A scenario-based approach for optimal operation of energy hub under different schemes and structures

Because of economic and environmental issues' importance in the energy field, moving toward smart energy systems and energy hubs (EHs) has accelerated. The impacts of uncertainties, e.g., stochastic behaviors of renewable distributed generations (DGs), on EHs are fundamental challenges that should be considered carefully. Although several studies have been done in the area of EHs, a knowledge gap exists about developing an approach considering uncertainties under different EHs' structures and topologies. This research purposes of responding to such a research gap. In this research, a scenario-based approach for EHs' optimal operation considering wind turbine (WT) and photovoltaic (PV) uncertainties is proposed. The proposed approach is applied to EH under different schemes. Using the k-means clustering algorithm decreases the computational burden, while the appropriate accuracy is achievable. The proposed stochastic optimization problem is solved using the genetic algorithm (GA). The comparative view is considered to investigate the impacts of cooling and heating components like the heat pump (HP), absorption chiller (AC), and heat storage (HS) on EH's optimal operation and energy cost. According to this research findings, the EH's daily energy cost under a scheme using the AC, HP, and HS is approximately 6.5% less than a scheme only using HP. Also, using the HS and HP alongside the AC leads to 5.6% and 6.4% cost-saving, respectively. But for a better comparison, the investment and operation and maintenance (O&M) cost are considered, in which case Structure 3 (AC + HP) is more efficient both in terms of energy consumption and investment costs.