Practical Power System Research Articles

Embedding scrapping criterion and degradation model in optimal operation of peak-shaving lithium-ion battery energy storage

Lithium-ion battery systems have been used in practical power systems for peak-shaving and demand response. However, a lithium-ion battery is degrading while cycling and would be scrapped when the capacity reduces to a certain experience-based threshold (e.g. 80%). The scrapping criterion could significantly affect battery lifetime and operation scheduling for grid application. Thus, such scraping criterion may not explore the maximum benefit from a new or re-used lithium-ion battery. In this paper, we propose a novel efficiency-based scrapping criterion for peak-shaving energy storage. This criterion is suitable for both new and re-used battery in grid application. Using the scraping criterion as a parameter, the maximum cycle number of battery is derived as a function of the depth of discharge, which makes the life model easy to embed in battery operation model. The results of the case study show that the operation method could maximize the benefit of peak-shaving energy storage while delaying battery degradation. Compared with the traditional 80% capacity-based scrapping criterion, our efficiency-based scrapping criterion can significantly improve the lifetime benefit of the battery by 97%.

System Redundancy Enhancement of Secondary Frequency Control Under Latency Attacks

Latency Attacks become emerging new types of cyber attacks with widely applied wireless wide-area network and Internet of Things (IoTs) technologies. The power system can suffer from several even tens of seconds of malicious time delay instead of normal latency of mere several milliseconds. Consequently, this exorbitant time delay or latency attack will significantly influence the system dynamics and stability. In this paper, from the perspective of parameter tuning, we study how to enhance the system redundancy of a secondary frequency control system with a high-order complex power system model under latency attacks. The tuning aims to achieve the maximal stability region under latency attacks and is performed with the aid of exhaustive and heuristic search methods. By testing the tuning method using a more practical and detailed power system model, it shows that the stability of the tuned system is greatly enhanced under latency attacks.

Reliability evaluation of power systems with multi-state warm standby and multi-state performance sharing mechanism

With the increasing interconnection of the power grids, the imbalanced distribution between power generation and demand in different areas has been effectively alleviated. In practical power systems, the subsystems in different areas need to meet the load requirements of each subsystem, and the performance sharing among different subsystems is one way to increase system reliability. Moreover, each subsystem can be configured with redundancy techniques especially warm standby, which consumes less energy than hot standby and has a shorter recovery time than cold standby. Furthermore, both the generating units and the performance sharing mechanism may have more than binary states in practice. Therefore, in this paper, the reliability evaluation of power systems with multi-state warm standby and multi-state performance sharing mechanism is proposed. Arbitrary state transition time distributions are allowed, and the successful activation probabilities for warm standby generating units are also embedded in the proposed model. The multi-state decision diagram (MSDD) technique is developed for system reliability evaluation. Time-dependent reliability is presented in illustrative examples to validate the proposed model and technique.

Decision Tree for Online Voltage Stability Margin Assessment Using C4.5 and Relief-F Algorithms

In practical power system operation, knowing the voltage stability limits of the system is important. This paper proposes using a decision tree (DT) to extract guidelines through offline study results for assessing system voltage stability status online. Firstly, a sample set of DTs is determined offline by active power injection and bus voltage magnitude (P-V) curve analysis. Secondly, participation factor (PF) analysis and the Relief-F algorithm are used successively for attribute selection, which takes both the physical significance and the classification capabilities into consideration. Finally, the C4.5 algorithm is used to build the DT because it is more suitable for handling continuous variables. A practical power system is implemented to verify the feasibility of the proposed online voltage stability margin (VSM) assessment framework. Study results indicate that the operating guidelines extracted from the DT can help power system operators assess real time VSM effectively.

