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

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