Power System Security Research Articles

A day-ahead wind speed correction method: Enhancing wind speed forecasting accuracy using a strategy combining dynamic feature weighting with multi-source information and dynamic matching with improved similarity function

Forecasting error of day-ahead wind speed (WS) seriously affects wind power integration and power system security and stability. In this regard, this paper fully considers the spatiotemporal correlation of wind farms (WFs) in different geographical locations, and proposes a day-ahead WS combined correction method that integrates multi-source station dynamic information weighting. Different from the previous WS correction methods, this paper fully considers the dynamic correlation of WS between the WFs, introduces an improved weighted similarity function to screen and dynamically weight the information of WFs with dynamic correlation, and introduces the dynamic weighting feature into the WS correction process. A combined decomposition mechanism is proposed, which combines sequential variational mode decomposition (SVMD) and feature mode decomposition (FMD) models to extract the most relevant trend components and non-stationary components of forecasted and measured WS. A combined correction model is introduced, and a combined architecture of Non-stationary Transformer combined with bidirectional long short-term memory network (Ns-Transformer-BILSTM) is used to correct the stationary WS component. A dynamic matching mechanism of fluctuation components considering improved similarity is proposed for the correction of non-stationary components. The proposed method is applied to several regional WFs in China. The experimental results show that the average correction of NRMSE, NMAE and R can reach 2.4 % ∼ 3.7 %, 2.0 % ∼ 3.0 % and 3.3 % ∼ 9.7 %, respectively. The NRMSE and NMAE corresponding to the corrected WS of certain individual WFs can be reduced by 10 % and 9 %, respectively, and R can be increased by 33 %.

Extreme weather impact on carbon-neutral power system operation schemes: A case study of 2060 Sichuan Province

The impact of increasingly frequent and intense climate change events on power supply and consumption has become a concern for many countries. This study evaluated the impact of extreme weather on the power system economy and security by conducting an empirical case study of the Sichuan power network to address uncertainty attributed to different extreme weather events under carbon neutrality scenarios. First, our paper proposed a CNPSSO model of four seasonal typical days by jointly considering the regional hourly disparities of load curves and renewable power output curves. Then, the model creatively introduced the effects of extreme weather on the power supply and demand curves based on various scenario analyses. The most cost-effective output strategy using various generation technologies was provided based on optimization models. Finally, Sichuan Province, a hydropower-dominated province was selected as an empirical case study. The results indicate that extreme weather significantly affects the hourly operational and dispatching schemes of Sichuan's power system. Compared to the "Normal weather scenario”, hydropower's share plummets from 83.3 % to 46.1 %, while pumped storage surges 7.1 billion kWh in summer in extremely hot and dry weather. Imported electricity rises by 24.4 %, 30.5 % and 24.8 % in dry scenario, extremely hot and dry scenario, and extremely cold scenario, respectively. Furthermore, extreme weather increases carbon emissions (mainly from thermal power and imported electricity) and costs. Specifically, when juxtaposed against normal weather, extreme drought, extreme heat drought and extremely cold lead to respective increases in carbon emissions by 32.9 %, 33.5 %, and 12.3 %. Regarding the levelized cost of electricity (LCOE), "Extremely dry scenario”, "Extremely hot and dry scenario”, and "Extremely cold scenario” drive up the LCOE by 7.2 %, 9.2 %, and 4.2 %, respectively.

Consensus-Based Power System State Estimation Algorithm Under Collaborative Attack.

Due to its vulnerability to a variety of cyber attacks, research on cyber security for power systems has become especially crucial. In order to maintain the safe and stable operation of power systems, it is worthwhile to gain insight into the complex characteristics and behaviors of cyber attacks from the attacker's perspective. The consensus-based distributed state estimation problem is investigated for power systems subject to collaborative attacks. In order to describe such attack behaviors, the denial of service (DoS) attack model for hybrid remote terminal unit (RTU) and phasor measurement unit (PMU) measurements, and the false data injection (FDI) attack model for neighboring estimation information, are constructed. By integrating these two types of attack models, a different consensus-based distributed estimator is designed to accurately estimate the state of the power system under collaborative attacks. Then, through Lyapunov stability analysis theory, a sufficient condition is provided to ensure that the proposed distributed estimator is stable, and a suitable consensus gain matrix is devised. Finally, to confirm the viability and efficacy of the suggested algorithm, a simulation experiment on an IEEE benchmark 14-bus power system is carried out.

