Iet Generation Transmission & Distribution最新文献

The integration of flexible interconnection devices (FIDs) into distribution networks introduces complexities in power flow management under both normal and transient/fault conditions, thereby decreasing the selectivity, sensitivity, and reliability of common current protection schemes. This article examines the impact of FIDs on existing current protection arrangements and proposes a novel method for recalculating the overcurrent protection setting. The AC output characteristics of the FID for a short period after AC fault occurrence are first investigated. It is followed by an analysis of FID's impact on the three-stage current protection scheme in flexible interconnected distribution networks, conducted through theoretical calculations in MATLAB and simulations in PSCAD/EMTDC. A quantitative assessment of the mal-operation and non-operation of current protection caused by FIDs of varying capacities, power flow directions, and locations, with a focus on the typical 10 kV 10MVA FID, is also performed. Based on the above findings, a calculation and modification method for Stage III overcurrent protection setting is presented to address mal-operation issues in FID-based distribution networks, with its feasibility validated through electromagnetic transient simulations.

According to the inherent characteristics of power systems, the voltage reduction is different in each part of power system. The voltage drop is directly proportional to the demand current and the total impedance between the source and the loads. Therefore, in distribution system voltage/VAr control is more important rather than transmission network, because of higher line R/X ratios rather than the transmission network and customers or end-users receive power through a higher impedance and experience a larger voltage drop in distribution system. This work presents multistage optimal reactive power planning and clustering the distribution network to implement distributed voltage/VAr control approach. At first, reactive power planning to reduce active power losses and save costs has been investigated. According to the result of the first stage, the power system is clustered into some partitions using spectral clustering and reactive power weighted matrix. The optimal number of clusters has been evaluated and determined by four criteria and the clusters are defined by fuzzy c-mean method which gives better vision about cluster boundaries. Finally, a sensitivity-based approach has been used to determine the precise location of VAr resources and the results show that voltage/VAr control in a clustered distribution system has more acceptable outcomes rather than centralized voltage/VAr control in the whole distribution network.

Reasonable scheduling and control of air-source heat pumps (ASHPs) contribute to reducing operational costs for users while encouraging their participation in grid demand response. This article proposes a two-layer optimal scheduling and control strategy for ASHP loads incorporating phase change energy storage (PCES). First, an electricity-heat coupling model for ASHP loads is proposed. This model integrates PCES technology and considers the influence of outlet water temperature, ambient temperature, and the cold island effect on the coefficient of performance. Subsequently, a PCES capacity configuration model is established with the objective of maximizing the overall benefits of the PCES device. Finally, a two-layer optimal scheduling and control strategy for ASHPs is introduced: The upper-layer model employs model predictive control to minimize heating costs based on user comfort, leveraging PCES and building thermal storage characteristics, in conjunction with time-of-use electricity pricing. The lower-layer model schedules periodic start–stop cycles of the ASHP units to respond to the upper-layer power demand and mitigates the impact of the cold island effect on ASHP performance through unit interval sequencing. The simulation results show that the proposed strategy reduces the daily operation cost and power consumption by 38.9% and 25.0%, respectively, significantly improving the building's regulation capability.

In a power system, the communication link can be compromised by intruders who can launch cyberattacks by capturing data packets, sending falsified packets, or stopping data packets from reaching their destination. Moreover, intruders can compromise control devices using supply chain attacks, firmware patching attacks, and insider attackers. Numerous cyberattacks have been reported previously, and cyberattacks are becoming more frequent since attackers are aware of their socioeconomic impacts. Extensive research has been conducted on developing platforms to simulate cyberattacks, studying different types of cyberattacks, investigating the adverse effects of a successful cyberattack on different components of the power system, designing ways to detect anomalies in the power system using electrical measurements, and proposing ways to mitigate the adverse effects of the detected cyberattack. This paper presents a review of state-of-the-art of cybersecurity in the power system, reviewing available simulation tools for studying the cybersecurity of the power system, classifying components of the power system vulnerable to cyberattacks, and summarizing the adverse effects of a successful cyberattack on each component in the power system. Furthermore, different types of cyberattacks and detection and mitigation methods are classified. Research gaps in the cybersecurity of the power system are also discussed.

During extreme meteorological events, the output of wind farms may undergo noticeable fluctuations within short timeframes, leading to wind power ramp events (WPREs), which can result in severe disruptions to the power system, potentially causing widespread outages. However, limited studies have dedicated to addressing the adverse impacts of WPREs on the power system. Moreover, few articles consider the influence of power correlations among multiple wind farms on WPREs. This paper proposes an improved multi-parameter segmentation algorithm for detecting WPREs based on the generation of wind farm output scenarios with different correlation coefficients. And a bi-level optimization model is developed for the allocation of electrochemical energy storage and thermal power units to address offshore WPREs, considering the correlations among wind farms. The effectiveness of the proposed approach is validated through case study based on the modified IEEE RTS-24 node system. Results demonstrate that stronger correlation among wind farms lead to fewer WPREs and lower requirements for thermal power units and electrochemical energy storage capacity, thereby reducing both investment and operational costs.

