Enhance Power System Research Articles

Accelerating optimal scheduling prediction in power system: A multi-faceted GAN-assisted prediction framework

This study introduces a comprehensive framework aimed at enhancing power system optimality through a two-stage optimization process and the development of a deep-based model for optimal scheduling prediction (OSP). Initially, a Bidirectional Long Short-term Memory (Bi-LSTM) architecture is employed to accurately forecast wind power in the first stage. Subsequently, a convolutional Generative Adversarial Network (GAN) model utilizes these predicted wind power values to generate synthetic scenarios. These scenarios, based on the preceding 10 days’ wind power predictions, serve as inputs for the subsequent power system optimization stage. To streamline computational efficiency, the power system optimization is conducted via a two-stage model. The outputs from this process, alongside other pertinent parameters, are utilized to train the proposed deep-based OSP model. The efficacy of the proposed model in rapidly and reliably predicting optimal scheduling is evaluated using the 118-bus power system. Results indicate that the innovative approach demonstrates exceptional speed and precision in determining optimal scheduling for the power system. Specifically, the proposed OSP model accurately forecasts optimal dispatch for ten days ahead in a mere 0.38 s, with an error rate below 0.001. Furthermore, the model exhibits a 92 % correlation in predicting optimal dispatched wind power. Sensitivity analysis highlights that optimizing the arrangement of the proposed deep-based model using an automatic hyperparameter optimization software framework (OPTUNA) can significantly enhance performance accuracy, potentially by up to 24 %.

Identification of vulnerable lines in power grids with wind power integration based on topological potential

Vulnerable transmission lines are one of the weak links in the operation of the power system, as their damage can trigger widespread blackouts and cascading failures. The integration of large-scale wind power generation into the grid has resulted in significant changes to both the topology and distribution patterns of power flows inside the system, exacerbating line vulnerabilities. Identifying these lines can enhance power system safety through vigilant monitoring or augmenting their carrying capacity. In this paper, a method for identifying vulnerable lines based on the theory of topological potential is proposed. The method is based on the N-1 fault model of optimal DC power flow, and a corresponding network is constructed for the power system. The task of pinpointing weak points in a complicated network is converted into an assessment problem of important nodes. The proposed index comprehensively considers the in-degree and out-degree topological potentials of nodes, and uses the entropy weight method for weight allocation. Meanwhile, the TLS distribution is used to fit the wind power prediction error. Finally, simulation experiments are conducted on the IEEE 39-bus system. Simulation results indicate that the most vulnerable transmission lines tend to be located at the hubs of the system’s topological network and in close proximity to wind farms.

Decentralized solutions for island states: Enhancing energy resilience through renewable technologies

Decentralized grid solutions could be a feasible alternative to improve resilience and mitigate cascading effects in island states. Our study explores approaches that reduce the risk of infrastructure failures and promote decentralized utility planning in islands. A novel framework is proposed to conduct a power system resilience assessment by integrating vulnerability assessments and energy system modelling approaches through network analysis. The framework is applied to an island context, where vulnerability to hydroclimatic hazards, geographic isolation, restricted access to energy sources, small population bases inadequate for substantial infrastructure investments, dependence on imported energy, lack of energy source diversification, and fragile ecosystems have exacerbated energy insecurity. As a case study, we have applied the framework to Cuba. We simulate disruptions in vulnerable network nodes in Cuba to determine the municipalities that are most impacted by the simulated cascading failures. We designed and optimized the lowest cost decentralized solutions to increase resilience either by acting as the baseload electricity source or as a complementary backup system to complement in case of a power outage. Then, the resilience of the designed system was assessed using power system resilience metrics. The study results show Regla municipality in Cuba as the most vulnerable hotspot for electricity distribution. Upon the different system comparisons, ancillary systems outperform backup systems in enhancing power system resilience, especially in the context of a disruptive event, supplying up to 53 MWh/day more, although they have higher investment costs. Based on this research, resource planners and policymakers can understand vulnerable node points and prioritize the necessary investments for the preferred system choice to alleviate impacts of energy insecurity on the Island States.

