Advancing resilient power systems through hierarchical restoration with renewable resources

Voltage source converters (VSCs) have emerged as the key components in modern power systems, facilitating efficient energy conversion and flexible power flow control. Understanding the fundamental circuit model of VSCs is essential for their accurate modeling and analysis in power system studies. A basic voltage source converter circuit model connected to an LC filter is essential because it lowers the harmonic distortions and enhances the overall power quality of the micro-grid. This guarantees a clean and steady power supply, which is necessary for the integration of multiple renewable energy sources and sensitive loads. A comprehensive methodology for developing a basic circuit model of VSCs, focusing on the key components and principals involved, is presented in this paper. The methodology includes the modeling of space vector pulse-width modulation (SVPWM) as well as the direct quadrature zero synchronous reference frame. Different design controls, including the design of current control loop in the S-domain, the design of the direct current (DC) bus voltage control loop in the S-domain, and the design of the alternating current (AC) voltage control loop in the S-domain, are explored to capture the dynamic behavior and control strategies of VSCs accurately. The proposed methodology provides a systematic framework for modeling VSCs, enabling engineers and researchers to analyze their performance and assess their impact on power system stability and operation. Future studies can be conducted by using case studies and simulation scenarios to show the efficiency and applicability of the developed models in analyzing VSC-based power electronics applications, including high-voltage direct current (HVDC) transmission systems and flexible alternating current transmission systems (FACTS). The significance of this work lies in its potential to advance the understanding and application of VSCs, contributing to more resilient and efficient power systems. By providing a solid foundation for future research and development, this study supports the ongoing integration of renewable energy sources and the advancement of modern electrical infrastructure.

Stochastic flexible transmission operation for coordinated integration of plug-in electric vehicles and renewable energy sources

The integration of renewable energy sources (RESs) has become more attractive to provide electricity to rural and remote areas, which increases the reliability and sustainability of the electrical system, particularly for areas where electricity extension is difficult. Despite this, the integration of hybrid RESs is accompanied by many problems as a result of the intermittent and unstable nature of RESs. The extant literature has discussed the integration of RESs, but it is not comprehensive enough to clarify all the factors that affect the integration of RESs. In this paper, a comprehensive review is made of the integration of RESs. This review includes various combinations of integrated systems, integration schemes, integration requirements, microgrid communication challenges, as well as artificial intelligence used in the integration. In addition, the review comprehensively presents the potential challenges arising from integrating renewable resources with the grid and the control strategies used. The classifications developed in this review facilitate the integration improvement process. This paper also discusses the various optimization techniques used to reduce the total cost of integrated energy sources. In addition, it examines the use of up-to-date methods to improve the performance of the electrical grid. A case study is conducted to analyze the impact of using artificial intelligence when integrating RESs. The results of the case study prove that the use of artificial intelligence helps to improve the accuracy of operation to provide effective and accurate prediction control of the integrated system. Various optimization techniques are combined with ANN to select the best hybrid model. PSO has the fast convergence rate for reaching to the minimum errors as the Normalized Mean Square Error (NMSE) percentage reaches 1.10% in 3367.50 s.

A literature survey on load frequency control considering renewable energy integration in power system: Recent trends and future prospects

Review of model-driven and data-driven/AI approaches for estimating frequency dynamics after a disturbance

