With the growing need for transitioning to clean energy, increasing the share of renewable energy sources has become a global priority. The integration of distributed energy resources (DERs), such as photovoltaic (PV) and wind turbine (WT) systems, is crucial for optimizing the current distribution systems. Despite its benefits, DER integration presents technical challenges. DERs can cause rapid power output changes, leading to voltage instability and overvoltage, thereby impacting the power quality and equipment reliability. Mitigating this instability often requires costly grid infrastructure upgrades. Therefore, optimizing existing networks is essential to reduce these costs. To achieve this, the voltage profile of the target area must be thoroughly assessed and locations suitable for DER integration must be identified to ensure stable operation and voltage stability. This study proposes a new evaluation index, the Voltage Margin Evaluation Index (VMEI), to address the limitations of the existing indices. This index evaluates voltage stability and promotes DER integration in a region. Using the VMEI, we assessed the suitability of DER integration in Jeju, South Korea. Results demonstrate that integrating a 0.5 MW DER at Node 37 in Feeder 5, the optimal point with a VMEI of 1.00066, maintained the maximum voltage at 22.97 kV within the regulated range and improved voltage stability. Conversely, Node 29 in Feeder 4, with the lowest VMEI of 0.97262, resulted in a maximum voltage of 23.84 kV, exceeding the voltage regulation range, demonstrating that VMEI effectively identifies suitable DER integration points. This study provides valuable insights for improving DER integration into distribution systems, aiding the global transition to sustainable energy.

The rapid deployment of renewable energy sources is highlighting the critical need for availability and efficiency in power systems, particularly within medium-voltage direct-current (MVDC) systems. Modular multilevel converters (MMCs) are pivotal in these systems, serving as key components for the efficient distribution and management of electricity. Consequently, MMCs must be managed as critical assets, necessitating maintenance strategies that minimize both maintenance and outage costs while ensuring high system reliability. Previous studies have primarily focused on maintenance methodologies targeting single MMC stations, assessing them based on availability. However, such approaches often overlook the systemic ripple effects of failures and the associated economic losses across the entire system. This study proposes a novel algorithm designed to determine the maintenance priority among multiple MMC converters within an MVDC system. The proposed algorithm optimizes the maintenance timing and priority by performing a sensitivity analysis of the system reliability indicators, from both the consumer and operator perspectives. Contrary to the traditional methods, this approach comprehensively considers the broader impacts on the system reliability and economic efficiency, providing a more holistic maintenance strategy. The effectiveness of the proposed algorithm is extensively compared and analyzed through detailed case studies, which benchmark it against the existing reliability-centered maintenance methodologies. The results indicate that the proposed algorithm is economically viable within the evaluation period while also enhancing the overall system availability by mitigating potential risks associated with the MMC failures. This approach facilitates more informed decision-making by incorporating sensitivity analysis and addressing the systemic ripple effects, thereby contributing to the optimization of system performance and cost-effectiveness.

This paper presents a data-driven decision-support framework for distribution system operations using logistic regression (LR) on the Voltage Margin Index (VMI). Treating VMI as the sole explanatory feature, the proposed two-stage workflow first fits an inferential LR model to establish statistical significance and perform valid statistical inference on the coefficients. Next, it trains a performance-optimized LR classifier with class-balanced sample weighting to produce calibrated violation probabilities. LR maps VMI to violation probability and analytically converts a calibrated probability threshold into an operator-ready VMI decision boundary. Applying 5-fold group cross-validation to 8816 node-level samples generated from a 22.9 kV Jeju Island model yields performance- and safety-oriented probability thresholds (θ_opt = 0.7891, θ_safe = 0.6880), which correspond to VMI decision boundaries VMI_DB,opt = 0.7893 and VMI_DB,safe = 0.8101. On an unseen 20% test set, the LR classifier achieves 99.94% accuracy (F₁ = 0.9977) under θ_opt and 100% recall under θ_safe. A random forest (RF) benchmark confirms comparable accuracy (=99.72%) but lacks analytical invertibility and transparency. This framework offers distribution system operators (DSOs) and virtual power plant (VPP) operators clear, evidence-based criteria for routine planning and risk-averse decision-making, and it can be applied directly to any distribution system with node-level voltage measurements and known regulation limits.