Power System Operation Research Articles

A blockchain consensus mechanism for real-time regulation of renewable energy power systems

With the ongoing development of renewable energy sources, information technologies and physical energy systems are further integrated, which leads to challenges in ensuring the secure and stable operation of renewable energy power systems in the face of potential cyber threats. The strengths of blockchain in cybersecurity make it a promising solution to these challenges. However, existing blockchains are not well-suited for control tasks due to their low real-time performance. Here, we present a consensus mechanism that enables real-time security control of systems, called Proof of Task. Instead of solving meaningless hash puzzles in Proof of Work, Proof of Task addresses problems closely related to the stable operation and control performance of these systems. With the proposed verification mechanism, Proof of Task significantly enhances the real-time performance of blockchain while mines its computational resources for tasks of interest. To demonstrate the effectiveness and necessity of Proof of Task, it is deployed across three renewable energy power systems. The results show that Proof of Task markedly fortifies the security and computing capability of these systems, ensuring their reliable and stable operation. This work highlights the promise of blockchain to facilitate security control and trusted computing of large-scale, complex-dynamic systems.

A multi-scale and multi-modal convolutional neural network for condition monitoring of transmission line

Abstract Monitoring the fatigue damage of transmission lines is crucial for stable power system operation. However, existing model-driven methods face challenges such as high computational complexity and reliance on expert knowledge, while data-driven methods require large amounts of abnormal state data. To address these issues, a multi-scale and multi-modal convolutional neural network (CNN) is proposed for real-time condition monitoring of transmission lines. Key steps include: firstly, empirical Fourier decomposition is used to decompose the original signals, extracting multi-scale state information at different frequency scales. Then, time-domain, frequency-domain, and time–frequency domain analyses are performed on the decomposed signals to capture multi-modal information. Based on this, a multi-modal fusion network is proposed based on a CNN to extract shallow and deep features, with a fully connected layer used for multi-modal feature fusion. Notably, the algorithm is implemented on a microprocessor for practical application. Experimental results show that the proposed model achieves a diagnostic accuracy of 93.06%, outperforming classical networks. It also surpasses models trained solely on time, frequency, or time–frequency features by 25.18%, 21.8%, and 19.3%, respectively.

Novel variable charging pricing strategy applied to the multi-objective planning of integrated fast and slow electric vehicle charging stations and distributed energy resources

Electric vehicle charging stations are increasingly being deployed to accommodate the rising demand for electric vehicles. However, this additional load on the distribution system can lead to significant challenges, including increased power losses and undervolage issues. Therefore, this paper proposes the optimized planning of electric vehicle charging stations, along with the integration of distributed energy resources considering the power system operator’s perspective. The approach focuses on minimizing the costs associated with public charging stations and distributed generators, as well as reducing power losses and voltage deviation of the system. Additionally, this study incorporates both fast and slow chargers to address user requirements, while promoting the use of slow charging to help reduce the peak load demand caused by the electric vehicle charging stations. A novel variable pricing strategy for slow charging is also proposed to decrease the operator’s expenses while promoting this mode of charging. The proposed methodology is validated through the integration of a 33-bus distribution test system with a 25-node traffic system. The analysis was conducted using three multi-objective optimization methods: Multi-Objective Cuckoo Search, Multi-Objective Flower Pollination Algorithm, and Non-dominated Sorting Genetic Algorithm II. From the results, it is possible to conclude that the proposed approach provides benefits not only to the system operator but also to EV users, given the reduction in their travel costs. Despite the increased charging demand, the optimization results show a reduction in voltage deviation by 11.57%–16.94% and power losses by 19.71%–24.20% compared to the original system.