Impact of grading capacitor on transient recovery voltage due to shunt reactor de-energization for different values of current chopping

This paper investigates the impact of grading capacitors on transient recovery voltage (TRV) during shunt reactor switching in high voltage systems, considering different levels of current chopping. Shunt reactor de-energization can result in voltage surges and instability due to current chopping effects. The study utilizes simulation models using ATP-Draw software to assess the effectiveness of grading capacitors in mitigating TRV under various operating conditions as well as of using a proposed method to mitigate the excessive TRV across the circuit breaker. The findings provide valuable insights into managing TRV during shunt reactor switching, enhancing power system stability and reliability. The results obtained showed that the TRV across the circuit breaker decreased by 61.5% by using circuit modification as well as adding a grading capacitor.

Robust LFC design using adaptive neuro‐fuzzy inference‐aided optimal fractional‐order PIDA control for perturbed power systems with solar and wind power sources

AbstractMaintaining stability in modern power systems is challenging due to complex structures, rising power demand, and load disturbances. The integration of renewable energy sources further threatens stability by causing imbalances between generation and demand. Conventional load frequency stabilization methods fall short in such scenarios. This paper proposes an optimal fractional‐order proportional‐integral‐derivative‐acceleration (FOPIDA) controller, enhanced by a robust adaptive neuro‐fuzzy inference system (ANFIS), to improve load frequency control and reliability in power systems with wind and solar generators. First, the dynamical model of a multi‐area interconnected power system, including a thermal power plant, wind turbine, and solar photovoltaic generators, is developed. A decentralized ANFIS‐FOPIDA controller is then designed for load frequency control objectives. The gains of this controller are optimized using the whale optimization algorithm (WOA), focusing on frequency deviation and tie‐line power exchange. Simulations on a New England IEEE 10‐generator 39‐bus power system demonstrate the approach's effectiveness under various disturbances, including random load‐generation disturbances and nonlinear generation behaviors. Comparisons with other strategies, such as fractional order (FO) beetle swarm optimization algorithm (FOBSOA)‐FOPIDA, WOA‐PIDA, and WOA‐ANFIS‐PIDA, and recent control approaches highlight the superior performance of the WOA‐ANFIS‐FOPIDA method in enhancing power system stability.

Aggregated model of an active distribution system for coordinated TSO–DSO fast frequency control

The growing integration of distributed energy sources (DERs) challenges the traditional independent operation of transmission system operators (TSOs) and distribution system operators (DSOs). The coordination between TSOs and DSOs becomes essential, as DERs not only offer local ancillary services but also enable the provision of these services at the distribution network level following disturbances in the transmission system. To enhance power system analyses in TSO–DSO coordination, the availability of reduced-order or aggregated models proves crucial, avoiding detailed modeling and reducing computational burden. Addressing the lack of reduced-order models capturing the dynamics of diverse distributed technologies and their control systems within a unified single-device model, this paper introduces a single equivalent device based on virtual synchronous machine (VSM) technology. This device effectively subsumes the frequency response of various DERs and loads. The proposed aggregation methodology undergoes validation on systems of different sizes and compositions of DERs. Results from simulated scenarios consistently demonstrate the accuracy and generalizability of the proposed aggregated model, irrespective of system size and included technologies.

Two-stage day-ahead and intra-day scheduling considering electric arc furnace control and wind power modal decomposition

As the uncertainty in energy supply increases, engaging various flexible resources in power systems has emerged as an effective strategy to address wind curtailment issues. Existing research insufficiently explores how EAFs can participate in reducing wind curtailment or optimizing flexible power system resources to decrease CO2 emissions from TTPs and enhance wind energy absorption across various timescales. This study introduces a dual timescale, dual-tier scheduling methodology incorporating EAF regulation and wind power modal decomposition. The day-ahead model integrates EAF demand response to decrease wind curtailment, a comprehensive wind power allocation, and a TTP carbon minimization model. The intra-day model employs wind power modal decomposition for optimizing BESSs within WFs and schedules TTPs to minimize CO2 emissions. Implemented through iterative genetic algorithms and CPLEX solver techniques, simulation results from a real-case scenario indicate that incorporating EAF loads reduces wind curtailment by 40.49 % and cuts CO2 emissions by 2.5 % in the day-ahead phase. Furthermore, by applying modal decomposition, TTPs and BESSs absorb fluctuating wind power components, ensuring maximal wind utilization and substantial CO2 reduction at TTPs. This approach offers vast potential to enhance power system flexibility, advance energy-intensive industries' transition, and foster low-carbon initiatives at TTPs.

