Mitigation of PV Variability Using Adaptive Moving Average Control

Variability of the power generated by photovoltaics due to passing clouds can cause instability in the utility grid. This variability can be mitigated by control strategies which include the integration of energy storage systems. This article proposes a combination of two novel methods, termed here as adaptive moving average control and adaptive state of charge control, to mitigate variability using energy storage. The adaptive moving average control uses an adjustable window size based on a variability metric quantified by a moving standard deviation of the input power, leading to improved mitigation. The adaptive state of charge control maintains flexibility of the storage system by keeping its state of charge close to a reference. This proposed combination reduces storage utilization during times of low variability, and improves the trade-off between the degree of smoothness achieved and the capacity requirement of the storage system. The advantage of the proposed strategy with respect to traditional algorithms is first presented in simulation. The result is further verified experimentally in a field test by using a battery energy storage system controlled by agents written for the Eclipse VOLTTRON platform.