The overuse of fossil fuels has caused a serious energy crisis and environmental pollution. Due to these challenges, the search for alternative energy sources that can replace fossil fuels is necessary. Hydrogen is a widely acknowledged future energy carrier because of its nonpolluting properties and high energy density. To realize a hydrogen economy in the future, it is essential to construct a comprehensive hydrogen supply chain that can make hydrogen a key energy carrier. This paper reviews the various technologies involved in the hydrogen supply chain, encompassing hydrogen production, storage, transportation, and utilization technologies. Then, the challenges of constructing a hydrogen supply chain are discussed from techno-economic, social, and policy perspectives, and prospects for the future development of a hydrogen supply chain are presented in light of these challenges.

In response to the ‘zero carbon’ goal, the development of renewable energy has become a global consensus. Among the array of renewable energy sources, wind energy is distinguished by its considerable installed capacity on a global scale. Accurate wind power prediction provides a fundamental basis for power grid dispatching, unit combination operation, and wind farm operation and maintenance. This study establishes a framework to bridge theoretical innovations with practical implementation challenges in wind power prediction. This work uses a narrative method to synthesize and discuss wind power prediction methods. Common classification angles of wind power prediction methods are outlined. By synthesizing existing approaches through multi-time scales, from the ultra-short term and short term to mid-long term, the review further deconstructs methods by model characteristics, input data types, spatial scales, and evaluation metrics. The analysis reveals that the data-driven prediction model dominates ultra-short-term predictions through rapid response to volatility, while the hybrid method enhances short-term precision. Mid-term predictions increasingly integrate climate dynamics to address seasonal variability. A key contribution lies in unifying fragmented methodologies into a decision support framework that prioritizes the time scale, model adaptability, and spatial constraints. This work enables practitioners to systematically select optimal strategies and advance the development of forecasting systems that are critical for highly renewable energy systems.

With the increasing integration of renewable energy sources such as wind and solar power, the regulation requirements of power systems have become more dynamic and complex. This paper provides a comprehensive review of multi-temporal scale regulation requirements and explores the application of diverse flexible resources across different time scales. The regulation demands are categorized into short, medium, and long-term scales, each presenting unique challenges that need to be addressed for efficient power system operation. Existing studies primarily focus on the adjustment of a single time scale, often overlooking the interplay between multiple time scales. This paper critically analyzes the limitations of previous approaches and demonstrates the need for more holistic and flexible solutions. The research results show that integrating diverse, flexible resources, including energy storage systems (ESS) and demand response (DR), can effectively meet regulation needs across various time scales. Additionally, the paper identifies the potential of hydrogen-based solutions to address emerging challenges in power system regulation. The findings highlight the importance of combining multiple flexible resources to improve system resilience and sustainability, offering a pathway toward more efficient and adaptable power systems.

In the context of large-scale wind power access to the power system, it is urgent to explore new probabilistic supply–demand analysis methods. This paper proposes a wind power stochastic and extreme scenario generation method considering wind power–temperature correlations and carries out probabilistic supply–demand balance analysis based on it. Firstly, the influence of temperature on wind power output is analyzed via Pearson coefficient to obtain the correlation between wind power and temperature. Secondly, based on the historical wind power curve, a large number of wind power output scenarios are randomly generated while fully preserving its characteristics, and probabilistic supply–demand analysis is carried out. Thirdly, for the extreme case of continuous multi-day extreme heat without wind, extreme scenarios are selected from the generated scenarios for supply–demand balance analysis. Finally, a practical example in a province in central-eastern China is used to verify the effectiveness of the proposed method. The results indicate that the scenario generation method can effectively capture the historical wind power characteristics and can be better applied to the diversified supply and demand balance analysis to obtain more accurate analysis results.

Renewable energy is an important means of addressing climate change and achieving carbon peaking and carbon neutrality goals. However, the uncertainty and randomness of renewable energy also have a certain impact on the flexibility, reliability, and transient voltage stability of the power system. These effects also pose great challenges to power system planning. In order to address the impact of renewable energy on power system planning, this paper proposes a two-layer optimization model for power sources and network planning which takes into account both reliability and transient voltage stability requirements. The upper-layer grid planning problem is formulated with consideration of the system reliability index, and the transient stability requirements and construction and operation costs are included in the lower-layer problem to determine a construction scheme for power generation and energy storage units. To solve the complex nonlinear problem efficiently, a two-layer iterative algorithm utilizing the adaptive particle swarm optimization (PSO) technique is proposed. The effectiveness of the proposed method is demonstrated via its application to the IEEE 33 test system. The results show that the proposed optimization approach effectively addresses the power system transmission and generation planning problem while improving the efficiency and reliability of the system’s operation. The findings can guide the design and implementation of future power system planning and operation strategies.