An easy method for simultaneously enhancing power system voltage and angle stability using STATCOM

The global electricity demand is growing rapidly and is expected to double by 2050. This growth requires significantly expanding the current high-voltage transmission lines, which is a long-term and costly solution. As an alternative, utilities use advanced technologies like Flexible Alternating Current Transmission System (FACTS) devices as a temporary measure, postponing the expansion. However, it is necessary to locate these devices in the network properly to avoid negative outcomes. Traditional methods for FACTS placement focus on voltage profile improvement and power loss reduction, neglecting rotor angle stability. This can lead to oscillatory instability, especially in weak power grids. To address this issue, the paper proposes a simple approach to optimize the placement of shunt FACTS devices in power systems. The method improves both voltage and rotor angle stability without compromising power loss. The proposed approach considers rotor angle stability constraints, which is essential for system stability during transient events. The study was demonstrated on Nigeria’s 50-bus 330 kV power system, with results indicating a 31% improvement in angle stability with an enhanced voltage profile. The method is straightforward, non-iterative, and provides a cost-effective solution for FACTS device placement in power systems.

OPTIMAL LOCATION OF AN IDEAL TRANSFORMER UPFC MODEL USING OPF FIRST-ORDER SENSITIVITIES

This paper presents a screening technique for greatly reducing the computation involved in determining the optimal location of a unified power flow controller (UPFC) in a power system. The first-order sensitivities of the generation cost with respect to UPFC control parameters are derived. This technique requires running only one optimal power flow (OPF) to obtain UPFC sensitivities for all possible transmission lines. To implement a sensitivity-based screening technique for guidance in optimally locating a single UPFC in a power system, we propose a new UPFC model, wrach consists of an ideal transformer with a complex turns ratio and a variable shunt admittance. In this model, the UPFC control variables do not depend explicitly on UPFC input and output currents and voltages. Accordingly, this model does not require adding extra buses for UPFC input and output terminals. IEEE five-, 14- and 30-bus systems were used to frustrate the technique. Index Terms-Flexible ac transmission systems (FACTS), FACTS location, first-order sensitivity, optimal power flow (OPF), screening technique, unified power flow controller (UPFC), UPFC ideal transformer model, UPFC placement, UPFC uncoupled model. This study focuses on the development of an Ideal Transformer Unified Power Flow Controller (UPFC) model and its integration with Optimal Power Flow (OPF) first-order sensitivities analysis for determining optimal UPFC locations in power systems. The abstract outlines the significance of UPFCs in enhancing power system stability and efficiency by controlling voltage and power flow. It highlights the objectives of the research, including the development of the UPFC model, analysis of OPF first-order sensitivities, and their combined application for screening optimal UPFC locations. The study aims to contribute to the improvement of power system operation and control through strategic UPFC placement. The abstract provides a concise overview of the study, summarizing the key aspects of the research. It outlines the development of an ideal transformer Unified Power Flow Controller (UPFC) model, the analysis of Optimal Power Flow (OPF) first-order sensitivities, and their application in determining optimal locations for UPFC installation in power systems

Optimizing power system stability: A hybrid approach using manta ray foraging and Salp swarm optimization algorithms for electromechanical oscillation mitigation in multi‐machine systems

