In western China, large-scale wind power is generally bundled with thermal power, and transmitted to eastern China by high-voltage direct current (HVDC) systems. This constitutes the wind–thermal bundled system transmitted by HVDC (WTBH). In this study, a typical studied system of WTBH is presented, and its models for eigenvalue analysis are introduced. To improve the efficiency of eigenvalue analysis, an improved block modelling method is proposed for building the state-space model of WTBH. By eigenvalue analysis and electromagnetic transient (EMT) simulation, the impact of doubly-fed induction generator (DFIG)-based wind farm integration on sub-synchronous torsional interaction (SSTI) between thermal generators (TGs) and HVDC was investigated. The sensitivity of the operating parameters of DFIG-based wind farms was analysed, including wind speed, wind farm scale, and distance between the wind farm and HVDC rectifier station. The obtained results show that the integration of DFIG-based wind farms can mitigate the SSTI between the TG and HVDC. The system becomes more stable as the wind speed and wind farm scale increase or the distance between the wind farm and HVDC rectifier station decreases. The conclusions of this study are validated through EMT simulations in PSCAD/EMTDC, and provide theoretical reference for practical WTBH projects.

Large-scale electric vehicle (EV) charging will bring new challenges to coordination of grid and transportation. To facilitate large-scale EV applications, optimal locating and sizing of charging stations have become essential. For the investors of a charging station, economic benefit is the primary and the only objective. In this context, this work studies the siting and sizing of EV stations based on the optimal economic benefit. Benefit changes with time, location and capacity. A planning model method considering net present value (NPV) and life cycle cost (LCC) is proposed to determine the site and the size of the charging stations. The model has integrated distribution network constraint, the user constraint and the traffic flow captured constraint. Origin–destination lines and voronoi diagram are selected to calculate the traffic flow and the service region of each charging station, respectively. The quantum genetic algorithm was adopted for a better convergence of the planning model. Finally, a coupled 33-node distribution system and a 36-node transportation system are used to simulate various scenarios in the coupled networks of grid and transportation. The simulation results show that by introducing a planning model method considering NPV and LCC, the charging station economical benefits can be further improved.

This study proposes a novel method based on data mining for islanding detection of synchronous distributed generators. The method uses a new technique called data mining of code repositories (DAMICORE), which is a powerful data mining tool for detecting patterns and similarities in various kinds of datasets. In addition, a trip logic was developed in this proposal in order to detect islanding and disconnect the distributed generator. One of the most relevant features of the proposed method is its capability to generalise, which reduces the need of big datasets for training purposes. This approach has been tested with islanding, load switching and fault simulations, presenting promising results concerning performance, as well as detection time. General results showed a better performance of the method if compared to traditional anti-islanding protection schemes, such as frequency-based relays.

The conventional hierarchical control in an islanded microgrid (MG) does not consider the long time-span dynamics of distributed storages (DSs). The main challenge in control of battery energy storage systems (BESSs) is different levels of stored energy in terms of state of charge (SoC). In power droop control, the energy of the BESSs with lower initial SoC is drained earlier, and their capacities become unachievable. Moreover, using droop control to balance the SoC of BESSs, deviates the steady state frequency and voltage from the nominal values. However, restoration of the MG frequency employing the conventional distributed secondary controllers disturbs SoC-balancing, since SoC of BESSs are ignored. In this paper, a new distributed storage secondary controller (DSSC) scheme is designed for restoration of the voltage and frequency of a stand-alone MG, and to provide power-sharing and SoC-balancing, using a distributed cooperative architecture. The cooperative DSs are controllable and exchange the information with neighbor DSs through a communication network. The unknown output power of the uncooperative renewable distributed generation (DG) is considered as external disturbance to the DSSC. The designed DSSC is robust against the variation of the communication configuration, and eliminates the necessity to communicate with uncontrollable DGs and loads.

The performance of the excitation system plays a very important role in the voltage stability of the power grid. The brushless excitation system with a brushless alternating current exciter offers a lower failure rate and thus higher reliability in comparison with the static system with high-rated power electronics converters, brushes, and slip rings. However, its dynamic response is impaired due to the exciter lag in producing the required field current. In this study, the optimal design of an exciter for the large-scale power-plant synchronous generator based on the magnetic equivalent circuit method is pursued. The design goals are reducing the overall volume, minimising the magnitudes of the cogging torque and the field current ripples while satisfying the grid code requirements such as the ceiling values of available field voltage and current, rise time and settling time of the rectified output voltage under dynamic conditions. In addition, the other electrical, magnetic, thermal and mechanical constraints are included in the design algorithm. The proposed design method is used for obtaining the optimal specifications of an exciter for a 25 MW, 11 kV power-plant generator in order to attain superior characteristics than the existing one. The analytical results are validated using two-dimensional finite-element studies and experimental measurements.

