Input-convex neural network modeling for battery optimization in power systems

Battery energy storage systems (BESS) play an increasingly vital role in integrating renewable generation into power grids due to their ability to dynamically balance supply. Grid-tied batteries typically employ power converters, where part-load efficiencies vary non-linearly. This non- linearity poses challenges for optimization, particularly in ensuring computational tractability. In this poster, a data-driven approach is introduced with the input-convex neural network (ICNN) to approximate the nonlinear efficiency with a convex function. This relaxed ICNN method is applied to battery optimization problems. Specifically, ICNN-based method appears to be promising for future battery optimization with desirable feasibility and optimality outcomes.