Identifying power outage hotspots to support risk management planning.

Secure and reliable power systems are vital for modern societies and economies. While there is a focus in the literature on predicting power outages caused by severe weather events, relatively little literature exists on identifying hot spots, locations where outages occur repeatedly and at a higher rate than expected. Reliably identifying hotspots can provide critical input for risk management efforts by power utilities, helping them to focus scarce resources on the most problematic portions of their system. In this article, we show how existing work on Moran's I spatial statistic can be adapted to identify power outage hotspots based on the types and quantities of data available to utilities in practice. The local Moran's I statistic was calculated on a grid cell level and a set of criteria were used to filter out which grid cells are considered hotspots. The hotspot identification approach utilized in this article is an easy method for utilities to use in practice, and it provides the type of information needed to directly support utility decisions about prioritizing areas of a power system to inspect and potentially reinforce.

Integrated resource strategic planning considering inter-regional flexibility supply–demand balance: A case study for the Northwest and Central Grid in China

China’s rapid advancement in renewable energy and regional power grid interconnection has highlighted the need for enhanced system flexibility to ensure the security and stability of regional power systems. To address this issue, this study presents a new framework for integrated resource strategic planning for inter-regional flexibility (IRSP-IF). This framework evaluates inter-regional flexibility demand and optimizes power resource allocation across regions. The northwest and central grids in China are used as case studies to evaluate inter-regional power resource allocation from 2022 to 2035. This analysis considers key factors including flexibility supply–demand balance constraints, regional power grid interconnections, and time scales for assessing flexibility demand. The results show that incorporating these constraints and interconnections into the strategic power plan enables greater integration of renewable energy while reducing both installed capacity and the overall cost of regional power resources. In the S3 scenario, which includes inter-regional flexibility supply–demand balance, the total integration of wind and solar power increases by 1.54%. Additionally, the combined average annual fixed and operating costs decrease by 1.44 billion yuan compared to the S2 scenario, which only considers single-region flexibility supply–demand balance. Furthermore, evaluating flexibility demand at a shorter time scale results in a higher proportion of flexible resources, with an average annual growth of 24.43%.

WITHDRAWN: Design of Group Key Agreement Method Based on Elliptic Curve Cryptography in New Energy Extensive Access Security Protection

Aiming at the security problem of power system communication network under the condition of extensive access of new energy, we design the group key agreement method based on elliptic curve cryptography in the security protection of new energy extensive access. Design the architecture of power system security protection network under the condition of extensive access to new energy. The network architecture adopts wireless communication technology to realize the transmission of new energy security monitoring information. For the data confidentiality problem in the wire communication technology, the group key agreement method based on elliptic curve cryptography is proposed, and the group key agreement model is constructed under the set conditions, and the group key is generated through the processes of main parameter selection, group composite public key distribution, group key agreement protocol design, group key update protocol for node joining event, and group key update protocol for members departure event, etc. The group key is utilized for the communication. The experimental results show that this method can generate the key with high randomness and excellent key sensitivity, which can effectively resist the attacks of character frequency statistics and information entropy and guarantee the security of communication information.

A Review on Power System Security Issues in the High Renewable Energy Penetration Environment

A Review on Power System Security Issues in the High Renewable Energy Penetration Environment

Security-constrained economic dispatch of power systems with line outage using firefly algorithm

The Security-constrained Economic Dispatch (SCED) problem is the problem of finding the optimal generation dispatch for a power system that minimizes the operating cost while ensuring the security of the system. The security of the system is defined as the ability of the system to withstand disturbances without violating any operational constraints. Line outage is a common disturbance that can affect the security of a power system. The Firefly Algorithm, inspired by the flashing behavior of fireflies in nature, is a metaheuristic optimization technique known for its effectiveness in solving complex and dynamic optimization problems. In this study, the FA is adapted to the SCED problem with a specific focus on enhancing grid resilience during line outage scenarios. The proposed method aims to simultaneously optimize the economic cost of power generation and the system's ability to withstand line outages. To demonstrate the effectiveness of the proposed approach, extensive simulations are conducted on standard IEEE test systems with varying degrees of complexity and network sizes. The results showcase the superior performance of the SCEDL-Firefly Algorithm compared to traditional optimization methods, as it provides a resilient dispatch solution that can effectively adapt to unexpected line outages while maintaining economic efficiency. Overall, this research contributes to the advancement of secure economic dispatch techniques and power system resilience by leveraging the Firefly Algorithm's capabilities. The findings offer valuable insights for power system operators and planners seeking to enhance grid reliability in the presence of line outages, ultimately promoting a more sustainable and resilient energy infrastructure.