Detecting False Data Injection Attacks in Smart Grids: A Semi-Supervised Deep Learning Approach

The dependence on advanced information and communication technology increases the vulnerability in smart grids under cyber-attacks. Recent research on unobservable false data injection attacks (FDIAs) reveals the high risk of secure system operation, since these attacks can bypass current bad data detection mechanisms. To mitigate this risk, this paper proposes a data-driven learning-based algorithm for detecting unobservable FDIAs in distribution systems. We use autoencoders for efficient dimension reduction and feature extraction of measurement datasets. Further, we integrate the autoencoders into an advanced generative adversarial network (GAN) framework, which successfully detects anomalies under FDIAs by capturing the unconformity between abnormal and secure measurements. Also, considering that the datasets collected from practical power systems are partially labeled due to expensive labeling costs and missing labels, the proposed method only requires a few labeled measurement data in addition to unlabeled data for training. Numerical simulations in three-phase unbalanced IEEE 13-bus and 123-bus distribution systems validate the detection accuracy and efficiency of this method.

An application of energy efficiency programs on multi-stage transmission network expansion and reactive power planning

Energy efficiency programs as an energy source with multiple advantages (low cost, more reliable and better clean) are considered as one of the most effective solutions to meet future energy needs. In this paper, energy efficiency programs are modelled as virtual sources of power generation to investigate their impacts on expanding transmission network and reactive power planning. A multi-stage model based on AC power flow has been used to indicate the possibility of postponing the investment actions as the investment deferral in transmission expansion problems. Two groups of regulatory support schemes are considered as investor income to promote participation in energy efficiency programs, namely purchasing certified emission reductions and shared saving model. In addition, two standard case studies (Garver & 46-bus Brazilian) are evaluated to demonstrate the promising potential of the proposed model in handling the planning problems of practical power systems. The simulation results show the effectiveness of the proposed model to solve such planning problems in the presence of energy efficiency programs.

An Analytical Model for Frequency Nadir Prediction Following a Major Disturbance

The frequency nadir is a significant indicator for the primary frequency response monitoring and control. It is imperative to predict the maximum frequency deviation and the time at which the maximum occurs with high efficiency and accuracy following a major disturbance. To develop an analytical model for the frequency nadir prediction, the closed-loop is broken and a parabolic frequency deviation is input for decoupling the calculation of governor response and frequency deviation. Following which, the polynomial fitting is adopted to depict the primary frequency response characteristic of each governor. The iterative numerical solution for the frequency nadir prediction model is carried out based on the insight into the features of governor response. Case studies are presented to verify the performance of the analytical model over WSCC 9-bus system, New England 39-bus system and practical provincial power system, where the frequency nadir prediction model demonstrates its advantages of easy implementation, minimum computation, and high accuracy.

Fast Heuristic AC Power Flow Analysis with Data-Driven Enhanced Linearized Model

Though the full AC power flow model can accurately represent the physical power system, the use of this model is limited in practice due to the computational complexity associated with its non-linear and non-convexity characteristics. For instance, the AC power flow model is not incorporated in the unit commitment model for practical power systems. Instead, an alternative linearized DC power flow model is widely used in today’s power system operational and planning tools. However, DC power flow model will be useless when reactive power and voltage magnitude are of concern. Therefore, a linearized AC (LAC) power flow model is needed to address this issue. This paper first introduces a traditional LAC model and then proposes an enhanced data-driven linearized AC (DLAC) model using the regression analysis technique. Numerical simulations conducted on the Tennessee Valley Authority (TVA) system demonstrate the performance and effectiveness of the proposed DLAC model.

Detection and Analysis of Electromechanical Oscillation in Power Systems with Low-Sampled Data Using Modal Analysis Methods

Electromechanical oscillations between interconnected generators are considered a major threat to the secure operation of power systems. Therefore, oscillation monitoring systems in real-time are of critical importance to detect the danger of poorly damped oscillations. For the detection and analysis of the oscillations, high-temporal-resolution measurements are required according to the Nyquist theorem. This paper proposes a novel algorithm for the identification of electromechanical oscillations using low-sampled data such as supervisory control and data acquisition (SCADA) measurements. The lack of temporal resolution of the data is compensated by using low-sampled data sets at multiple different locations. At a target location, a high-sampled data-signal can be reconstructed using mode shape information obtained from model-based modal analysis. The variable projection method is then used to detect oscillations and estimate oscillation components including frequency and damping ratio. Case studies based on practical Korean power systems are presented to evaluate the performance of the proposed method. Simulation results show that the proposed method can detect and identify electromechanical oscillations with low-sampled data.

