As distributed energy resources (DERs) proliferate power systems, power grids face new challenges stemming from the variability and uncertainty of DERs. To address these problems, virtual power plants (VPPs) are established to aggregate DERs and manage them as single dispatchable and reliable resources. VPPs can participate in the day-ahead (DA) market and therefore require a bidding method that maximizes profits. It is also important to minimize the variability of VPP output during intra-day (ID) operations. This paper presents mixed integer quadratic programming-based scheduling methods for both DA market bidding and ID operation of VPPs, thus serving as a complete scheme for bidding-operation scheduling. Hourly bids are determined based on VPP revenue in the DA market bidding step, and the schedule of DERs is revised in the ID operation to minimize the impact of forecasting errors and maximize the incentives, thus reducing the variability and uncertainty of VPP output. The simulation results verify the effectiveness of the proposed methods through a comparison of daily revenue.

North Korean cyber-attack groups such as Kimsuky, Lazarus, Andariel, and Venus 121 continue to attempt spear-phishing APT attacks that exploit social issues, including COVID-19. Thus, along with the worldwide pandemic of COVID-19, related threats also persist in cyberspace. In January 2022, a hacking attack, presumed to be Kimsuky, a North Korean cyber-attack group, intending to steal research data related to COVID-19. The problem is that the activities of cyber-attack groups are continuously increasing, and it is difficult to accurately identify cyber-attack groups and attack origins only with limited analysis information. To solve this problem, it is necessary to expand the scope of data analysis by using BGP archive data. It is necessary to combine infrastructure and network information to draw correlations and to be able to classify infrastructure by attack group very accurately. Network-based infrastructure analysis is required in the fragmentary host area, such as malware or system logs. This paper studied cyber ISR and BGP and a case study of cyber ISR visualization for situational awareness, hacking trends of North Korean cyber-attack groups, and cyber-attack tracking. Through related research, we estimated the origin of the attack by analyzing hacking cases through cyber intelligence-based profiling techniques and correlation analysis using BGP archive data. Based on the analysis results, we propose an implementation of the cyber ISR visualization method based on BGP archive data. Future research will include a connection with research on a cyber command-and-control system, a study on the cyber battlefield area, cyber ISR, and a traceback visualization model for the origin of the attack. The final R&D goal is to develop an AI-based cyber-attack group automatic identification and attack-origin tracking platform by analyzing cyber-attack behavior and infrastructure lifecycle.

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