AbstractThis paper emphasizes the significance of ensuring adequate damping of electromechanical oscillations in power systems to ensure stable operation. Power System Stabilizers (PSSs) are influential in enhancing system damping and refining dynamic characteristics during transient conditions. However, the efficacy of PSSs is notably contingent on parameter values, particularly in the case of lead‐lag PSSs. In response to this challenge, the paper introduces a Tilt‐Integral‐Derivative (TID)‐based PSS, optimized through a novel optimization algorithm called Hybrid Manta Ray Foraging and Salp Swarm Optimization Algorithms (MRFOSSA). The MRFOSSA algorithm demonstrates robustness and enhanced convergence, validated through benchmark function tests, and outperforms competing algorithms. These superior characteristics of MRFOSSA were employed in optimal tuning of TID‐PSSs to uphold the stability of multi‐machine power systems. The MRFOSSA algorithm demonstrates robustness and enhanced convergence, outperforms competing algorithms in the optimal tuning of TID‐PSS within the Western System Coordinating Council (WSCC)‐3‐machines 9‐bus test system. In summary, the proposed TID‐PSS, coupled with the MRFOSSA algorithm, presents a promising avenue for enhancing power system stability.

A Resilient Frequency Regulation for Enhancing Power System Security Against Hybrid Cyber-Attacks

A Resilient Frequency Regulation for Enhancing Power System Security Against Hybrid Cyber-Attacks

Optimisation Methods Based on Soft Computing for Improving Power System Stability

In order to keep electrical grids running reliably and efficiently, power system stability is essential. The optimisation of power systems is fraught with complexity and uncertainty, yet soft computing approaches have been shown to be an excellent tool for dealing with these issues. In order to improve the reliability of the power system, this article presents a summary of optimisation techniques that rely on soft computing techniques. , the methods of evolutionary computing, such as particle swarm optimisation (PSO) and genetic algorithms (GA), are discussed. By efficiently searching for optimum solutions in huge solution spaces, these algorithms are used to optimise power system parameters and control techniques. In general, optimisation techniques based on soft computing provide effective and flexible ways to enhance power system stability. These technologies improve grid operation by using swarm intelligence, evolutionary computing, fuzzy logic, and neural networks to tackle the issues of power systems, which are dynamic and unpredictable.

A new virtual consensus‐based wide area differential protection

AbstractThis paper introduces a virtual consensus‐based wide area differential protection method through cooperative control concepts and graph theory. Using multi‐agent systems eliminates the need for a direct telecommunication connection between the protective relays to implement the proposed differential protection. In addition, applying telecommunication subgraphs facilitates the establishment of numerous differential protection areas. Collaboration between protected areas is facilitated by common agents, establishing a wide‐area cooperative protection network. The capability of the network operator to define various protection areas and the collaboration between these areas ensure the versatility of the proposed method for various protection purposes. The present paper primarily represents the application of the proposed protection system as a wide‐area supervisor protection for distance relays. Performance evaluations on an IEEE 39‐bus test system illustrate the method's effectiveness in various scenarios. The results show enhanced power system stability and resilience, particularly when traditional methods struggle to detect power swings with high impedance variation rates.

Enhancing power system reliability: Hydrogen fuel cell-integrated D-STATCOM for voltage sag mitigation

The focus of this study is to investigate the critical matter of voltage sags in power systems, which have a substantial adverse effect on both the functionality of equipment and the quality of power. Central to this investigation is the integration of hydrogen fuel cells and a D-STATCOM system, which is suggested as a substitute approach for mitigating voltage fluctuations. The hydrogen fuel cell serves as the pivotal component, renowned for its ability to significantly improve power quality through the efficient mitigation of voltage sag concerns. The paper provides a comprehensive analysis of the contribution that hydrogen fuel cell integration with D-STATCOM makes to the reliability of power systems. A systematic methodology is employed, supported by simulations and modeling, to emphasize the critical significance of hydrogen fuel cells in surmounting obstacles related to voltage injection and enhancing power quality. By utilizing a Type-3 Fuzzy system, the research evaluates the dependability of the control system and proposed model. The effectiveness of this method, which is focused on hydrogen fuel cells, is verified by means of MATLAB simulations, which demonstrate its superior performance in comparison to conventional PI and ANFIS controllers.

