Grid Stability Research Articles

Research on the Circuit Breaker State Judgment Method Based on Ant Colony Optimization Dempster-Shafer Evidence Theory

The paper proposes a method for circuit breaker state judgment based on ant colony algorithm-optimized Dempster-Shafer evidence theory.It can improve the accuracy and robustness of state judgment. As a key device in the power system, the state judgment of circuit breakers is crucial for the safety and stability of the power grid. Existing methods have limitations in handling conflicts and uncertainties of multi-source data, and a single model is difficult to meet the needs of complex data fusion. Therefore, the paper applies the ant colony algorithm to optimize the basic probability assignment in Dempster-Shafer evidence theory to improve the fusion effect of multi-source data. The ant colony algorithm, through its global search and adaptive characteristics, can effectively optimize evidence combination and enhance the accuracy of the fusion results. The experiment used a support vector machine model based on current signals and a decision tree model based on vibration signals for data fusion and discrimination. The results showed that the Dempster-Shafer evidence theory model optimized by the ant colony algorithm achieved a discrimination accuracy of 75% under various circuit breaker conditions. Compared to the Dempster-Shafer evidence theory fusion model, it improved by approximately 8.3%, and compared to the current research’s Dempster-Shafer evidence theory and neural network methods, it improved by 5%.. This method has broad application prospects in enhancing the operational stability of power grid equipment and improving fault detection efficiency.

Transmission grid stability with large interregional power flows

We propose a general methodology for identifying critical lines in the long-distance transmission of power across large electric grids. When the system is pushed to its operational limit, for instance by large power imbalances such as those generated by high penetration of variable renewable energy sources, the network gets destabilized and loses synchrony. We investigate a model of the synchronous AC grid of continental Europe under tunable large interregional power flows. When those flows exceed some critical value, we find that instabilities emerge due to topological constraints. We identify two different scenarios triggering these instabilities. In the first one, specific sets of lines reach their maximal load simultaneously, causing the grid to split into two desynchronized zones. In the second one, one or a few lines become overloaded, which eventually renders one Lyapunov exponent positive. The first scenario is obviously less generic and we develop a numerical approach to force the splitting of the AC grid into disconnected areas. Remarkably, the critical lines identified in this way match those that triggered the separation of the synchronous grid of continental Europe in two instances in 2021. We further discuss how the modes of the system provide information on which areas are more susceptible to lose synchrony with each other. Published by the American Physical Society 2025

Coordinated frequency modulation strategy considering source–load uncertainty

IntroductionWind turbine generators (WTGs) and electric vehicles (EVs) are used as source-side and load-side resources, respectively. And the uncertainties of WTGs output and EVs charging load seriously affect the frequency stability of the power system. For the wind speed prediction error and the uncertainty of EV off-grid time, a cooperative frequency modulation (FM) strategy is proposed.MethodsFirstly, the wind speed interval is divided based on the operating characteristics of WTGs and the load reduction rate. On the basis, a load reduction operation strategy based on rotor speed control and pitch Angle control is proposed to enable WTGs to have bidirectional FM capability. The adjustable capacity of WTGs is determined based on the wind speed prediction error and the operation strategy of load reduction; Secondly, based on the controllable domain model of an EV considering the off-grid time uncertainty, the adjustable capacity of EV clusters is determined by state grouping of the state of charge (SOC) according to its charging urgency. By defining EV FM capability parameters and charging urgency parameters, the EV priority list for FM is determined and the power allocation strategy is proposed; Then, based on the urgency of EV charging and the economy of WTGs load reduction operation, a cooperative FM task assignment strategy is proposed.ResultsSimulations demonstrate the strategy enhances FM capability and improves FM effect by 6.05% compared to fixed-proportion task allocation. It strengthens frequency stability by leveraging complementary strengths.DiscussionConsideration of wind speed prediction errors can improve WTG adjustable capacity estimation, boosting FM accuracy; Coordinated task allocation minimizes WTGs intermittent FM output impacts, ensuring stable grid frequency. This dual-source-load approach offers a robust solution for modern power systems with high renewable penetration.

