Multilayered Defense Against Oscillations: Mitigation of Oscillation Risk in Inverter-Dominated Power Grids

Risk Assessment at the Edge: Applying NERC CIP to Aggregated Grid-Edge Resources

Increased penetration of renewable energy sources poses new challenges to the power grid. Grid integrated energy storage combined with fast-ramping conventional generation can help to address challenges associated with power output variability. This paper proposes a risk mitigating optimal power flow (OPF) framework to study the dispatch and placement of energy storage units in a power system with wind generators that are supplemented by fast-ramping conventional back-up generators. This OPF with storage charge/discharge dynamics is solved as a finite-horizon optimal control problem. Chance constraints are used to implement the risk mitigation strategy. The model is applied to case studies based on the IEEE 14 bus benchmark system. First, we study the scheduling of spinning reserves and storage when generation and loads are subject to uncertainties. The framework is then extended to investigate the optimal placement of storage across different network topologies. The results of the case studies quantify the need for storage and reserves as well as suggest a strategy for their scheduling and placement.

Phasor measurement units (PMUs) can be effectively utilized for the monitoring and control of the power grid. As the cyber-world becomes increasingly embedded into power grids, the risks of this inevitable evolution become serious. In this paper, we present a risk mitigation strategy, based on dynamic state estimation, to eliminate threat levels from the grid's unknown inputs and potential cyber-attacks. The strategy requires (a) the potentially incomplete knowledge of power system models and parameters and (b) real-time PMU measurements. First, we utilize a dynamic state estimator for higher order depictions of power system dynamics for simultaneous state and unknown inputs estimation. Second, estimates of cyber-attacks are obtained through an attack detection algorithm. Third, the estimation and detection components are seamlessly utilized in an optimization framework to determine the most impacted PMU measurements. Finally, a risk mitigation strategy is proposed to guarantee the elimination of threats from attacks, ensuring the observability of the power system through available, safe measurements. Case studies are included to validate the proposed approach. Insightful suggestions, extensions, and open problems are also posed.

In recent years, situation awareness and risk mitigation have become the challenging issues in large‐scale power grids. This study presents a novel pair‐wise relative energy function for real‐time transient stability analysis and emergency control. The proposed energy function is able to accurately identify the clusters of critical and non‐critical generators significantly faster when compared with previous methods. Additionally, a new emergency control criterion is proposed in order to stabilise the identified critical generators within a comparatively short interval after fault clearance. The emergency control scheme computes the capacity of the requisite generation curtailment using the pre‐calculated relative energy of the equivalent post‐fault system. Finally, the relative energy oscillation trajectories that occur in the critical cluster are utilised in order to locate the most appropriate generator to launch the emergency control. When compared with the existing methods, it is evident that the novel approach can be applied practically for power systems transient stability analysis and emergency control. The effectiveness of the proposed method is demonstrated and evaluated using the New England 10 machines 39‐bus and 16 machines 68‐bus systems.

Power grids are becoming increasingly intelligent. In this regard, they benefit considerably from the information technology (IT) networks coupled with their underlying operational technology (OT) networks. While IT networks provide sufficient controllability and observability of power grid assets such as voltage and reactive power controllers, distributed energy resources, among others, they make those critical assets vulnerable to cyber threats and risks. In such systems, however, several technical and economic factors can significantly affect the patching and upgrading decisions of their components including, but not limited to, limited time and budget as well as legal constraints. Thus, resolving all vulnerabilities at once could seem like an insuperable hurdle. To figure out where to start, an involved decision maker (e.g. a security team) has to prudently prioritize the possible vulnerability remediation actions. The key objective of prioritization is to efficiently reduce the inherent security risk to which the system in question is exposed. Due to the critical role of power systems, their decision makers tend to enhance the system resilience against extreme events. Thus, they seek to avoid decision options associated with likely severe risks. Practically, this risk attitude guides the decision-making process in such critical organizations and hence the sought-after prioritization as well.Therefore, the contribution of this work is to provide an integrated risk-based decision-support methodology for prioritizing possible remediation activities. It leverages the Time-To-Compromise security metric to quantitatively assess the risk of compromise. The developed risk estimator considers several factors including: i) the inherent assessment uncertainty, ii) interdependencies between the network components, iii) different adversary skill levels, and iv) public vulnerability and exploit information. Additionally, our methodology employs game theory principles to support the strategic decision-making process by constructing a chain of security games. Technically, the remediation actions are prioritized through successively playing a set of dependent zero-sum games. The underlying game-theoretical model considers carefully the stochastic nature of risk assessments and the specific risk attitude of the decision makers involved in the patch management process across electric power organizations.

