Iet Generation Transmission & Distribution最新文献

This study introduces a novel and effective approach to address the multi-area dynamic economic dispatch problem, with the primary objective of minimizing both operational costs and water consumption associated with the dispatch process. This research is motivated by the growing complexity of modern power systems, which require efficient management of both operational costs and resource consumption (e.g. water) to ensure sustainability and reliability. The proposed model simultaneously optimizes two critical objectives: the total operational cost, comprising thermal energy production, wind energy integration, power transfer between regions, pumped energy storage operations and water consumption and the overall water usage. To enhance the model's relevance to contemporary power systems, several key features are incorporated, including the integration of wind energy, the deployment of energy storage systems, the interconnection of geographically diverse regions within the power grid and the implementation of a demand response (DR) mechanism to mitigate peak loads and improve system efficiency. To tackle the complexity of balancing multiple objectives and constraints, a novel optimization method based on the combined whale optimization algorithm and grey wolf optimizer is developed. By integrating these techniques, the method effectively explores a broader solution space, offering a more accurate and efficient optimization process without the need for additional chaotic mechanisms or differential evolution. Solving the optimization problem on a 40-unit test system without DR resulted in a 6% reduction in water consumption compared to the initial conditions. With the integration of DR, the hybrid method achieved further improvements, reducing the total cost by 9.56% and water consumption by 3.05% compared to the case without DR. These results demonstrate the effectiveness of the proposed approach and the added value of DR in improving both economic and environmental performance. This study contributes to the ongoing efforts in designing more efficient, sustainable and resilient power systems.

With the development of smart grids, transmission line UAV intelligent inspection technology has been widely used. Pin defect detection is a common task in the intelligent inspection process, but due to the small size of the pin bolts in the inspection image, it is difficult for the existing detection algorithms to accurately recognise the pin defects in the complex background. In this paper, an Adaptive Focusing Multi-Scale Feature Network (AFMFNet) is proposed. First, the Path-Interleaved Deformation Convolution (PIDC) is proposed to further enhance the feature extraction ability for the irregular pose of the pin bolt. Second, the Small Target Enhanced Pyramid (STEP) is constructed. It realises the effective fusion of multi-scale features of small targets through the differentiated processing between different layers and the global perception capability granted by CSP_OmniKernel. Finally, the improved Wise-MPDIoU loss function is utilised to improve the convergence speed and regression accuracy of the model. AFMFNet enhances detection accuracy for normal and defective pins by 7.5% and 13.4%, respectively compared to the baseline model, achieving a reasoning speed of 141.2 f/s on PC, meeting real-time detection needs. Its robustness is verified in complex scenarios, offering a new intelligent approach for transmission line inspection.

Climate change intensifies extreme weather events, creating major challenges for energy distribution systems. This paper presents a method to quantify resilience in sector-coupled energy systems by weighting sectors via operating costs and aggregating results from multiple disruption severities into a single resilience index. A complementary cost assessment framework is introduced to evaluate the economic viability of mitigation strategies. To demonstrate its practical application, the approach is tested with data from a German distribution operator, focusing on heavy rainfall events as a representative case. Scenario intensities with different return periods are simulated and aggregated probabilistically, assessing impacts on an urban medium-voltage grid with mitigation options including vehicle-to-grid, gas-to-power, and substation hardening. Results indicate that while conventional hardening is most effective in cost minimization, vehicle-to-grid significantly enhances resilience by mitigating initial disruptions. The framework provides actionable guidance for operators and policymakers, supporting investment decisions and advancing sector-coupling strategies for climate-resilient energy systems.

With the increasing penetration of renewable energy, the limited ramping capability of power systems within short timeframes has become a critical challenge for maintaining system security and stability. This imposes higher requirements on the accuracy of ramping demand forecasting. Accurate ramping demand prediction relies on reliable net load forecasting. However, traditional methods often struggle to capture complex temporal patterns, including periodic fluctuations, long-term trends, and abrupt anomalies.

To address these challenges, this paper proposes an Adaptive Enhanced model based on Time-Frequency Fusion Networks (AETFFN) for net load ramping demand forecasting. First, a dynamic frequency selection module is designed to adaptively identify key frequency components by analysing spectral energy and sparsity, which suppresses high-frequency noise and enhances core periodic features. Second, a gated time-frequency fusion module is constructed by integrating one-dimensional convolution and Fast Fourier Transform to extract frequency-domain features, while dynamic weighting ensures effective fusion of time and frequency information. Additionally, a lightweight convolutional module is introduced, combining multi-scale convolution with dual attention mechanisms to improve both local detail extraction and global pattern recognition while maintaining computational efficiency. The proposed method is evaluated on multiple datasets, with both ablation and comparative experiments conducted to validate its effectiveness. Results show that AETFFN outperforms other benchmark models across three net load datasets, achieving an average RMSE reduction of 27.91%. This confirms the effectiveness of the proposed approach in net load forecasting and its ability to accurately estimate ramping demand.

