一种用于直流微电网的多层级家庭能源管理系统

It is essential for Home Energy Management Systems (HEMSs) to minimize the system operating cost while maintaining the user comfort under forecasting uncertainties of solar and electricity load demand. However, the existing HEMS excessively relies on a single battery system and may not effectively assess user comfort. To this end, a hierarchical HEMS, i.e., system- and local-level, is proposed in this article to coordinate the dispatch of home resources including battery energy storages and supercapacitors (SC). The system-level HEMS consists of long-term (LT) and short-term (ST) optimization based on Model Predictive Control (MPC). The LT optimization optimizes resource dispatch by using forecasted load and solar generation to minimize house operating costs and maximize the user comfort. The ST layer one is proposed to track the optimal power scheduling to minimize the cost error, refine the dispatch of resources and ensure a safe operational level of hybrid energy storage systems including the SC. The SC is employed to compensate the transient power and alleviate the battery degradation effects. The local-level HEMS is used to achieve DC voltage restoration, power sharing, voltage recovery of SC and state of charge (SoC) balance between batteries. The interaction between system- and local-level is also discussed. By using the dataset from NREL and Ameren Illinois Company, the test results show that this methodology can potentially reduce the system operating cost by 4.3500%, 7.7600%, and 37.7253% compared to the other single and multi-layer HEMSs.