The increasing use of photovoltaics (PVs) in distribution systems owing to the low-carbon policy has given rise to the need for various technological changes. In particular, the operation of on-load tap changers (OLTCs) has attracted attention. In traditional distribution systems, the OLTC operates via a line-drop compensator (LDC), which focuses on the load to solve the low-voltage problem; however, the problem of over-voltage caused by PVs persists. Currently, a method for operating an OLTC using the measured voltage is being researched; however, solving the voltage problem for several feeders connected to a main transformer (MT) is not viable. Therefore, this study proposes an OLTC operation method to address the feeder with the largest voltage problem depending on the direction of power flow. The proposed method selects a point where the OLTC operates using the difference between the measured and reference voltages. Setting the reference voltage can solve the problem that occurs due to the direction of power flow. Finally, the effectiveness of the proposed method is verified via case studies. Based on the results, we can conclude that the proposed method effectively solves the voltage problem, and an increase in hosting capacity can be expected.

Renewable energy sources are being increasingly deployed to achieve carbon neutrality, thereby boosting photovoltaic (PV) system adoptions. Accordingly, vertical PV systems designed for specific installations have been developed. We propose a strategy to enhance the PV hosting capacity of a connected distribution line (DL) by combining vertical installations with modules facing east-west and conventional PV systems with modules facing south at an installation angle of approximately 30°. The data were obtained from a real testbed located in South Korea, which is situated in a mid-latitude region. We analyzed the generation patterns of vertical and combined PV systems (vertical and conventional) to enhance the hosting capacity. The results showed that vertical PV combination ratios of 40–60% effectively flattened the peak generation curve. Additionally, the DL hosting capacity improved by 40% under real-world conditions. In an actual industrial scenario, the system feasibility was validated to be within the voltage maintenance range and thermal capacity of lines in South Korea, indicating that this approach can mitigate the need for additional line installations and renewable energy curtailments. Furthermore, the issue of Duck Curves in the power grid can be addressed by smoothing the power production of the PV systems, particularly during low-demand periods.

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.