Abstract:As more and more power electronic based generation units are integrated into power systems, the stable operation of power systems has been challenged due to the lack of system inertia. In order to solve this issue, the virtual synchronous generator (VSG), in which the power electronic inverter is controlled to mimic the characteristics of traditional synchronous generators, is a promising strategy. In this paper, the representation of the synchronous generator in power systems is firstly presented as the basis for the VSG. Then the modelling methods of VSG are comprehensively reviewed and compared. Applications of the VSG in power systems are summarized as well. Finally, the challenges and future trends of the VSG implementation are discussed.

Abstract:Plug-in electric vehicle (PEV) load modeling is very important in the operation and planning studies of modern power system nowadays. Several parameters and considerations should be taken into account in PEV load modeling, making it a complex problem that should be solved using appropriate techniques. Different techniques have been introduced for PEV load modeling and each of them has individual specifications and features. In this paper, the most popular techniques for PEV load modeling are reviewed and their capabilities are evaluated. Both deterministic and probabilistic methods are investigated and some practical and theoretical hints are presented. Moreover, the characteristics of all techniques are compared with each other and suitable methods for unique applications are proposed. Finally, some potential research areas are presented for future works.

Abstract:The uncertainties from renewable energy sources (RESs) will not only introduce significant influences to active power dispatch, but also bring great challenges to the analysis of optimal reactive power dispatch (ORPD). To address the influence of high penetration of RES integrated into active distribution networks, a distributionally robust chance constraint (DRCC)-based ORPD model considering discrete reactive power compensators is proposed in this paper. The proposed ORPD model combines a second-order cone programming (SOCP)-based model at the nominal operation mode and a linear power flow (LPF) model to reflect the system response under certainties. Then, a distributionally robust optimization (WDRO) method with Wasserstein distance is utilized to solve the proposed DRCC-based ORPD model. The WDRO method is data-driven due to the reason that the ambiguity set is constructed by the available historical data without any assumption on the specific probability distribution of the uncertainties. And the more data is available, the smaller the ambiguity would be. Numerical results on IEEE 30-bus and 123-bus systems and comparisons with the other three-benchmark approaches demonstrate the accuracy and effectiveness of the proposed model and method.

Abstract:The increasing penetration of highly intermittent wind generation could seriously jeopardize the operation reliability of power systems and increase the risk of electricity outages. To this end, this paper proposes a novel data-driven method for operation risk assessment of wind-integrated power systems. Firstly, a new approach is presented to model the uncertainty of wind power in lead time. The proposed approach employs k-means clustering and mixture models (MMs) to construct time-dependent probability distributions of wind power. The proposed approach can also capture the complicated statistical features of wind power such as multimodality. Then, a non-sequential Monte Carlo simulation (NSMCS) technique is adopted to evaluate the operation risk indices. To improve the computation performance of NSMCS, a cross-entropy based importance sampling (CE-IS) technique is applied. The CE-IS technique is modified to include the proposed model of wind power. The method is validated on a modified IEEE 24-bus reliability test system (RTS) and a modified IEEE 3-area RTS while employing the historical data of wind generation. The simulation results verify the importance of accurate modeling of short-term uncertainty of wind power for operation risk assessment. Further case studies have been performed to analyze the impact of transmission systems on operation risk indices. The computational performance of the framework is also examined.

Abstract:The design of an effective protection system for inverter-based microgrids is a complicated engineering challenge. This is due to the fact that inverters have limited fault current capabilities, and that the conventional overcurrent protection is not suitable for inverter-based microgrids. This paper introduces a novel protection method for inverter-based microgrid using a current-only polarity comparison. The proposed method is based on the phase difference between the pre-fault and fault current components. The method responds to faults in both grid-connected and autonomous operation modes and provides a new way to identify faulted sections. Simulations of an inverter-based microgrid with a relay model are conducted using PSCAD/EMTDC software. The results show that the proposed method can detect faults in inverter-based microgrids.

Abstract:Newly proposed power system control methodologies combine economic dispatch (ED) and automatic generation control (AGC) to achieve the steady-state cost-optimal solution under stochastic operation conditions. However, a real power system is subjected to continuous demand disturbance and system constraints due to the input saturation, communication delays and unmeasurable feed-forward load disturbances. Therefore, optimizing the dynamic response under practical conditions is equally important. This paper proposes a state constrained distributed model predictive control (SCDMPC) scheme for the optimal frequency regulation of an interconnected power system under actual operation conditions, which exist due to the governor saturation, generation rate constraints (GRCs), communication delays, and unmeasured feed-forward load disturbances. In addition, it proposes an algorithm to handle the solution infeasibility within the SCDMPC scheme, when the input and state constraints are conflicting. The proposed SCDMPC scheme is then tested with numerical studies on a three-area interconnected network. The results show that the proposed scheme gives better control and cost performance for both steady state and dynamic state in comparison to the traditional distributed model predictive control (MPC) schemes.

