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The inherited intermittency of solar photovoltaic (PV) systems impacts the power grid by creating power fluctuations, which are mitigated by the integration of battery energy storage systems (BESS) augmented with a smoothing controller. However, the conventional charging and discharging schemes result in a repetitive and chaotic state of charge (SOC) which might degrade the BESS performance. This paper proposes a reliant monotonic charging/discharging controller (RMCC) to reduce battery degradation while maintaining smoothing efficiency. In this configuration, two battery units operate concurrently, one charging, and the other discharging, which helps to reduce battery cycles and extend BESS lifetime. In addition, the paper presents a smart RMCC (S-RMCC) controller to control the operation of a BESS made up of an even number of battery units greater than two. The controller forecasts the short-term irradiance and identifies the existence of power ramps using a ramp event detector. Hence, the forecasted irradiance is then utilized to obtain the PV output power and to compute the forecasted needed charging and discharging energy to compensate for PV power intermittency. These estimates are then compared to the remaining energy of battery units to determine whether to work with the entire BESS system or a subset of units. The proposed controllers were tested for battery degradation, ramp rate reduction, and power smoothing efficiency. The battery degradation is significantly minimal when using RMCC-operated units, accounting for 0.1877% and 0.1918%, as compared to 9.1514% when using a single BESS for smoothing.

Clouds passing over solar photovoltaic (PV) power system causes power fluctuations, which contributes to power quality issues. Power fluctuations are usually compensated by an energy storage system (ESS) integrated with a filtering or smoothing controller. However, there is a great burden to tune and determine the filtering time constant (TF) that leads to better alleviation of PV power fluctuations because the irradiance variability varies from day to day. Lack of cloud information leads to incorrect selection of TF values which leads to poor smoothing performance when setting low values on cloudy days, or to unnecessary ESS operation when setting high values on clear days. This paper introduces a novel power smoothing framework to avoid the arbitrary selection of TF values and reduce the stress on ESS based on predictive and adaptive smoothing mechanisms. The proposed controller contains two layers, the first layer predicts the next day's irradiance profile and uses a clear sky model to identify the cloud class. Next, the controller sets the initial values of the filter time constants for cloudy, moderate, and mild days, and disables smoothing on clear and overcast days. The second layer adaptively adjusts the initial filter time constant based on the current power ramp rate and shares power for a hybrid energy storage system (HESS). The performance of the proposed controller is tested against the forecasting errors and compared to fixed time constant-based smoothing techniques and ramp rate control in a range of scenarios, including smoothing, ramp rate reduction, and battery degradation assessment.