In response to the evolving dynamics of electric energy substitution and the integration of distributed new energy sources, the distribution network’s supply form and load structure are undergoing significant transformations. This shift has precipitated a notable shortfall in distribution transformer area capacity resources, necessitating upgrades to accommodate the remaining capacity. Traditional solutions, such as expanding transformer capacity, not only escalate distribution network investment but also diminish transformer operating efficiency, inadequately addressing short-term heavy overload issues. As the electricity market liberalizes, an array of flexible resources is becoming available for grid interaction, prompting the development of demand response strategies and methods for flexible load management within distribution networks. This paper introduces a multi-objective optimal scheduling algorithm that incorporates various flexible resources and considers the potential of energy storage devices in transformer virtual capacity augmentation. Through epsilon-constraint method, a Pareto solution set is derived, from which a compromise solution is selected based on minimal deviation from the origin principle. The methodology demonstrates the feasibility of achieving dynamic transformer capacity augmentation across different areas, while balancing system regulation costs and customer satisfaction loss.