Hydrogen is becoming an increasingly important energy carrier in sector integration for fuel cell transportation, heat and electricity. Underground salt caverns are one of the most promising ways to store the hydrogen obtained from water electrolysis using power generation from renewable energy sources (RES). At the same time, the production of hydrogen can be used to avoid energy curtailments during times of low electricity demand or low prices. The stored hydrogen can also be used during times of high energy demand for power generation, e.g., with fuel cells, to cover the fluctuations and shortages caused by low RES generation. This article presents an overview of the techniques that were used and proposed for using excess energy from RES for hydrogen production from water and its storage techniques, especially in underground salt caverns, for the aforementioned purpose, and its feasibility. This paper compares and summarizes the competing technologies based on the current state-of-the-art, identifies some of the difficulties in hydrogen production and storage, and discusses which technology is the most promising. The related analysis compares cost and techno-economic feasibility with regard to hydrogen production and storage systems. The paper also identifies the potential, technical challenges and the limitations associated with hydrogen integration into the power grid.
This paper deals with the Differential Evolution (DE) based method for identification of the heat equation parameters applied for the estimation of a bare overhead conductor`s temperature. The parameters are determined in the optimization process using a dynamic model of the conductor; the measured environmental temperature, solar radiation and wind velocity; the current and temperature measured on the tested overhead conductor; and the DE, which is applied as the optimization tool. The main task of the DE is to minimise the difference between the measured and model-calculated conductor temperatures. The conductor model is relevant and suitable for the prediction of the conductor temperature, as the agreement between measured and model-calculated conductor temperatures is exceptional, where the deviation between mean and maximum measured and model-calculated conductor temperatures is less than 0.03 °C.
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