Virtual power plant (VPP) serves as an effective solution for maintaining internal power balance and participating in external peak shaving auxiliary services within grid-connected microgrid involved in multi-type flexible resources (FRs). However, with increasing prominence of the feature heterogeneity in response behaviors of diverse FRs and their coupling in peak shaving poses challenges in the accurate decomposition of VPP scheduling commands. This paper proposes a de-aggregation strategy, utilizing discrete choice model and feature matching methods, to dynamically sequence FRs responses while optimizing VPP's peak shaving capability. Initially, heterogeneous features are refined and modeled to characterize the response capability of multi-type FRs in meeting the scheduled demand of grid-connected microgrid (SDGM). Subsequently, a feature difference quantification model and matching priority criterion are formulated to describe the feature mapping relationship and guide dynamic decision-making process. On this basis, the multi-type FRs are co-scheduled in the considered VPP to form a dynamic response sequence achieving peak shaving objectives. Case studies based on real data from a region-connected microgrid demonstrate the proposed strategy's performance in improving return on investment by 6.1 %, reducing peak shaving deviation and power exchange with main grid by 70 % and 13.1 %, respectively, and effectively improve the ability of grid-connected microgrid to balance the power and participate in peaking auxiliary services.

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.