Two-stage configuration optimization of a novel standalone renewable integrated energy system coupled with hydrogen refueling

Abstract

Two-stage optimization framework of a novel hybrid renewable integrated energy system is proposed. Firstly, system topological structure and corresponding behaviors are constructed. In this system, renewable energy and conventional thermal power satisfy electricity, heating and cooling demands of the system, and hydrogen storage subsystem is extended for heavy-load fuel cell vehicle refueling. Next, a multi-objective model is constructed to minimize life-time cost of comprehensive loads (LCCL), energy externality rate (EER), and hydrogen hiatus rate (HHR). As for model solution stage, Kernel functions are applied to create typical day scenarios, which contain uncertain information and profoundly ease computational difficulty. Enhanced particle swarm optimization algorithm based on immune clone concept is applied to search non-inferior solutions. Additionally, preference of decision-makers is expanded in solution selection stage that includes more than objectives in the first stage. In the second stage, best Worst Method and Multi-attributive Border Approximation Area Comparison are applied to determine the optimal alternative that corresponds with subjective preference the best. To validate the model, a case in Datong city, Shanxi province in China is selected to validate the framework. Optimization result shows good performance, with LCCL 2.31 Yuan/kWh, EER 6.66%, and HHR 1.09%. Moreover, scenario analysis shows that 1) preference of decision-makers could dramatically influence the ideal solution; 2) double hydrogen refueling demands could be ensured. 3) solar photovoltaic alone could also support the system in Datong. 4) current carbon trade policy almost does not influence the system.