With the wide application of high voltage/ultra-high voltage (HV/UHV) DC transmission technology, the impact of DC grounding electrode location selection on the surrounding power grid has become increasingly prominent, especially the problem of DC bias hazard caused by DC grounding electrodes at provincial grid boundaries needs to be solved urgently. This paper studies the assessment and prevention of trans-regional DC bias risk, and proposes an inversion method of earth resistivity model based on the measured data of neutral current in the provincial boundary area. Firstly, the DC bias risk of provincial boundary power grid is simulated and calculated, and the influence of reasonable selection of earth model on the accuracy of risk assessment results is explained. Based on the classical Fletcher-Reeves conjugate gradient method and the measured neutral current data, a reduced-order inversion method for the earth resistivity parameters of the provincial boundary power grid is proposed. On this basis, a set of DC bias risk control scheme is formulated for the actual project of Gannan power grid. Finally, the feasibility of the scheme is verified by simulation and measured data.

Numerous recent papers have found important relationships between network structure and risks within networks. These results indicate that network structure can dramatically affect the relative effectiveness of risk identification and mitigation methods. With this in mind this paper provides a comparative analysis of the topological and electrical structure of the IEEE 300 bus and the Eastern United States power grids. Specifically we compare the topology of these grids with that of random, preferential-attachment and small-world networks of equivalent sizes and find that power grids differ substantially from these abstract models in degree distribution, clustering, diameter and assortativity, and thus conclude that these abstract models do not provide substantial utility for modeling power grids. To better represent the topological properties of power grids we introduce a new , the minimum distance graph, that produces networks with properties that more nearly match those of known power grids. While these topological comparisons are useful, they do not account for the physical laws that govern flows in electricity networks. To elucidate the electrical structure of power grids, we propose a new method for representing electrical structure as a weighted graph. This analogous representation is based on electrical distance rather than topological connections. A comparison of these two representations of the test power grids reveals dramatic differences between the electrical and topological structure of electrical power systems.

Twenty-five years of research has produced a wide range of models of the causal processes linking solar wind to geomagnetic disturbances to geomagnetically induced currents (GICs) and grid vulnerability. This review places the main concepts of each of the publications from the last 25 years in context with the others, categorizing them according to the following four themes: 1) flow of energy and momentum from the Sun to the Earth via the solar wind; 2) response of the terrestrial system to solar wind energy and momentum in the form of geomagnetic disturbances; 3) generation of quasi-DC electric currents (geomagnetically induced currents/GIC) in electric power grids as a consequence of geomagnetic disturbances (GMD); or 4) impact of GIC on operations of power grids. This review also reveals gaps in the knowledge of modeling, geophysical parameters, and the implications of GICs for power grids. More measurements from space, of geomagnetic disturbances, and on power systems are needed. There is scope to guide policy on the mitigation of societal risk and improve space weather forecasts for regular operational use by utilities.

The rising multifaceted nature of cyber threats to critical Power Grid Infrastructure calls for new approaches in ensuring power systems resiliency and stability. Existing methods based on deep learning and predictive analysis, have shown significant gains in the area of threat mitigation and response time. However, such approaches have been rendered limited in their scope by their lack of dynamic reaction to the shifts in cyberattack tactics, particularly in sequential and multi-dimensional settings. This paper presents the novel Power Grid Security Alert and Emergency Response System through the use of integrating Long Short-Term Memory (LSTM) networks into a Quantum Entropy Q-Learning (QEQ) model. The hybrid LSTM-QEQ framework utilizes the complementarity between the temporal structure modeling abilities of the LSTMs and decision making based on entropy optimization by QEQ in detecting, predicting, and responding to cyber threats more efficiently. The developed system overcomes some of the shortcomings of existing methods by improving resilience to complex attack scenarios and guaranteeing optimal responses through quantum-inspired learning. This solution enables real-time detection of anomalies, pre-emptive failure prevention, and appropriate responses to a scenario. The main benefits include higher threat detection rates, lower response time to incidents, and the ability to operate in large and flexible grid networks. With the application of AI and quantum concepts, this work provides a solution to further enhance the performance of critical energy assets while strengthening the security against contemporary cyber threats. The results suggest that there has been a notable improvement in the threat detection rate, now standing at 99%, and a reduction in incident response time of over 75% allowing witness the potential artificial intelligence holds for efficient and timely identification, as well as the mitigation of cyber-related risks. Moreover, deep learning algorithms have made it possible to forecast failures and cyber-attacks, enhancing proactivity and cyber security management greatly with regards to Power Grid Infrastructure.