Dynamic Damping of Power Oscillations in High-Renewable-Penetration Grids with FFT-Enabled POD-P Controllers

The growing integration of renewable energy sources, particularly photovoltaic (PV) and wind power, presents challenges such as reduced system inertia and increased susceptibility to inter-area oscillations. These issues, coupled with stricter regulatory demands for grid stability, highlight the urgent need for effective damping solutions. This study proposes a novel method for detecting and mitigating inter-area oscillations using a power oscillation damping (POD) controller enhanced by applying a Fast Fourier Transform (FFT). The controller’s parameters are optimized through the Nobel Bat Algorithm (NBA) and fully implemented in DIgSILENT PowerFactory (DSPF). Simulations conducted on the New England IEEE-39 power system model under varying levels of renewable energy penetration demonstrate the model’s capability to dynamically detect, mitigate, and deactivate oscillations once stability is achieved. This work addresses emerging regulations requiring oscillation damping systems and offers a framework for certifying POD controllers for real-world implementation, ensuring their adaptability to diverse energy systems and regulatory contexts.

Holistic Hosting Capacity Enhancement Through Sensitivity-Driven Flexibility Deployment and Uncertainty-Aware Optimization in Modern Distribution Networks

This study presents a novel sensitivity-driven distributionally robust optimization framework designed to enhance hosting capacity in renewable-powered distribution networks through targeted flexibility resource deployment. The proposed approach integrates temporal sensitivity mapping with robust optimization techniques to prioritize resource allocation across high-sensitivity nodes, addressing uncertainties in renewable energy generation and load demand. By leveraging a dynamic interaction between sensitivity scores and temporal system conditions, the framework achieves efficient and resilient operation under extreme variability scenarios. Key methodological innovations include the incorporation of a social force model-based sensitivity mapping technique, a layered optimization approach balancing system-wide and localized decisions, and a robust uncertainty set to safeguard performance against distributional shifts. The framework is validated using a synthesized test system, incorporating realistic renewable generation profiles, load patterns, and energy storage dynamics. Results demonstrate a significant improvement in hosting capacity, with system-wide enhancements of up to 35% and a 50% reduction in renewable curtailment. Moreover, sensitivity-driven resource deployment ensures efficient utilization of flexibility resources, achieving a peak allocation efficiency of 90% during critical periods. This research provides a comprehensive tool for addressing the challenges of renewable integration and grid stability in modern power systems, offering actionable insights for resource allocation strategies under uncertainty. The proposed methodology not only advances the state-of-the-art in sensitivity-based optimization but also paves the way for scalable, resilient energy management solutions in high-renewable penetration scenarios.

Blockchain-Enhanced Demand-Side Management for Improved Energy Efficiency and Decentralized Control

Blockchain technology introduces significant advancements in demand-side management (DSM) by enabling decentralized, transparent, and secure data handling within energy systems. This study explores a blockchain-based approach aimed at improving electricity efficiency through enhanced DSM strategies. We present a comprehensive framework that integrates blockchain technology with real-time data analytics to optimize energy consumption, stimulate consumer participation, and support efficient energy utilization. The experimental analysis demonstrates that blockchain integration significantly strengthens DSM operations by reducing operational costs, increasing participation in demand response programs, and enhancing grid stability. The findings highlight the effectiveness and potential of the proposed approach as a forward-looking solution for energy management systems.

Photovoltaic solar energy prediction using the seasonal-trend decomposition layer and ASOA optimized LSTM neural network model

As the global energy demand continues to produce, photovoltaic (PV) solar energy has emerged as a key Renewable Energy Source (RES) due to its sustainability and potential to reduce dependence on fossil fuels. However, accurate forecasting of Solar Energy (SE) output remains a significant challenge due to the inherent variability and intermittency of solar irradiance (SI), which is affected by factors such as weather conditions, geographic location, and seasonal patterns. Reliable prediction models are crucial for optimizing energy management, ensuring grid stability, and minimizing operational costs. To address these challenges, this research introduces an innovative method that integrates Robust Seasonal-Trend Decomposition (RSTL) with an Adaptive Seagull Optimisation Algorithm (ASOA)-optimized Long Short-Term Memory (LSTM) neural network. Using RSTL to differentiate between time series data into development, seasonal in nature, and residual factors, this methodology addresses SI’s unpredictable nature and intermittent operation and provides the basis for accurate predictions. ASOA improves LSTM features by constantly finding and exploiting resources and adopting motivation from seagulls’ collecting and migration behaviours. Parameter standardization employing ASOA, the RSTL decomposition approach, and the conceptual model of LSTM networks are all presented in this research work. The proposed method has been contrasted with conventional methods by applying a testing environment incorporating essential Meteorological Factors (MF) and historical SE datasets. The study of performance measurements (RMSE, MAE, and R2) demonstrates significant improvements in the accuracy of predictions. The research results highlight significant implications regarding subsequent studies and real-world uses in SE prediction, accentuating the positive impacts of incorporating accurate data decomposition and adaptive optimized performance.

