Subsynchronous oscillation (SSO) is classified as subsynchronous resonance, torsional interaction, or control interaction, depending on which devices or controllers are involved. Researchers have conducted numerous studies and developed methodologies on how to analyze SSO cases in different types of power systems. For these diverse mechanisms, complicated systems, and analytical methods, the overall summary and categorization of the SSO phenomena have been crucial, and numerous reviews have been published to this end. However, with the emerging inverter-based and power-electronics-based devices, in addition to the high computational capability, more advanced analytical methods have recently been researched, and more general, up-to-date literature surveys are thus needed. This study reviews the various SSO types depending on the interaction mechanisms, before investigating a number of representative SSO events in terms of the devices. Following this, the study evaluates the existing cutting-edge methods for SSO analysis and compares them to ascertain the appropriate method for specific SSO types. The review provides distinct practical considerations for the analysis to simplify the entire procedure. Ultimately, this paper presents a summary of modern SSO-damping and mitigation methods for pragmatic insight and future perspectives for a reader when dealing with old and the latest power systems.

Because of Korea’s rapid expansion in photovoltaic (PV) generation, forecasting long-term PV generation is of prime importance for utilities to establish transmission and distribution planning. However, most previous studies focused on long-term PV forecasting have been based on parametric methodologies, and most machine learning-based approaches have focused on short-term forecasting. In addition, many factors can affect local PV production, but proper feature selection is needed to prevent overfitting and multicollinearity. In this study, we perform feature-selective long-term PV power generation predictions based on an ensemble model that combines machine learning methods and traditional time-series predictions. We provide a framework for performing feature selection through correlation analysis and backward elimination, along with an ensemble prediction methodology based on feature selection. Utilities gather predictions from various sources and need to consider them to make accurate forecasts. Our ensemble method can produce accurate predictions using various prediction sources. The model with applied feature selection shows higher predictive power than other models that use arbitrary features, and the proposed feature-selective ensemble model based on a convolutional neural network shows the best predictive power.

Transmission system operators (TSOs) are procuring increasing services through distributed energy resources (DERs), many of which are distribution-level renewable energy sources (RESs). However, RES inherent uncertainties have negative impacts on distribution system operations, which is a critical problem when distribution system operators (DSOs) respond to the TSO's flexibility requirement. This raises the question of how the DSO can arrange flexible resources in each different time stage so that the DSO can offer the preset flexibility at as little economic loss as possible. In this context, a novel three-stage flexibility provision framework for radial distribution systems considering uncertainties is proposed. In the first stage, the DSO responds to the TSO's specific flexibility requirement by scheduling priced-based demand response (PBDR), battery energy storage systems (BESSs), and electric vehicles (EVs) on a day-ahead (DA) horizon. In the second stage, the BESS and EV scheduling are re-optimized to respond to the most recent intraday short-term predictions of RESs and loads. In the third stage, real-time frequency control is provided by BESSs and EVs to alleviate frequency variations and power imbalance at the TSO–DSO interface. The simulation results on a modified IEEE 33-bus system demonstrate the effectiveness of the proposed flexibility provision framework.

The penetration of renewable energy sources (RESs) is increasing in modern power systems. However, the uncertainties of RESs pose challenges to distribution system operations, such as RES curtailment. Demand response (DR) and battery energy storage systems (BESSs) are flexible countermeasures for distribution-system operators. In this context, this study proposes an optimization model that considers DR and BESSs and develops a simulation analysis platform representing a medium-sized distribution system with high penetration of RESs. First, BESSs and DR were employed to minimize the total expenses of the distribution system operation, where the BESS model excluding binary state variables was adopted. Second, a simulation platform based on a modified IEEE 123 bus system was developed via MATLAB/Simulink for day-ahead scheduling analysis of the distribution system with a high penetration of RESs. The simulation results indicate the positive effects of DR implementation, BESS deployment, and permission for electricity sales to the upper utility on decreasing RES curtailment and distribution system operation costs. Noticeably, the RES curtailments became zero with the permission of bidirectional power flow. In addition, the adopted BESS model excluding binary variables was also validated. Finally, the effectiveness of the developed simulation analysis platform for day-ahead scheduling was demonstrated.