## 1 Introduction  
### Background  
As climate change and its associated environmental challenges intensify globally, the transition from fossil fuel-based energy systems to sustainable alternatives has become an urgent priority [1]. Renewable energy has emerged as a critical solution for replacing fossil fuels, driving countries worldwide to restructure their energy systems around renewable sources [2]. These efforts aim to not only reduce greenhouse gas emissions, widely recognized as the primary driver of climate change, but also address the depletion of energy resources [3]. In South Korea, the Ministry of Trade, Industry, and Energy's \"The 10 th basic plan for electricity supply and demand\" sets ambitious goals to expand renewable energy generation capacity to levels capable of replacing fossil fuel-based power generation by 2030 [4].  
Notably, Jeju Island has positioned itself as a leader in South Korea's renewable energy transition through its \"Carbon Free Island 2030\" (CFI 2030) initiative [5]. This project aims to transform Jeju into a region powered exclusively by renewable energy sources, such as wind and solar power. However, a significant challenge faced by Jeju Island is the overproduction of electricity from renewable energy sources, particularly during periods of low demand [6]. This overgeneration necessitates the implementation of curtailment measures to ensure grid stability and prevent potential disruptions [7]. To address these challenges, Jeju Island has also initiated a pilot project for implementing a VPP, which integrates DERs, such as wind turbines, PV, and ESS [8].  
However, the VPP currently lacks clear standards for control and curtailment, leading to inefficiencies in resource utilization and grid management [9]. In the field of VPP control, research utilizing Multi-Agent Systems (MASs) has focused on the efficient integration of DERs and environmentally sustainable operations [10]. This approach optimizes energy exchange by considering the unique characteristics and environmental impacts of each resource. However, such studies have highlighted the lack of a clear and standardized control framework applicable to the entire VPP, which remains a significant barrier to enhancing the operational efficiency of VPPs [11]. Similarly, research employing Model Predictive Control (MPC) has aimed to improve the stability and efficiency of VPPs by generating optimal operational curves through predictive algorithms and controlling distributed power plants based on these curves [12]. Despite its effectiveness, this approach also faces limitations, as it relies heavily on specific algorithms and simulations, lacking generalized standards or guidelines that can be applied across diverse scenarios [13].  
Several DER-related challenges and benchmarks have been studied globally, providing valuable insights for improving VPP control. For instance, DER systems in Australia face operational complexities due to diverse generator ownership and market-driven dispatch mechanisms, necessitating dynamic market rules and pricing models [14]. Similarly, in Denmark, integrating high shares of wind energy into a small-scale grid has revealed challenges in balancing supply and demand during extreme weather conditions, leading to the exploration of the real-time demand response and flexible ESS deployment [15]. These examples underscore the importance of addressing not only curtailment strategies but also broader operational scenarios, such as market participation and extreme event handling, to ensure sustainable and efficient VPP operations.