A hybrid SATS algorithm based security constrained optimal power flow using FACTS devices

In the realm of power systems, achieving optimal operation while ensuring security remains a paramount challenge. The security constrained optimal power flow (SCOPF) problem deals with optimizing power system operations while taking into account security limitations. Flexible alternating current transmission system (FACTS) is a system consisting of static equipment used for transmitting electrical energy in the form of AC. The static synchronous series compensator (SSSC) is a specific form of series FACTS device. The unified power flow controller (UPFC) is a FACTS device that is connected in parallel and series with a transmission line. In this research, hybrid simulated annealing and tabu search (hybrid SATS) algorithm is designed to solve SCOPF problems that involve use of FACTS devices. The combination of simulated annealing and tabu search is intended to improve algorithm's pace of convergence and the quality of its solutions. Hybrid SATS with FACTS devices are used to investigate line flow limit violations during single line failures and ensure power flows remain within their security limitations. The efficacy of proposed algorithm is demonstrated through case studies utilizing IEEE 30 bus system. These case studies demonstrate algorithm's capabilities to achieve optimal and secure power system functioning to demonstrate its effectiveness.

Optimal Reactive Power Dispatch Using Artificial Gorilla Troops Optimizer Considering Voltage Stability

The power system has been expanded to supply and fulfil the consumers’ requirements for reliability, affordability, and power quality. Power loss reduction and voltage stability enhancement are important points and have been considered interesting subjects for researchers and utilities. Furthermore, reactive power plays an important role in power system stability, security, and voltage improvement, and it is known as reactive power dispatch (RPD). In this paper, a newly developed meta-heuristic optimization technique that inspired the gorilla troop’s social intelligence in nature is applied. It is named Artificial Gorilla Troop Optimization (GTO). In addition, GTO is utilized to solve the optimal reactive power dispatch (ORPD) problem, whose real active power and voltage deviation reduction are the objective functions of this study. Generator voltage, transformer tap-changers, and reactive power compensators are the controlled variables that are optimized for achieving the minimum real power loss and bus voltage deviation. To illustrate the efficiency and performance of the proposed algorithm, IEEE 14-bus and 30-bus systems are employed. Moreover, the obtained results are compared with those obtained with other three already existing optimization algorithms, including the genetic algorithm (GA), particle swarm optimization (PSO), and whale optimization algorithm (WOA). Obviously, the proposed approach can prove the optimal values of controlled variables in solving the ORPD problem by giving the minimum real power loss and voltage deviation than those from compared techniques with less computation time.

Research Methods for Transient Stability Analysis of Power Systems under Large Disturbances

Transient stability analysis is critical for maintaining the reliability and security of power systems. This paper provides a comprehensive review of research methods for transient stability analysis under large disturbances, detailing the modeling concepts and implementation approaches. The research methods for large disturbance transient stability analysis are categorized into five main types: simulation methods, direct methods, data-driven methods, analytical methods, and other methods. Within the analytical method category, several common analytical strategies are introduced, including the asymptotic expansion method, intrusive approximation method, and other analytical methods. The fundamental principles, characteristics, and recent research advancements of these methods are detailed, with particular attention to their performance in various aspects such as computational efficiency, accuracy, applicability to different system models, and stability region estimation. The advantages and disadvantages of each method are compared, offering insights to support further research into transient stability analysis for hybrid power grids under large disturbances.

