Input-convex neural network modeling for battery optimization in power systems
Omidi, Arash
Omidi, Arash
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Abstract
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.
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Graduate
Date
2025-06-02