Fine-grained simulation model for PV power output interval based on two-stage scenario clustering and dual-ensemble compatible learning

Photovoltaic (PV) power simulation is indispensable for the planning and operation of renewable-rich power systems. Among various simulation methods, data-driven indirect simulation methods are the most efficient, primarily involving scenario extraction and power output generation. Typical scenario extraction methods often feature coarse characterizations, which also cannot be expressed by traditional power output generation models lacking the ability of quantifying the uncertainty of the output results. This paper proposes a fine-grained simulation model for PV power output interval, i.e., upper bound and lower bound, based on two-stage clustering of multiple typical scenarios and dual-ensemble compatible learning. The first stage of scenario extraction, considering the impact of total daily irradiance on PV output, employs a line distance index for preliminary scenario division of daily PV output. These scenarios are further divided into five characteristic scenarios with an angular distance index considering the impact of intra-day irradiance variations on power output. PV power output interval is generated from dual-ensemble compatible learning including Stacking and Bagging, which are adaptively selected with a Bias-Variance Selection Index (BS) calculated using the value of scenario characteristics. Stacking and Bagging are driven by three neural network base learners with Bayesian layers, which can output the lower bound and upper bound with confidence at a certain level, thus quantifying the uncertainty of the result accurately. The actual data of Daxing and Gangjialing power stations are used in case study. Results form case study show that compared with the existing methods, the proposed method can improve the Power Coverage Rate (PCR) and Average Power Simulation Accuracy (APA) of uncertainty simulation results by 27.44% and 5.39%, respectively. Meanwhile, the Average Daily Maximum Power Simulation Accuracy (AMPA) is similar to that of existing methods.

An adaptive assessment method of power system transient stability considering PMU data loss

AbstractTransient stability assessment (TSA) plays an important role in ensuring the reliable operation of power systems. With the popularity of phasor measurement units (PMUs), data‐driven TSA methods have been widely concerned. However, the performance of TSA model may deteriorate when data loss occurs due to PMU failure. This paper proposes an adaptive assessment method for transient stability of power systems considering PMU data loss. First, considering the importance of temporal features, a collection of PMU clusters is constructed to minimize the failure risk and maintain full observability of the whole buses of the grid. Secondly, a weighted integrated assessment model based on PMU clusters is constructed by using an improved eXplainable Convolutional neural network for Multivariate time series classification (XCM) as a TSA classifier. The model can make full use of time series information to carry out adaptive TSA and maintain the robustness of the assessment performance even when PMU failure occurs. Finally, it is verified in a modified IEEE 39‐bus system with wind power and solar power. The effect of the proposed method shows high accuracy and strong anti‐noise interference ability in case of data loss.

Asymmetric LKF approach for stability analysis of multi-area load frequency control systems with time-varying delays

ABSTRACT The widespread use of communication networks in power systems to send and receive control signals causes time delays in the feedback loop. Improper handling of these delays might degrade controller performance or cause system instability. This work presents new stability criteria that are less conservative in analysing the influence of delays on the stable operation of PI controller-based multi-area power systems with uncertainties and disturbances. An asymmetric Lyapunov-Krasovskii functional technique is used to reduce conservatism by relaxing the condition that all matrices associated with the LKF formulation need not be symmetric or have positive definiteness. In contrast, the symmetric LKF requires all matrices to be symmetric. Two tight-bound integral inequalities are implemented to compute the quadratic integral terms included in the derivative of asymmetric LKF. The less conservative stability conditions are derived for multi-area LFC systems subjected to delays, parameter uncertainties, and disturbances by utilising asymmetric LKF. The superiority of the proposed stability criterion is determined both theoretically and through simulation. According to the analysis, the proposed stability criterion provides higher delay margins than existing techniques.

Optimal DSSC’s deployment in power system using PSO

Owing to the high cost of installation and operation, distributed flexible AC transmission system (FACTS) technology gives opportunity to provide cost-effective solution in power system operation and control. A distributed static series compensator (DSSC) is a series FACTS device and it is placed equidistantly placed on the existing line to vary the line current. Active power can be controlled in the line with the help of DSSC. This paper presents DSSC for active power flow control to enhance system loadability index and to minimize reactive power generation by generators. Since DSSC is of low power. a small value of reactance is emulated in the line. Large number of DSSC’s are distributed along the transmission line at regular intervals to realize considerable change in the current. A particle swarm optimization is implemented to determine DSSC’s emulated reactance optimally. In this condition, all the lines flowing power in their limits with increased loading condition. Maximum system loadability index is evaluated by employing optimal number of DSSC’s on the line. A multiobjective problem is formulated. One objective function is formed such that no line would become overloaded even when loading is increased. Other objective is formulated to minimize reactive power generation by generators. A compromise solution is investigated for optimally connected of DSSC devices to achieve both the objective functions. IEEE 14 bus system is taken for MATLAB simulation of DSSC compensated system.

