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This paper studies the critical topic of optimal sizing of energy resources, focusing specifically on the configuration of storage system solutions within a microgrid framework. A microgrid is a localized energy system that can operate independently or in conjunction with the main power grid, and it is increasingly recognized for its potential to enhance energy resilience, efficiency, and sustainability. In this study, we examine a microgrid that integrates three key components: photovoltaic (PV) systems, battery storage, and fuel cell (FC) systems. Each of these technologies plays a vital role in the overall energy management strategy of the microgrid. In addition to installation costs, we also focus on minimizing net present costs (NPC), which encompass the total cost of ownership over the lifespan of the energy systems, including initial capital expenditures, operational and maintenance costs, and any potential revenue from energy sales or savings. By carefully analyzing the trade-offs between different system configurations, we seek to identify the most economically viable options. Another critical metric we consider is the loss of load expectation (LOLE), which quantifies the reliability of the energy supply. LOLE represents the expected number of hours per year during which the energy demand exceeds the available supply. By minimizing LOLE, we enhance the reliability and stability of the microgrid, ensuring that it can meet the energy needs of its users even during periods of high demand or low generation. Through a comprehensive analysis of these factors, this paper aims to provide valuable insights into the optimal sizing of energy resources within microgrids. By leveraging advanced modeling techniques and optimization algorithms, we seek to identify configurations that not only reduce costs but also enhance the overall performance and reliability of hybrid energy systems. Ultimately, our findings will contribute to the ongoing efforts to develop sustainable and resilient energy solutions that can meet the challenges of a rapidly changing energy landscape. The teaching-learning-based optimization (TLBO) metaheuristic algorithm is employed for configuring microgrids. Results from the algorithm demonstrate that TLBO offers a more effective resource configuration than alternative approaches.