Hardware-in-the-Loop testing for low-inertia grids

Abstract Extensive testing of various control algorithms using comprehensive testing frameworks enables us to assess their effectiveness in managing system dynamics and maintaining grid stability. In order to validate the algorithms in the real world, testing of the developed applications in an empirical laboratory environment is essential and the testing parameters in the laboratory have to closely resemble real-world conditions. The fundamental aspect of such testing is to monitor and analyze the dynamics and interaction between the various components in synchronous mode, thereby assessing the system’s ability to operate the grid without violations. The experimental test setup and script-based testing explained in this paper helps to develop innovative control algorithms, with a key focus on medium voltage and low voltage grids with low rotating masses. The test platform makes extended use of a Typhoon Real-Time Simulator (RTS). Firstly, it is used for the simulation of the distributed energy resources and the overlying distribution grid as well as the implementation of control algorithms. Secondly, the RTS acts as a control and SCADA system for the whole testing platform. While the physical components provide measurements in regular intervals, the control algorithms and the energy management system are implemented directly in the Typhoon platform. The data acquisition is performed either via programmable logic controllers for the nodal measurements or through faster measurements such as current transformers. This ensures that the developed algorithms are empirically tested and optimized in close to real-world conditions. The control algorithms provide the set points for the individual components, and the results show the advantages of script-based testing in HIL testing to ensure deterministic and reliable operation of innovative grid components.

The Impact of Integrating Variable Renewable Energy Sources into Grid-Connected Power Systems: Challenges, Mitigation Strategies, and Prospects

Although the impact of integrating solar and wind sources into the power system has been studied in the past, the chaos caused by wind and solar energy generation has not yet had broader mitigation solutions notwithstanding their rapid deployment. Many research efforts in using prediction models have developed real-time monitoring of variability and machine learning predictive algorithms in contrast to the conventional methods of studying variability. This study focused on the causes and types of variability, challenges, and mitigation strategies used to minimize variability in grids worldwide. A summary of the top ten cases of countries that have successfully managed variability in their electrical power grids has been presented. Review shows that most of the success cases embraced advanced energy storage, grid upgrading, and flexible energy mix as key technological and economic strategies. A seven-point conceptual framework involving all energy stakeholders for managing variability in power system networks and increasing variable renewable energy (VRE)-grid integration has been proposed. Long-duration energy storage, virtual power plants (VPPs), smart grid infrastructure, cross-border interconnection, power-to-X, and grid flexibility are the key takeaways in achieving a reliable, resilient, and stable grid. This review provides a useful summary of up-to-date research information for researchers and industries investing in a renewable energy-intensive grid.

Study on evolution characteristics of energy dissipation and vortex in pump-turbine during load rejection transition process

The widespread adoption of intermittent wind and solar energy sources disrupts the stability of power grids, necessitating frequent load rejection processes for pump-turbine. This jeopardizes the stability of pump-turbine units. To investigate the operating behavior of pump-turbine units during load rejection, this study focuses on a Francis reversible pump-turbine. Employing the results from a one-dimensional characteristic line method as boundary conditions, three-dimensional numerical simulations are conducted. The entropy production theory was employed to quantify energy loss in different components, including direct dissipation, turbulent dissipation, and wall shear stress. And the pressure load curves of runner blades at different spans are extracted. The flow characteristics, entropy production losses, and pressure loads within the passage are analyzed at various moments during the load rejection process. Results demonstrate that when a unit undergoes load rejection, it repeatedly transitions into and out of the S-characteristic region. The flow state within the passage is most unfavorable under reverse pump conditions, with the entropy production loss reaching its maximum value of 339 160 W/K. When the unit's rotational speed increased beyond 1.1nd, the unit's water thrust and torque underwent drastic changes. Moreover, the pressure load undergoes significant variations under runaway conditions, with the pressure load difference among various blades reaching up to 33 000 kPa. These findings provide a scientific basis for ensuring the safe and reliable operation of pump-turbine units during load rejection processes.