Evaluation of grounding grid corrosion extent based on laser-induced breakdown spectroscopy (LIBS) combined with machine learning

As one of the most widely used forms of energy, the safety and stability of power systems are crucial to modern society. Grounding grids dissipate current and reduce touch and pace voltage during lightning strikes or fault currents, ensuring the safety of personnel and equipment. However, prolonged submersion in soil causes inevitable corrosion, compromising grounding efficacy by increasing resistance and reducing current dissipation. This deterioration can result in unsafe local potential differences. This study uses Laser-Induced Breakdown Spectroscopy (LIBS) to measure corrosion degrees in grounding grids. Spectral data from samples with varying corrosion extent were collected, with outliers removed using the Local Outlier Factor (LOF) algorithm. Principal Component Analysis (PCA) reduced data dimensionality, revealing clustering in spectral data corresponding to corrosion extent. Three machine learning models were compared: Adaptive Boosting - Backpropagation Neural Network (Adaboost-BP), Support Vector Machine (SVM), and Random Forest (RF). The RF model showed the highest accuracy in predicting corrosion degree (R²=0.9845, MSE=0.0296), outperforming Adaboost-BP and SVM, especially for intermediate corrosion extent. These findings validate the effectiveness and reliability of combining LIBS with machine learning for predicting grounding grid corrosion, providing a theoretical foundation for the safe operation of power systems.

Status of Overseas Utilization of Distributed Energy Sources for Power System Operation

Status of Overseas Utilization of Distributed Energy Sources for Power System Operation

Integrated model and automatically designed solver for power system restoration

Power system restoration strategies schedule the power plants to re-energize the power network and loads after a blackout. These strategies are of great significance to ensure a resilient power system. Most power system restoration models consider either certain restoration stage(s) or all stages, with each or some of the stages managed independently. They destroy the essential characteristics of the interaction among the restoration stages and lead to sub-optimal solutions. To address this issue, in this paper, we first propose an improved integrated power system restoration (IPSR) model covering the entire restoration process without decoupling and sectionalization. We then develop both mathematical and metaheuristic solvers for the proposed model. In particular, we introduce the automated solver design paradigm to the highly non-linear constrained restoration problem-solving. By the paradigm, we automatically design the metaheuristic solver that gains enhanced performance beyond handcrafted solvers without algorithmic expertise. It is suggested that the automatically designed metaheuristic solver is a promising new option in variable and complicated power system operation scenarios without algorithmic expertise. Finally, a comprehensive case study demonstrates the proposed model’s significance and the proposed solver’s efficiency. Numerical tests show that the proposed IPSR model and solver obtained over 20% lower restoration time than current restoration methods, demonstrating IPSR’s and the proposed solver’s efficiency and effectiveness.

Enhanced AC/DC optimal power flow via nested distributed optimization for AC/VSC-MTDC hybrid power systems

