Reliable Operation Of Power System Research Articles

ENHANCING POWER SYSTEM SECURITY THROUGH RESILIENT FREQUENCY REGULATION IN THE FACE OF HYBRID CYBER-ATTACKS

The growing reliance on modern communication technologies in power systems for Frequency Regulation (FR) introduces vulnerabilities to cyberattacks, posing significant threats to system stability and reliability. These attacks can disrupt the coordination among various components, such as sensors, control centers, and actuators, thereby compromising the integrity of FR analysis. In response, this article proposes a resilient solution in the form of a deep-learning-based Attack Detection and Mitigation system. By integrating advanced AI techniques, this system aims to fortify the security of FR operations within the cyber-physical framework, swiftly identifying and neutralizing cyber threats. Ultimately, this approach ensures the continuous and reliable operation of power systems, mitigating the risks posed by hybrid cyberattacks and safeguarding critical infrastructure. The proposed system represents a proactive approach to mitigating the escalating risks associated with cyberattacks targeting FR in power systems. Through its deep-learning algorithms, the system can dynamically adapt to emerging threats, enhancing the resilience of FR analysis against malicious intrusions. By bolstering security measures within the cyber-physical model, the system minimizes the potential impact of cyberattacks on power system stability and reliability. Moreover, its ability to detect and mitigate threats in real-time ensures uninterrupted operation, thereby safeguarding the functionality of power systems even amidst the evolving landscape of hybrid cyber threats. This resilient solution represents a crucial step towards fortifying power system security and maintaining essential services in the face of adversarial cyber activities. Keywords: Attack detection and mitigation (ADM) system, Frequency Regulation(FR), hybrid power system, Renewable Energy Sources (RESs), resiliency.

Power Quality Improvement of Three-Phase Electrical Systems Using Active-Passive Hybrid Harmonic Filter

An overreliance on nonlinear load can result in electric current harmonics, which will ultimately result in a decrease in the quality of the electrical power. If a malfunction arises as a consequence of the internal integration of a power system in any of the internal networks, it will have an adverse effect on the overall operation of the power system. Because of this issue, there is a requirement for harmonic filters, which can minimize the levels of harmonics by referencing the IEEE-519 standards for enhancing power quality. This research offers an active-passive hybrid harmonic power filter to eliminate harmonic pollution in three-phase electrical systems. A MATLAB simulation and laboratory experiment proceeded for 240 Vac with nonlinear loads that represent by uncontrolled rectifier and series RLC load, and selected results were presented to verify the performance of the active-passive harmonic filter component. Upon completion of this research, an analysis of the conditions of a three-phase electrical system will be possible under four distinct circumstances: in the absence of a filter, with an active filter, with a passive filter, and with a hybrid filter. Furthermore, this model performs a comparative analysis of the percentage reduction in THD value achieved by the filter compared to the previous researcher. The result analysis reveals the proposed filter's good performance in eliminating harmonic pollution to 58.63 %, thereby significantly improving power quality. The findings presented in this study contribute to the ongoing efforts to ensure the reliable and efficient operation of modern power systems.

Wind power forecasting technologies: A review

This study addresses the critical role of wind power forecasting in ensuring stable and reliable power system operations. Wind Power Forecasting is critical for the efficient operation of plant, time scheduling, and it’s balancing of power generation with grid integration systems. Due to its dependency on dynamic climatic conditions and associated factors, accurate wind power forecasting is challenging. The research delves into various aspects, including input data, input selection techniques, data pre-processing, and forecasting methods, with the aim of motivating researchers to design highly efficient online/offline models on weather-based data. The overarching goal is to enhance the reliability and stability of power systems while optimizing energy resource utilization. The analysis reveals that hybrid models offer more accurate results, highlighting their significance in the current era. This study investigates different Wind Power Forecasting (WPF) models from existing literature, focusing on input variables, time horizons, climatic conditions, pre-processing techniques, and sample sizes that affect model accuracy. It covers statistical models like ARMA and ARIMA, along with AI techniques including Deep Learning (DL), Machine Learning (ML), and neural networks, to estimate wind power.

