The hydrogen energy system based on the multi-energy complementary of renewable energy can improve the consumption of renewable energy, reduce the adverse impact on the power grid system, and has the characteristics of green, low carbon, sustainable, etc., which is currently a global research hotspot. Based on the basic principles of hydrogen production technology, this paper introduces the current hydrogen energy system topology, and summarizes the technical advantages of renewable energy complementary hydrogen production and the complementary system energy coordination forms. The problems that have been solved or reached consensus are summarized, and the current status of hydrogen energy system research at home and abroad is introduced in detail. On this basis, the key technologies of multi-energy complementation of hydrogen energy system are elaborated, especially in-depth research and discussion on coordinated control strategies, energy storage and capacity allocation, energy management, and electrolysis water hydrogen production technology. The development trend of the multi-energy complementary system and the hydrogen energy industry chain is also presented, which provides a reference for the development of hydrogen production technology and hydrogen energy utilization of the renewable energy complementary system.

As the proportion of wind power in the world's electricity generation increases, improving wind power prediction accuracy is vital for making full use of wind energy and ensuring the safe and stable operation of the power grid. Given the uncertainty and volatility of wind power and the weak generalization ability of the current wind power prediction models, we propose a wind power prediction model that combines Adaboost algorithm with extreme learning machine optimized by particle swarm optimization (PSO-ELM). First, particle swarm optimization is used to optimize the initial thresholds and input weights of the ELM to obtain the PSO-ELM basic prediction model. Then, combined with the Adaboost algorithm, a series of PSO-ELM weak predictors with input weights and thresholds optimized by PSO and containing different hidden layer nodes are composed. Finally, each weak predictor is weighted and fused into a strong prediction model of wind power, and the final prediction results are output. In this paper, the Adaboost-PSO-ELM model is verified by a wind turbine's measured data in Turkey. The prediction indicators are compared with the current wind power prediction methods including optimized neural networks and ensemble learning models. The results show that the Adaboost-PSO-ELM wind power prediction model has higher accuracy and better generalization ability.

The hydrogen production technology by wind power is an effective mean to improve the utilization of wind energy and alleviate the problem of wind power curtailment. First, the basic principles and technical characteristics of the hydrogen production technology by wind power are briefly introduced. Then the history of the hydrogen production technology is reviewed, and on this basis, the hydrogen production system by wind power is elaborated in detail. In addition, the prospect of the application of the hydrogen production technology by wind power is analyzed and discussed. In the end, the key technology of the hydrogen production by wind power and the problems to be solved are comprehensively reviewed. The development of hydrogen production technology by wind power is analyzed from many aspects, which provides reference for future development of hydrogen production technology by wind power.