Renewable energy is identified as a solution for the growing future electricity demand. Photovoltaic (PV) is a leading type of\nrenewable energy source used for electricity generation. Among the PV systems, distributed PV systems are becoming popular\namong the domestic consumers and hence the number of domestic PV installations is on the rise continuously. Intermittent\noutput power variations and inability to use the PV power during the night peak hours are major issues with PV systems. Energy\nstorage is a possible mitigation technique for these issues. In order to effectively utilize local generations, storage, and loads, energy\nmanagement system (EMS) becomes an essential component in future domestic PV installations. EMS for domestic consumers\nneeds to be inexpensive, while a reasonable accuracy level is maintained. In this paper, optimization problem-based EMS and rulebased\nEMS were developed and compared to investigate the accuracy and the processing speed, thereby to select a fast and\naccurate EMS for a domestic PV installation. Furthermore, in the proposed EMS, a day-ahead generation and load profiles are\ngenerated from predictions, and thus the batteryâ??s state of charge (SoC) levels over a day is estimated through the EMS. In order to\nutilize the storage effectively, time-varying local maximum and minimum SoC limits for the battery are introduced, which are\ninside the global maximum and minimum SoC limits. With the aid of real-PV profiles and typical loading profiles, the EMS was\nimplemented using optimization- and rule-based techniques with local SoC limits. The results verified that the rule-based EMS\nproduced accurate results in comparison to optimization-based EMS with lesser processing time. Further results verified that the\nintroduction of local SoC limits improved the performance of the EMS in the unforeseen conditions.
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