Grid-forming converters (GFMCs) are expected to replace the function of synchronous generators (SGs) in the future power system with high integration rate of power electronics converters. Transient stability of such systems will be significantly impacted by the proper design of controller parameters of GFMCs. The domain of attraction (DA) can quantify the transient stability region of multi-machine systems. But high computational costs pose significant challenges for analysing various possible controller parameter values. Catering to this technical gap, this paper proposes a controller parameter optimization method for GFMCs by maximizing the DA estimate of the multi-GFMC multi-SG power system. Sum-of-squares programming is employed to incorporate the controller parameter optimization and expansion of the boundary of DA estimates. Numerical results are obtained with respect to a 1-GFMC-2-SG and a 5-GFMC-5-SG power system, respectively. It is found that the transient stability boundary of the test systems has been improved significantly through the controller parameter optimization.

The identification of vulnerabilities within distribution networks, impacted by complex factors such as ring configurations and the integration of distributed energy resources, remains a significant challenge. This study presents a novel enhanced hierarchical analysis fault chain comprehensive assessment model to address this issue. The proposed model develops four new weak indicators for assessing the nodes of a distribution network based on complex network theory: an improved indicator of node degree, an improved indicator of node PageRank, a power flow increment impact indicator and a node voltage stability indicator. It introduces the weighting method with the binomial coefficient for optimal distribution of weight in the fault chain framework to further enhance effectiveness in refined hierarchical analysis, aimed at improving objectivity and precision. Besides, it employs an integrated analytic hierarchy process in which system indicators and operational parameters under various conditions can be integrated and dynamically calculate the fault rate of a weak link in the network. These further enhance the adaptability and precision of the model in solving inherent complexities within modern distribution networks. Simulation case studies performed using an IEEE-69 node complex active distribution network have demonstrated the efficacy and superiority of the proposed method for the correct identification of weak nodes.

State of charge (SOC) is a critical performance indicator for battery operation, and its precise estimation is essential for ensuring the safe operation of the battery system. This paper introduces a novel cascaded hybrid SOC estimation model that integrates an adaptive extended Kalman filter (AEKF) and a long short-term memory (LSTM) network. The model employs RC circuit configuration and utilises a forgetting factor recursive least squares algorithm for parameter identification. Initially, the AEKF is used to derive SOC estimates from the circuit model. Subsequently, an LSTM network corrects the errors in these initial SOC estimates, resulting in improved accuracy. The paper provides a detailed model description and validates it across various operating conditions. Experimental results demonstrate that this model offers outstanding estimation accuracy and generalisation performance, with a root mean square error maintained within 0.34%, a maximum error within 2.10%, and a mean absolute error within 0.23%.

The coordination and interconnections of power networks are crucial for power systems dispatch with high-proportion variable renewable energy (VRE). Recent studies have separately explored the transmission-distribution system integration and the interconnections of transmission (distribution) systems via voltage source converter multi-terminal direct current (VSC-MTDC) grids. However, few researches concentrate on the combination of these two technologies, which contains admirable potential operation flexibility to absorb massive VREs. This paper proposes a dispatch model for VSC-MTDC-based integrated transmission-distribution systems based on the distributionally robust joint chance-constrained programming (DRJCCP) framework. At first, two approximation approaches of network losses according to different reactance-resistance ratios are proposed for transmission and distribution grids, respectively. Then, the extended affine strategy is embedded in the DRJCCP framework to map the relation between VRE uncertainties and dispatch decisions. Finally, based on the hierarchy, a decentralised optimisation algorithm based on analytical target cascading is utilised, which divides the whole dispatch problem into the bi-level problem of transmission and distribution grids. Numerical tests on different scale systems demonstrate that the proposed method balances decision-making realism and robustness, highlighting the strategic and operational aspects of coordinated dispatch across different hierarchies and regions.

With the large-scale integration of distributed energy sources, considering their intermittent and volatile nature, grid voltage regulation has become more challenging than ever before. Issues such as voltage fluctuations and even two-way over-limit violations have become increasingly prominent. To achieve the optimized dispatch of reactive power and the stable operation of the grid voltage, a comprehensive review of the reactive power regulation device and related issues in the ancillary service market is conducted. Firstly, the development history and future trend of the reactive power market are systematically summarized, with a comparison of different power market architectures and their impact on reactive power regulation. Subsequently, the relevant mathematical theories of reactive power pricing are analysed, followed by typical optimization models of the reactive power market. Finally, the current deficiencies and challenges of the reactive power market are concluded. It is noted that research on the reactive power market, adapted to the context of high renewable energy integration, remains scarce, and the collaborative dispatch mechanism for distributed reactive power sources requires improvement. Future research directions are provided in the end.

Resilience assessment is also relevant for highly reliable power systems, such as meshed networks. The paper deals with resilience of an urban dense-meshed low voltage network to supply feeder outages and subsequent cascading failures. Presented resilience analysis evaluates the amount of preserved load during multiple feeder outages. The simulations account for a time-span of 1 year of network operation with regard to load variations. Based on disturbances simulation results, the weakest elements of the network are identified. To increase resilience, corrective measures are proposed and incorporated into the simulations. Resilience improvements of applied measures are evaluated and discussed.