The rapid integration of renewable energy sources (RES) and distributed energy resources (DER) into modern power systems introduces significant challenges for distribution network expansion planning (DNEP). These challenges primarily arise from the inherent uncertainty in renewable generation and load demand, which traditional deterministic and robust optimization models struggle to address effectively. This study proposes a hybrid stochastic optimization framework that integrates Monte Carlo simulation with scenario reduction techniques to improve decision-making under uncertainty. The framework employs stochastic programming to minimize investment and operational costs while ensuring system reliability across diverse scenarios. Monte Carlo simulation models high-dimensional uncertainties, and scenario reduction methods enhance computational efficiency by selecting representative scenarios. By leveraging interdisciplinary methods, including optical sensing technologies for real-time monitoring and data acquisition, and intelligent grid management, the framework supports the development of smart cities, where energy systems must be sustainable, resilient, and efficient. Case studies on a modified IEEE 33-bus system demonstrate the framework’s ability to achieve significant cost savings—up to 9% compared to deterministic methods—while improving reliability by 15%. Sensitivity analysis further reveals the framework’s robustness to variations in scenario numbers and distributions. This scalable and computationally efficient approach provides a practical solution for real-world power system planning, particularly in the context of smart cities transitioning toward higher renewable penetration and intelligent energy infrastructures. Future research directions include exploring advanced scenario generation techniques and extending the framework to dynamic, multi-period planning under evolving uncertainties.

Guest Editorial: Situational awareness of integrated energy systems

Microgrids (MGs) are a critical component in modern power systems, comprising interconnected loads and distributed energy resources (DERs) that can operate autonomously or in conjunction with the main grid. This review examines the role of MGs in advancing smart grid technologies, emphasizing the optimal integration of renewable energy sources (RES) and energy storage systems (ESS) to enhance their resilience and efficiency. It provides a detailed analysis of various MG architectures, including AC, DC, and hybrid topologies, alongside their operational modes. The paper highlights the pivotal role of control systems in optimizing energy management within MGs. Additionally, the challenges associated with MG integration, such as harmonic distortion, system stability, and regulatory constraints, are discussed. The review further explores the application of intelligent control strategies, including Fuzzy Logic Controllers (FLCs) and Genetic Algorithms (GAs), to optimize battery energy storage systems (BESS) and improve overall MG performance. The findings emphasize the need for innovative optimization techniques and intelligent control systems to overcome current limitations, thus enhancing the potential of MGs in future energy systems

ST-AGNet: Dynamic power system state prediction with spatial–temporal attention graph-based network

Purpose of the Study. The study aims to investigate various approaches to mitigating the impact of renewable energy sources on the stability of the power system and to determine the optimal combination of conventional and renewable energy sources to ensure stable and reliable energy supply. The effectiveness of different approaches to minimizing the impact of renewable energy on grid stability is evaluated. Materials and Methods. Methods of eigenvalue analysis and transient process modeling were used. Correlation and regression analysis methods were used to assess the impact of renewable energy sources (RES) on the stability of the energy system. Data from the system operator «Ukrenergo» were used, including power system load schedules (hourly, daily, seasonal). Data from the Ukrainian Hydrometeorological Center for the years 2023–2024 were used to model RES generation: data on solar radiation (hourly, daily) and wind speed (hourly, daily). Technical specifications of solar panels and wind turbines installed in Ukraine were also used. The QGIS software was utilized for geoinformation analysis. The study employed artificial neural network theory, optimization methods based on linear programming and logical production rules, as well as methods for assessing and regulating steady-state operating conditions of the system, taking into account energy storage devices. Results. The scientific novelty lies in the development of an innovative method for assessing the reduction of the impact of renewable energy sources on power system stability. The feasibility of using artificial neural networks to forecast wind and solar power generation was demonstrated, as well as the optimization of the model structure and parameters to improve forecasting accuracy. The optimal combination of conventional and renewable energy sources was determined to ensure stable and reliable operation of the power system. Practical Significance. Based on experimental and theoretical research, specific recommendations and solutions are provided aimed at ensuring the stability of the power system during the integration of renewable energy sources, including optimization of management, use of new technologies, and infrastructure development

In modern power systems, efficient ground fault line selection is crucial for maintaining stability and reliability within distribution networks, especially given the increasing demand for energy and integration of renewable energy sources. This systematic review aims to examine various artificial intelligence (AI) techniques employed in ground fault line selection, encompassing artificial neural networks, support vector machines, decision trees, fuzzy logic, genetic algorithms, and other emerging methods. This review separately discusses the application, strengths, limitations, and successful case studies of each technique, providing valuable insights for researchers and professionals in the field. Furthermore, this review investigates challenges faced by current AI approaches, such as data collection, algorithm performance, and real-time requirements. Lastly, the review highlights future trends and potential avenues for further research in the field, focusing on the promising potential of deep learning, big data analytics, and edge computing to further improve ground fault line selection in distribution networks, ultimately enhancing their overall efficiency, resilience, and adaptability to evolving demands.

