  Batteries can effectively improve the security of energy systems and mitigate
climate change by facilitating wind and solar power. The installed capacity of
battery energy storage system (BESS), mainly the lithium ion batteries are
increasing significantly in recent years. However, the battery degradation
cannot be accurately quantified and integrated into energy management system
with existing heuristic battery degradation models. This paper proposed a
hierarchical deep learning based battery degradation quantification (HDL-BDQ)
model to quantify the battery degradation given scheduled BESS daily
operations. Particularly, two sequential and cohesive deep neural networks are
proposed to accurately estimate the degree of degradation using inputs of
battery operational profiles and it can significantly outperform existing fixed
or linear rate based degradation models as well as single-stage deep neural
models. Training results show the high accuracy of the proposed system.
Moreover, a learning and optimization decoupled algorithm is implemented to
strategically take advantage of the proposed HDL-BDQ model in
optimization-based look-ahead scheduling (LAS) problems. Case studies
demonstrate the effectiveness of the proposed HDL-BDQ model in LAS of a
microgrid testbed.
