Safe Bayesian Optimization for Data-Driven Power Electronics Control Design in Microgrids: From Simulations to Real-World Experiments

Abstract

Micro- and smart grids (MSG) play an important role both for integrating renewable energy sources in electricity grids and for providing power supply in remote areas. Modern MSGs are largely driven by power electronic converters due to their high efficiency and flexibility. Controlling MSGs is a challenging task due to requirements of power availability, safety and voltage quality within a wide range of different MSG topologies resulting in a demand for comprehensive testing of new control concepts during their development phase. This applies, in particular, to data-driven control approaches such as reinforcement learning, of which the stability and operating behavior can hardly be evaluated on an analytical basis. Therefore, the OpenModelica Microgrid Gym (OMG) package, an open-source software toolbox for the simulation and control optimization of MSGs, is proposed. It is capable of modeling and simulating arbitrary MSG topologies and offers a Python-based interface for plug & play controller testing. In particular, the standardized OpenAI Gym interface allows for easy data-driven control optimization. The usage and benefits of OMG for designing and testing data-driven controllers are demonstrated utilizing Bayesian optimization. Both the current and voltage control loops of a voltage source inverter operating in standalone, grid-forming mode for a remote MSG are automatically tuned given an uncertain application environment. Finally, the transfer to real-world laboratory experiments is successfully demonstrated.