Secure Operation Of Power Systems Research Articles

A Hybrid Nonlinear Forecasting Strategy for Short-Term Wind Speed

The ability to predict wind speeds is very important for the security and stability of wind farms and power system operations. Wind speeds typically vary slowly over time, which makes them difficult to forecast. In this study, a hybrid nonlinear estimation approach combining Gaussian process (GP) and unscented Kalman filter (UKF) is proposed to predict dynamic changes of wind speed and improve forecasting accuracy. The proposed approach can provide both point and interval predictions for wind speed. Firstly, the GP method is established as the nonlinear transition function of a state space model, and the covariance obtained from the GP predictive model is used as the process noise. Secondly, UKF is used to solve the state space model and update the initial prediction of short-term wind speed. The proposed hybrid approach can adjust dynamically in conjunction with the distribution changes. In order to evaluate the performance of the proposed hybrid approach, the persistence model, GP model, autoregressive (AR) model, and AR integrated with Kalman filter (KF) model are used to predict the results for comparison. Taking two wind farms in China and the National Renewable Energy Laboratory (NREL) database as the experimental data, the results show that the proposed hybrid approach is suitable for wind speed predictions, and that it can increase forecasting accuracy.

A Grid-Support Strategy With PV Units to Boost Short-Term Voltage Stability Under Asymmetrical Faults

Modern grid codes demand the integration of voltage support capability with photo-voltaic (PV) generators to ensure a secure operation of power systems. On the other hand, short-term voltage instability (STVI) of distribution networks (DNs) is one of the key issues to be addressed. It can occur due to high proportion of induction motor loads. However, the existing literature lacks a thorough analysis of short-term voltage stability (STVS) of DNs following an asymmetrical fault. Furthermore, an effective voltage-support strategy for PV units, which can improve the STVS while mitigating the excessive voltage swell, is essential for secure operation. Hence, firstly this paper thoroughly investigates the STVS of DNs subjected to asymmetrical faults. It is perceived that voltage support through conventional methods can increase the risk of STVI and excessive voltage swell. Secondly, a new voltage-support strategy is proposed based on the negative sequence voltage at point of common coupling (PCC) aimed at mitigating the STVI and regulating the phase voltages within the margins. The key features of the proposed strategy are, fast and accurate estimation of the network's impedance at PCC is not required and it can be easily re-designed considering the network's dynamic behaviors. The efficacy of the proposed method is validated on two IEEE benchmark test systems, and the results demonstrate the effectiveness in improving the STVS and alleviating over voltage issues.

Convolutional neural network-based power system transient stability assessment and instability mode prediction

Online transient stability assessment (TSA) is vital for power system control as it provides the basis for operators to decide emergency control actions. But none of previous TSA research has taken into consideration the difference between two instability modes (aperiodic instability and oscillatory instability), which may threaten secure operation of power system. To address this problem, a TSA and instability mode prediction method based on convolutional neural network is proposed. The method takes the bus voltage phasor sampled by phasor measurement units (PMUs) during a short observation window after disturbance as input, and outputs the prediction result promptly: stable, aperiodic unstable or oscillatory unstable. The end-to-end model automatically extracts needed features from the raw measurement data, thus freeing itself from reliance on expertise. At the offline training stage, stochastic gradient descent with warm restart (SGDR) optimization algorithm is employed so that the model tends to converge to 'flat' and 'wide' minima with better generalization ability. Case studies conducted on New England 39-bus system and Western Electricity Coordinating Council (WECC) 179-bus system demonstrate superior accuracy, adaptability and scalability of the proposed method compared with conventional machine learning methods. Furthermore, the proposed model is empirically proven to be robust to PMU noise and loss.

