Refined update frequency selection in multi-agent systems

Abstract

The evolution of microgrids stands as a contemporary global phenomenon, showcasing remarkable adaptability in harnessing energy from renewable sources. Integration of simulation capabilities, particularly through co-simulation, enhances the flexibility in designing and testing energy systems. Multi-agent systems further augment operational efficiency by facilitating seamless communication among intelligent agents and converters, enabling autonomous decision-making. In this study, it is employed co-simulation in a DC micro-grid comprising simulated power converters in Power Simulator Powersim©(PSIM) and a Python-based multi-agent system. The addition of new elements may increase data traffic towards the multi-agent system, potentially impacting system stability. The primary contribution of this work lies in exploring the simulation time within the system environment and examining how the update rate of the multi-agent system by agents influences overall system performance. Through this analysis, light is shed on the intricacies of system scalability and performance under varying conditions, offering insights for the advancement of microgrid technologies.