Modeling and optimization for distributed microgrid based on Modelica language

Abstract Penetration rates of intermittent renewables increase in smart grid due to environmental issues. As a significant part of smart grid, distributed microgrids (DMGs) have huge application prospects for its flexibility, high efficiency and fast recovery ability. In order to improve the effective penetration of renewable energy in distributed microgrids at low cost, we proposed a dynamic model-based configuration and scheduling coupling optimization method in this study. We established the multi-domain joint simulation model for DMGs based on the Modelica acausal modeling language to realize the coupling of distributed generation (DG), energy conversion/storage components and user loads with multiscale time-varying characteristics, and validated its accuracy by using the measured data. On the basis of the model established above, branch and bound method and meta-heuristic algorithm are used to optimize the double layer configuration and scheduling of microgrids in four typical scenarios. Moreover, a novel evaluating indicator, the effective penetration of renewable energy (EPRE), is proposed to improve the system self-utilization rate of renewable energy. Study results show that by using the newly developed model-based optimization method with the new indicator, the self-utilization rate in DMGs can be increased up to 58.05% and the average daily interactive power of electricity fed back to the regional grid is reduced significantly.