Low Carbon Multi-Objective Unit Commitment Integrating Renewable Generations

Unit commitment is an intractable issue aiming to reduce the overall economic cost of power system operation while maintaining the system constraints. Due to the emerging scenario of global warming, many countries are vigorously developing renewable energy to replace the traditional fossil power plant, in order to reduce the environmental and carbon emission. The increasing penetration of renewable generation significantly challenge the economic and security of power system operation. In this paper, a low carbon multi-objective objective unit commitment model considering economic cost, environmental cost and, more importantly, the carbon emission is established, integrating wind and solar power and therefore generating a multi-objective, high-dimensional, strong non-linear, multi-constraint and mixed integer optimization problem. The non-dominated sorting genetic algorithm-III is tailored and adopted for solving the proposed challenging task, where the decision-making scheme is designed according to the normalization method and weighted sum function. Numerical results show that the proposed complex many-objective low carbon unit commitment model can be successfully solved by the proposed algorithm and the carbon emission is effectively reduced by the integration of renewable generations.

Recurrent Graph Convolutional Network-Based Multi-Task Transient Stability Assessment Framework in Power System

Reliable online transient stability assessment (TSA) is fundamentally required for power system operation security. Compared with time-costly classical digital simulation methods, data-driven deep learning (DL) methods provide a promising technique to build a TSA model. However, general DL models show poor adaptability to the variation of power system topology. In this paper, we propose a new graph-based framework, which is termed as recurrent graph convolutional network based multi-task TSA (RGCN-MT-TSA). Both the graph convolutional network (GCN) and the long short-term memory (LSTM) unit are aggregated to form the recurrent graph convolutional network (RGCN), where the GCN explicitly integrate the bus (node) states with the topological characteristics while the LSTM subsequently captures the temporal features. We also propose a multi-task learning (MTL) scheme, which provides joint training of stability classification (Task-1) as well as critical generator identification (Task-2) in the framework, and accelerate the process with parallel computing. Test results on IEEE 39 Bus system and IEEE 300 Bus system indicate the superiority of the proposed scheme over existing models, as well as its robustness under various scenarios.

An Integrated LHS–CD Approach for Power System Security Risk Assessment with Consideration of Source–Network and Load Uncertainties

Large-scale wind power integrated into power grids brings serious uncertainties and risks for power system safe operation, and it is imperative to evaluate power system security risk pertinent to high-level of uncertainties. In this paper, a comprehensive source–network–load probabilistic model, representing the typical uncertainties penetrated in power generation transmission consumption portion, is firstly set for power system operation. Afterwards an integrated LHS–CD approach based on the Latin hypercube sampling (LHS) and Cholesky decomposition (CD) is tailored to effectively conduct the security risk assessment, in which the LHS is utilized to stratified sample the uncertainties of wind power and thermal power, transmission line outage, and load demands, while the CD part is adopted to address the correlations of uncertainties by rearranging the sampled matrix generated by LHS. Moreover, static voltage risk and transmission line overloaded risk index are properly defined for quantitatively evaluating power system operational security risk. Simulation results of a modified New England 39-bus system confirm that the proposed integrated LHS–CD approach is effective and efficient for power system security risk assessment with consideration of source–network–load demand uncertainties.

RETRACTED: Blockchain-Enabled Charging Right Trading Among EV Charging Stations

Increasing penetration of electric vehicles (EVs) gives rise to the challenges in the secure operation of power systems. The EV charging loads should be distributed among charging stations in a fair and incentive-compatible manner while ensuring that power transmission and transformation facilities are not overloaded. This paper first proposes a charging right (or charging power ration) trading mechanism and model based on blockchain. Considering all kinds of random factors of charging station loads, we use Monte Carlo modeling to determine the charging demand of charging stations in the future. Based on the charging demand of charging stations, a charging station needs to submit the charging demand for a future period. The blockchain first distributes initial charging right in a just manner and ensures the security of facilities. Given that the charging urgency and elasticity differences vary by charging stations, all charging stations then proceed with double auction and peer-to-peer (P2P) transaction of charging right. Bids and offers are cleared via double auctions if bids are higher than offers. The remaining bids and offers are cleared via the P2P market. Then, this paper designs the charging right allocation and trading platform and smart contract based on the Ethernet blockchain to ensure the safety of the distribution network (DN) and the transparency and efficiency of charging right trading. Simulation results based on the Ethereum private blockchain show the fairness and efficiency of the proposed mechanism and the effectiveness of the method and the mechanism.

