About Optimal discharge depth of energy storage battery
In this study, we investigated a BESS management strategy based on deep reinforcement learning that considers depth of discharge and state of charge range while reducing the total operating cost.
In this study, we investigated a BESS management strategy based on deep reinforcement learning that considers depth of discharge and state of charge range while reducing the total operating cost.
Battery energy storage (BES) plays an important role for mitigation of microgrids power imbalance induced by the intermittency of renewable sources and load cha.
Based on these results, an optimal battery control method for improving the battery stability and energy efficiency is proposed.
The proposed model in this paper includes the Depth of Discharge (DOD) of battery through the determination of battery life loss cost.
Extend lithium battery lifespan by managing Depth of Discharge (20%-80%), using BMS to prevent over-discharge, and adopting partial charging strategies.
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