Enhancing Electricity Load Forecasting with Machine Learning and Deep Learning

The electricity load forecasting handles the process of determining how much electricity will be available at a given time while maintaining the balance and stability of the power grid. The accuracy of electricity load forecasting plays an important role in ensuring safe operation and improving the reliability of power systems and is a key component in the operational planning and efficient market. For many years, a conventional method has been used by using historical data as input parameters. With swift progress and improvement in technology, which shows more potential due to its accuracy, different methods can be applied depending on the identified model. To enhance the forecast of load, this paper introduces and proposes a framework developed on graph database technology to archive large amounts of data, which collects measured data from electrical substations in Pristina, Kosovo. The data includes electrical and weather parameters collected over a four-year timeframe. The proposed framework is designed to handle short-term load forecasting. Machine learning Linear Regression and deep learning Long Short-Term Memory algorithms are applied to multiple datasets and mean absolute error and root mean square error are calculated. The results show the promising performance and effectiveness of the proposed model, with high accuracy in load forecasting.

Maximizing Wind Turbine Power Generation Through Adaptive Fuzzy Logic Control for Optimal Efficiency and Performance

Wind power output fluctuations, driven by variable wind speeds, create significant challenges for grid stability and the efficient use of wind turbines, particularly in high-wind-penetration areas. This study proposes a combined approach utilizing an ultra-capacitor energy storage system and fuzzy-control-based pitch angle adjustment to address these challenges. The fuzzy control system dynamically responds to wind speed variations, optimizing energy capture while minimizing mechanical stress on turbine components, and the ultra-capacitor provides instantaneous buffering of power surpluses and deficits. Simulations conducted on a 50 kW DFIG wind turbine powering a 23 kW load demonstrated a substantial reduction in power fluctuations by a factor of 3.747, decreasing the power fluctuation reduction scale from 13.04% to 3.48%. These results highlight the effectiveness of the proposed system in improving the stability, reliability, and quality of wind energy, thereby advancing the broader adoption of renewable energy and contributing to sustainable energy solutions.

Role of energy storage technologies in enhancing grid stability and reducing fossil fuel dependency

Role of energy storage technologies in enhancing grid stability and reducing fossil fuel dependency

Decentralized Framework for Securing Smart Grids Using Blockchain and Machine Learning

Abstract: The transition to smart grids has revolutionized energy distribution, enabling more efficient and flexible power management through advanced communication and control systems. However, this interconnected structure makes smart grids vulnerable to cyberattacks, such as False Data Injection Attacks (FDIA), Distributed Denial of Service (DDoS) attacks, and data manipulation. These threats undermine the stability and reliability of the grid, and existing centralized security frameworks are often ill-equipped to address them due to their susceptibility to single points of failure and limited scalability. To overcome these challenges, this paper introduces a decentralized security framework that combines blockchain technology with machine learning (ML). The framework leverages blockchain to provide a transparent, immutable, and decentralized ledger, employing consensus mechanisms like Practical Byzantine Fault Tolerance (PBFT) or Proof of Authority (PoA) to ensure secure data validation. Alongside this, ML models, including Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN), are used to detect anomalies in time-series data, such as FDIA, with high precision. Smart contracts embedded in the blockchain enable automated, real-time responses to threats, such as isolating compromised nodes or rerouting energy flows to maintain grid stability. Through simulations replicating real-world cyberattacks, the proposed framework demonstrated over 95% detection accuracy, a 30% reduction in response times, and enhanced computational efficiency with lower energy consumption. These results affirm the effectiveness of the framework as a scalable and resilient solution for modern smart grid security.

Strategic modelling for sustainable power grids: a comprehensive study on energy storage integration in India

Purpose This study aims to address the critical need for innovation in the power grid sector, driven by global carbon reduction commitments. It highlights the pivotal role of critical success factors (CSFs) in enhancing system adaptability and environmental mitigation within India’s power industry. Design/methodology/approach This research is grounded on transition management theory to identify and validate the CSFs necessary to integrate energy storage systems (ESS). Here, exploratory factor analysis (EFA) and total interpretive structural modeling (TISM) are integrated to evaluate the model’s effectiveness in reducing CO2 emissions while ensuring grid stability and flexibility. Findings The research develops a seven-level hierarchical model illustrating the interaction of ESS components for a stable power grid, clean energy and a profitable electric industry. It emphasizes the strategic significance of managing key factors to reduce CO2 emissions and ensure grid stability. The study recommends continuous monitoring at tactical and operational levels to enhance overall performance. Research limitations/implications The study provides policymakers with strategic insights for the successful implementation of smart grid initiatives, facilitating effective decarbonization of the electricity industry. Additionally, it offers a comprehensive framework for minimizing the environmental impact associated with electricity generation, thereby enhancing overall operational sustainability and efficiency. Originality/value The originality of this study lies in its integration of EFA and TISM for robust model assessment and the application of transition management theory to identify and validate CSFs in the integration of ESS. This approach offers a novel perspective on enhancing the sustainability and efficiency of power grids.