Transient Stability of Power Systems Under High Penetrations of Wind Power Generation

This paper investigates the impact of high levels of penetration of wind power generation in the problem of transient stability of power systems. The investigation takes into account the stability issues related to the disconnection of wind power plants due to violation of voltage limits defined by the low-voltage ride-through curve. Different levels of penetration are investigated, starting at 10.59% for wind power plants based on types 1, 2, 3 and 4 wind turbine generators, until 75.24% for those based on type 3. Simulation results show that the secure power system operation can be maintained in most of the situations, from the point of view of transient stability, for the crescent penetration of wind power plants.

Network-constrained unit commitment with RE uncertainty and PHES by using a binary artificial sheep algorithm

With increasing share of renewable energy (RE) in power system, the unit commitment of hybrid power system have become more complicated due to network security demand and integration of pumped hydro energy storage (PHES). In this paper, a network-constrained unit commitment (NCUC) problem considering RE uncertainty and modulation of PHES has been raised. The NCUC model, with AC network security constraints and environmental constraints as well as traditional terms of constraints, is complicated to be solved. A novel Binary Artificial Sheep Algorithm (BASA) is used to solve the NCUC model. In the frame of BASA, the NCUC problem is decomposed as a master UC problem to determine start/stop status and economic load dispatch and a sub problem to check the AC network constraints. Based on the NCUC model and the solving method, the joint impacts of RE uncertainty and PHES have been studied. Test systems with different size have been adopted to verify the feasibility and effectiveness of the BASA. It is seen that the BASA has achieve an overall competitive performance on terms of operation cost and convergence speed by comparing it with existing methods. A modified IEEE 30-bus system with wind and photovoltaic power stations has been designed to reveal the impacts of RE and PHES. The results demonstrate that uncertainty of RE affects operation cost and security of the hybrid power system, and the adverse impact would aggravate as the RE forecasting error increase. The results also confirm the significant effect of PHES in restraining the negative influence of RE uncertainty.

Risk Assessment of Rare Events in Probabilistic Power Flow via Hybrid Multi-Surrogate Method

The risks associated with rare events threatening the security of power system operation are of paramount importance to power system planners and operators. To analyze the risks caused by high-impact, low-frequency rare events, an immensely large number of samples are typically required for the Monte-Carlo (MC) method on the high-fidelity power system model to achieve a sufficient accuracy, thereby rendering this approach computationally prohibitive. To handle this problem efficiently, it is desirable to construct a surrogate model for the power system response. However, the straightforward MC sampling of the low-fidelity surrogate can lead to biased results in the low-probability tail regions that are vital to risk assessment. Moreover, a single surrogate is unable to handle the topology uncertainties caused by random branch outages. To overcome these issues, we propose a hybrid multi-surrogate (HMS) method based on the polynomial chaos expansion (PCE) with low-probability tail events reevaluated by the high-fidelity model through a probabilistic analysis. This method improves the computational efficiency of the MC method for rare-event risk assessment by leveraging multi-fidelity models while retaining the desired accuracy. Simulations conducted in three test systems verify the excellent performances of the HMS method.

The Impact of Ramp-Induced Data Attacks on Power System Operational Security

This paper analyzes a malicious data attack in which the attacker targets at the generation side of the system and aims to compromise the system security by causing large power imbalance in the real-time operations. Such an attack is called the ramp-induced data (RID) attack in the literature which revealed that the attacker can manipulate the limits of generator ramp constraints in real-time dispatch (RTD) and, thus, impact the power market operations. In this paper, we propose an optimal attack model to analyze the impact of the RID attack on power system operational security and show that the attacker can introduce large power imbalance into real-time operations that can cause security issues or even catastrophic consequences. To address the risk of such an attack, a countermeasure is presented that reassesses the regulation reserve adequacy against a given risk level of the attack. Simulations on the IEEE 118-bus system verify the impact of the proposed RID attack and the effectiveness of the regulation assessment approach.

