Optimal micro-grid battery scheduling within a comprehensive smart pricing scheme

The challenge of optimizing battery operating revenue while mitigating aging costs remains inadequately addressed in current literature. This paper introduces a novel cost-benefit approach for scheduling battery energy storage systems (BESS) within microgrids (MGs) that features smart grid attributes. The proposed comprehensive approach accounts for fluctuations of real-time pricing, demand charge tariffs, and battery degradation cost. Using the dynamic programming technique, a novel high-speed BESS scheduling optimization algorithm that incorporates a LiFePO4 battery degradation cost model is developed, achieving substantial monthly operational cost savings for the MG with a fine-grained sampling interval of nine minutes and execution time under one minute. The algorithm utilizes day-ahead forecasts for MG load profiles and photovoltaic output power, enabling the prediction of BESS's optimal power profile a day in advance. The algorithm's rapid execution enables real-time adaptability, allowing BESS scheduling to dynamically respond to grid fluctuations. The proposed approach outperforms existing methods in the literature, delivering MG operational cost savings ranging from 33.6% to 94.8% across various scenarios. Consequently, this approach enhances MG operational efficiency and provides significant cost savings.