Wind Farm Voltage Drop Stabilisation Using SVC Inverter Based on FACTS

The study focus on maintain of voltage factor in the near-unity network (1pu) using a Static Variables Compensator (SVC). In order to determine the effectiveness of this device to improve the stability of a power system with distributed genetartion in presence of wind farm based on MADA, the power flow is calculated without the existence of the SVC at first, and then when the SVC is integrated. This operation is performed to make a comparison and evaluate the role of the device in the system. However, in order to improve voltage stability as well as minimize power losses for practical power systems, it is important to locate the appropriate place of SVC. Various methods have been developed. The particular CPF method has been proven effective in determining SVC placement. The obtained results are discussed and analysed, it is found that this device provides a considerable reduction in the voltage drop and appreciable control of the voltage at the concerned busbar.

Effect of accurate modelling of converter interfaced generation on a practical bulk power system

With increased penetration of converter interfaced generation (CIG) in power systems, the suitability of modelling approaches and models dependent on the system conditions has to be studied. However, their suitability has yet to be systematically analysed. In this study, the authors first develop a methodology for identifying the maximum penetration of aggregated rooftop sources that can be represented as negative loads in both the steady state and dynamic analysis. Then they verify the suitability of a positive-sequence performance-based model and a detailed electromagnetic transient (EMT) model for utility-scale photovoltaic (PV) plants under different fault conditions. Based on the results, they perform dynamic simulations on a large practical system with both positive-sequence transient stability (TS) and EMT-TS hybrid simulation. This study shows that positive-sequence performance based PV plant model is more suitable for study cases with faults relatively far away from the point of connection. In addition, the results indicate that positive-sequence models and TS simulation approach can produce comparable results with reference to the EMT-TS hybrid simulation with EMT detailed models for normally-cleared single-line-to-ground faults, but the inadequacy of positive-sequence based model and simulation approach becomes obvious for prolonged-tripping of unbalanced faults in the proximity of the PV plants.

An algorithm for the protection of distribution feeder using Stockwell and Hilbert transforms supported features

Faults' recognition in the distribution feeders (DFs) is extremely important for improving the reliability of distribution system. Therefore, this manuscript proposed the technique to identify the faults on the DF using Stockwell Transform (ST) dependent variance feature and Hilbert transform (HT) by utilizing current signals. By element to element multiplication of H-index, computed using HT aided decompositions of current waveforms and VS-index, calculated through ST aided decomposition of current waveforms. By utilizing the decision rules, various faults are classified. Different faults studied in this work are line to ground, double line, double line to ground and 3-Φ to ground. For the high fault impedance also, this technique is effectively utilized. Further, variation in the fault incidence angles also performed to test the performance of the proposed technique. To perform the proposed algorithm, IEEE-13 bus system is developed in MATLAB/Simulink software. Algorithm effectively classified the faults with accuracy greater than 98%. Algorithm is also successfully validated on IEEE-34 bus test system. Further, algorithm successfully validated on the practical power system network. It is recognized that the developed method performed better than the discrete Wavelet transform (DWT) and ruled decision tree based protection scheme reported in literature.