The deployment of voltage source converters (VSC) to facilitate flexible interconnections between the AC grid, renewable energy system (RES) and Multi-terminal DC (MTDC) grid is on the rise. However, significant challenges exist in exploiting coordinated operations for such AC/VSC-MTDC hybrid power systems. One of the most critical issues is how to achieve the optimal operation of such wide-area systems involving several power entities with as minimal communication burden as possible. To address this issue, an enhanced AC/DC optimal power flow (OPF) is specifically proposed. Firstly, a mixed-integer convex AC/DC OPF model is explicitly formulated to describe the optimal operation of such hybrid power systems. Subsequently, a nested distributed optimization method with double iteration loops is developed to offer optimal system-wide decision-making through a more “thorough” distributed communication architecture. In the outer iteration, the original AC/DC OPF problem is decomposed into several slave problems (SPs) associated with systems (including the AC grid and RESs) and one master problem (MP) associated with the integrated VSC-MTDC grid. Generalized Benders decomposition (GBD) serves to solve the master and slave problems iteratively. Techniques such as multi-cut generation and asynchronous updating are utilized to upgrade the GBD performance of computation efficiency and address communication delays. In the inner iteration, the master problem is continuously decomposed into multiple sub-MPs associated with individual VSCs. The alternating direction method of multipliers (ADMM) is employed to solve these sub-MPs iteratively. Proximal terms and heuristic approaches are embedded to enable parallel computation and handling of integer variables. Numerical experiment results finally validate the effectiveness of the proposed enhanced AC/DC OPF. The constructed AC/DC OPF model exhibits acceptable accuracy in terms of power flow calculation, and the developed nested distributed optimization method showcases decent convergence rate and solution optimality performances.

Transformer fault diagnosis technology based on AdaBoost enhanced transferred convolutional neural network

Transformer fault diagnosis is an important means to ensure the stable operation of power systems, and Dissolved Gas Analysis (DGA) is a widely used method. However, existing intelligent diagnostic methods have problems such as insufficient expression of gas features, poor generalization of classification models, and lack of specialized deep optimization. On this basis, the authors proposed a transformer fault diagnosis technology based on AdaBoost enhanced transferred convolutional neural network, and constructed a transformer fault diagnosis scheme of “feature engineering, classification models, specialized deep optimization”. Firstly, the original five-dimensional gas features were expanded into new twenty-one-dimensional gas features to address the problem of insufficient expression of gas features. Factor analysis method was used to extract key information from twenty-one-dimensional gas features, and twelve-dimensional common factors were transformed to create the high-quality DGA data set. Secondly, the ensemble learning idea was referenced to construct ensemble classification models, and the best performing AdaBoost was obtained to solve the problem of poor generalization of single classification models. Finally, the transfer learning idea was referenced, the convolutional neural network was used as the basic classification model for AdaBoost, learning parameters were passed, and AdaBoost-TCNN with superior and stable classification effect was constructed to solve the problem of lack of specialized deep optimization. Test results on both private and public data sets show that, ensemble classification models achieved higher classification accuracy than single classification models, with significant advantages in Precision, Recall, and F1-score compared to single classification models. Among them, AdaBoost-TCNN achieved the highest classification accuracy of 97.33% and 95.35%, respectively, and obtained the most stable and significant Precision, Recall, and F1-score, all of which were superior to the ensemble classification models in the latest research. In addition, AdaBoost-TCNN also demonstrated a focus on key fault types. Therefore, the necessity of conducting this study, as well as the feasibility, practicality, and superiority of the technology proposed in this paper, have been fully demonstrated.

Network-aware electric vehicle charging/discharging scheduling for grid load management in a hierarchical framework

The increasing adoption of electric vehicles (EVs) poses significant challenges for power system operations, requiring scalable coordination to mitigate their negative impacts and leverage their potential to enhance grid conditions. This paper introduces a scalable, three-layer hierarchical framework for optimal EV charge and discharge scheduling (EVCDS) that coordinates key agents: EVs, EV aggregators (EVAs), and the distribution network operator (DNO). The optimization problem is developed as an exchange problem and solved using the alternating direction method of multipliers (ADMM) in a decentralized approach. The proposed EVCDS addresses economic factors by minimizing battery degradation costs at the EV level and charging costs at the EVA level, while managing technical aspects at the DNO level by minimizing load curve variance and limiting power capacity. Moreover,voltages at network nodes are calculated using the DistFlow model to simplify the optimization and ensure compliance with standard operational limits. Compared to uncoordinated EV charging, EVCDS reduces load profile deviations by 85% and total costs by 91%, while also improving bus voltage profiles.

