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The aim of this paper is to analyze through simulation how the energy efficiency of a single electrolysis stack at various loads is affected by shunt currents and to determine the energy-optimal load distribution between lines in a multiline electrolysis system under various magnitudes of shunt current losses. A dynamic energy and mass balance model of an industrial 3 MW , 16 bar alkaline water electrolyzer (AWE) process was developed using MATLAB. The optimization goal is to determine the power supply for each AWE line so that it can meet any hydrogen demand while minimizing the global specific energy consumption (SEC). The Particle Swarm Optimization (PSO) algorithm is used to minimize the objective function. According to the results of the single stack investigation, shunt current reduction could significantly improve the energy efficiency of partial-load operation. In addition, the optimization study revealed that whenever two or more lines are required to run in order to satisfy the hydrogen demand, the global SEC is minimized when the lines operate at equal loads. [ABSTRACT FROM AUTHOR]

With the development of fluid transmission and distribution network in the direction of intelligent pipe network, it is urgent to solve the power supply problem of many instruments in the pipe network. However, solving the problem of power supply for intelligent pipe network systems in remote areas and poor site conditions is a challenging task, and the power supply problem can be solved if the fluid energy in the pipe network can be harvested to power instruments such as sensors. Valve is essential equipment in pipe network system, and installing impeller in their spool can harvest fluid energy for power generation. In this paper, in order to improve the ability of the impeller to harvest fluid energy, the blade profile is optimized by parametrically defined method based on the Isight platform. The energy harvesting under different parameters is obtained through CFD simulation software, the Kriging model is established according to the sample data, and the NSGA-II algorithm is used to optimize. After optimization, the impeller shaft power is increased by 7.22%, the energy harvesting efficiency is increased by 7.50%, and the optimal blade profile is obtained. The ability of the impeller to harvest fluid energy is significantly improved. [ABSTRACT FROM AUTHOR]