Abstract:

Abstract:This paper introduces a dynamic network model together with a phasor measurement unit (PMU) measurement model suitable for power system state estimation under spoofing attacks on the global positioning system (GPS) receivers of PMUs. The spoofing attacks may introduce time-varying phase offsets in the affected PMU measurements. An algorithm is developed to jointly estimate the state of the network, which amounts to the nodal voltages in rectangular coordinates, as well as the time-varying attacks. The algorithm features closed-form updates. The effectiveness of the algorithm is verified on the standard IEEE transmission networks. It is numerically shown that the estimation performance is improved when the dynamic network model is accounted for compared with a previously reported static approach.

Abstract:In the past decade, dramatic progress has been made in the field of machine learning. This paper explores the possibility of applying deep learning in power system state estimation. Traditionally, physics-based models are used including weighted least square (WLS) or weighted least absolute value (WLAV). These models typically consider a single snapshot of the system without capturing temporal correlations of system states. In this paper, a physics-guided deep learning (PGDL) method is proposed. Specifically, inspired by autoencoders, deep neural networks (DNNs) are used to learn the temporal correlations. The estimated system states from DNNs are then checked against physics laws by running through a set of power flow equations. Hence, the proposed PGDL is both data-driven and physics-guided. The accuracy and robustness of the proposed PGDL method are compared with traditional methods in standard IEEE cases. Simulations show promising results and the applicability is further discussed.

Abstract:This paper develops a novel approach to track power system state evolution based on the maximum correntropy criterion, due to its robustness against non-Gaussian errors. It includes the temporal aspects on the estimation process within a maximum-correntropy-based extended Kalman filter (MCEKF), which is able to deal with both nonlinear supervisory control and data acquisition (SCADA) and phasor measurement unit (PMU) measurement models. By representing the behavior of the state variables with a nonparametric model within the kernel density estimation, it is possible to include abrupt state transitions as part of the process noise with non-Gaussian characteristics. Also, a novel strategy to update the size of Parzen windows in the kernel estimation is proposed to suppress the effects of suspect samples. By properly adjusting the kernel bandwidth, the proposed MCEKF keeps its accuracy during sudden load changes and contingencies, or in the presence of bad data. Simulations with IEEE test systems and the Brazilian interconnected system are carried out. The results show that the method deals with non-Gaussian noises in both the process and measurement, and provides accurate estimates of the system state under normal and abnormal conditions.

Abstract:In this paper, a new fully distributed state estimation (DSE) based on weighted least square (WLS) method and graph theory is proposed for power system. The proposed method is fully distributed so that the centralized facilities, e.g., supervisory control and data acquisition (SCADA) and centralized estimators, are not required. Also, different from the existing DSE methods, the proposed method is a bus-level DSE method, in which the power system is not required to be partitioned into several areas. In order to realize the proposed fully distributed DSE method, a novel information propagation algorithm is developed in this paper. This algorithm has great potential in future applications since it is useful to broadcast the local information of the nodes to the entire system in a fully distributed network. The proposed DSE method is compared with the conventional centralized state estimation method and existing multi-area DSE method in different models in this paper. The results show that the proposed method has better performance than the traditional methods.

Abstract:State estimation (SE) usually serves as the basic function of the energy management system (EMS). In this paper, the time-scale characteristics of the integrated heat and electricity networks are studied and an SE model is established. Then, a two-stage iterative algorithm is proposed to estimate the time delay of heat power transportation in the pipeline. Meanwhile, to accommodate the measuring resolutions of the integrated network, a hybrid SE approach is developed based on the two-stage iterative algorithm. Results show that, in both steady and dynamic processes, the two-stage estimator has good accuracy and convergence. The hybrid estimator has good performance on tracking the variation of the states in the heating network, even when the available measurements are limited.

Abstract:In order to obtain an accurate state estimation of the operation in the combined heat and power system, it is necessary to carry out state estimation. Due to the limited information sharing among various energy systems, it is practical to perform state estimation in a decentralized manner. However, the possible communication packet loss is seldomly considered among various energy systems. This paper bridges this gap by proposing a relaxed alternating direction method of multiplier algorithm. It can also improve the computation efficiency compared with the conventional alternating direction of the multiplier algorithm. Case studies of two test systems are carried out to show the validity and superiority of the proposed algorithm.