An Optimal Load Frequency Control in a Two-Area Power System Using a Fractional Order Proportional- Integral-Derivative-Based Zebra Optimization Algorithm

Load frequency control systems are crucial for maintaining the stability and reliability of power grids. They help ensure that the power supply matches the demand, preventing fluctuations in grid frequency. However, Load frequency control systems have inherent limitations, such as the potential for instability and oscillation if not properly controlled. In this work, A fractional order proportional integral derivative controller is proposed to address this issue, which exhibits strong capabilities in managing parameter uncertainties, rejecting disturbances, and handling non-linear systems controllers. The novel approach in this search is the application of the zebra optimization algorithm to fine-tune controller parameters, a technique not previously used in load frequency control systems. In this Study the two-area power system with a reheat turbine is used a test case for fractional order proportional integral derivative controller, and the system is simulated using MATLAB/SIMULINK. The objective function integral time of square error is used that acts as a bridge of communication between the system's behavior and the control strategy. The performance of this controller is evaluated under disturbance 0.04. The results of the analysis demonstrated that the fractional order proportional integral derivative based zebra optimization algorithm controller outperformed the traditional fractional order proportional integral derivative controller in terms of three essential criteria: overshoot, settling time, and steady-state error. This research contributes to the advancement of load frequency control systems, ensuring reliable and stable power system operation.

A hybrid K-means and KNN approach for enhanced short-term load forecasting incorporating holiday effects

Accurate short-term load forecasting (STLF) is essential for efficient power system operations, particularly during holidays when demand patterns vary significantly. Traditional methods often struggle with these non-linear changes, leading to increased operational costs. This research introduces a hybrid model combining K-means clustering and K-nearest neighbors (KNN) classification to enhance forecast accuracy by accounting for holiday effects. Using historical electricity consumption data, the model applies K-means to classify load patterns and KNN to predict load clusters based on the day of the week, month, and holiday status. Advanced forecasting algorithms for each cluster, including linear regression, neural networks with Bayesian optimization, Support Vector Regression with Bayesian Optimization, and long short-term memory with Bayesian optimization, were evaluated using mean absolute percentage error (MAPE), ANOVA, and Tukey's HSD test. Results show significant improvements in forecast precision, especially during holidays, with the LSTM-BO model achieving the lowest MAPE values. The proposed hybrid model achieved an average 56.1 % improvement in forecasting accuracy across various scenarios, providing a reliable tool for power system planners and operators to optimize performance and reduce costs.

Multi-objective hierarchical energy management strategy for fuel cell/battery hybrid power ships

The energy management strategy and the local controller in the ship energy management system are interconnected, impacting the performance of the hybrid propulsion system. To achieve the efficient operation of the hydrogen fuel cell (FC) and battery hybrid power system, based on the modelling and analysis of the hybrid power system, a nonlinear model predictive control (NMPC) based energy management strategy is proposed, and a dynamic virtual impedance droop controller and a classical proportional-integral (PI) controller are designed as local controllers. By simulating the designed random load conditions, pulse load conditions, and actual sailing conditions using hardware-in-the-loop (HiLs) technology, six different energy management strategies and their comprehensive performance with local controllers are compared and analysed. Comparing performance in terms of energy consumption, operating pressure, control accuracy, real-time performance, and robustness, it has been proven that the energy management strategy based on NMPC, coupled with a PI controller, is superior to other strategies overall. It can balance hydrogen consumption and the stable operation of the hybrid power system. Compared to existing energy management strategies, the proposed NMPC+PI strategy can reduce hydrogen consumption by 7.00 % and 40.29 %, and FC operating pressure by 44.96 % and 49.88 %, respectively, under both designed navigation conditions and actual navigation conditions.

