The operational stability of isolated industrial and mining microgrids is critically challenged by the diverse response characteristics of heterogeneous flexible resources (FRs), such as varying ramp rates and response times. Conventional scheduling methods, which often overlook these multi-granularity attributes, result in significant power imbalances, mismatched execution of the dispatch commands, and degradation of industrial product quality. To address this gap, this paper proposes a novel dispatch command disaggregation strategy for the precise coordination of these resources. The strategy's core innovation begins with establishing a pioneering quantitative flexibility model for lithium mine loads (LMLs), mechanistically exploring the thermo-electric dynamics of the salt-lake lithium extraction process. This specific model is then integrated within a unified framework designed to systematically characterize and quantify the multi-granularity flexibility attributes of diverse resources. Finally, a Discrete Choice Model (DCM) is employed to optimize the dispatch priority of FRs, effectively translating the microgrid's aggregate dispatch command into individualized and feasible setpoints. Validated on a real-world microgrid in Xizang, the proposed strategy reduces system power deviation by 89.05% and decreases the total operating cost by 25.25%. This work provides a practical framework for enhancing the control precision and economic efficiency of isolated microgrids through effective coordination of diverse flexibility assets.

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.