Microgrids, characterized by their ability to operate autonomously or in conjunction with the main grid, have emerged as a promising solution for enhancing energy resilience and sustainability. This study presents a comprehensive analysis of an on-grid microgrid system, focusing on the integration of photovoltaic (PV) systems, utility grid reliance, battery storage, and dynamic pricing strategies. Through the evaluation of four distinct operational scenarios, including varying levels of component integration and optimization techniques, we assess the impact on operational costs, energy utilization, and overall system performance. Notably, our analysis highlights the effectiveness of Genetic Algorithm (GA) optimization in reducing operational costs by up to 24% compared to baseline scenarios. Furthermore, dynamic pricing strategies, coupled with real-time energy management, demonstrate significant potential for improving cost-effectiveness and grid stability. The findings underscore the importance of advanced optimization techniques and dynamic pricing strategies in enhancing the economic viability and sustainability of microgrid systems. These insights contribute to the ongoing discourse on efficient energy management and pave the way for the widespread adoption of microgrid solutions in the transition toward a more resilient and sustainable energy future.