Abstract

With global energy consumption increasing year by year, the industrial sectors of the world’s countries have never been more urgent in achieving carbon reduction. Renewable energy plays a crucial role in the industrial decarbonization process. As the most mature renewable energy technology, wind and photovoltaic power generation have made significant progress in recent years. However, the intermittent characteristics hinder the efficient use of renewable energy. Existing research provides sufficient support for the flexible scheduling of large-scale renewable energy. Hence, this paper proposes a combined energy system composed of wind power-photovoltaic-energy storage salt cavern with hydrogen as the energy scheduling carrier. The system mainly realizes energy conversion through electrolytic water equipment and fuel cells. Then, Qianjiang City, Hubei Province, is taken as the analysis object, and the working conditions of each component in the system are optimized with the help of an improved particle swarm optimization algorithm. According to the results, the system’s contribution to the energy system is discussed, and the system's economy and carbon reduction effect is studied. The analysis proves that the improved particle swarm optimization algorithm has a stronger solving ability. The combined energy system can effectively improve the economy and renewable energy utilization rate, meet the regional electricity demand, and significantly reduce carbon emissions. The economic analysis of the system shows that the operation and maintenance cost of hydrogen storage salt caverns accounts for the largest proportion of the total cost, about 60 %. Compared with several existing large-scale energy storage technologies, it is found that the energy efficiency of the whole system is only about 40 % due to the limitation of technology maturity. Nevertheless, the flexibility of the hydrogen storage system layout is relatively high. The required underground space volume is only equivalent to 1/6 and 1/2 of pumped storage and compressed air storage with the same scheduling capacity. The system will provide a theoretical support for the optimization of energy pattern and layout.

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