Iet Generation Transmission & Distribution最新文献

The demand for electric power has consistently been on the rise, owing to urbanisation and technological improvements. On the generation side, renewable sources have been favoured over their polluting, exhaustible, non-renewable counterparts. These changes in the power system have necessitated a system for maintaining the supply-demand balance, to maintain system stability. A complex power system is also more prone to blackouts and grid failure. Islanding helps in provision of supply to consumers in a microgrid, reducing the possibility of a blackout. Depending on the power demand and generation, loads need to be shed or restored to mitigate power imbalances. A wide area distribution management system (WADMS) is proposed to dynamically shed and restore loads in the islanded mode, with the aid of micro phasor measurement units (µPMUs). A priority and consumer-based load shedding and restoration (PCLS) algorithm is proposed in the WADMS that preferentially sheds or restores loads based on their assigned load priority indices and number of consumers. The algorithm has been tested on a modified IEEE 13 bus system, incorporated with a solar photovoltaic (PV) system, diesel generators (DGs) and an energy storage system (ESS) in MATLAB Simulink.

This study employs a sophisticated bi-level optimization methodology to model the most efficient operation of microgrids (MGs) within the operational framework of distribution companies (DCs). In this bi-level optimization problem, the upper level strives to maximize the profits of both MGs owners and DCs, while the lower level is dedicated to ensuring load balance, managing distributed generation, and implementing load curtailment strategies. The coordination of power transmission is facilitated by the DCs. At the upper level of decision-making, the optimal pricing strategies for power transactions are determined, accounting for various factors such as market prices, demand response programs, and uncertainties in wind speed. Through the utilization of a bi-level optimization framework, this study comprehensively captures the complex interactions between MGs and DCs, taking into consideration the objectives and constraints of both entities. This approach offers a more precise representation of the decision-making process in retail electricity markets, thereby providing valuable insights into the optimal operation of MGs within the DCs setting.

Recent years have seen high penetration of renewable energies, which have significantly reduced the inertia of bulk power systems. As a result, the frequency behaviour of power systems is becoming more complex. To resolve this technical challenge, there is a particularly strong interest in developing analytical solutions for frequency dynamics studies. This study first describes a second-order frequency dynamics model for power systems with renewable energies. A non-linear perturbation approach is suggested to drive the analytical solution of the model. It is shown that, under many circumstances, frequency dynamics can be effectively approximated using a linear model. Subsequently, the article describes a fourth-order linear frequency dynamics model that takes into account governor-turbines. A polynomial eigenvalue method is proposed to identify the dominant and non-dominant modes of the solution of the four-order model. It is demonstrated that the dominant mode has a decisive impact on frequency behaviour, while the non-dominant modes influence the relative frequency oscillations only. Finally, the study derives the analytical expressions of the standard frequency performance metrics and examines the impact of damping and inertia parameters. The introduced results are verified using two test systems, demonstrating the accuracy and effectiveness of the suggested method.

During the operation of the ITER machine, hundreds of MW/Var of active and reactive power will be exchanged with the grid. The E-STATCOM scheme composed of the Modular Multilevel Converter (MMC) and split supercapacitor energy storage has been proposed to improve the power compensation performance of the existing reactive power compensation system in the previous study. However, one of the main technical challenges which is lack of research is to precharge all submodule capacitors and supercapacitors from zero to their nominal voltage values efficiently during a startup process. As the capacitance and operating voltage of supercapacitors are much different from capacitors in each submodule, the startup of E-STATCOM is a more complicated process. To coordinate the energy exchange between submodule capacitors and supercapacitors, submodule capacitors and the grid, this paper presents an optimized four-stage AC side startup strategy for the E-STATCOM. The proposed method minimizes the use of current-limiting resistors while suppressing the surge current in the zero-voltage startup process of supercapacitors, in addition to optimizing the energy consumption. The pulse current charging, constant current charging and constant power charging strategies of supercapacitors are adopted in different charging stages, and the detailed coordinated control scheme between submodule capacitors and supercapacitors are described and analyzed. The effectiveness and performance of the proposed method are verified by simulation results and hardware-in-the-loop (HIL) experiments.

The weak AC grid in a modular multilevel converter modular multilevel converter based high voltage direct current (MMC-HVDC) receiving-end system requires faster reactive power support from the receiving-end MMC. However, under the MMC traditional constant AC voltage dual-loop control strategy, the increase in MMC's reactive power support speed will reduce the system stability. The reactive power cascade control strategy is a novel MMC control strategy. The analysis in this paper demonstrates that it possesses advanced performance under faster reactive power support, which is required by the MMC connected to a weak AC grid, but, some of its control links and operations can cause impacts exceeding that a weak AC grid can undertake. To solve this problem, based on the original control strategy, this paper proposes an improved reactive power cascade control strategy for weak AC grid conditions. The complete framework and the detailed design of primary control links of the improved cascade control strategy are displayed in this paper. Finally, a digital simulation has been conducted to validate the analysis and the proposed improved control strategy in this paper.

