An alternate optimization method for energy hub siting and sizing considering supply-demand balance

Energy hubs （EHs） can effectively facilitate the consumption of renewable energy with high uncertainty. Still， the existing EH planning methods have not fully considered the correlation between siting and sizing. Hence， this study introduces an alternate optimization method considering the supply-demand balance. Firstly， utilizing graph theory and weighted similarity， the study defines the energy moment （EM）， a concept that represents the energy transmission cost， and proposes an optimal partitioning criterion for energy internet system with minimum total EM as the goal. Subsequently， the supply-demand balance index within each EH is calculated based on the time-series energy interaction information of the energy supply and demand entities. The paper then proposes a sizing strategy for multi-energy coupling units within EH， aiming to maximize the average supply-demand balance of the entire energy internet system. On this basis， the paper constructs an alternate optimization model for EH siting and sizing based on K-means clustering， and achieves an optimal solution through iteration of EHs. Finally， case studies are presented， and their results indicate that the proposed method can significantly improve the accuracy and economy of EH planning.