The growing adoption of photovoltaic (PV) systems and battery storage in residential areas presents new challenges in optimizing household energy management. Traditional systems often fail to efficiently coordinate energy flow among PV generation, battery storage, and consumption, especially under variable weather and fluctuating demand. This research aims to develop a simulation-based energy management system that maximizes self-consumption of renewable energy through intelligent control algorithms and smart scheduling. The project includes the creation of a configurable simulation framework to test various system parameters, device configurations, and weather conditions. Key objectives include optimizing energy distribution, scheduling high-power loads, and producing data-driven reports to evaluate system performance over defined timeframes. The theoretical framework explores energy modelling, weather impact, optimization strategies, and the role of model-based engineering. The practical component validates the system using real weather data, simulates household consumption patterns, and benchmarks algorithm performance. This work contributes to the advancement of smart energy systems by offering a scalable solution for increasing renewable energy self-consumption in private households.