Controlled electric vehicle (EV) charging at commercial locations has been seen as the key solution to mitigate the negative effects of uncontrolled charging on the power grid. In the scientific literature, EV users’ willingness to participate in charging control has been analyzed, and various control algorithms have been studied. However, there is a gap regarding the best practices to encourage users to participate in charging control and the potential influences of the EV users’ decisions on charging site operator's profits. In this article, the EV users’ perspective on charging control is assessed to form a user-friendly charging control approach and compensation scheme for commercial charging locations. Then, simulations are carried out using real charging session data to analyze the potential influences of EV users’ decisions on charging site operator's profits. According to the results, the profits of the charging site operator are more heavily dependent on the number of customers than the optimality of the charging control. Hence, charging site operators should carefully consider the attractiveness of the implemented control strategy to maximize profits.

The high uncertainty of wind power output greatly affects the rapid reactive power optimization of power systems. This paper proposes a neural network-based comprehensive reactive power optimization method for large-scale wind power grids, effectively addressing the challenges of rapid reactive power optimization in power systems. Firstly, by constructing typical wind-power-load scenarios, the generalization ability of the neural network is improved. Then, focusing on the comprehensive reactive power optimization problem after integrating typical wind-power-load scenarios into the system, the improved Harris hawks optimization algorithm (HHO) is compared with the particle swarm optimization algorithm and traditional HHO algorithm, highlighting its advantages. Finally, HHO is utilized for solving, thereby constructing a comprehensive reactive power optimization strategy tag set. Furthermore, through deep fitting of the neural network between the power grid operating state and the comprehensive reactive power optimization strategy, the computational complexity and decision-making time of reactive power optimization are reduced.

Microgrids play a pivotal role in modern power distribution systems, necessitating precise control methodologies to tackle challenges such as performance instability, especially during islanding operations. This paper introduces an advanced control strategy that employs artificial intelligence, specifically deep neural network (DNN) predictions, to enhance microgrid performance, particularly in an islanding mode where voltage and frequency (VaF) deviations are critical concerns. By utilizing real-time data and historical trends, the proposed controller accurately forecasts power demand and generation patterns, enabling proactive planning and optimization of efficiency, reliability, and sustainability in microgrid management. One significant aspect of this approach is to establish an intelligent distributed control system that minimizes reliance on communication devices while ensuring that VaF remains within acceptable limits. Moreover, it consolidates the roles of primary and secondary controllers within the microgrid and facilitates the prediction of load changes and load injection processes. This capability significantly reduces microgrid VaF deviations, enhancing system performance through precise power distribution and balanced coordination among distributed generators. Consequently, it ensures the stability and reliability of the system. In summary, the integration of DNN-based predictive control represents a significant advancement in microgrid management, providing a solution to address performance challenges and optimize operational efficiency, reliability, and sustainability.

Most of the existing methods for harmonic analysis are from frequency-domain perspective. In fact, the essential factor for generating harmonics is non-linear characteristic or time-varying property. Therefore, the harmonic generation mechanism and the harmonic source location method are investigated from the time domain perspective in this paper. Firstly, a general model of non-harmonic sources is established. The non-harmonic sources will match well with the proposed general model while the harmonic source will not. Then correlation coefficient that reflects compliance degree between each device and the general model is defined for distinguishing harmonic sources from non-harmonic sources. On this basis, a novel time-domain harmonic source location method is proposed. Numerous simulations and experiments have demonstrated that the proposed method has good performance under fluctuations, saturations, pre-distortions, transient process, and resonances. The defined correlation coefficient can reflect non-linearity/time-varying degree of the harmonic source, which can be used for evaluating harmonic emission level of each harmonic source. On this basis, a simple harmonic responsibility division method is proposed, which is immune to possible fluctuations, pre-distortions, saturations, resonances, and impedance variation.

There is a change in the traditional power system structure as a result of the increased incorporation of microgrids (MGs) into the grid. Multi-area MGs will emerge as a result, and issues related to them will need to be addressed. Load frequency control (LFC) is a challenge in such structures, which are more complicated due to variations in demand and the stochastic characteristics of renewable energy sources. This paper presents a cascade fuzzy active disturbance rejection control technique to deal with the LFC problem. In order to tune different parameters of controllers, a newly developed heuristic algorithm called the Gazelle optimization algorithm (GOA) is also employed. Moreover, due to the fact that multi-area MGs are regarded as cyber-physical systems (CPSs), a relatively new concern for LFC problems is their resilience to cyberattacks such as false data injection (FDI) and denial of service (DoS) attacks. Therefore, this research also presents a novel machine learning approach called parallel attack resilience detection system (PARDS) to deal with the LFC problem in the presence of cyberattacks. The efficiency of the proposed strategy is investigated under different scenarios, such as non-linearities in the power system or server cyberattacks.