Efficient transient simulation of large power grid is of great importance for system operation and dispatching, fault repetition, operator training etc. Online stability estimation and testing on system control and protection require hard real-time performance of transient simulation. The conventional real-time simulation, which splits an entire grid into sub-grids and solves them in parallel, lacks generality for various case systems. This study proposes a new efficiency enhancement method without grid partitioning. Three key techniques are involved. First, parallel solving format is proposed to distribute two-layer computational burdens into central processing unit cores in balance. Second, to calculate the fault changing the grid structure, the inverse current compensation is employed instead of re-forming impedance matrix and repeating lower-upper (LU) decomposition. Third, dual-loop forward and backward substitutions to solve grid equations are modified to a single-loop iteration, which further improves the computational efficiency. At last, the validity and effectiveness of the proposed method are verified by tests.

Based on the analysis on the uncertain nature of wind power output, the Weibull distribution parameters of regional wind speed at different periods are calculated to obtain the probability density function (PDF) and the cumulative distribution function (CDF) of the wind power. The spinning reserve (SR) requirements for wind power incorporation are determined according to the obtained PDF and CDF, and by converting the wind power into a chance constrained form, a model for dynamic economic emission dispatch with wind power is constructed. A hybrid multi-objective algorithm that integrates differential evolution (DE) and particle swarm optimisation (PSO) algorithm is put forward to solve the proposed model. The algorithm is implemented based on the Pareto dominance theory and a dynamic external archive set, and fully exploits the advantages of DE and PSO. An improved calculation method of crowding distance and Pareto solution set reduction rule are also employed to enhance the performance of the proposed algorithm. Also, three performance indicators are introduced to evaluate the performance of the algorithm. Two distinct test systems are performed to verify the proposed model and algorithm, and the results show that they are effective and reasonable.

Changes in system topology, such as branch breaking and the loss of a generator or load, may profoundly influence the operation security of the power system. This study introduces a novel deep-learning based fault diagnosis method using power flow to diagnose topology changes in the power system. Power flow samples with different system states and topologies are first computed numerically; then, they are transformed into computer-visualised images. Using massive power-flow image samples, a convolutional neural network that aims to identify the system state is trained. A feature-map restriction technique is used to restructure the network. To enhance the robustness of the network, the random noise of branch flow is considered in the sample generation process. The results show that the proposed deep-learning based method may diagnose system faults effectively.

Volt/VAr optimisation (VVO) is one of the important techniques used for reduction of energy consumption, energy losses, and peak demand. The proper load modelling is necessary in order to accurately schedule the volt/VAr control (VVC) devices. In the past, most of the studies have evaluated energy losses of the network by considering constant power type of loads. Some studies also have considered different types of loads such as constant power (), constant current (), and constant impedance (). However, there is no literature on the effect of load models on the scheduling of VVC devices. The purpose of this study is to show the impact of load models on various operating parameters in VVC. In this study, the impact of different load models on the scheduling of VVC devices is analysed. Time-series simulations are carried on the modified IEEE-123 node unbalanced radial distribution network, where industrial, commercial, and residential loads are connected at various locations. A comparative study is performed under VVO framework to analyse energy consumption, losses, and peak power demand. The important objective of this study is to find the best settings of VVC devices with various load models while minimising apparent energy losses of the network.

High-voltage direct current (HVDC) can speed up the recovery of a power system. However, both rectifier and inverter sides of HVDC are weak systems during a black start. To coordinate frequency characteristics of both sides within the frequency deviation constraint, a frequency regulation strategy based on variable-parameter frequency limit control (FLC) during a black start is proposed. First, the mathematical models of asynchronous interconnected AC–DC system are established, and the optimal FLC parameters leading to minimum frequency deviation of both sides are obtained. Subsequently, based on the characteristics of the optimal FLC parameters with the parameters of the equivalent generator and the capacity of the AC system, a frequency regulation strategy is proposed to adapt to the continuously changing parameters and configurations of the AC power system during a black start. Finally, the simulation model is established using PSCAD/EMTDC software to verify the control strategy and the correctness of the optimal FLC parameters.

In this study, a ‘microgrid community’ (MGC) consisting of multiple microgrids (MGs) is investigated. Each MG is a self-governed entity and independently decides how much power is in need or can provide. Neighbouring MGs are aggregated by a multi-terminal energy router (ER), forming a MGC, which is then connected to the distribution network at one terminal. Other terminals are connected to those MGs and all terminals share a common DC link. A concise trading mechanism is proposed to coordinate the involved MGs. Every MG submits its power requirement to the ER in real time and the ER decides how much to accept, fully or with a discount, according to the DC-link voltage level. Meanwhile, a price stimulation mechanism based on the DC-link voltage deviation is designed to exploit the potential of the MGs in power generation and consumption. The ER has no central controllers and the proposed bargaining process is achieved at each terminal without mutual communications. The control strategy is fully distributed and will benefit the scalability and plug-and-play of such an MGC. Simulation results are provided to validate the proposed solution for the operation of multi-microgrid systems.