Navigating the Intersection of Microgrids and Hydrogen: Evolutionary Trends, Challenges, and Future Strategies

Growing interest in sustainable energy has gathered significant attention for alternative technologies, with hydrogen-based solutions emerging as a crucial component in the transition to cleaner and more resilient energy systems. Following that, hydrogen-based microgrids, integrated with renewable energy sources including wind and solar, have gained substantial attention as an upcoming pathway toward long-term energy sustainability. Hydrogen, produced through processes such as electrolysis and steam methane reforming, can be stored in various forms including compressed gas, liquid, or solid-state hydrides, and later utilized for electricity generation through fuel cells and gas turbines. This dynamic energy system offers highly flexible, scalable, and resilient solutions for various applications. Specifically, hydrogen-based microgrids are particularly suitable for offshore and islanded applications, with geographical factors, adverse environmental conditions, and limited access to conventional energy solutions. This is critical for energy independence, long-term storage capacity, and grid stability. This review explores topological and functional-based classifications of microgrids, advancements in hydrogen generation, storage, and utilization technologies, and their integration with microgrid systems. It also critically evaluates the key challenges of each technology, including cost, efficiency, and scalability, which impact the feasibility of hydrogen microgrids.

Research on Multi-Objective Reactive Power Optimization of Distribution Grid with Photovoltaics

With the introduction of large distributed photovoltaic (PV) power and electric vehicles, the inherent volatility of their output makes it difficult for traditional power grid structures and reactive power optimization methods to meet the needs of the safe operation of distribution grids and economic benefits. Therefore, a multi-objective reactive power optimization method for a distributed grid is proposed under a distributed PV power generation scenario. Aiming at the two objectives of the network loss and voltage fluctuation rate, the improved multi-objective particle swarm optimization algorithm is used to solve the model under the condition that the output of each device does not exceed the constraint and the optimal solution that can reduce the distribution grid loss and improve the voltage stability of the distribution grid is obtained. The simulation was conducted on the IEEE 33-node and 113-node distribution networks to verify the proposed method’s feasibility.

A Review of Synergies Between Advanced Grid Integration Strategies and Carbon Market for Wind Energy Development

The integration of wind energy into power systems is essential for achieving global decarbonization goals but poses significant challenges, including transmission losses, grid instability, and risks of wind farm disconnection during contingencies. This review focuses on advanced grid stability technologies, optimization strategies, and carbon trading mechanisms, proposing a synergistic framework to address these issues. By enhancing transmission efficiency and maintaining grid stability, these solutions reduce energy losses, contribute to carbon reduction, and create economic incentives through carbon credits. Moreover, optimization models enable wind farms to remain operational during severe faults, ensuring their active participation in carbon markets. This review connects recent technical advancements with economic and policy frameworks, offering a comprehensive pathway to achieving sustainable and stable power systems while maximizing the economic potential of wind energy.

Hybrid Variable Renewable Power Plants: A Case Study of ROR Hydro Arbitrage

Wind and solar energy sources, while sustainable, are inherently variable in their power generation, posing challenges to grid stability due to their non-dispatchable nature. To address this issue, this study explores the synergistic optimization of wind and solar photovoltaic resources to mitigate power output variability, reducing the strain on local grids and lessening the reliance on balancing power in high-penetration renewable energy systems. This critical role of providing stability can be effectively fulfilled by run-of-river hydropower plants, which can complement fluctuations without compromising their standard operational capabilities. In this research, we employ a straightforward energy balance model to analyze the feasibility of a 100 MW virtual hybrid power plant, focusing on the northern region of Portugal as a case study. Leveraging actual consumption and conceptual production data, our investigation identifies a specific run-of-river plant that aligns with the proposed strategy, demonstrating the practical applicability of this approach.

The application study of deep learning technology in intelligent power supervision systems

Abstract This study explores the application of a combined model incorporating bidirectional long short-term memory networks and gated recurrent units within intelligent power supervision systems, demonstrating its efficacy in recognizing anomalies within power grids. Based on historical operational data of two critical indicators—grid sectional security margin and generation-consumption balance margin—this paper constructs an anomaly detection model. This model effectively processes time series data, promptly identifying equipment malfunctions and abnormal load fluctuations, thereby enhancing the safety and stability of power grids.