Network transition security for transmission switching

The smart grid with flexible topologies receives intensive attention recently. Transmission switching (TS) alters the power system topology during operation, and has been demonstrated for the advantage of economic and secure operation of power systems. TS includes a chain of sequential switching actions which bring disturbances to the system if the switching actions are not properly designed. Unfortunately, it is not considered or well-studied in existing works. In this paper, a new multi-period TS model that considers the transition security and a two-stage iterative method are proposed. In the TS model, we take into account the fact that only one line is permitted to switch up or down at a time and the security of each switching action is considered. The proposed iterative solution makes the TS model more tractable under AC framework. Case studies on a 6-bus system and the IEEE 57-bus test system have varified the effectiveness of the proposed model. Numerical results show that: ① the consideration of transition security of TS is essential; ② the transition path is directly related to secure and fast the transmission switching; ③ the proposed model and solution method give an effective way to determine the switching sequence and switching timing under transition security criteria.

Robust Forecasting-Aided State Estimation for Power System Against Uncertainties

Accurate forecasting-aided state estimation plays a vital role in reliable and secure operation of power systems. However, most of existing methods are unable to deal with the uncertainties that might be caused by uncertain model parameters or uncertain noise statistics. Therefore, the performance of these methods may be inevitably degraded significantly. To address these issues, based on the robust control theory, in this paper, by incorporating the modified innovation based Sage-Husa estimator of noise statistics and the proposed estimation error covariance matrix adaptive technique, a novel adaptive $H_{\infty }$ extended Kalman filter (AHEKF) is developed to realize robust forecasting-aided state estimation for power system with model uncertainties. Extensive simulations carried out on several different test systems demonstrate the efficiency and robustness of the proposed method.

Multi-class support vector machines for static security assessment of power system

Abstract Accurate determination of present operating state of power system takes utmost importance for utility operator for reliable and secure operation of power system. Conventional power flow methods have the limitations of larger memory requirement, longer computational time and infeasible options for real time applications for security assessment. Moreover, using performance index based on bus voltage deviations and line loadings for security assessment suffers with masking problem and fails to discriminate the contingency cases with the closer violations. Hence, composite security index has been formulated in this work addressing hyper-ellipse encompassed within a hyper-box concept to address multiclass classification problem of power system security using support vector machine (SVM). Sequential forward selection has been implemented for optimal feature selections to achieve the highest classification accuracy and the least mis-classification rate. The results of the proposed method have been validated and compared with existing method for three IEEE standard test systems.

Generation Scheduling of a Hydrodominated Provincial System Considering Forecast Errors of Wind and Solar Power

The integration of large-scale uncontrollable renewable power greatly affects the security and reliability of power system operations. An effective way of coping with this challenge is to employ traditional dispatchable generation to buffer the uncontrollable power. This study focuses on the coordinated operation of a hydro-wind-solar system. The Yunnan power grid with 62.4 GW hydropower capacity and 10.8 GW wind and solar power capacity is selected as an example. A methodology for the generation scheduling of a hydrodominated provincial system with large-scale wind and solar power is developed. This methodology introduces additional positive reserve, negative reserve, and ramping response constraints to handle the forecast errors of wind and solar power. Moreover, the methodology merges renewable power into the original loads to determine an equivalent load curve for hydropower plants. An optimization model with a peak-shaving objective is formulated and solved by a two-phase approach. The first phase uses knowledge rules to check and adjust the generation schedules of hydropower plants with predetermined schedules or dispatching modes. The second phase presents a multidimensional search method to optimize other major plants to respond to peak loads. The methodology is demonstrated by day-ahead scheduling of the given provincial system, which includes 397 plants. The obtained generation schedules provide a reasonable reserve capacity for buffering wind and solar power fluctuations and meet the peak demands of the power grid. A detailed sensitivity analysis for different dates is presented to illustrate the seasonal characteristics of renewable power.

A Review of Power System Flexibility With High Penetration of Renewables

The notion of secure operation of power systems, with its present semantic, dates back to the last decade. Since then, tremendous research effort has investigated secure operation of the power system. Nevertheless, operators are still faced with security issues, with even larger uncertainty, however, caused by the integration of renewable energy sources (RES). The system's ability to cope with the volatility of RES and load becomes a critical issue in power system operation. This paper surveys the literature on the concepts of power system flexibility, indices of flexibility, and implementation of the concept of flexibility in power system security. The paper proceeds to review the origin of the reserve problem, the meaning of reserve, its technical classification and related economical aspects. The paper highlights the effect of renewables on these aspects, and suggests new research directions.