Accurate wind prediction is critical across engineering disciplines. For coastal infrastructure, it determines wave loads and storm surge resilience, directly impacting millions in vulnerable low-lying regions. The energy sector relies on precise forecasts to optimize wind farm output and stabilize power grids, while agriculture uses wind data to time pesticide applications and protect crops. Aviation and shipping industries leverage predictions for fuel-efficient routing and hazard avoidance, and urban engineers integrate wind models for skyscraper design and air pollution management. As climate change amplifies wind extremes, advancing predictive capabilities has become an urgent cross-sector priority for adaptive planning and risk mitigation. In coastal applications, empirical wave models (e.g., SWAN and WAVEWATCH III) heavily depend on accurate wind inputs, where errors can lead to underestimation of extreme events and compromise structural safety. This study introduces a novel deep learning framework, integrating advanced data preprocessing, structured neural networks, and explainable AI techniques, to enhance short-term (hourly) wind speed forecasting for coastal engineering applications, addressing the gap in region-specific deep learning frameworks for operational forecasting. The proposed method in this study addresses critical gaps in traditional methods by combining physical constraints with data-driven learning. It presents an innovative framework for wind speed data processing and prediction, integrating deep learning architectures with comprehensive meteorological analysis. Our research implements a sophisticated neural network model that processes high-frequency wind data from Bowen, incorporating multiple environmental parameters through a systematic data pipeline. The methodology encompasses three key components: (1) advanced data preprocessing, including time series standardization and cyclical feature encoding; (2) a deep learning architecture featuring three hidden layers (128-64-32 nodes) with ReLU activation and dropout regularization; and (3) comprehensive performance evaluation using five-fold cross-validation. The model achieved remarkable accuracy metrics: R 2 = 0.957, RMSE = 0.449 m/s, demonstrating robust performance across varying weather conditions. Analysis revealed distinct performance patterns across wind speed ranges (low-speed MAE: 0.295 m/s; high-speed MAE: 0.433 m/s). The SHAP (SHapley Additive exPlanations) analysis provided deeper insights into feature importance and model interpretability, revealing Wind Direction (0.713 SHAP value) as the most influential predictor, followed by Relative Humidity (0.609) and Barometric Pressure (0.563). Temporal features (month, hour, and day) exhibited lower but consistent influence (SHAP values < 0.239). This research advances the field of environmental data science by providing: (1) a reproducible framework for wind speed prediction, (2) insights into feature significance and model behavior, and (3) practical applications for renewable energy planning and meteorological forecasting. The demonstrated methodology offers a foundation for future research in environmental modeling and time series prediction.

Components of the electric power grid that were traditionally deployed in physically isolated networks, are now using IP based, interconnected networks to transmit Supervisory Control and Data Acquisition (SCADA) messages. SCADA protocols were not designed with security in mind. Therefore, in order to enhance security, access control and risk mitigation, operators need detailed and accurate information about the status, integrity, configuration and network topology of SCADA devices. This paper describes a comprehensive system architecture that provides situational awareness (SA) for SCADA devices and their operations in a Smart Grid environment. The proposed SA architecture collects and analyzes industrial traffic and stores relevant information, verifies the integrity and the status of field devices and reports identified anomalies to operators.

The power grid safety is achieved by eliminating or decreasing to an acceptable level of all grid operation-related hazards for the society and human beings. The development of the smart grid (SG) concept stipulates the need for implementing novel approaches to grid safety analysis and risk mitigation. These approaches must be developed considering the informational technologies and innovations. Nuclear Power Plants (NPPs) are an integral part of the existing and future grids. Safe and reliable operation of an NPP implies efficiency, safety, and reliability of the grid to which it is connected and vice versa. The paper presents principles and techniques of SG safety assessment based on accident risk-analysis using dynamical and hierarchical criticality matrices. The critically of the grid system failure is taken as a safety index. The SG safety assessment considering the NPP influence could be extended due to analysis and prediction of changes in the state of SG systems or changes in the failure probability caused by operational environment, other failures or time factor. The shut down of Zaporizhzhya NPP unit 2 was investigated by means of dynamical and hierarchical criticality matrices-based analysis.

