<p>Modernization trends are transforming electric power distribution, driven by technological advancements and environmental responsibility. These changes include the rise of distributed generation (DG), microgrids, energy storage, and demand-side management. This research develops an optimal scheduling framework for a distribution microgrid, incorporating various resources, including photovoltaic (PV), wind turbines (WT), micro-turbines (MT), fuel cells (FC), load management, and a reserve provision mechanism. A multi-objective optimization model is formulated, considering uncertainties in load, electricity prices, and renewable DG output. The model is solved using a multi-objective Particle Swarm Optimization (MOPSO) algorithm, which is well-suited for its fast convergence and ability to efficiently identify the Pareto front, providing a set of optimal trade-offs between cost and emissions. To evaluate the effectiveness of the proposed approach, a sample microgrid with industrial and residential loads is examined under four scenarios: (1) DG using MT and FC, (2) integration of renewable energy sources (PV and WT), (3) incorporation of energy storage systems (ESS), and (4) implementation of advanced load and reserve management strategies. The results demonstrate that compared to Scenario 1, integrating renewables in Scenario 2 reduces energy costs by up to 6.67% and emissions by 14.29%. Scenario 3, with energy storage, further lowers costs by 16.67% and emissions by 33.33%. The most comprehensive approach, Scenario 4, achieves the highest efficiency, reducing energy costs by 68.75% and emissions by 77.78%. These findings highlight the significant advantages of the proposed optimization framework in enhancing both economic and environmental performance, making it a highly effective tool for sustainable microgrid operation.</p>