Efficient operation of the stand-alone solar photovoltaic power system

Efficient operation of the stand-alone solar photovoltaic power system

A Review of Harmonic Detection, Suppression, Aggregation, and Estimation Techniques

The rapid growth of power electronics-based devices, such as electric vehicles and renewable energy systems, has introduced nonlinear components into power systems, generating high-frequency harmonics that distort current and voltage waveforms. These distortions pose significant risks to the stability of power grids, potentially leading to equipment malfunctions, reduced efficiency, and even system failures. To address these challenges, advanced harmonic detection, suppression, and estimation techniques are required to ensure the reliable operation of modern power systems. This paper comprehensively reviews the most widely used methods for managing harmonic distortions, focusing on recent harmonic detection, suppression, and estimation advancements. Key techniques, such as Fourier analysis and wavelet transforms, are compared alongside emerging machine learning-based approaches and adaptive filtering methods, which offer enhanced accuracy in real-time and dynamic environments. Additionally, advancements in harmonic suppression technologies, including passive, active, and hybrid filtering, are discussed for their effectiveness in mitigating harmonic impacts. Furthermore, the paper explores harmonic aggregation techniques that assess the cumulative impact of multiple harmonic sources and innovative estimation models that improve harmonic quantification under complex grid conditions. With the growing integration of renewable energy and electric vehicles, this review highlights the importance of advanced harmonic management strategies to ensure the safety, efficiency, and long-term stability of power systems.

Research and Applications of Knowledge Graphs in the Power Sector: A Review

With the rapid development of science and technology, power system has become the lifeblood of modern society, an increasingly large power system complicates the management, operation, and maintenance of the grid. In order to effectively use a large number of operating data and prior knowledge of power system, the knowledge graph is introduced into the field of power systems. This paper introduces the research and application of knowledge graph in power system and emphasizes the importance of knowledge graphs in addressing the increasing complexity of power systems and the rising demand for intelligence, the knowledge graph integrates heterogeneous data in a structured way, which helps to improve the technical level of power system management and control, especially in fault diagnosis, equipment monitoring and market transactions. This paper first introduces the basic concept, construction method and main application scenarios of knowledge graph in power system, including data management, equipment operation and maintenance, power system operation management and decision support, Through specific examples, such as a question-answering system for identifying defects in power equipment based on knowledge graphs, the identification method of transmission line missing bolts, intelligent planning of distribution network, etc., the paper shows knowledge graph can effectively improve the operation efficiency and management level of power system. Finally, the paper looks forward to the future development trend of knowledge graph in power system, points out its broad application prospects in smart grid construction, multidisciplinary cross integration and other fields, and emphasizes the importance of knowledge graph for promoting the intelligent development of power system.

Optimal Sizing and Placement of Distributed Generators and Capacitors in Nepal's Sankhu Feeder Using the Water Cycle Algorithm

Minimizing power loss and improving voltage stability are crucial aspects of power systems, driven by transmission line contingencies, financial losses for utilities, and potential power system blackouts. Optimal allocation comprising the sizing and operating power factor—of Distributed Generation (DG) units and capacitor banks (CBs) significantly enhances power system efficiency. Efforts by power system operators and researchers focus on addressing issues related to power loss, energy loss, voltage profiles, and voltage stability through the strategic placement of DGs and CBs. Additionally, optimal DG and CB allocation protects the distribution system from unforeseen events and enables operators to run the system in islanding mode when necessary. The integration of DG units and CBs in distribution systems aims to enhance overall system performance. This research paper introduces a Water Cycle Algorithm (WCA) for the optimal placement and sizing of DGs and CBs. The proposed method targets both technical and economic benefits, considering multiple objective functions: minimizing power losses, reducing voltage deviation, lowering total electrical energy costs, and improving the voltage stability index. The WCA emulates the natural water cycle, from streams to rivers and rivers to the sea. Five different operational scenarios are evaluated to test the performance of this methodology. Simulations are conducted on distribution systems: the IEEE 69-bus test system and the Sankhu feeder network, a real system. The results demonstrate the superior performance of the proposed WCA compared to other optimization algorithms. The findings highlight the WCA's flexibility, efficiency, and significant improvements in economic benefits, establishing it as a promising approach for optimizing the placement of DG and CB in distribution systems.