Malaysia’s National Energy Transition Roadmap (NETR) aims for net-zero carbon emissions by 2050, emphasizing greater adoption of photovoltaic (PV) systems coupled with battery energy storage systems (BESS). However, many existing PV–BESS installations use static, rule-based controls that do not adapt to fluctuating solar input, demand, or tariffs, leading to underutilized storage and reduced overall efficiency. In this study, four control modes – General, Peak Shaving, Economic, and an Adaptive Hybrid strategy – were tested on a 5 kW PV–10.24 kWh battery system under Malaysia’s commercial time-of-use tariff. One year of real operational data was utilized to simulate each mode’s performance in terms of grid energy usage, electricity cost, and carbon dioxide emissions. All proposed modes improved performance over a grid-only baseline. Notably, the Adaptive Hybrid mode reduced grid energy consumption by ~16.8%, cut electricity costs by ~9.7%, and lowered CO₂ emissions by ~16.8% compared to the baseline. These results demonstrate that adaptive control algorithms can significantly enhance renewable energy utilization and cost savings in PV–BESS systems. The findings support Malaysia’s sustainable energy goals by highlighting the impact of intelligent energy management in commercial building applications.

Malaysia’s National Energy Transition Roadmap (NETR) aims for net-zero carbon emissions by 2050, emphasizing greater adoption of photovoltaic (PV) systems coupled with battery energy storage systems (BESS). However, many existing PV–BESS installations use static, rule-based controls that do not adapt to fluctuating solar input, demand, or tariffs, leading to underutilized storage and reduced overall efficiency. In this study, four control modes – General, Peak Shaving, Economic, and an Adaptive Hybrid strategy – were tested on a 5 kW PV–10.24 kWh battery system under Malaysia’s commercial time-of-use tariff. One year of real operational data was utilized to simulate each mode’s performance in terms of grid energy usage, electricity cost, and carbon dioxide emissions. All proposed modes improved performance over a grid-only baseline. Notably, the Adaptive Hybrid mode reduced grid energy consumption by ~16.8%, cut electricity costs by ~9.7%, and lowered CO₂ emissions by ~16.8% compared to the baseline. These results demonstrate that adaptive control algorithms can significantly enhance renewable energy utilization and cost savings in PV–BESS systems. The findings support Malaysia’s sustainable energy goals by highlighting the impact of intelligent energy management in commercial building applications.