Event-Driven Day-Ahead and Intra-Day Optimal Dispatch Strategy for Sustainable Operation of Power Systems Considering Major Weather Events

As the proportion of renewable energy installations in modern power systems increases, major weather events can easily trigger significant fluctuations in new energy generation and electricity load, presenting the system with the dual challenges of ensuring power supply and renewable energy consumption. Traditional dispatch models need more coordination and optimization of flexible resources under major weather events and risk management of system operations. This study focuses on provincial-level transmission systems, aiming to achieve the coordinated and optimized dispatch of flexible resources across multiple time scales in response to the complex and variable environments faced by the system. Firstly, by profoundly analyzing the response mechanisms of power systems during major weather events, this study innovatively proposes an event-driven day-ahead and intra-day optimal dispatch strategy for power systems. This strategy can sense and respond to major weather events in the day-ahead phase and adjust dispatch decisions in real time during the intra-day phase, thereby comprehensively enhancing the adaptability of power systems to sudden weather changes. Secondly, by considering the variability of renewable energy sources and electricity demand in the day-ahead and intra-day dispatch plans, the strategy ensures efficient and reliable power system operation under normal and major weather event scenarios. Finally, the method’s effectiveness is validated using actual data from a provincial-level power grid in China. The proposed dispatch strategy enhances the resilience and adaptability of power systems to major weather events, which are becoming increasingly frequent and severe due to climate change. The research demonstrates that an event-driven day-ahead and intra-day optimal dispatch strategy can enhance the economic efficiency and robustness of power system operations through the coordinated dispatch of flexible resources during major weather events, thereby supporting the transition toward sustainable energy systems that are resilient against the challenges of a changing climate.

Boosting efficiency in state estimation of power systems by leveraging attention mechanism

Ensuring stability and reliability in power systems requires accurate state estimation, which is challenging due to the growing network size, noisy measurements, and nonlinear power-flow equations. In this paper, we introduce the Graph Attention Estimation Network (GAEN) model to tackle power system state estimation (PSSE) by capitalizing on the inherent graph structure of power grids. This approach facilitates efficient information exchange among interconnected buses, yielding a distributed, computationally efficient architecture that is also resilient to cyber-attacks. We develop a thorough approach by utilizing Graph Convolutional Neural Networks (GCNNs) and attention mechanism in PSSE based on Supervisory Control and Data Acquisition (SCADA) and Phasor Measurement Unit (PMU) measurements, addressing the limitations of previous learning architectures. In accordance with the empirical results obtained from the experiments, the proposed method demonstrates superior performance and scalability compared to existing techniques. Furthermore, the amalgamation of local topological configurations with nodal-level data yields a heightened efficacy in the domain of state estimation. This work marks a significant achievement in the design of advanced learning architectures in PSSE, contributing and fostering the development of more reliable and secure power system operations.

Analysis of multiple-faults of high-voltage circuit breakers based on non-negative matrix decomposition

High-voltage circuit breakers are the core equipment in power networks, and to a certain extent, are related to the safe and reliable operation of power systems. However, their core components are prone to mechanical faults. This study proposes a component separation method to detect multiple mechanical faults in circuit breakers that can achieve online real-time monitoring. First, a model and strategy are presented for obtaining mechanical voiceprint signals from circuit breakers. Subsequently, the component separation method was used to decompose the voiceprint signals of multiple faults into individual component signals. Based on this, the recognition of the features of a single-fault voiceprint signal can be achieved. Finally, multiple faults in high-voltage circuit breakers were identified through an experimental simulation and verification of the circuit breaker voiceprint signals collected from the substation site. The research results indicate that the proposed method exhibits excellent performance for multiple mechanical faults, such as spring structures and loose internal components of circuit breakers. In addition, it provides a reference method for the real-time online monitoring of high-voltage circuit breakers.