A Distributed Robust Power System State Estimation Approach Using $t$-Distribution Noise Model

In practical power system applications, the distribution of the measurement noise is sometimes unknown and deviates from the assumed Gaussian noise model due to measurement outliers. In such cases, the performances of the state estimators based on Gaussian noise assumption may deteriorate significantly. In this article, we propose a fully distributed and robust power system state estimation approach based on the $t$ -distribution noise model and the maximum likelihood criterion. The $t$ -distribution is used to model Gaussian and non-Gaussian statistics in the field of robust statistics. The matrix-splitting techniques are employed to carry out the extensive matrix inversion of the gain matrix in a distributed way to achieve efficient computation. In the proposed distributed estimation framework, each local control area only requires limited data exchange with its neighboring areas. Simulations on the IEEE 14-bus, 118-bus and 300-bus systems are used to verify the effectiveness and robustness of the proposed distributed state estimation algorithm.

Operational Bottleneck Identification Based Energy Storage Investment Requirement Analysis for Renewable Energy Integration

Operational bottlenecks are commonly observed in power systems and lead to severe system security issues, which may be caused by the fluctuating and uncertain nature of renewable energy. This paper presents an approach to define, identify and eliminate such bottlenecks in the scope of system balance for renewable energy integrated bulk power systems, so as to quantify the requirement of energy storage. A mixed-integer linear programming (MILP) formulation for system operational bottleneck identification is proposed given renewable generation profile, in order to obtain operational restriction indices to assess the adequacy of power adjustment margin and power ramp rate. Cosine similarity based density-based spatial clustering of applications with noise (DBSCAN) method is used to cluster a large number of scenarios by operational restriction indices, then scenarios with bottlenecks are attributed to corresponding clusters. Finally, various bottleneck elimination options, including energy storage with different technologies, are compared for each cluster. Case studies of an eight-bus test system and a practical Chinese power system are presented to verify the proposed approach, the numerical results indicate energy storage is the most effective option to eliminate bottlenecks identified in power downward adjustment margin and ramp rate dominated clusters aforementioned.

Online monitoring of voltage stability margin using PMU measurements

With the growing smart grid concept it becomes important to monitor health of the power system at regular intervals for its secure and reliable operation. Phasor Measurement Units (PMUs) may play a vital role in this regard. This paper presents voltage stability monitoring in real time framework using synchrophasor measurements obtained by PMUs. Proposed approach estimates real power loading margin as well as reactive power loading margin of most critical bus using PMU data. As system operating conditions keep on changing, loading margin as well as critical bus information is updated at regular intervals using fresh PMU measurements. Simulations have been carried out using Power System Analysis Toolbox (PSAT) software. Accuracy of proposed Wide Area Monitoring System (WAMS) based estimation of voltage stability margin has been tested by comparing results with loading margin obtained by continuation power flow method (an offline approach for accurate estimation of voltage stability margin) under same set of operating conditions. Case studies performed on IEEE 14-bus system, New England 39-bus system and a practical 246-bus Indian power system validate effectiveness of proposed approach of online monitoring of loading margin.

Synchrophasor Estimation for Three-Phase Systems Based on Taylor Extended Kalman Filtering

Synchronized phasor and frequency measurements are key tools for the monitoring and management of modern power systems. Under dynamic conditions, it is vital to define algorithms that allow accurately measuring time-varying signals with short latencies and high reporting rates. A dynamic phasor model can help the design of these algorithms and, in particular, of those based on the Kalman filter approach. This article proposes a three-phase synchrophasor estimator based on the extended Kalman filter; state variables are obtained from Taylor expansions of amplitudes and phase angles. The underlying dynamic model considers the inherent relationship among the phases and includes harmonics in an effective way. The process noise covariance matrix that allows representing the uncertainty introduced by the dynamic model has been written by considering that practical ac power systems are nearly three-phase symmetric during typical operation. This a priori information allows improving noise rejection and increasing accuracy in the presence of amplitude modulation, as highlighted by the reported simulation results.

