Optimal design of combined operations of wind power-pumped storage-hydrogen energy storage based on deep learning

Multi energy complementary system is a new method of solving the problem of renewable energy consumption. This paper proposes a wind-pumped storage-hydrogen storage combined operation system based on deep learning and intelligent optimization, which introduces deep neural network to predict wind power generation. With the goal of minimizing power fluctuation and maximizing economic benefits, the system is optimized by multi-objective genetic algorithm for the basic parameters of wind turbine arrangement, electrolyzer and pumped storage power station. After getting the Pareto frontier solutions, we use Technique for Order Preference by Similarity to Ideal Solution(TOPSIS) to select the best scheme. Taking a specific case study for example, the system reduces the daily power fluctuation from 104.20 MW to 23.37 MW, a drop of 77.60%, and produces daily economic benefit of 139,638.5 yuan. Finally, by comparing the system with 3 and 9 wind turbines, we confirm the flexibility and universality of our system.