Abstract:Distribution state estimation (DSE) is an essential part of an active distribution network with high level of distributed energy resources. The challenges of accurate DSE with limited measurement data is a well-known problem. In practice, the operation and usability of DSE depend on not only the estimation accuracy but also the ability to predict error variance. This paper investigates the application of error covariance in DSE by using the augmented complex Kalman filter (ACKF). The Kalman filter method inherently provides state error covariance prediction. It can be utilized to accurately infer the error covariance of other parameters and provide a method to determine optimal measurement locations based on the sensitivity of error covariance to measurement noise covariance. This paper also proposes a generalized formulation of ACKF to allow scalar measurements to be incorporated into the complex-valued estimator. The proposed method is simulated by using modified IEEE 34-bus and IEEE 123-bus test feeders, and randomly generates the load data of complex-valued Wiener process. The ACKF method is compared with an equivalent formulation using the traditional weighted least squares (WLS) method and iterated extended Kalman filter (IEKF) method, which shows improved accuracy and computation performance.

Abstract:Cyber-attacks that tamper with measurement information threaten the security of state estimation for the current distribution system. This paper proposes a cyber-attack detection strategy based on distribution system state estimation (DSSE). The uncertainty of the distribution network is represented by the interval of each state variable. A three-phase interval DSSE model is proposed to construct the interval of each state variable. An improved iterative algorithm (IIA) is developed to solve the interval DSSE model and to obtain the lower and upper bounds of the interval. A cyber-attack is detected when the value of the state variable estimated by the traditional DSSE is out of the corresponding interval determined by the interval DSSE. To validate the proposed cyber-attack detection strategy, the basic principle of the cyber-attack is studied, and its general model is formulated. The proposed cyber-attack model and detection strategy are conducted on the IEEE 33-bus and 123-bus systems. Comparative experiments of the proposed IIA, Monte Carlo simulation algorithm, and interval Gauss elimination algorithm prove the validation of the proposed method.

Abstract:With the advent of advanced energy management systems in distribution systems, there is a growing interest in rapid and reliable code for distribution system state estimation (DSSE) in large-scale systems. Fast DSSE methods employed in the industry are based on load scaling as they are well suited to the abundance of pseudo-measurements. Due to the paucity of real-time measurements in DSSE, phasor measurement units (PMUs) have been proposed as a potential solution to increase the estimation accuracy. However, load scaling methodologies are not extendable for exploiting PMUs. This paper proposes a high-performance DSSE method that can handle the PMUs together with all common measurement types in industrial DSSE. By using Wirtinger calculus, the method operates entirely in complex variables and employs the latest version of advanced vector extensions (AVX-2) to reap the maximum potential of computer processing units. The paper highlights the derivation of complex DSSE in matrix form, from which one can infer the implications on code reliability and maintenance. Numerical results are reported on large-scale multi-phase distribution systems, and they are contrasted with a publicly available code for DSSE in real variables. The simulation results show that loop unrolling in AVX-2 contributes about a two-fold increase in the solving speed.

Abstract:The dynamic characteristic evaluation is an important prerequisite for safe and reliable operation of the medium-voltage DC integrated power system (MIPS), and the dynamic state estimation is an essential technical approach to the evaluation. Unlike the electromechanical transient process in a traditional power system, periodic change in pulse load of the MIPS is an electromagnetic transient process. As the system state suddenly changes in the range of a smaller time constant, it is difficult to estimate the dynamic state due to periodic disturbance. This paper presents a dynamic mathematical model of the MIPS according to the network structure and control strategy, thereby overcoming the restrictions of algebraic variables on the estimation and developing a dynamic state estimation method based on the extended Kalman filter. Using the method of adding fictitious process noise, it is possible to solve the problem that the linearized algorithm of the MIPS model is less reliable when an abrupt change occurs in the pulse load. Therefore, the accuracy of the dynamic state estimation and the stability of the filter can be improved under the periodic disturbance of pulse load. The simulation and experimental results confirm that the proposed model and method are feasible and effective.

Abstract:This paper is concerned about the impact of network parameter errors on the reliable operation and management of electricity markets. Specifically, the paper investigates the so-called critical parameters in a network model whose errors cannot be detected or estimated due to the lack of local measurement redundancy. Due to this property of critical parameters, it will be impossible to detect, identify and correct errors in these parameters. Given the fact that electricity market applications are heavily model-dependent, the locational marginal prices (LMPs) can be shown to be seriously distorted in the presence of critical parameter errors. Furthermore, if such errors are maliciously injected by adversaries, they will go undetected. Meanwhile, prices and revenues associated with power transactions may be strategically manipulated. An approach for quantifying the impact of critical parameters on the management of electricity markets is proposed. Conditions related to network topology and measurement configuration leading to the appearance of critical parameters are classified, and meter placement strategies for avoiding critical parameters are presented as well. Simulation results obtained by using IEEE test systems are given to verify the proposed analysis and design methods.