Magnifying effect of weak grid connection for a PMSG to induce torsional sub‐synchronous oscillations under the condition of open‐loop modal resonance

When open-loop modal resonance (OLMR) happens, a grid-connected PMSG may induce torsional sub-synchronous oscillations (SSOs) in a power system. This study examines the magnifying effect of a weak grid connection for the PMSG to induce torsional SSOs under the condition of OLMR. Theoretical analysis is carried out in this study to indicate that the weak grid connection magnifies the impact of OLMR, when the resonant mode of PMSG is dominated by the dynamics of a phase-locked loop (PLL) or the DC voltage control of the grid side converter. The magnifying effect of weak grid connection is analytically proved to have two aspects. First, the magnitude of the real part of the open-loop residue of the PMSG increases when the grid connection weakens. Second, the magnitude of the real part of the resonant open-loop oscillation mode of PMSG decreases under the condition of weak grid connection. According to the OLMR theory, the weakly connected PMSG is more likely to cause growing torsional SSOs when the grid connection is weak. This study helps to understand the mechanism of recent SSO incidents in a practical Chinese power system is analysed. Study cases are presented to demonstrate and evaluate the analysis and conclusions made in this study.

Home Energy Management Systems under Effects of Solar‐Battery Smart Inverter Functions

Rooftop solar panels paired with home‐scale batteries are increasingly popular in the residential area and they bring a fantastic opportunity to households, considered as prosumers, to make benefits from trading electricity. Unlike research in the literature, we consider the trading with technical influences of utility requirements: Volt‐Watt and Volt‐Var functions. As possible active power curtailments may occur and lead to economic losses to households, our work reveals how a home energy management system (HEMS) maximizes profit from trading energy with an aggregator and an electricity retailer. Appropriate optimization problems are formulated with technical constraints, these functions, and home appliances. HEMS responses are optimized in two phases. The first phase is for planning an optimal day‐ahead energy commitment to the aggregator with a consideration of the predicted power curtailments. On the day of operation, the second phase tries to follow the commitment by reoptimizing the responses in the real condition. Simulation of a practical power system shows that addressing these functions can mitigate power curtailment, reduce economic loss, and improve home profits. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Electrical Load Analysis for Shipboard Power Systems Using Load Survey Data

Electric load analysis is an important task in early new stage for design of shipboard power systems (SPSs). The installation generation capacity onboard depends on the accurate result of the analysis, which significantly affects the system economic and secure operations. In the traditional electrical load analysis, the demand factor is often obtained from engineering experiences which may lead to inaccurate analysis results. In order to obtain better results, a methodology for electrical load analysis in SPSs is proposed in this article. The load analysis spreadsheet is adopted to find the power consumption of key electric loads in SPSs. The actual power consumptions of selected loads are collected by intelligent meters. The statistical analysis is then performed to find the critical parameter of each selected load in the load analysis under different operating conditions based on the power measurement of field test and load analysis is performed for estimating system power demand accordingly. The large practical maritime vessel power systems are selected for numerical analysis to ensure the performance of the proposed method. The proposed method has been adopted by local ship builders in Taiwan to better understand the load characteristics in shipboard power systems to support various functions of system planning and operation in a more effective manner.

Vulnerability assessment of distributed load shedding algorithm for active distribution power system under denial of service attack

In order to deal with frequency deviation and supply-demand imbalance in active distribution power system, in this paper a distributed under frequency load shedding (UFLS) strategy is proposed. Different from conventional centralized UFLS schemes, no centralized master station gathering all the buses' information is required. Instead, each bus decides its own load shedding amount by only relying on limited peer-to-peer communication. However, such UFLS strategy may suffer from some unexpected cyber-attacks such as integrity attacks and denial of service (DoS) attack. The latter DoS attack aims to degrade the system performance by jamming or breaking the communication, which is of high probability to happen in practical power system. To assess the vulnerability of proposed distributed UFLS algorithm, the effect of DoS attack on distributed average consensus algorithm is theoretically derived, which indicates that the final consensus value can be estimated by a given attack probability. It is also investigated that such DoS attack does harm to the load shedding amount and finally affects the system frequency performance in the active distribution power system. Several case studies implemented on an IEEE 33-bus active distribution power system are conducted to verify the effectiveness of the theoretical findings and investigate the vulnerability of the considered power system.