Collaborative modeling and optimization of energy hubs and multi-energy network considering hydrogen energy

To achieve the goal of carbon neutrality, integrated energy hubs (EHs) based on coupled energy sources such as electricity, gas and heat are gaining increasing attention due to their efficient and flexible energy supply characteristics. The introduction of integrated EHs may lead to the reconstruction of the existing superior energy network to accommodate the suitable flow of the new network. In this study, a multi-objective optimization model is proposed that synergistically considers the optimal configuration of EHs and the reconstruction of higher-level energy networks, with the objectives of minimizing total annual energy supply costs and CO2 emissions. First, an EH and multi-energy network (MEN) topology model considering hydrogen energy is developed. Secondly, the the joint output of wind and solar power under typical scenarios is sampled and obtained by employing the Monte Carlo sampling method and the synchronous back-propagation scenario analysis method. To address the nonlinear constraint problem associated with multi-energy storage output, the Big-M method is introduced to improve the optimization capability of spatial-temporal coupling of energy storage. Moreover, the utilization potential of hydrogen and renewable energy is explored by considering constraints on the nonlinear dynamic pricing of hydrogen. Finally, a numerical analysis is conducted using an illustrative example. The Pareto frontier optimal results, derived from fuzzy theory membership functions, are achieved using both classical and heuristic algorithms. The classical mixed-integer linear programming algorithm yields relatively superior optimization outcomes in terms of annual energy costs and carbon emissions compared to the heuristic algorithm. However, it suffers from longer computation times.