Fast Screening of High-Risk Lines Under False Data Injection Attacks

False data injection attacks can compromise power system operational security by causing severe branch overloads. A set of transmission lines can be more vulnerable to the attack. These lines can be quickly identified by a high-risk line fast screening (HLFS) approach proposed in this paper. The HLFS has a high computational efficiency which facilitates its applications in large-scale systems. In the meantime, for an indepth security assessment, a critical-line identification tool is also presented to identify a subset of critical lines with simultaneous tripping risk under the attack. The effectiveness and efficiency of the proposed approach are verified by benchmarking its performance against a basic linear-programming checking method in the case study which includes simulations on multiple large-scale MATPOWER cases.

Electric Vehicle Battery Lifetime Extension through an Intelligent Double-Layer Control Scheme

Electric vehicles (EVs) are recognized as promising options, not only for the decarbonization of urban areas and greening of the transportation sector, but also for increasing power system flexibility through demand-side management. Large-scale uncoordinated charging of EVs can impose negative impacts on the existing power system infrastructure regarding stability and security of power system operation. One solution to the severe grid overload issues derived from high penetration of EVs is to integrate local renewable power generation units as distributed generation units to the power system or to the charging infrastructure. To reduce the uncertainties associated with renewable power generation and load as well as to improve the process of tracking Pareto front in each time sequence, a predictive double-layer optimal power flow based on support vector regression and one-step prediction is presented in this study. The results demonstrate that, through the proposed control approach, the rate of battery degradation is reduced by lowering the number of cycles in which EVs contribute to the services that can be offered to the grid via EVs. Moreover, vehicle to grid services are found to be profitable for electricity providers but not for plug-in electric vehicle owners, with the existing battery technology and its normal degradation.

A novel approach for the identification of critical nodes and transmission lines for mitigating voltage instability in power networks

Voltage collapse is a major issue combating the effectiveness and optimal operation of modern power systems in recent times. This is a great threat to the security and reliability of modern power systems and, in recent times, it has been a growing concern for power system engineers, researchers and utilities. A prompt identification of transmission lines whose outage could lead to a cascading failure and the sets of nodes where voltage collapse could erupt, during critical outages, is therefore a vital issue for a reliable and secure power system operation. An alternative approach to solving these problems is therefore presented in this paper. The problem is viewed from the graph-theoretical perspective, considering the topological properties of power networks. Application of the fundamental circuit theory is employed and the bus-to-line matrix (BLM) is formulated. This matrix provides clearer insights into the interconnections of the components within the network. This valuable information is captured and used for identifying the critical elements where a suitable location for reactive power support could be placed to avoid voltage collapse of the network. The effectiveness of the proposed approach is tested using a simple 10-bus power network. The results obtained are compared with those obtained from the existing approaches. The results obtained show a strong correlation and agreement between the proposed and the existing approaches.

Distributed State Estimation of Multi-region Power System based on Consensus Theory

Effective state estimation is critical to the security operation of power systems. With the rapid expansion of interconnected power grids, there are limitations of conventional centralized state estimation methods in terms of heavy and unbalanced communication and computation burdens for the control center. To address these limitations, this paper presents a multi-area state estimation model and afterwards proposes a consensus theory based distributed state estimation solution method. Firstly, considering the nonlinearity of state estimation, the original power system is divided into several non-overlapped subsystems. Correspondingly, the Lagrange multiplier method is adopted to decouple the state estimation equations into a multi-area state estimation model. Secondly, a fully distributed state estimation method based on the consensus algorithm is designed to solve the proposed model. The solution method does not need a centralized coordination system operator, but only requires a simple communication network for exchanging the limited data of boundary state variables and consensus variables among adjacent regions, thus it is quite flexible in terms of communication and computation for state estimation. In the end, the proposed method is tested by the IEEE 14-bus system and the IEEE 118-bus system, and the simulation results verify that the proposed multi-area state estimation model and the distributed solution method are effective for the state estimation of multi-area interconnected power systems.