Abstract:The rapid development of economy and society stimulates the increase of power demand. Wind power has received great attention as a typical renewable energy, and the share of wind power is continually increasing in recent years. However, the high integration of wind power brings challenges to the secure and reliable operation of power grid due to the intermittent characteristic of wind power. In order to solve the operation risk caused by wind power uncertainty, this paper proposes to solve the problem of stochastic security-constrained unit commitment (SCUC) by considering the extreme scenarios of wind power output. Firstly, assuming that the probability density distribution of wind power approximately follows a normal distribution, a great number of scenarios are generated by Monte Carlo (MC) simulation method to capture the stochastic nature of wind power output. Then, the clustering by fast search and find of density peaks (CSFDP) is utilized to separate the generated scenarios into three types: extreme, normal and typical scenarios. The extreme scenarios are identified to determine the on/off statuses of generators, while the typical scenarios are used to solve the day-ahead security-constrained economic dispatch (SCED) problem. The advantage of the proposed method is to ensure the robustness of SCUC solution while reducing the conservativeness of the solution as much as possible. The effectiveness of the proposed method is verified by IEEE test systems.

Abstract:Phasor measurement units (PMUs) can provide real-time measurement data to construct the ubiquitous electric of the Internet of Things. However, due to complex factors on site, PMU data can be easily compromised by interference or synchronization jitter. It will lead to various levels of PMU data quality issues, which can directly affect the PMU-based application and even threaten the safety of power systems. In order to improve the PMU data quality, a data-driven PMU bad data detection algorithm based on spectral clustering using single PMU data is proposed in this paper. The proposed algorithm does not require the system topology and parameters. Firstly, a data identification method based on a decision tree is proposed to distinguish event data and bad data by using the slope feature of each data. Then, a bad data detection method based on spectral clustering is developed. By analyzing the weighted relationships among all the data, this method can detect the bad data with a small deviation. Simulations and results of field recording data test illustrate that this data-driven method can achieve bad data identification and detection effectively. This technique can improve PMU data quality to guarantee its applications in the power systems.

Abstract:Due to the low dispatchability of wind power, the massive integration of this energy source in power systems requires short-term and very short-term wind power output forecasting models to be as efficient and stable as possible. A study is conducted in the present paper of potential improvements to the performance of artificial neural network (ANN) models in terms of efficiency and stability. Generally, current ANN models have been developed by considering exclusively the meteorological information of the wind farm reference station, in addition to selecting a fixed number of time periods prior to the forecasting. In this respect, new ANN models are proposed in this paper, which are developed by: varying the number of prior 1-h periods (periods prior to the forecasting hour) chosen for the input layer parameters; and/or incorporating in the input layer data from a second weather station in addition to the wind farm reference station. It has been found that the model performance is always improved when data from a second weather station are incorporated. The mean absolute relative error (MARE) of the new models is reduced by up to 7.5%. Furthermore, the longer the forecasting horizon, the greater the degree of improvement.

Abstract:This paper presents a P-Q coordination based high-voltage ride through (HVRT) control strategy for doubly fed induction generators (DFIGs) based on a combined Q-V control and P-V de-loading control. The active/reactive power injection effect of DFIG on transient overvoltage is firstly analyzed and the reactive power capacity evaluation of DFIG considering its de-loading operation is then conducted. In the proposed strategy, the reactive power limit of DFIG can be flexibly extended during the transient process in coordination with its active power adjustment. As a result, the transient overvoltage caused by DC bipolar block can be effectively suppressed. Moreover, key outer loop parameters such as Q-V control coefficient and de-loading coefficient can be determined based on the voltage level of point of common coupling (PCC) and the available power capacity of DFIG. Finally, case studies based on MATLAB/Simulink simulation are used to verify the effectiveness of the proposed control strategy.

Abstract:The problem of optimal placement and sizing (OPS) of renewable distributed generation (RDG) is followed by numerous technical, economical, geographical, and ecological constraints. In this paper, it is investigated from two viewpoints, namely the simultaneous minimization of total energy loss of a distribution network and the maximization of profit for RDG owner. The stochastic nature of RDG such as the wind turbine and photovoltaic generation is accounted using suitable probabilistic models. To solve this problem, a hybrid metaheuristic algorithm is proposed, which is a combination of the phasor particle swarm optimization and the gravitational search algorithm. The proposed algorithm is tested on an IEEE 69-bus system for several cases in two scenarios. The results obtained by the hybrid algorithm shows that it provides high-quality solution for all cases considered and has better performances for solving the OPS problem compared to other metaheuristic population-based techniques.