Renewable energy sources provide an important means of reducing reliance on conventional fuels. However, some renewable energy resources such as wind and solar energies are intermittent, and their uncertainty threatens the operating security of the power grid. To solve this problem, this study proposes the use of intervals to model the power output of renewable energy resources and the power load demand, and accordingly develops an interval voltage control model, i.e. interval reactive power optimisation model. The proposed model considers the control modes of renewable energy power generators and can safeguard the security of power grids by ensuring that the voltages reside within established limits. An adaptive genetic algorithm is employed to solve the proposed model, where a newly developed interval power-flow (IPF) calculation is used to solve the IPF equations, and penalty functions are applied to express inequality constraints. The proposed method is introduced in detail, and simulation results are presented to demonstrate its performance in comparison with a previously proposed interval voltage control method, as well as its applicability to large systems with various fluctuations of input data. The proposed approach provides robust convergence, obtains lower system power losses, and substantially reduces the computation time.

This study proposes a time-series analysis approach and a non-linear dynamics originated method to detect sub-synchronous oscillation in power systems. Mathematical expressions of the fundamental instantaneous signal and sample discrete signal of peak values are derived to examine the phenomenon of interaction between power system components. The results of the circulating trajectory are shown in a two-dimensional map of the calculated root-mean-square value and estimated Floquet multiplier when two signals of different modes are mixed. Without applying a digital filter or frequency decomposition, non-linear oscillation detection is possible by monitoring a non-linear oscillatory index based on the maximum Lyapunov exponent.

High intermittency in today's renewable-rich power systems, and the prohibitive cost of upgrading the network infrastructure along with the load growth, has rendered voltage instability an imminent threat for many power systems. This necessitates faster and more efficient ways of identifying the voltage stability (VS) limits, associated with specific bifurcation points of power system model, which are suitable for real-time applications. To date, continuation power flow (CPF) has conventionally been used to identify bifurcation points of power systems, through plotting power–voltage (P–V) curves. However, existing CPF methods are complex and computationally demanding. To tackle this issue, in this study, accurate identification of both saddle-node and limit-induced bifurcation points of power systems is carried out by using a new and efficient continuous power flow algorithm, in which all the complexities associated with the existing CPF methods are relaxed. Low execution time (as compared to the existing CPF methods), ease of implementation, and automated applicability, make the proposed algorithm highly suitable for fast and accurate VS assessment of renewable-rich, uncertain, power systems. Experiments, carried out on several different size power systems, verify that the proposed method can be effectively used to identify the VS limits of practical real-life power systems, despite its ease of implementation and lower computational burden.

Power system measurement is extremely crucial for the stable power system operation and results in the generation of bulk data. This enormous volume of data must be transferred from field devices to the control centre and must be preserved for future references. Analysis of practical generation scheduling and monitoring data indicates its repetitive and slow varying nature. A simple, low-computational compression algorithm, the differential binary encoded algorithm (DBEA), is developed for compressing such information and a high compression ratio is achieved for the majority of practical data sets. To overcome the constraints of the DBEA, extended version of DBEA (E-DBEA) is developed which increases the range of input at the expense of compression ratio. Resumable load data compression algorithm (RLDA) is a differential coding-based algorithm developed for compressing load profile data. Comparison of the performance obtained by the DBEA, E-DBEA and RLDA with practical data sets clearly indicates the effectiveness of the DBEA and E-DBEA. The online test bench of the DBEA and E-DBEA consists of two inter connected PCs, one working as virtual load despatch centre and other as virtual generating station or sub-station. Due to the simplicity of the proposed work, it can be useful for data storage and data transfer both at high-level PCs and low-level microcontrollers.

This article presents a novel electric vehicle (EV) charging system model. The model introduces power constraints in the grid-to-battery converter to improve modelling accuracy. Simulation results of the presented model indicate EV charging impact on a low-voltage electricity grid. Even though most of the battery charging load is spread evenly during desired times (e.g. off-peak load during night), power constraints of the EV model result in narrow peak loads. Plug-in EVs bring additional load to the electricity grid. If not managed properly, high EV deployment may lead to unnecessary grid investments due to high-peak currents of EV charging. Rising numbers of grid connected EVs is a challenging task in the future electricity grid planning. Thus an accurate EV charging system model is essential for reliable analysis of EV deployment.

The following article published in IET Generation, Tranmission and Distribution, Nikoobakht, A., Aghaei, J., Mardenah, M., ‘Optimal transmission switching in the stochastic linearised SCUC for uncertainty management of the wind power generation and equipment failures’, Gener. Transm. Distrib., 2017, 11, (10), pp. 2664 – 2676, doi:10.1049/iet-gtd.2016.1956, has been withdrawn by agreement between the authors, the Editors-in-Chief, Innocent Kamwa and Christian Rehtanz, and the Institution of Engineering and Technology. This is because the incorrect text was uploaded due to a technical error. The correct version has now been published as: Nikoobakht, A.,Aghaei, J., Mardenah, M. et al, 'Moving beyond the optimal transmission switching: stochastic linearised SCUC for the integration of wind power generation and equipment failures uncertainties', Gener. Transm. Distrib., 2018, 12, (15), pp. 3780 – 3792, 10.1049/iet-gtd.2017.0617.