Abstract:The development of renewable energy and the increasing peak-valley difference of load demand lead to an increasing requirement of spinning reserve (SR). However, the traditional operation reliability analysis of power system mainly focuses on the up SR and neglects the down SR. Therefore, the operation reliability of power system considering the impacts of down SR is investigated in this paper. Firstly, the constraints of down SR are incorporated into the day-ahead unit commitment (UC) model to obtain the generation scheduling and reserve allocation of power systems. Based on the dispatch results of UC model, the re-dispatched energy and load interruption can be determined using optimal power flow (OPF) model in the real-time operation in various contingency states. Operation reliability indices are calculated based on the load curtailment to represent the reliability performances of power systems. The proposed approaches are validated using the modified IEEE reliability test system. Case studies demonstrate that down SR can improve the operation reliability of power systems.

Abstract:In recent years, the advent of microgrids with numerous renewable energy sources has created some fundamental challenges in the control, coordination, and management of energy trading between microgrids and the power grid. To respond to these challenges, some techniques such as the transactive energy (TE) technology are proposed to control energy sharing. Therefore, this paper uses TE technology for energy exchange control among the microgrids, and applies three operation cases for analyzing the energy trading control of four and ten microgrids with the aim of minimizing the energy cost of each microgrid, respectively. In this regard, Monte Carlo simulation and fast forward selection (FFS) methods are respectively exerted for scenario generation and reduction in uncertainty modeling process. The first case is assumed that all microgrids can only receive energy from the network and do not have any connection with each other. In order to maximize the energy cost saving of each microgrid, the second case is proposed to provide a positive percentage of cost saving for microgrids. All microgrids can also trade energy with each other to get the most benefit by reducing the dependency on the main grid. The third case is similar to the second case, but its target is to indicate the scalability of the models based on the proposed TE technology by considering ten commercial microgrids. Finally, the simulation results indicate that microgrids can achieve the positive amount of cost saving in the second and third cases. In addition, the total energy cost of microgrids has been reduced in comparison with the first case.

Abstract:The increasing number of photovoltaic (PV) generation and electric vehicles (EVs) on the load side has necessitated an aggregator (Agg) in power system operation. In this paper, an Agg is used to manage the energy profiles of PV generation and EVs. However, the daily management of the Agg is challenged by uncertain PV fluctuations. To address this problem, a robust multi-time scale energy management strategy for the Agg is proposed. In a day-ahead phase, robust optimization is developed to determine the power schedule. In a real-time phase, a rolling horizon-based convex optimization model is established to track the day-ahead power schedule based on the flexibilities of the EVs. A case study indicates a good scheduling performance under an uncertain PV output. Through the convexification, the solving efficiency of the real-time operation model is improved, and the over-charging and over-discharging problems of EVs can be suppressed to a certain extent. Moreover, the power deviation between day-ahead and real-time scheduling is controllable when the EV dispatching capacity is sufficient. The strategy can ensure the flexibility of the Agg for real-time operation.

Abstract:Resilience against major disasters is the most essential characteristic of future electrical distribution systems (EDSs). A multi-agent-based rolling optimization method for EDS restoration scheduling is proposed in this paper. When a blackout occurs, considering the risk of losing the centralized authority due to the failure of the common core communication network, the available agents after disasters or cyber-attacks identify the communication-connected parts (CCPs) in the EDS with distributed communication. A multi-time interval optimization model is formulated and solved by the agents for the restoration scheduling of a CCP. A rolling optimization process for the entire EDS restoration is proposed. During the scheduling/rescheduling in the rolling process, CCPs in EDS are re-identified and the restoration schedules for CCPs are updated. Through decentralized decision-making and rolling optimization, EDS restoration scheduling can automatically start and periodically update itself, providing an effective solution for EDS restoration scheduling in a blackout event. A modified IEEE 123-bus EDS is utilized to demonstrate the effectiveness of the proposed method.

Abstract:With the widespread use of communication and information technology, power system has been evolving into cyber-physical power system (CPPS) and becoming more vulnerable to cyber-attacks. Therefore, it is necessary to enhance the ability of the communication and information system in CPPS to defend against cyber-attacks. This paper proposes a method to enhance the survivability of the communication and information system in CPPS. Firstly, the communication and information system for critical business of power system is decomposed into certain types of atomic services, and then the survivability evaluation indexes and their corresponding calculation method for the communication and information system are proposed. Secondly, considering the efficacy and cost defensive resources, a defensive resource allocation model is proposed to maximize the survivability of communication and information system in CPPS. Then, a modified genetic algorithm is adopted to solve the proposed model. Finally, the simulation results of CPPS for an IEEE 30-node system verify the proposed method.

