MATLAB/Simulink Simulation model accompanying publication: Distributed MPC for Cost-Optimal Control of FC-Battery Shipboard Microgrids

The provided optimization scripts were used in the case study and creation of results, accompanying the publication: Degradation-aware Predictive Energy Management for Fuel Cell-Battery Ship Power System with Data-driven Load Forecasting
The scripts are made public to act as supplementary data to the published article, recreation of results and to support other researchers for further developments in the field.
The provided code implements a plant model of a modular FC-battery ship power system with 4xfuel cell and 2xbattery modules with different ratings and degradation states.
The code features a distributed energy management strategy based on Lagrangian dual decomposition to find an optimal power allocation for mid-length time intervals for all modules. The distributed model predictive controller (MPC) is intended to use inputs from data-driven load predictions to optimize the power allocation.
The system can be run in three modes: predictive control/MPC, instantaneous optimization/ECMS, and filter-based control, to compare the performance of different approaches. The load prediction can be selected to assume a constant load, assume a perfect load prediction or use externally provided data-driven load forecasts.
Measurement data and load predictions are not included due to confidentiality, but any arbitrary load profile matching the totally installed power are admissable.
The system and controller parameters can be adjusted in the provided scripts to evaluate the performance of different strategies and settings.