Analyzing the Impact of Renewable Energy Integration on Power System Reliability

The integration of renewable energy sources is pivotal in addressing climate change and ensuring sustainable energy provision. Reliability management in power systems plays a crucial role in this endeavor, ensuring a consistent supply of clean energy while reducing carbon emissions. Extensive national and international research has demonstrated the effectiveness of modeling techniques, analytical methods, and management strategies in mitigating the volatility and uncertainty inherent in renewable energy. Reserve capacity planning, intelligent control systems, and energy storage technologies are instrumental in enhancing power system reliability. Case studies and empirical analyses offer valuable insights for other regions dealing with renewable energy challenges. With continuous technological advancements and policy support, the obstacles of renewable energy integration can be surmounted, moving us closer to a sustainable energy future. Ongoing research and international cooperation provide further opportunities and solutions to achieve clean energy objectives.

Advancing solar energy integration: Unveiling XAI insights for enhanced power system management and sustainable future

Solar energy has emerged as a vital renewable alternative to fossil fuels, enhancing environmental sustainability in response to the pressing need to reduce carbon emissions. However, the integration of solar power into the electrical grid faces challenges due to its unpredictable nature, as a result of solar energy production variability. This research presents an advanced Explainable Artificial Intelligence (XAI) framework to explicate machine learning models decision-making processes, thereby improving the predictability and management of solar energy distribution. The influence of critical parameters such as solar irradiance, module temperature, and ambient temperature on energy yield is studied using the Local Interpretable Model-Agnostic Explainer (LIME). Rigorous testing using four advanced regression models identified Random Forest Regressor as the superior model, with an R2 score of 0.9999 and a low Root Mean Square Error (RMSE) of 0.0061. Furthermore, Partial Dependency Plots (PDP) are used to emphasize the intricate dependencies and interactions among features in the dataset. The application of XAI techniques for solar power generation extends beyond explainability, addressing challenges due to various parameters in solar radiation pattern analysis, error estimation in solar performance, degradation of the battery function, and also provides interpretable insights for enhancing the lifespan of solar panels, contributing to advancements in sustainable energy technologies. The results of this study show how XAI has the potential to transform power system management (PSM) and strategic planning, propelling us toward a future of energy that is more resilient, efficient, and environmentally friendly.

MPC-based framework incorporating pre-disaster and post-disaster actions and transportation network constraints for weather-resilient power distribution networks

Enhancing power system resilience to extreme weather requires an effective coordination of pre-disaster preventive response actions and post-disaster repair and restoration services. In this context, this article presents a two-stage framework to enhance resilience of power distribution networks to severe weather events. It considers operating and resource constraints in interdependent power and transportation networks based on official guidance from transport appraisal studies. It also incorporates sources of information available in a smart city context, such as energy measurements, traffic flows, and geographical information systems. In the first stage, a pre-disaster operational planning strategy is defined to prepare the grid for a high-risk outage scenario with the objective of minimizing the value of lost loads, formulated as a mixed-integer quadratic programming model. In the second stage, a post-disaster corrective strategy is implemented over a receding time horizon to compensate for deviations between the actions computed in the first stage and the actions required to minimize power outages, embedded into a model predictive control scheme. The effectiveness of the proposed framework is demonstrated on a real-world large-scale distribution network in the United Kingdom over a range of outage scenarios. Simulation results show that the proposed framework is capable of effectively minimizing first-stage operational costs and second-stage deviations between the projected and actual load demand supply. Numerical results obtained with the proposed framework indicate that the load energy unserved is at least twice smaller than with typical practices adopted by distribution network operators and computational times take two minutes or less. Therefore, it can be effectively used by distribution network operators to ensure an appropriate level of preparedness to power outages caused by extreme weather along with prompt restoration and repair services.

