Power-to-hydrogen systems, particularly the most mature alkaline electrolyzers (AELs), are increasingly deployed in modern energy systems due to their pivotal role in green hydrogen production and decarbonization. Proper modeling is vital for optimizing AEL lifecycle decisions, including design, operation, and investment. Despite numerous proposed models, a review focusing on their applications in system-level decision-making (e.g., operation and planning) remains lacking. This paper bridges this gap by reviewing over 100 peer-reviewed articles to offer an in-depth overview of AEL models employed in system-level decision-making. Followed by clarifying modeling requirements across different levels of AEL system analysis, three types of AEL models are classified in system-level decision-making: linear electricity–hydrogen (LEHM), nonlinear electricity–hydrogen (NEHM), and integrated electricity-heat-hydrogen models (IEHHM). This classification is based on representing the AEL with different levels of multi-physics detail and energy conversion assumptions. LEHM assumes a constant electricity-to-hydrogen conversion efficiency of typically about 60%–70%, while NEHM and IEHHM allow modeling of dynamic efficiency variations in the typical range of 60%–80%, where the IEHHM uniquely integrates thermal dynamics. Their modeling principles, characteristics, strengths, and limitations are systematically reviewed, followed by an in-depth overview of their applications and impacts across four applications: economic operation, grid services, heat recovery, and capacity planning. It reveals that LEHM, NEHM, and IEHHM are employed in 35%, 42%, and 23% of these applications, respectively. Finally, a discussion of current modeling limitations and future direction is provided. This paper offers valuable insights and guidance for selecting appropriate AEL models in decision-making studies and identifying pathways for advancing AEL modeling.

The DGPS technique can considerably improve the accuracy of stand-alone GPS positioning, but the improvement depends on the distance between the user and the reference station (spatial correlation), the latency of differential corrections (temporal correlation), and the quality of differential corrections. Therefore, how to correctly generate differential corrections as well as their precision is one of the keys to the DGPS positioning technique. This paper presents a new algorithm for generating differential GPS corrections. This algorithm directly uses code and carrier observations in the measurement model of a Kalman filter, so that it is possible to use a simple stochastic observation model and to use the standard algorithm of the Kalman filter. The algorithm accounts for biases like multipath errors and instrumental delays in code observations and it clearly shows how differential corrections are differently affected by code biases when dual or single frequency data are used. In addition, the algorithm can be integrated with a recursive quality control procedure. As a consequence, the quality of differential corrections can be guaranteed with certain probability.