• Energy Research

This study proposed a probabilistic methodology based on a confidence interval with the aim of overcoming the limitations of deterministic methods. A stability evaluation technique was required because the output variability of renewable energy can lead to instability of the distribution system. The proposed method can predict the possibility of violating stability in the future. It can also provide a theoretical basis for securing distribution system stability and improving operational efficiency by assessing the in-stability risk and worst-case scenarios. Because of steady-state analysis in the distribution system to which solar power is connected, the probability of violating the standard voltage during the daytime when PV fluctuations are severe was the highest. Moreover, as a result of a simulation of a three-phase short-circuit in the distribution system that is connected to the PV and WT, it was observed that it could violate the allowable capacity of the CB owing to the effects of the power demand pattern and output variability.

To validate microgrid systems, precise simulations are necessary beforehand. Traditional Hardware-in-the-Loop Simulation (HILS) is used to validate systems by creating a digital twin environment that integrates software and hardware to mimic reality. However, HILS requires high investment costs for hardware, posing a significant hurdle for companies. To address this issue, this study proposes a Software-in-the-Loop Simulation (SILS) framework using SCADA/EMS and MATLAB/Simulink(R2024a). The proposed SILS framework is highly compatible with Energy Management Systems (EMSs) and Supervisory Control and Data Acquisition (SCADA), allowing near real-time data exchange and scenario-based analysis without relying on physical hardware. According to the simulation results, SILS effectively replicates the dynamic behavior of microgrid components such as solar power generation systems, energy storage systems (ESSs), and diesel generators. Solution providers can quickly conduct feasibility tests through systems that simulate actual power systems. They can test the operation of SCADA/EMS at a lower cost and reduce on-site time, thereby reducing business costs and preemptively addressing potential issues in the field. This paper demonstrates how SILS can contribute to establishing optimal operation strategies and power supply stability through case studies, including daily operation optimization and autonomous operation scenarios for microgrids. This research provides a foundation for the feasibility of microgrid solution construction by enabling software performance evaluations and the verification of economic expected returns in the early stages of a project.

Under disaster conditions, an intentional islanding operation in distribution systems is required to enhance resilience. This paper proposes a strategic islanding operation utilizing grid-forming (GFM) inverter-based distributed generation (DG) within a grid containing both GFM and grid-following (GFL) inverters. Unlike previous studies, this work uniquely addresses the temporary overvoltage (TOV) constraints that arise during multi-fault and consecutive fault conditions in islanded systems, which is critical for stable grid operation in disaster scenarios. The islanding operation problem is formulated as a knapsack problem and solved by a genetic algorithm, enabling optimal load supply and enhanced DG utilization under diverse disaster scenarios. Numerical simulations validate the effectiveness of the proposed method by exploring the impact of varying the percentage of ungrounded loads, the capacity of GFM inverter-based DGs, and the location of GFM inverters. The results emphasize that the reduction of grounded sources, which has not been evaluated in existing studies, negatively affects TOV and, therefore, impacts the reliability of islanding operations.

The expected increase in renewable energy sources (RESs) and electric vehicles (EVs) connected to distribution systems will result in many technical constraints. A meshed network is a promising solution; however, some remarkable challenges must be overcome. Among these, this paper mainly focuses on the line overload and short circuit current of a networked distribution system (NDS) in Korea, an advanced form of meshed network. An NDS refers to a system in which there exists permanent linkages between four feeders and N × N communication-based protection. We propose a method, which employs the tap changing control algorithm of the series reactor to control line overload and short circuit current. MATLAB/Simulink was used to evaluate the proposed method. Three different types of distribution system were employed. First, the utilization rate and feeder imbalance were analyzed in steady-state condition. Subsequently, the short circuit current was analyzed in short circuit condition. The results revealed that the proposed method can effectively prevent line overload in up to 82.7% of cases, enhance the utilization rate by up to 79.9%, and relieve the short circuit current; that is, it can contribute to system stability and the economic operation of an NDS.

Distributed energy resources (DERs), recently introduced into distribution systems, are mainly inverter-based distributed generations (IBDGs), which have different short-circuit behaviors from conventional synchronous-based distributed generations (SBDGs). Hence, this study presents a comprehensive analysis of the short-circuit behaviors of distribution systems with IBDGs, based on sequence networks and superposition, from the perspectives of interconnected transformers, and observes the flow of zero-sequence fault currents with different transformer topologies. Moreover, two- and three-winding transformers with various bank connection types and groundings are investigated. It was concluded that the transformer topology and its grounding influence the fault current contribution in zero-sequence networks, and the high penetration of IBDGs alters the fault current magnitude and phase angles.

The scenario of renewable energy generation significantly affects the probabilistic distribution system analysis. To reflect the probabilistic characteristics of actual data, this paper proposed a scenario generation method that can reflect the spatiotemporal characteristics of wind power generation and the probabilistic characteristics of forecast errors. The scenario generation method consists of a process of sampling random numbers and a process of inverse sampling using the cumulative distribution function. In sampling random numbers, random numbers that mimic the spatiotemporal correlation of power generation were generated using the copula function. Furthermore, the cumulative distribution functions of forecast errors according to power generation bins were used, thereby reflecting the probabilistic characteristics of forecast errors. The wind power generation scenarios in Jeju Island, generated by the proposed method, were analyzed through various indices that can assess accuracy. As a result, it was confirmed that by using the proposed scenario generation method, scenarios similar to actual data can be generated, which in turn allows for preparation of situations with a high probability of occurrence within the distribution system.

This paper presents a new smart grid infrastructure for active distribution systems that will allow continuous and accurate monitoring of distribution system operations and customer utilization of electric power. The infrastructure allows a complete array of applications. The paper discusses four specific applications: a) protection against downed conductors; b) load levelization; c) loss minimization; and d) reliability enhancement.

Recently, mobile edge computing (MEC) technology was developed to mitigate the overload problem in networks and cloud systems. An MEC system computes the offloading computation tasks from resource-constrained Internet of Things (IoT) devices. In addition, several convergence technologies with renewable energy resources (RERs) such as photovoltaics have been proposed to improve the survivability of IoT systems. This paper proposes an MEC integrated with RER system, which is referred to as energy-harvesting (EH) MEC. Since the energy supply of RERs is unstable due to various reasons, EH MEC needs to consider the state-of-charge (SoC) of the battery to ensure system stability. Therefore, in this paper, we propose an offloading scheduling algorithm considering the battery of EH MEC as well as the service quality of experience (QoE). The proposed scheduling algorithm consists of a two-stage operation, where the first stage consists of admission control of the offloading requests and the second stage consists of computation frequency scheduling of the MEC server. For the first stage, a non-convex optimization problem is designed considering the computation capability, SoC, and request deadline. To solve the non-convex problem, a greedy algorithm is proposed to obtain approximate optimal solutions. In the second stage, based on Lyapunov optimization, a low-complexity algorithm is proposed, which considers both the workload queue and battery stability. In addition, performance evaluations of the proposed algorithm were conducted via simulation. However, this paper has a limitation in terms of verifying in a real-world scenario.