Abstract:The integration of distributed energy resources (DERs) into distribution networks is becoming increasingly important, as it supports the continued adoption of renewable power generation, combined heat and power plants, and storage systems. Nevertheless, inadvertent islanding operation is one of the major protection issues in distribution networks connected to DERs. This study proposes an intelligent islanding detection method (IIDM) using an intrinsic mode function (IMF) feature-based grey wolf optimized artificial neural network (GWO-ANN). In the proposed IIDM, the modal voltage signal is pre-processed by variational mode decomposition followed by Hilbert transform on each IMF to derive highly involved features. Then, the energy and standard deviation of IMFs are employed to train/test the GWO-ANN model for identifying the islanding operations from other non-islanding events. To evaluate the performance of the proposed IIDM, various islanding and non-islanding conditions such as faults, voltage sag, linear and nonlinear load and switching, are considered as the training and testing datasets. Moreover, the proposed IIDM is evaluated under noise conditions for the measured voltage signal. The simulation results demonstrate that the proposed IIDM is capable of differentiating between islanding and non-islanding events without any sensitivity under noise conditions in the test signal.

Abstract:Pole-to-ground (PTG) fault analysis is of vital importance for high-voltage direct current (HVDC) grid. However, many factors are not considered in the existing studies such as the asymmetrical property of PTG fault, the coupling issue between DC transmission lines and the complexity of the structure of DC grid. This paper presents a PTG fault analysis method, which is based on common- and differential-mode (CDM) transformation. Similar to the symmetrical component method in AC system, the transformation decomposes the HVDC grid into CDM networks, which is balanced and decoupled. Then, a transfer impedance is defined and calculated based on the impedance matrices of the CDM networks. With the transfer impedance, analytical expressions of fault characteristics that vary with space and time are obtained. The proposed PTG fault analysis method is applicable to arbitrary HVDC grid topologies, and provides a new perspective to understand the fault mechanism. Moreover, the analytical expressions offer theoretical guidance for PTG fault protection. The validity of the proposed PTG fault analysis method is verified in comparison with the simulation results in PSCAD/EMTDC.

Abstract:This paper addresses an optimal design of low-voltage (LV) distribution network for rural electrification considering photovoltaic (PV) and battery energy storage (BES). It aims at searching for an optimal topology of an LV distribution system as well as the siting and sizing of PV and storage over a time horizon of 30 years. Firstly, the shortest-path algorithm (SPA) and first-fit bin-packing algorithm (FFBPA) are used to search for the optimal radial topology that minimizes the total length of the distribution line and improves the load balancing. Then, the optimal siting of decentralized BES (DeBES) is determined using a genetic algorithm (GA) to eliminate the under-voltage constraints due to the load consumption. Two iterative techniques are elaborated to size the maximum peak power of PV and the minimum number of DeBES that can be connected to an LV network without violating the voltage and current constraints. Then, the sizing strategy of centralized BES (CeBES) is developed to avoid reverse power flows into the medium-voltage (MV) network. Finally, a Monte Carlo approach is used to study the impact of load profile uncertainties on the topology. A non-electrified village in Cambodia has been chosen as a case study.

Abstract:Due to the swift expansion and the deployment of distributed generation, protection systems of active distribution networks are more expected to be fast. In loop-based active distribution networks, directional overcurrent relays (DOCRs) are caught in different chains. These chains stand as the severe obstacle to follow fast-response protection, which remains a significant challenge. In this paper, to overcome this challenge, a fast protection scheme is proposed to break the chains in the corresponding loops by deploying auxiliary DOCRs. The most effective constraint associated with each chain is relaxed during the coordination process. Then, the auxiliary relays are employed to play the backup roles instead of conventional backup relays in the relaxed constraints. To avoid the misoperation of relays in the proposed scheme, low bandwidth communication links are suitably employed. Furthermore, the auxiliary relays are optimally placed and adjusted. The proposed approach demonstrates a mixed-integer nonlinear programming model which is tackled by particle swarm optimization (PSO) algorithm. Detailed simulation studies are carried out to verify the performance of the proposed approach.