Multi-Feature Fusion in Graph Convolutional Networks for Data Network Propagation Path Tracing

With the rapid development of information technology, the complexity of data networks is increasing, especially in electric power systems, where data security and privacy protection are of great importance. Throughout the entire distribution process of the supply chain, it is crucial to closely monitor the propagation paths and dynamics of electrical data to ensure security and quickly initiate comprehensive traceability investigations if any data tampering is detected. This research addresses the challenges of data network complexity and its impact on the security of power systems by proposing an innovative data network propagation path tracing model, which is constructed based on graph convolutional networks (GCNs) and the BERT model. Firstly, propagation trees are constructed based on the propagation structure, and the key attributes of data nodes are extracted and screened. Then, GCNs are utilized to learn the representation of node features with different attribute feature combinations in the propagation path graph, while the Bidirectional Encoder Representations from Transformers (BERT) model is employed to capture the deep semantic features of the original text content. The core of this research is to effectively integrate these two feature representations, namely the structural features obtained by GCNs and the semantic features obtained by the BERT model, in order to enhance the ability of the model to recognize the data propagation path. The experimental results demonstrate that this model performs well in power data propagation and tracing tasks, and the data recognition accuracy reaches 92.5%, which is significantly better than the existing schemes. This achievement not only improves the power system’s ability to cope with data security threats but also provides strong support for protecting data transmission security and privacy.

AC breakdown and decomposition products detection characteristics of eco-friendly insulating medium in gas insulated switchgear

Gas Insulated Switchgear (GIS) was a crucial electrical equipment that provides the safety and security of power systems, and finding the eco-friendly alternatives of sulphur hexafluoride (SF6) as insulating medium in GIS has been an urgent demand in past decades. However, few studies reported the specific dielectric strength under various electrodes and partial discharge (PD) decomposition products detection of applying the eco-friendly gas in GIS. In this paper, the insulation properties among C4F7N/CO2 mixtures, dry air and CO2 were explored under the reduced-scale gas-insulated experimental device. The relationship between AC breakdown voltage and gas pressure was gained under the different electrodes and gap distances, and 0.6 MPa 7% C4F7N/93% CO2, 0.9 MPa dry air, 0.9 MPa CO2 all possess the capacity to be the insulating medium in eco-friendly gas insulated switchgear. Furthermore, the partial discharge experimental was also carried out to realize the decomposition products detection of dry air, which designs three common defect types including metal protrusion defects, air gap defects, and metal contamination defects. The decomposition products detection result shows that the contains of CO2, CO, and NO2 linearly increase with the increasing applied voltages and times, and the partial discharge defects are distinguished according to the ratios of c(CO2 + CO)/c(NO2) and c(CO2)/c(CO). The results can provide the basis for the further development of eco-friendly gas insulated switchgear.

Uncertain Scheduling of the Power System Based on Wasserstein Distributionally Robust Optimization and Improved Differential Evolution Algorithm

The rapid development of renewable energy presents challenges to the security and stability of power systems. Aiming at addressing the power system scheduling problem with load demand and wind power uncertainty, this paper proposes the establishment of different error fuzzy sets based on the Wasserstein probability distance to describe the uncertainties of load and wind power separately. Based on these Wasserstein fuzzy sets, a distributed robust chance-constrained scheduling model was established. In addition, the scheduling model was transformed into a linear programming problem through affine transformation and CVaR approximation. The simplex method and an improved differential evolution algorithm were used to solve the model. Finally, the model and algorithm proposed in this paper were applied to model and solve the economic scheduling problem for the IEEE 6-node system with a wind farm. The results show that the proposed method has better optimization performance than the traditional method.

Robust optimization dispatch for PV rich power systems considering demand response and energy storage systems

In recent years, the ever-rising penetration of distributed photovoltaics (PV) power has presented substantial challenges in power system dispatch due to its inherent randomness and unpredictability. To bridge this gap, this paper proposes a two-stage robust optimization method for power system security dispatch considering traditional generators as well as flexible resources, such as load demand response and energy storage systems. Specifically, a price-based demand response model is established to optimize the system’s load curve during a day. On this basis, a two-stage optimization problem for day-ahead and intra-day power system dispatch model is proposed. The dispatch objective is to minimize the overall cost in worst-case scenarios through properly scheduling unit commitment (UC) as well as flexible resources in each dispatch interval. Column and constraint generation (C&CG) algorithm is adopted for problem solving. The effectiveness of the proposed method is validated by case studies based on a modified 6-node system and a 24-node system. Simulation results indicate that through appropriately scheduling the energy storage system and load demand response, the proposed dispatch method can significantly reduce the total operation cost of a PV rich power system, which in turn facilitates the integration of PV power.