Abstract:Accurate fault area localization is a challenging problem in resonant grounding systems (RGSs). Accordingly, this paper proposes a novel two-stage localization method for single-phase earth faults in RGSs. Firstly, a faulty feeder identification algorithm based on a Bayesian classifier is proposed. Three characteristic parameters of the RGS (the energy ratio, impedance factor, and energy spectrum entropy) are calculated based on the zero-sequence current (ZSC) of each feeder using wavelet packet transformations. Then, the values of three parameters are sent to a pre-trained Bayesian classifier to recognize the exact fault mode. With this result, the faulty feeder can be finally identified. To find the exact fault area on the faulty feeder, a localization method based on the similarity comparison of dominant frequency-band waveforms is proposed in an RGS equipped with feeder terminal units (FTUs). The FTUs can provide the information on the ZSC at their locations. Through wavelet-packet transformation, ZSC dominant frequency-band waveforms can be obtained at all FTU points. Similarities of the waveforms of characteristics at all FTU points are calculated and compared. The neighboring FTU points with the maximum diversity are the faulty sections finally determined. The proposed method exhibits higher accuracy in both faulty feeder identification and fault area localization compared to the previous methods. Finally, the effectiveness of the proposed method is validated by comparing simulation and experimental results.

Abstract:With the rapid development of inverter-based generators (IGs), power grid is faced with critical frequency stability challenges because the existing IGs have no inertia. To equip IGs with inertial response, researchers have proposed several virtual inertia control methods, which can be classified into two categories: virtual synchronous generator (VSG) control and droop control based on rate of change of frequency (ROCOF-droop control). In this paper, the comparison between both virtual inertia control methods is conducted from three perspectives: mathematical model, output characteristic and small-signal stability. State-space models are firstly built to analyze the control mechanism of VSG control and ROCOF-droop control methods. Simulation and eigenvalue analysis are conducted to study the transient responses and oscillation characteristics of both methods, which is helpful to understand the advantages and limitations of existing virtual inertia control methods. Finally, the obtained theoretical results are validated through real-time laboratory (RT-LAB) hardware-in-loop simulation platform.

Abstract:The high-voltage direct current (HVDC) circuit breaker is becoming popular with the rapid development of the flexible HVDC grid for efficient DC fault ride-through purposes. This paper proposes a novel module for reciprocating HVDC circuit breaker topology, whose branch connections are able to switch between series and parallel modes to limit the rising rate and interrupt the DC fault currents. Diode-bridge sub-modules (DBSMs) are used to compose the main branch for current interruption. Besides fault clearance, the proposed topology has the advantageous function of DC fault current limiting by employing DBSMs with bi-directional conduction capability. The topology can easily switch among branch connection modes through the assembled trans-valves, and their resistance and reactance are very small in the normal state when branches are in parallel and the values become promptly large in the transient state when the branches are series connected. With the modular design, it is easy to change the number of branches or sub-modules and the types of sub-modules to adapt to more specific needs. A 6-terminal modular multi-level converter (MMC) based HVDC grid is established in PSCAD/EMTDC, and various simulation scenarios are carried out to validate the proposed topology.

Abstract:Islanding refers to a condition where distributed generators (DGs) inject power solely to the local load after electrical separation from power grid. Several islanding detection methods (IDMs) categorized into remote, active, and passive groups have been reported to detect this undesirable state. In active techniques, a disturbance is injected into the DG's controller to drift a local yardstick out of the permissible range. Although this disturbance leads to more effective detections even in well-balanced island, it raises the total harmonic distortion (THD) of the output current under the normal operation conditions. This paper analyzes the power quality aspect of the modified sliding mode controller as a new active IDM for grid-connected photovoltaic system (GCPVS) with a string inverter. Its performance is compared with the voltage positive feedback (VPF) method, a well-known active IDM. This evaluation is carried out for a 1 kWp GCPVS in MATLAB/Simulink platform by measuring the output current harmonics and THD as well as the efficiency under various penetration and disturbance levels. The output results demonstrate that since the proposed disturbance changes the amplitude of the output current, it does not generate harmonics/subharmonics. Thereby, it has a negligible adverse effect on power quality. It is finally concluded that the performance of the sliding mode-based IDM is reliable from the standpoints of islanding detection and power quality.

Abstract:Real-time simulation of power electronics has been recognized by the industry as an effective tool for developing power electronic devices and systems. Since there is no energy transfer during the course of the usage, real-time simulation has a lot of advantages in the process of development and experimentation. From the perspective of real-time simulation, this paper focuses on the main problems in modeling accuracy, system bandwidth and stability, limitations on communication interface and energy interface, and the cost of platform construction. Finally, we provide further research directions.