Analysis of safe electricity consumption on load side based on attack and defence game model

AbstractWith the improvement of digitalization and intelligence in the power system, the safe operation of the power system is facing enormous challenges. The safe use of electricity on the load side is the key to achieving safe and reliable power system operation. The detection party needs amounts of human and material resources when the power network is attacked. In response to the current difficulties of low detection ability and high detection costs, this paper proposes an attack and defence game model that considers the differences between different nodes, ensuring the safety and economy of electricity consumption while reducing energy waste. At first, the structure of smart meters and the attack characteristics of intruders are summarized, and a basic attack and defence game model is constructed. The Nash equilibrium is then solved, and the optimal strategy for the game between the defender and the intruder is given to balance the relation between detection performance and energy consumption. In response to the differences generated by each node, strategies for attackers to launch attacks on different nodes and the setting of optimal thresholds for other nodes in the defence system are explored. Finally, case studies verify that the proposed model could reduce the cost of intruder detection while ensuring a specific detection rate.

Modeling the impact of extreme summer drought on conventional and renewable generation capacity: Methods and a case study on the Eastern U.S. power system

Across recent years, there has been a growing prevalence of extreme weather events throughout the United States, posing significant challenges to the reliable and resilient operation of power systems. Specifically, summer droughts threaten to severely reduce available generation capacity to meet regional electricity demand, potentially leading to power outages. This underscores the importance of accurate resource adequacy (RA) assessment to ensure the reliable operation of the nation’s energy infrastructure. Accurately evaluating the usable capacity of regional generation fleets is a challenging undertaking due to the intricate interactions between power systems and hydro-climatic systems. This paper proposes a systematic and analytical framework to evaluate the impacts of extreme summer drought events on the available capacity of various generating technologies, incorporating both meteorological and hydrologic factors. The framework provides detailed plant-level capacity derating models for hydroelectric, thermoelectric, and renewable power plants, facilitating evaluations with high temporal and spatial resolution. The application of the proposed impact assessment framework to the 2025 generation fleet of the real-world power system within the PJM and SERC regions of the United States yields insightful results. By analyzing the daily usable capacity of 6,055 at-risk generators across the study region, it shows that the summer capacity deration is most significant for hydroelectric and once-through thermal power plants, followed by recirculating thermal power plants and combustion turbines. In the event of the recurrence of the 2007 southeastern summer drought event in the near future, the generation fleet could experience a substantial reduction in available capacity, estimated at approximately 8.5 GW, compared to typical summer conditions. The sensitivity analysis reveals that the usable capacity of the generation fleet would suffer an even more significant decrease under conditions of increasingly severe summer droughts. The proposed approach and the findings of this study provide valuable methodologies and insights, empowering stakeholders to bolster the resilience of power systems against the potentially devastating effects of future extreme drought events.

Learning model combining convolutional deep neural network with a self-attention mechanism for AC optimal power flow

Alternating current optimal power flow (OPF) analysis is critical for efficient and reliable operation of power systems. For large systems or repetitive computations, the traditional methods such as the direct and gradient methods, or non-traditional methods, such as the genetic algorithm and simulating annealing, are time-consuming and unsuitable for real-time computing. The work in this paper proposes a novel framework to obtain the optimal solution of power flow in real-time using a combination of convolutional neural networks and a self-attention mechanism. All parameters of the power networks are rearranged in an image-like shape of a multi-channel image where each channel is a two-dimensional matrix. The proposed approach is adaptive with every input size of power systems as well as frequent variations of network topologies without intervention to the framework core. The encompassment of all power system contexts in which all parameters of internal elements, generation costs, and topology information are included, contributes to the higher accuracy of inference compared to other current machine-learning-based OPF-solving methods. Besides, the proposed framework established on ubiquitous platforms is effortlessly integrated into current infrastructures of power systems, and the great efficiency along with the computation speed may serve as a critical point for practical implications, such as enabling faster decision-making during real-time operations, predicting system contingencies, and remedial actions based on an offline pre-trained model. This supervised learning process is applied to the dataset of four case studies of meshed power systems: the IEEE 5-bus system (IEEE-5), the IEEE 30-bus system (IEEE-30), the IEEE 39-bus system (IEEE-39), and the IEEE 57-bus system (IEEE-57) to prove the efficacy of the proposed method.

