, A. Frohner, und C. Karner, “Konzeptentwicklung für ADRES – Autonome Dezentrale Erneuerbare Energie Systeme”, Endbericht, Wien, 2012.
C. W. H. a. M. A. M. William F. Holmgren, "pvlib python: a python package for modelingsolar energy systems," Journal of Open Source Software, 03 August 2018.
D. Faiman, "Assessing the outdoor operating temperature of photovoltaic modules," Progress in Photovoltaics, 21 Feb 2008.
Published
How to Cite
Conference Proceedings Volume
Section
License
Copyright (c) 2025 Tim Kappler, Lukas Strobel, Bernhard Schwarz, Nina Munzke, Marc Hiller
This work is licensed under a Creative Commons Attribution 4.0 International License.
Accepted 2025-07-13
Published 2025-08-27
Funding data
-
Bundesministerium für Wirtschaft und Klimaschutz
Grant numbers 03EE1135A