A Cloud–Fog Based Adaptive Framework for Optimal Scheduling of Energy Hubs

The energy hub has gained a lot of importance as a way to optimize multicarrier energy infrastructure to enhance its flexibility and proficiency. In the presence of energy storage in energy hubs, it is possible to efficiently coordinate energy supply and demand while taking advantage of changing energy tariffs and local renewable energy supply sources. In this article, we propose a novel method based on gray wolf optimization (GWO), which breaks down the complex optimization problem into subproblems, including the storage scheduling and another part in the grid, and comes up with the respective solutions via GWO and interior point. As a result, the proposed method avoids the drawbacks of numerical ways and artificial intelligence techniques, which tend to converge only to a local minimum or require heavy computational times. Furthermore, the article proposes a multilayer cloud computing framework for optimally scheduling energy hubs. The present study applies the new technique to a two-hub and three-hub system during 24 h. The approach is also examined in the context of an 11-hub system to investigate the efficiency and potential applications. The outcomes prove that the approach can achieve a very near-global point, which is confirmed through an analytical method, as well as being rapid sufficient to permit for online implementations with receding time horizons.