An efficient generator maintenance scheduling model based on unit clustering and linear relaxation

The formulation of periodic maintenance schedules for generators is crucial for the efficient and reliable operation of power systems. Traditional generator maintenance scheduling models often have low temporal resolution and ignore significant information. To solve this problem, this paper proposes a mid/ long-term generator maintenance scheduling model with an hourly time resolution. Given the challenges of large integer variable quantities and computational complexity in directly solving mid/ long-term maintenance schedules for power system generation units, an efficient solution method is proposed based on unit clustering and linear relaxation. Initially, a basic maintenance model is established based on the Unit Commitment (UC) model. Subsequently, the basic UC maintenance model is linearly relaxed to obtain an efficient approximate model, termed the Relaxed Clustered Unit Commitment (RCUC) maintenance model, which involves the operation of annual regulating hydropower stations, energy storage devices, pumped storage stations, wind power plants, and photovoltaic (PV) plants. Simulation results from an adapted IEEE RTS-79 system and a provincial scale system case study are provided to demonstrate the effectiveness of the proposed model in coping with mid/long-term maintenance scheduling and annual hydropower allocation problems of large-scale power systems.

Probabilistic net load forecasting based on transformer network and Gaussian process-enabled residual modeling learning method

Accurate net load forecasting plays an increasingly pivotal role in ensuring the reliable operation and scheduling of power systems. This paper introduces a novel probabilistic net load forecasting approach that combines the strengths of a Transformer network with Gaussian process regression. The state-of-the-art Transformer network is first employed to capture the net load pattern utilizing relatively abundant historical training samples. The remarkable temporal feature extraction ability allows it to discover the complex structure in net loads. Subsequently, the Gaussian Process is applied to capture the behavior of the Transformer network by modeling its forecasting residual utilizing a specific composite kernel. The modeling of the forecasting residual not only provides valuable uncertainty quantification of net load but also improves the forecasting performance based on the Transformer network. Comparative tests utilizing real-world data verify the superiority of the proposed method over other state-of-the-art net load forecasting algorithms.

A Region-Based Approach for the Operational Reliability Evaluation of Power Systems With Renewable Energy Integration

Power system operational reliability evaluation is computationally time-consuming due to the need to analyze numerous system states resulting from uncertain renewable energies and loads as well as random outages of components. This paper presents a load feasible region (LFR) approach to accelerate the process of operational reliability evaluation. The LFR of each state, which is defined as a polyhedron in the load space, can be built in advance offline. The inner points of the LFR correspond to the loads that can be satisfied under the system operational constraints. Then, the preestablished LFR can be corrected with little computing effort considering the renewable energy fluctuation during the operation phase. The optimal load shedding under each state can be obtained directly by calculating the minimum Manhattan distance to the LFR's boundary. Multiple application examples are presented to demonstrate the efficiency and scalability of the proposed region-based approach.

Optimization and analysis of distributed power carrying capacity of distribution network based on DR-DQN

The booming development of distributed power sources in power systems has drawn attention to the carrying capacity and stability of the power grid, becoming a key challenge for the power industry. This study aims to develop a comprehensive deep learning model by combining deep recurrent double Q network (DR-DQN) and deep convolutional neural network (DCNN), and use meta-learning to optimize the model as a whole to simultaneously optimize the power grid. Distributed power supply carrying capacity and predicting the voltage fluctuations of the grid. The comprehensive model is designed to consider distributed power capacity optimization and voltage fluctuation prediction holistically. Through the DR-DQN model, the maximum distributed power capacity is determined under different grid conditions and the distributed power configuration of the grid is optimized. At the same time, the DCNN model is used to analyze the power grid time series data and predict the voltage fluctuation of the power grid. The results are presented in graph form, showing trends in maximum capacity and voltage fluctuations under different grid conditions. Experimental results show that the overall model achieves satisfactory results in distributed power capacity optimization and voltage fluctuation prediction. Performance evaluation and comparison highlight the comprehensive model’s excellent performance in terms of prediction accuracy and computational efficiency, providing new possibilities for efficient management and reliable operation of power systems. The successful development of the model provides practical and reliable solutions for the future development of power systems.

