The paper describes a mid-term daily peak load forecasting method using recurrent artificial neural network (RANN). Generally, the artificial neural network (ANN) algorithm is used to forecast short-term load pattern and many ANN structures have been developed and commercialized so far. Otherwise, learning and estimation for long-term and mid-term load forecasting are hard tasks due to lack of training data and increase of accumulated errors in long period estimation. The paper proposes a mid-term load forecasting structure in order to overcome these problems by input data replacement for special days and a recurrent-type NN application. Also, the proposed RANN gives good performances on estimating sudden and nonlinear demand increase during heat waves. The results of case studies using load data of South Korea are presented to show performances and effectiveness of the proposed RANN.

The high penetration of distributed energy resources (DERs) in the distribution system brings new challenges related to voltage variations and reverse power flow. The sudden changes in electric power generation from the DERs due to climate conditions or their internal malfunction might cause high fluctuations in system voltage. To enhance the voltage stability when a disturbance occurs, this paper proposes a new reactive power management method to control each DER based on voltage sensitivity analysis. The voltage variation has the different features in both steady-state and transient conditions. In particular, its transient behavior depends on the type of DERs. Therefore, the particular optimal control for improving the dynamic response of voltage is difficult to apply for all types of DERs. In contrast, the voltage variation in steady-state can be controlled with the reactive power management by the sensitivity analysis between reactive power generation and system voltage. Even though this paper focuses on the reduction of voltage variation in steady-state, the relationship between the voltage variations in steady-state and transient conditions is also analyzed. The effectiveness of the proposed method is verified with several case studies on the practical distribution system in South Korea by using the time-domain simulation based on the PSCAD/EMTDC^® software. The results show that the proposed reactive power management method can improve the dynamic voltage responses in both steady-state and transient conditions when the distribution system has the high penetration of renewables.

This paper presents the design of the advanced voltage management system (AVMS) including manual operation mode in order to control reactive power generation in a practical power system. The existing secondary voltage controller (SVC) has a demerit occurring as a large transient phenomenon shortly after the SVC begins to work. This malfunction results from the difference of the operation statuses before and after the SVC operates. For solving the problem and implementing the voltage control safely, the AVMS including the manual operation mode is developed by Korea Electric Power Corporation. Aside from the fact that the AVMS overcomes the drawback, it can also execute the coordination control of both continuous voltage controller (CVC) and discrete voltage controller (DVC). The CVC controls reactive power generation sources such as generator and flexible ac transmission system devices which can generate reactive power continuously and linearly. On the other hand, the DVC controls the discrete reactive power sources such as switched shunt capacitors and reactors. The performances of the proposed AVMS are carried out by time-domain simulation in the Jeju Island power system with real-time digital simulator.

This paper proposes a wind power probabilistic model (WPPM) using the reflection method and multi-kernel function kernel density estimation (KDE). With the increasing penetration of renewable energy sources (RESs) into power systems, several probabilistic approaches have been introduced to assess the impact of RESs on the power system. A probabilistic approach requires a wind power scenario (WPS), and the WPS is generated from the WPPM. Previously, WPPM was generated using a parametric density estimation, and it had limitations in reflecting the characteristics of wind power data (WPD) due to a boundary bias problem. The paper proposes a WPPM generated using the KDE, which is a non-parametric method. Additionally, the paper proposes a reflection method correcting for the boundary bias problem caused by the double-bounded characteristic of the WPD and the multi-kernel function KDE minimizing the effect of tied values. Six bandwidth selectors are used to calculate the bandwidth for the KDE, and one is selected by analyzing the correlation between the normalized WPD and the calculated bandwidth. The results were validated by generating WPPMs with WPDs in six regions of the Republic of Korea, and it was confirmed that the accuracy and goodness-of-fit are improved when the proposed method is used.

So far, the short circuit ratio (SCR) has been widely used to assess grid robustness at inverter points of interconnection. However, its mathematical and geometric foundations have not been fully established, and its applicability across various X/R ratios and power factors remains an open question. This paper provides a geometric interpretation of the SCR based on the two-dimensional direct-quadrature axis framework from the perspective of voltage stability. The analysis incorporates the grid’s equivalent impedance, specifically accounting for the X/R ratio, as well as the inverter’s power factor and the reactive power contribution of shunt filter capacitors. The proposed method is validated through multiple case studies conducted under diverse conditions using an electromagnetic transient (EMT) model derived from a real power system. Simulations are performed with the Power System Computer Aided Design/Electromagnetic Transient including DC (PSCAD/EMTDC^TM) platform.

As renewable energy penetrates the power system, system operators are required to curtail output power from generation units to balance the power supply and demand. However, large curtailment from wind power plants (WPPs) may instantly cause excessive output power decrement, causing system frequency to drop significantly before reaching its nominal value. In order to solve this problem, this paper proposes a cooperative control framework to determine the operation of WPPs in two control methods, which are the stepwise inertial control (SIC) method and the curtailed control method. The proposed framework first determines the WPPs to operate in the curtailed control method to provide the required power curtailment. Next, it determines the WPPs to operate in the SIC method considering their releasable kinetic energy to provide an effective inertial response and compensate for the sudden excessive output power decrement caused by other WPPs operated in the curtailed control method. Therefore, each WPP is operated in one of two control methods to provide required power curtailment while reducing the sudden excessive output power decrement. To verify the effectiveness of the proposed cooperative control framework, several case studies are carried out on the practical South Korea electric power system.