The frequency analytical method for regional power systems considering the emergency frequency control

With the continuous construction of high-voltage direct-current (HVDC) transmission projects, higher requirements are placed on the frequency security of the regional power systems. In order to ensure the frequency security of regional power systems, this paper proposes a transient frequency analytical method considering the emergency frequency control (EFC). Firstly, the aggregated system frequency response (SFR) model is constructed by reducing the order of the generator governor equation, the EFC equation, and the load model equation. Then, based on the aggregation model, the analytical solution of the transient frequency nadir of the regional power system is derived. The model can quickly and accurately calculate the transient frequency nadir of the regional power system considering EFC after the HVDC block fault. Finally, based on an actual regional power system model, the accuracy and applicability of the proposed method under HVDC block fault are verified.

A new comprehensive framework for cascading outage simulation in power systems

Simulating accurately and fast cascading outages play a critical role in power system security and resilience assessment. This paper proposes a new comprehensive framework for cascading outage simulation, articulated around an extended AC power flow. To address the key modelling features of cascading outage simulation, the proposed framework develops new algorithms for: a) detecting possible islanding, b) frequency control in each island: primary frequency control (PFC), under-frequency load shedding (UFLS) and over-frequency generator tripping (OFGT) and c) improved under-voltage load shedding (UVLS) and load models to avoid the divergence of AC power flow and allow handling voltage instability during the cascade. Tests on a realistic 60-bus system show that the proposed cascading outage simulator can successfully handle islanding, PFC, UFLS, OFGT, and UVLS; it simulates the cascade until there is no violation of system frequency, voltage, and power flow limits in islands that do not experience blackout.

Machine learning optimization for hybrid electric vehicle charging in renewable microgrids

Renewable microgrids enhance security, reliability, and power quality in power systems by integrating solar and wind sources, reducing greenhouse gas emissions. This paper proposes a machine learning approach, leveraging Gaussian Process (GP) and Krill Herd Algorithm (KHA), for energy management in renewable microgrids with a reconfigurable structure based on remote switching of tie and sectionalizing. The method utilizes Gaussian Process (GP) for modeling hybrid electric vehicle (HEV) charging demand. To counteract HEV charging effects, two scenarios are explored: coordinated and intelligent charging. A novel optimization method inspired by the Krill Herd Algorithm (KHA) is introduced for the complex problem, along with a self-adaptive modification to tailor solutions to specific situations. Simulation on an IEEE microgrid demonstrates efficiency in both scenarios. The predictive model yields a remarkably low Mean Absolute Percentage Error (MAPE) of 1.02381 for total HEV charging demand. Results also reveal a reduction in microgrid operation cost in the intelligent charging scenario compared to coordinated charging.

Solution of optimal reactive power dispatch by Lévy-flight phasor particle swarm optimization

Optimal reactive power dispatch (ORPD) problems are important tools for the sake of security and economics of power systems. The ORPD problems are nonlinear optimization problems to minimize the real power losses and voltage profile enhancement by optimizing several discrete and continuous control variables. This paper proposes a Lévy-flight phasor particle swarm optimization (LPPSO) for solving ORPD problems while considering real power losses and voltage profile in two standard power systems. The simulation results demonstrate that the LPPSO algorithm proves itself as an acceptable method for reaching a more optimal solution for the ORPD problems.

A novel nature-inspired nutcracker optimizer algorithm for congestion control in power system transmission lines

In the restructured power system, where uncertainties are common, managing congestion becomes a crucial aspect of power system operation and control. Congestion management aims to alleviate the power system transmission line congestion while meeting the system constraints at minimal cost. This research introduces a generation rescheduling method for congestion management in the electricity market, leveraging an innovative nutcracker optimizer algorithm. The nutcracker optimizer algorithm, inspired by nutcrackers’ food accumulation mechanisms, is a recently developed nature-inspired algorithm. The efficacy of this proposed approach is assessed across modified IEEE 30-bus, and IEEE 118-bus test systems, considering the system parameters. The effectiveness of the proposed congestion management with the nutcracker optimizer algorithm is analyzed by comparing its results with those generated by other recent optimization techniques. Results demonstrated that the nutcracker optimizer algorithm surpasses other comparative methods, requiring fewer fitness function evaluations, avoiding local optima, and displaying encouraging convergence traits. Implementing this approach can assist the system operators in swiftly addressing contingencies, ensuring secure and reliable power system operation within a deregulated environment.