Optimization framework for efficient and robust renewable energy hub operation.

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.