Impact of Shading on Energy Management Strategies

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.