Abstract: The impact of climate change, electricity price fluctuations, and electricity consumption behavior on the flexible regulation boundaries of the load side constrains the economic efficiency of day-ahead energy management strategies in distributed energy systems. To address this issue, a two-stage stochastic optimization method based on the potential for flexible load regulation is proposed. In the first stage, the objective is to minimize the comprehensive operating costs of the distributed energy system, encompassing equipment maintenance, energy procurement, carbon emissions, and compensation costs for flexible electric and thermal loads. The second stage incorporates a compensation mechanism to mitigate deviations in renewable energy output and flexible load regulation boundaries, formulating a dynamic optimization model that accounts for intraday reserve costs. Case study results demonstrate that the proposed method effectively reduces total system operating costs and enhances renewable energy utilization. Further sensitivity analysis indicates that, compared to adjustable electric load uncertainty, adjustable thermal load uncertainty exerts a more pronounced impact on system operation.