The deployment of hybrid renewable energy systems is increasing due to their capacity to lower both greenhouse gas emissions and energy expenses. However, their efficiency and reliability are impacted by various parameters such as soiling, temperature, and fluctuating solar irradiation levels. This system proposes a comprehensive approach that utilizes a hybrid Digital Twin implementation, including the PV system and battery, to enhance the efficiency of a photovoltaic-battery system under changing circumstances. The proposed approach involves soiling modeling using linear regression and maximum power point tracking (MPPT) using incremental conductance to improve harvesting of energy. In addition, a novel Sustainable Energy Management and Optimization (SEMO) control algorithm is introduced, which prioritizes charging the battery with the power output of the PV system while maintaining microgrid stability. The proposed approach was validated through simulation experiments, which showed a significant improvement in the energy yield and battery lifespan under different scenarios. It was observed that the PV output was reduced by 6% for a single panel, considering a 30% soiling level. The hybrid digital twin implementation enables active monitoring and control of the system, whereas SEMO algorithm ensures optimal use of available resources. Moreover, the incorporation of the soiling effect model and MPPT techniques provided a robust solution to the challenges posed by low solar irradiance.