Multi-objective economic energy management strategy in thermal and electrical grids with energy hubs including renewable units and storage systems

Current study presents the sustainable computing-based economic energy scheduling according to reliability and emission management for renewable energy hubs including stationary and mobile storages in electric and thermal networks. Scheme minimizes energy loss, operating cost, unfed energy, and emissions of networks by adopting Pareto formulation according to the weighted sum of functions method as objective function. It incorporates hubs operation formulation and reliability-constrained linearized optimal power flow for networks. Hubs encompass limitations of wind system, solar units, bio-waste facilities, hydrogen and thermal storage, and electric vehicles. Bio-waste system generates electrical and thermal energy. There is uncertainty situation for energy pricing, renewable power, load, mobile storage, and network equipment availability. Study considers hybrid stochastic-adaptive robust optimization to effectively model uncertainties. It obtains sustainable computing-based robust operation for hubs in the face of uncertainty prediction errors, while simultaneously obtaining accurate value for reliability index and minimizing computational time. Findings of studies confirm effectiveness of this design in enhancing environmental, reliability, economic and operational performance of networks through implementation of suitable energy management strategies for the identified hubs. Sustainable computing-based optimal scheduling for stationary and mobile storage devices in renewable hubs resulted in a significant improvement of 27%-53% in operational performance, 35% in economic situation, 94% in environmental state, and 69.9% in reliability performance of energy networks, as compared to power flow analyses conducted under worst-case scenarios. Study demonstrates that the utilization of storage in renewable hubs enhances operational efficiency, thereby boosts resilience against a maximum prediction error of uncertainties, estimated at 45%.