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.

The large market penetration of non-dispatchable renewable power sources (vRES), i.e., wind and photovoltaic, may be hampered by an increasing need for large scale energy storage capacity and the challenges of balancing the power grid. Novel technologies integrating waste gasification with reversible Solid-Oxide Cell systems have been proposed to provide flexible grid balancing services. The rSOC system operated in electrolysis mode uses excess power from vRES to generate hydrogen (H2), which is combined with syngas derived from waste gasification to produce methane (CH4). The rSOC system can also be operated in fuel cell mode by oxidising syngas to produce electricity. This paper presents a well-defined case study which aimed to estimate the potential deployment of a novel rSOC technology in a future power system dominated by intermittent renewables. The hourly power grid residual loads (i.e., the difference between load and vRES power generation) and the availability of low-grade organic waste and residues are quantified and matched for the southern Italian peninsula in 2030. The results show that the theoretical grid flexibility needs approximately 10 TW h of overproduction and 5 TW h of underproduction in 2030 to ensure the complete disposal of the municipal organic waste generated in 2030 (6.7 Mt) and that production of renewable CH4 will need to be 1.4–2.4 Mt, pointing to an intriguing perspective for the deployment of rSOC systems at a large scale. The multifunctionality of the system proposed is an added value that can make it a convenient and efficient piece of the puzzle of technologies required in a climate-neutral and circular economy. The results and methods here presented are intended to form the basis for estimations of future potential deployment and economic and environmental assessments of competing technologies.