Photovoltaic (PV) power generation is inherently intermittent, as it depends on variable weather conditions, leading to output fluctuations that can compromise grid stability. To address this challenge, this study proposes a real-time PV output smoothing approach based on an Alpha-Beta filter algorithm. The method forecasts short-term power variations and dynamically regulates the charging and discharging of a Battery Energy Storage System (BESS), intervening only when significant deviations occur. This selective control reduces unnecessary cycling, improves State of Charge (SOC) management, and extends battery lifespan. Unlike conventional smoothing techniques, the proposed framework maintains the original PV generation profile while mitigating disruptive fluctuations. Implemented in Python and evaluated using OpenDSS simulations with real PV data from a high-penetration microgrid, the method achieved a 91.7% reduction in power variation and maintained voltage stability within regulatory limits. These results demonstrate the effectiveness and practicality of the Alpha-Beta filter-based strategy for stabilizing PV output and optimizing energy storage operation in distributed energy systems.

This paper aims to solve the minimum annual electricity bill problem of a campus microgrid with distributed generation and a battery energy storage system via the proposed optimal hybrid approach with sparrow search algorithm (SSA) and bin packing method (BPM). First, the hourly, daily, monthly, and annual electricity bill computing modules were built. The historical electrical consumption and photovoltaic generation were analyzed to statistic the net load distribution and duration over one year. Next, the time-of-use rate for high-voltage customers with demand charge and energy charging of Taipower for the high-voltage campus microgrid was formulated as an optimization function and coded in python language. The charging and discharging schedule of the 1 MW/1.26 MWh battery energy storage system (BESS), according to the annual net load, were considered in this minimum annual electricity bill problem. We obtained the optimal contract capacity and the corresponding minimum annual electricity bill. The optimal hybrid approach was applied to solve this optimization problem. The results demonstrate that the proposed method could effectively solve the annual minimum electricity bill problem.