Abstract:In a cyber-physical power system, active distribution network (ADN) facilitates the energy control through hierarchical and distributed control system (HDCS). Various researches have dedicated to develop the control strategies of primary devices of ADN. However, an ADN demonstration project shows that the information transmission of HDCS may cause time delay and response lag, and little model can describe both the ADN primary device and HDCS as a cyber-physical system (CPS). In this paper, a hybrid system based CPS model is proposed to describe ADN primary devices, control information flow, and HDCS. Using the CPS model, the energy process of primary devices and the information process of HDCS are optimized by model predictive control (MPC) methodology to seamlessly integrate the energy flow and the information flow. The case study demonstrates that the proposed CPS model can accurately reflect main features of HDCS, and the control technique can effectively achieve the operation targets on primary devices despite the fact that HDCS brings adverse effects to control process.

Abstract:A system combining photovoltaic power generation and cogeneration is proposed to improve the photoelectric absorption capacity. First, a time-of-use price strategy is adopted to guide users to change their electricity consumption habits for participation in the demand response, and a demand response model is established. Then, particle swarm optimization (PSO) is used with the aim of minimizing the operation cost of the microgrid to achieve economic dispatching of the microgrid. This considers power balance equation constraints, unit operation constraints, energy storage constraints, and heat storage constraints. Finally, the simulation results show the improved level of photoelectric consumption using the proposed scheme and the economic benefits of the microgrid.

Abstract:To obtain a larger controllable range of output/input power of droop-control sources, a multi-objective optimization segmented droop control suitable for economic dispatch for a DC microgrid is proposed. According to the small-signal analysis, the worst point of the stability in the droop-control curve is determined through the analysis of a simplified model with multiple droop-control sources. By considering the worst points of stability as constraints, an elitist non-dominated sorting genetic algorithm is used to search the better turning points of the proposed droop-control curves after obtaining the new rated operation points from the system-layer economic dispatch. Simultaneously, optimization objectives, including the influence of eliminating the line resistance and capacity matching, are considered in the search process. Finally, the simulation results of the DC microgrid simulation model based on RT-Lab are presented to support the stability conclusion and proposed droop control.

Abstract:The increasing electric vehicle (EV) penetration in a distribution network triggers the need for EV charging coordination. This paper firstly proposes a hierarchical EV charging coordination model and an algorithm based on Lagrangian relaxation. A barrier to the implementation of the coordination algorithm is that there usually does not exist a reliable coordinator of charging stations. This paper shows that an unreliable coordinator may collude with some charging stations and behave dishonestly by disobeying the coordination algorithm. Thus, the collusion coalition can gain more profits while lowering the profits of others and the total social welfare. To provide reliable coordination of charging stations, a novel blockchain-based coordination platform via Ethereum is established, including a coordination structure and a smart contract. A mathematical analysis is given to show that the proposed platform can mitigate the collusion behaviors in the coordination. Simulation results show the consequence of collusion and how blockchain can prevent the collusion.

Abstract:Being capable of aggregating multiple energy resources, the energy service company (ESCO) has been regarded as a promising alternative for improving power system flexibility and facilitating the consumption of renewable resources in the electricity market. Considering the uncertain variables in day-ahead (DA) market trading, an ESCO can hardly determine their accurate probability distribution functions. Traditional interval optimization methods are used to process these uncertain variables without specific probability distribution functions. However, the lower and upper bounds of the intervals may change due to extreme weather conditions and other emergent events. Hence, a dual interval optimization based trading strategy (DIOTS) for ESCO in a DA market with bilateral contracts (BCs) is proposed. First, we transfer the dual interval optimization model into a simple model consisting of several interval optimization models. Then, a pessimistic preference ordering method is applied to solve the derived model. Case studies illustrating an actual test system corroborate the validity and the robustness of the proposed model, and also reveal that ECSO is critical in improving power system flexibility and facilitating the ability of absorbing renewable resources.

Abstract:Advanced connectivity in substations brings along cybersecurity considerations. Especially, the use of standardized data objects and message structures stipulated by IEC 61850 makes them much more vulnerable to unauthorized access and manipulation. In order to tackle these vulnerabilities, different methods are investigated by researchers all over the world. An important aspect of such efforts is the real-time performance consideration since power systems are bound by the rules of physics and all control/communication tasks need to be completed in a certain time frame. Security schemes for substation communication have been proposed in the recent literature. However, they must be improved to ensure a full security solution. Recently published IEC 62351 standard aims to fill this gap. Node authentication is vital for substation communication networks based on IEC 61850 to mitigate a variety of attacks such as man-in-the-middle (MITM) attack. This short communication presents a node authentication mechanism based on transport layer security (TLS) with certificates to address this knowledge gap. It also investigates the real-time performance by implementing the proposed scheme with Python.