Multi-objective multiverse optimization for optimal allocation of distributed energy resources: The optimal parallel processing schemes

Utilizing Distributed Generators (DGs) and Energy Storage Systems (ESSs) enhances power system reliability, drawing significant research attention. However, these systems pose challenges, compelling scientists to explore optimization methods. Our paper presents an innovative solution, Parallel Multi-Objective Multi-Verse Optimization (PMOMVO), aimed at optimizing DGs and Battery Energy Storage Systems (BESSs) allocation. This optimization addresses voltage violations and operation costs, crucial concerns for system operators and consumers. By leveraging a parallel approach, PMOMVO significantly accelerates the optimization process. We compared its results with a base case scenario, demonstrating the superior efficiency of our parallel method. It not only enhances the optimization performance but also proves its efficacy by generating optimal solutions from the Pareto front set. This research showcases the benefits of PMOMVO, offering a faster, more efficient, and reliable way to optimize power systems, benefiting both operators and consumers.

Cooperative control of the DC-link voltage in VSC-MTDC grid via virtual synchronous generators

Introduction: Virtual Synchronous Generators (VSGs) are used in Voltage Source Converter-based Multi-Terminal High-Voltage Direct Current (VSC-MTDC) systems to enhance power system stability. However, the MTDC framework can lead to instability due to reduced inertia in certain grid areas, especially during load switching at VSC stations. This instability is exacerbated by untimely adjustments of the VSG's power setpoint, leading to voltage and frequency oscillations.Methods: This study introduces a cooperative control approach for the DC voltage of the VSG, employing a consensus algorithm and Model Predictive Control (MPC). This method aims to achieve incremental power for the VSG and provide interactive power commands for both the grid side and the wind farm side. The consensus algorithm ensures coherent system adjustments, while the MPC algorithm tracks DC-side voltage changes in real time.Results: The application of this cooperative control approach significantly enhances DC voltage regulation performance. It effectively reduces the extent of frequency drops and mitigates secondary frequency drop (SFD) issues, particularly those arising from the use of wind farms for frequency regulation and the associated speed recovery in wind turbine units.Discussion: The increase in supplemental power effectively utilizes the energy stored in DC-side capacitors for power balance regulation and introduces additional inertial power into the system. Electromagnetic transient simulations have confirmed the effectiveness of the Consensus MPC-VSG method, demonstrating its ability to optimize the dynamic performance of VSC-MTDC systems and promote stability in DC voltage and frequency.Conclusion: The findings suggest that employing the Consensus MPC-VSG method offers a promising solution for enhancing the stability and operational efficiency of VSC-MTDC systems, addressing the challenges posed by the inherent segmentation of the grid and the integration of renewable energy sources like wind farms.

Employing advanced control, energy storage, and renewable technologies to enhance power system stability

As the world witnesses a surge in the adoption of renewable energy sources to meet the surging global power demands, the dynamic and intermittent nature of these sources emerges as a significant hurdle. This article extensively explores the potential of advanced control systems, energy storage technologies, and renewable resources to fortify stability within power systems. Advanced control methodologies are strategically amalgamated with energy storage deployment and the utilization of renewable energy, to advance the reliability, predictability, and sustainability of power systems. The stability analysis, with a dedicated focus on Input-to-input-to-state stability (ISS), is conducted meticulously by applying the Lyapunov function and the Reciprocally Convex Approach, resulting in an impressive stability rate of 23.6%. Additionally, Positive Realness is substantiated by extracting Linear Matrix Inequalities (LMI) in the context of Enhancing Grid Stability with Wind Power. The study places particular emphasis on evaluating ISS and Passivity in both delayed and non-delayed systems, with a specific focus on neutral time-delay systems. This evaluation involves the selection of an appropriate Lyapunov-Krasovskii Functional (LKF) and its derivation, coupled with the integration of reciprocally convex methods, descriptive approach, and Jensen inequality. The outcomes of these analyses shed light on the causes of excess energy and its effective storage, along with highlighting the synergistic impact of integrating renewable sources and controlling grid frequency, voltage, and power in real time. The study also accentuates the robustness achieved through Enhanced Stability and the mitigation of Reduced Fluctuations, especially in the context of renewable energy sources.