Automatic Diagnosis Method for Short Circuit Faults in Power Measurement Instruments Based on CNN

The accuracy of short-circuit fault diagnosis methods for power measuring instruments is still low, which can directly lead to abnormal power data statistics. To address this issue, a convolutional neural network is used to construct an automatic diagnosis model for short circuit faults in power measuring instruments. The model utilizes wavelet packet energy spectrum to extract crucial features from the signal. The model identifies and determines the fault type based on the obtained operation characteristic data and the set short-circuit fault diagnosis criteria. The Sparrow optimization algorithm is used to optimize the model's parameters and enhance its performance. Experimental analysis revealed that after the signal features were extracted by using the wavelet energy spectrum distribution, the error value fluctuated in the range of 0.00 ~ 0.04 with regard to the measured value and was mainly around 0.02. The extracted features achieved high accuracy levels. The designed model exhibited an average diagnostic accuracy of 97.03%, surpassing the other three models by 9.24%, 7.10%, and 4.25%. The presented model can improve the precision and productivity of fault detection, support the safety and reliability of power system operations, and facilitate the collection and analysis of power usage data

Power system operational reliability assessment based on the data center energy consumption elastic space

In the era of big data, data centers with high energy consumption, interconnectivity, and load flexibility have developed rapidly. However, due to data privacy issues, the traditional power-system operational reliability assessment (ORA) struggles to precisely consider the load flexibility of data centers, leading to inaccurate evaluation. To this end, this article proposes an ORA method considering the load flexibility of data centers via the energy consumption elastic space. By transforming the inner operation constraints of data centers into an equivalent elastic space, the ORA does not require any private data to complete the evaluation. Specifically, the energy consumption model of data centers is established to accurately describe the load flexibility. Then, based on multi-parametric programming techniques, the energy consumption elastic space of data centers is characterized by data centers’ power demand constraints, which do not involve privacy data, and no privacy concerns exist. Finally, the ORA model and the evaluation method based on the energy consumption elastic space can be constructed. With a lot of data center operation constraints being replaced by power demand constraints, the proposed method can complete an evaluation faster without accuracy loss. Its effectiveness is validated through simulations using the IEEE RTS 24-bus system and a provincial 661-bus system.

Stability Study of New Power System Based On Multi-Intelligent Body Collaboration

Developing, implementing, and maintaining a multi-intelligent body collaboration system necessitates significant investments in finances, time, and expertise. While multi-intelligent body collaboration has the potential to enhance power system stability significantly, it also comes with challenges related to interoperability, security, system complexity, and resource allocation. Resource allocation and training costs can be substantial. Addressing these challenges is crucial to harnessing the full benefits of this approach and ensuring the reliable and efficient operation of power systems. Effective communication and coordination strategies among intelligent agents are integral to maintaining power system stability. Timely information exchange, load balancing, disturbance management, and the integration of AI contribute to a more resilient and adaptive energy grid. As technology advances, refining these strategies will be essential to meet the growing demands of an ever-evolving power landscape. As technology marches forward, it becomes increasingly evident that the refinement of these strategies is paramount. The dynamism of the power landscape, driven by technological advancements and evolving needs, necessitates an agile and adaptable power system. The fusion of multi-intelligent bodies and modern technology stands as a testament to our collective pursuit of a more reliable, efficient, and sustainable energy future. In this ever-evolving landscape, the innovation and enhancement of these strategies are our compass, guiding us toward a brighter and more efficient future.

Hybrid attention-based improved temporal convolutional BiGRU approach for short-term load forecasting

Accurate load forecasting can ensure the safe and reliable operation of power systems, reduce generation costs, and improve economic efficiency. To improve the accuracy and performance of short-term load forecasting, this paper proposes a hybrid short-term load forecasting method composed of an improved temporal convolutional network (TCNPlus) with an attention mechanism and a bidirectional gated recurrent unit (BiGRU). Firstly, the collected pre-processed training data is reconstructed using a fixed-length sliding window. Secondly, using the self-attention mechanism (SA) in the improved TCN to further enhance the weight of key features, and introducing residual connections can allow the input to propagate forward faster and improve the representation ability and efficiency of error backpropagation of the network, to eliminate the impact of interference signals. Finally, BiGRU is used to learn the forward and backward dependencies of the load sequence in both directions and predict the true load value. Based on the real load data of a national power grid in South China, through experimental comparison of multiple models, the results show that this model still has higher short-term load forecasting accuracy with fewer input features.

