<p>This research proposes an advanced optimization framework for renewable energy hubs within integrated electrical and thermal networks, aimed at improving energy management. The motivation stems from the need for a more flexible and efficient solution that addresses the variability of renewable energy sources, such as wind and bio-waste units, while integrating storage solutions like hydrogen and thermal systems. The hypothesis is that combining a market-clearing price model with robust decision-making frameworks can optimize both economic viability and operational efficiency. The methodology adopts a two-tier optimization approach: the upper tier maximizes hub profits, and the lower tier minimizes operational costs through a market-clearing price model. The study also incorporates a robust optimization model that accounts for decision-dependent uncertainties, with a novel class of polyhedral uncertainty sets used for improved decision-making. Numerical results from case studies demonstrate that the proposed method increases the objective function by approximately 3%, and achieves a 25% faster solution time compared to the Benders decomposition approach. These findings support the conclusion that the proposed framework enhances both flexibility and economic performance of energy hubs, offering a viable solution for modern energy systems.</p>