## 4 Conclusions  
In this paper, we developed a sensitivity-based control algorithm designed to optimize curtailment and enhance the control efficiency within the VPP. The algorithm leverages the electrical characteristics of VPP resources to address inherent limitations present in traditional curtailment methods. Implemented using actual power system data from Jeju Island within an integrated OpenDSS-MATLAB framework, the algorithm's robustness and efficacy were rigorously tested through simulation scenarios.  
Scenario 1 focused on managing voltage stability during a single line-to-ground fault. The simulation results demonstrated that the proposed algorithm significantly outperformed traditional methods, such as LIFO and Pro-Rata. By prioritizing control actions based on the electrical sensitivity of each DER, the algorithm achieved minimal curtailment while maintaining grid stability. It dynamically adjusted resource control according to voltage deviations, effectively reducing unnecessary interventions and optimizing energy utilization. The sensitivity-based approach ensured that the DERs contributing most significantly to voltage changes were prioritized for adjustments, leading to efficient fault recovery. Scenario 2 addressed voltage violations arising from load fluctuations, focusing on the minimum control capacity using ESS, EV, and FC. During periods of load reductions and increases, the sensitivity-based control algorithm was tested at critical moments, specifically at 12 PM for overvoltage and 8 PM for undervoltage. The algorithm demonstrated superior performance, achieving the least curtailment compared to LIFO and Pro-Rata. By intelligently managing resource output based on their sensitivity, it effectively mitigated voltage violations, ensuring grid reliability while maximizing DER utilization. This dynamic adaptability highlights the algorithm's capacity to handle real-time grid changes efficiently.  
Future research will extend this work by exploring the integration of additional DERs and investigating the application algorithm under diverse grid conditions and market structures. Additionally, to conduct a detailed analysis of the impact of VPP operations on the distribution network, inverter-based resource (IBR) models and machine learning techniques will be applied.  
Conceptualization, S.H. and Y.C.; data curation, J.P.; formal analysis, S.H.; investigation, G.K.; methodology, S.H.; project administration, J.P. and G.K.; resources, J.P. and G.K.; software, S.H.; supervision, Y.C.; validation, S.H. and Y.C.; visualization, S.H.; writing--original draft preparation, S.H.; writing--review and editing, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Korea Electric Power Corporation (Grant number: R23 DG02).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
This work is financially supported by the Ministry of Trade, Industry & ENERGY(MOTIE) through the fostering project of project for industry-university convergence district build-up.  
The authors declare no conflicts of interest.