Economic Dispatch Optimization Strategies and Problem Formulation: A Comprehensive Review

Economic Dispatch Problems (EDP) refer to the process of determining the power output of generation units such that the electricity demand of the system is satisfied at a minimum cost while technical and operational constraints of the system are satisfied. This procedure is vital in the efficient energy management of electricity networks since it can ensure the reliable and efficient operation of power systems. As power systems transition from conventional to modern ones, new components and constraints are introduced to power systems, making the EDP increasingly complex. This highlights the importance of developing advanced optimization techniques that can efficiently handle these new complexities to ensure optimal operation and cost-effectiveness of power systems. This review paper provides a comprehensive exploration of the EDP, encompassing its mathematical formulation and the examination of commonly used problem formulation techniques, including single and multi-objective optimization methods. It also explores the progression of paradigms in economic dispatch, tracing the journey from traditional methods to contemporary strategies in power system management. The paper categorizes the commonly utilized techniques for solving EDP into four groups: conventional mathematical approaches, uncertainty modelling methods, artificial intelligence-driven techniques, and hybrid algorithms. It identifies critical research gaps, a predominant focus on single-case studies that limit the generalizability of findings, and the challenge of comparing research due to arbitrary system choices and formulation variations. The present paper calls for the implementation of standardized evaluation criteria and the inclusion of a diverse range of case studies to enhance the practicality of optimization techniques in the field.

Dynamic Aggregation Method for Load Aggregators Considering Users’ Deviation Electricity

Constructing a new energy-based power system is not only an important direction for the transformation and upgrade of China’s power system, but also a key to achieving peak carbon and carbon neutrality. How to fully utilize situation awareness technology to adapt to diverse and differentiated scenarios has become a crucial breakthrough point for ensuring the reliable, safe, high-quality, low-carbon, and economical operation of the new power system. Starting from the distribution network demand resources, this paper proposes a dynamic aggregation method for load aggregators considering the user deviation quantity, to deal with the current situation that the adjustable load-side resource points are multi-faceted and wide, and the operating subjects are complex and difficult to participate directly in the grid dispatch. First, considering there is subjectivity in the electricity behavior of users under the jurisdiction of the load aggregator, a deviation amount may be generated during the actual aggregation process, which reduces the profit of the load aggregator. Therefore, a load aggregator-level user deviation dynamic volume forecasting method based on the Markov chain is proposed, which is used to predict the deviation quantity of users during the dispatch cycle and achieve a dynamic status estimate on the load side of the new power system. On this basis, a dynamic aggregation model for load aggregators based on the deviation volume was constructed with the objective of maximizing the revenue of load aggregators. The examples, by comparing the aggregation results of users under three scenarios, show the proposed method can effectively guarantee the income of load aggregators, verify the effectiveness of the proposed dynamic aggregation strategy, and provide technical support for the operation situation awareness of the load side of the new power system.

Co-evaluation of power system frequency performance and operational reliability considering the frequency regulation capability of wind power

With the increasing proportion of renewable energy, the power system inertia decreases, and the operation uncertainty rises. It brings concerns about the system frequency and operational reliability. However, the impacts of the power system frequency performance on the reliability parameters of generation units have not been fully investigated. This paper studies the frequency performance and the operational reliability co-evaluation for power systems considering wind turbines. Firstly, a power system frequency regulation model is established considering the regulation capability of wind turbines. Then, the cluster of equivalent wind turbines is incorporated into the frequency regulation architecture of thermal power units, which accelerates the analysis of frequency performance. Then, the frequency performance of the power system with the participation of wind turbines under the operation uncertainty and the unit random faults is quantitatively analyzed. A frequency-dependent generator reliability parameter model is derived. Next, a multi-time scale co-evaluation framework is proposed to realize the co-evaluation of frequency performance and operational reliability. Case studies are carried out on the modified IEEE RTS-79 system and a provincial power system. Results show that compared with the existing research, the proposed method can obtain the frequency performance and reliability results efficiently.