Compared with traditional reactors, lead-bismuth fast reactors have broader development prospects. Based on the operating characteristics of these reactors, this article proposes a design scheme for steam turbine generators suitable for small-scale lead-bismuth fast reactors. To achieve the design requirements of high efficiency and high-power density for steam turbine generators simultaneously, a multi-objective optimization method based on a feedforward neural network surrogate model is proposed. First, the generator losses and power density are analyzed to obtain the structural parameters that affect the generator optimization objectives. The selected structural parameters are then subjected to sensitivity analysis and data sampling. Subsequently, a feedforward neural network model is used to replace the finite element model, and based on this, a multi-objective genetic algorithm is employed to globally optimize the efficiency and power density of the generator. The final preferred scheme is obtained from the solved Pareto solution set. Meanwhile, the finite element method is used to verify and analyze the optimization results. The optimization results show that while ensuring the generator efficiency, the power density is further improved. Finally, the temperature rise of the generator is analyzed, and the results show that the temperature distribution of the generator is reasonable.

This work proposes an innovative methodology for the optimal placement of grid-forming converters (GFM) in converter-dominated grids while accounting for multiple stability classes. A heuristic-based methodology is proposed to solve an optimisation problem whose objective function encompasses up to 4 stability indices obtained through the simulation of a shortlist of disturbances. The proposed methodology was employed in a modified version of the 39-bus test system, using DigSILENT Power Factory as the simulation engine. First, the GFM placement problem is solved individually for the different stability classes to highlight the underlying physical phenomena that explain the optimality of the solutions and evidence the need for a multi-class approach. Second, a multi-class approach that combines the different stability indices through linear scalarisation (weights), using the normalised distance of each index to its limit as a way to define its importance, is adopted. For all the proposed fitness function formulations, the method successfully converged to a balanced solution among the various stability classes, thereby enhancing overall system stability.

To address the challenges posed by frequent source and load fluctuations in existing loop closing current calculation methods, this paper proposes an online loop closing current calculation method that considers source and load uncertainties. First, a dual-stack dynamic monitoring system is utilized to obtain real-time voltage and current variations before and after disturbances. Second, Thevenin's theorem is employed to build an equivalent model of the distribution network, simplifying the complex network into a combination of an independent voltage source and a series impedance. Then, the steady-state loop closing current is calculated based on the open-circuit voltage and equivalent impedance at both sides of the loop closing point. Next, the optimal frequency method is applied to determine the equivalent impedance and attenuation time constant at a specific frequency, achieving accurate calculation of the transient loop closing current. Finally, simulations are conducted to model the fluctuations in distributed generation and load, analysing the steady-state and transient loop closing currents. The simulation results demonstrate that the proposed method accurately captures the effects of source and load fluctuations on the loop closing current in dynamic environments, with minimal calculation error, indicating its high practicality.

To mitigate subsequent commutation failure (SCF) in line-commutated converter-based high-voltage direct current transmission systems, the response and limitations of the control system are analysed. The results reveal that due to the prolonged negative deviation of the extinction angle during recovery, the integral output of the proportional–integral controller in constant extinction angle (CEA) significantly reduces the advanced firing angle order, even below normal operating angle. This directly leads to a delayed transition between control strategies and diminishes the effectiveness of CEA. Furthermore, the influence of fault severity, fault type, and AC system strength on SCF is examined. Based on these findings, an enhanced CEA control method to suppress SCF is proposed. By setting an expected firing angle and adaptively adjusting the integral parameter, the control strategy switching moment can be advanced, improving the CEA's control margin and enhancing capability. Moreover, introducing a notch filter reduces firing angle fluctuations and strengthens CEA's ability to suppress SCF. Finally, the theoretical analysis and the effectiveness of the proposed optimization method are validated.

The integration of renewable energy sources and the increasing demand for reliable power have posed significant challenges in the design and operation of distribution networks under uncertain conditions. The inherent variability in renewable energy generation and fluctuating consumer load demand requires advanced strategies for Distributed Energy Resources (DERs) allocation and sizing to enhance grid resilience and operational efficiency. This article introduces an innovative framework for optimizing distribution network design under these uncertainties. The approach integrates deep learning-assisted Distributionally Robust Optimization (DRO) with Generative Adversarial Networks (GANs) to dynamically model and manage the inherent variability in renewable sources and demand fluctuations. Employing a combination of nonlinear optimization techniques and advanced statistical methods, the framework robustly optimizes network configurations to minimize losses and improve voltage stability. The model's efficacy is rigorously tested on the IEEE 33-bus system, achieving a 15% reduction in power distribution losses and a 20% improvement in voltage stability compared to traditional models. Utilizing open-source computational tools, the method not only boosts operational reliability and efficiency but also adapts effectively to the increasing integration of volatile renewable energy sources. These results underscore the framework's potential as a scalable and robust solution for modern power network design challenges.