The increasing integration of inverter-based renewable energy sources has significantly altered power system dynamics and reduced inertia. Identifying coherent generators in such low-inertia systems remains a major challenge due to the dynamic influence of renewable variability. Previous research considered rotor angles and rotor speed separately. Also, the fast dynamics introduced by inverter-based renewables on power system variables make it pertinent to simultaneously consider rotor angles and rotor speed dynamics for coherency detection. Moreover, many authors have established coherency detection schemes but few have validated their methods with controlled islanding making their methods less practical for the modern grid with renewables. This paper proposes a unified coherency detection framework that combines rotor angles and rotor speeds within a dynamic state vector to capture both oscillatory and speed dynamics. An adaptive coherency threshold is computed from the geometric mean of the Euclidean distances between rotor state time series of distinct generators, allowing the threshold to adjust to changing system conditions. The framework is validated on the IEEE 30-bus system and applied to controlled islanding based on network topology and generation–load balance. The proposed framework achieved smooth dynamic responses following disturbances. These results confirm the method’s suitability for real-time application and its potential to improve resilience in modern power systems with high renewable integration.

A reactive scheduling and control framework for integration of renewable energy sources with a reformer-based fuel cell system and an energy storage device

Cybersecurity is crucial for modern power systems due to their high digitization. The open information and communication technology, that is (ICT) being used for the operation of such systems, is highly vulnerable to cyber threats. The adopted smart grid concept around the globe enables the utilization of demand-side for providing ancillary services based on well-known demand response (DR) programs. These programs aggregate smart appliances in homes and electric vehicles (EVs) for providing vital services such as frequency regulation and voltage support. Since the aggregation is based on cyber layer, any cyber threat could affect the ancillary services that are being delivered from the aggregators, which might lead to stability and security issues resulting in brownout or massive blackouts. This chapter discusses the cybersecurity in DR program and shows its importance for modern and future smart power systems due to their stability and security margins. Furthermore, the cyberattack case study is implemented in a power system with demand side program responsible for providing primary frequency support ancillary service, where the results confirm the high vulnerability of modern power systems to cyber threats on DR-active power reserve providers. Moreover, technical suggestions are provided for enhancing the cybersecurity in DR programs in power systems with high power share from renewable energy sources.

The cybersecurity of electric power grids is emerging as a critical challenge for the power industry in the transformation of modern power systems towards the future smart grid. It is of great importance to enhance the resilience of power systems against potential cyber threats. In this paper, a distributionally robust recovery resource allocation method based on the tri-level defender-attacker-defender (D-A-D) model is proposed to enhance the resilience of power systems in the face of malicious cyberattacks. The proposed recovery resource allocation method is able to optimally distribute the recovery resources among the substations in the grid to mitigate the impacts of successful cyberattacks during the recovery process, and improve the resilience performance of power systems against the attacks. In the proposed model, the recovery resource represents the available resource that can be expended to support the recovery efforts of the cyber-physical power system. It is expected to accelerate the recovery process of the system after successful attacks. The recovery resource may include necessary software, hardware, and labor force. Meanwhile, a distributionally robust optimization (DRO) model is proposed to address the uncertainty of the power system operation conditions, e.g., renewable energy resources (RESs). In order to verify the proposed system defense method, case studies were conducted on the IEEE Reliability Test System RTS-79. The results of the case studies show that the proposed method can provide a robust solution to recovery resource allocation for mitigating the risk of potential cyberattacks.