Security, access control and risk mitigation in the smart grid are matters of great impact for this important sector of the critical infrastructure. Situational awareness requires a means of aggregating information and presenting that information in a manner conducive to assessing risk. While major components of the electric power grid were traditionally deployed in physically isolated networks, they are now utilising IP-based, open, interconnected networks to transmit and manage the supervisory control and data acquisition (SCADA) messages. Unfortunately, SCADA protocols used for communications and the systems that implement those protocols were not originally designed with security in mind. Therefore, in order to enhance security and detect potential malicious behaviour, smart grid operators need detailed and accurate information about the status, integrity, configuration and network topology of SCADA devices as well as information about any threats that may impact the grid. This paper describes a comprehensive framework that provides situational awareness (SA) for SCADA devices and their operations in a smart grid environment. Situational awareness is achieved by processing information collected by monitoring sensors and understanding threats that may affect operations. The proposed framework employs a threat modelling methodology to support this mission.

The rising global demand for energy, coupled with the transition to renewable sources, has significantly increased the need for efficient battery storage systems. These systems are essential for balancing energy supply and demand, stabilizing the power grid, and supporting the expansion of electric vehicles and decentralized energy networks. This article explores the critical role of battery storage in modern energy infrastructure, highlighting its ability to facilitate renewable energy integration, ensure grid reliability, and enhance energy security. Furthermore, it examines how data analytics play a transformative role in optimizing battery performance through predictive maintenance, demand forecasting, and real-time monitoring. Effective project management is also essential in the deployment of battery storage solutions, ensuring strategic planning, risk mitigation, and seamless integration with existing power systems. Through a case study of an innovative approach, this paper demonstrates how a data-driven and well-managed implementation of battery storage solutions can improve energy efficiency, reduce costs, and enhance grid stability. As the energy sector continues to evolve, the synergy between technology, analytics, and structured project management will be pivotal in shaping a sustainable and resilient future.

Enhanced design and development simulation and testing using digital twins represent a significant advancement in the field of renewable energy systems. Digital twins, which are virtual replicas of physical assets, allow for comprehensive simulation and testing of various scenarios and new technologies within a virtual environment. This approach offers numerous benefits, including the ability to optimize designs, predict performance, and troubleshoot potential issues before actual deployment. By leveraging high-fidelity models and real-time data, digital twins provide an invaluable tool for improving the efficiency, reliability, and sustainability of renewable energy systems. The use of digital twins in renewable energy extends across different stages of the project lifecycle. During the design phase, engineers can test multiple configurations and materials, ensuring that the final design meets all performance and durability requirements. This reduces the need for physical prototypes, saving both time and costs. In the development phase, digital twins enable the simulation of operational conditions, helping to identify and mitigate risks early. For instance, they can model the impact of environmental factors such as wind, temperature, and load variations on wind turbines or solar panels. Once the systems are operational, digital twins continue to play a crucial role in maintenance and optimization. They provide continuous monitoring and diagnostics, predicting failures before they occur and suggesting maintenance schedules that minimize downtime. This proactive maintenance approach enhances the longevity and efficiency of renewable energy assets. Moreover, digital twins facilitate the integration of renewable energy systems with existing power grids, ensuring smooth and efficient operation. The implementation of digital twins also supports the testing of innovative technologies and strategies, such as energy storage solutions and smart grid applications, in a controlled, virtual environment. This capability is essential for advancing renewable energy technologies and achieving higher levels of sustainability and energy efficiency. As the renewable energy sector continues to evolve, digital twins will undoubtedly become an integral part of the design, development, and operational processes, driving innovation and improving the overall effectiveness of renewable energy systems. Digital twins offer a transformative approach to enhancing the design and development of renewable energy systems. By enabling detailed simulation and testing in a virtual environment, they provide significant advantages in terms of optimization, risk mitigation, and operational efficiency, ultimately contributing to the advancement of sustainable energy solutions. Keywords: Design Optimization, Stimulation, Digital Twin, Virtual Environment.