Iet Generation Transmission & Distribution最新文献

We focus on blackouts in electric distribution systems that have a large cost to customers. To quantify resilience to these events, we show how to calculate risk metrics from the historical outage data routinely collected by utilities' outage management systems. Risk is defined using a customer cost exceedance curve. The exceedance curve has a heavy tail that implies large fluctuations in large blackout costs, and this makes estimating the mean large cost in the usual way impractical. To avoid this problem, we use new resilience metrics describing the large event risk; these metrics are the probability of a large cost event, the annual log cost resilience index, and the average of the logarithm of the cost of large-cost events or the slope magnitude of the tail on a log–log exceedance curve. Resilience can be improved by planned investments to upgrade system components or speed up restoration. The benefits that these investments would have had if they had been made in the past can be quantified by “rerunning history” with the effects of the investment included, and then recalculating the large event risk to find the improvement in resilience. An example using utility data shows a 2% reduction in the probability of a large cost event due to 10% wind hardening and 6%–7% reduction due to 10% faster restoration in two different areas of a distribution utility. This new data-driven approach to quantify resilience and resilience investments is realistic and much easier to apply than complicated approaches based on modeling all the phases of resilience. Moreover, an appeal to improvements to past lived experience may well be persuasive to customers and regulators in making the case for resilience investments.

With a high share of renewable energy and electric vehicle loads, distribution networks face insufficient carrying capacity due to structural weaknesses, reverse power flow, and weakened supply capability. Flexible interconnection devices optimize line power flow, while energy routers (ERs) regulate node-level power. Their combination effectively addresses these challenges. This paper proposes a two-layer planning method integrating the rotary power flow controller (RPFC) and ER across spatial and temporal dimensions to improve economy and carrying capacity. RPFC advantages over other flexible devices are analysed, and mathematical models for RPFC and ER are developed. The upper layer minimizes total network cost for siting and sizing, while the lower layer coordinates operation using indicators of stability, balance, economy, and flexibility. A hybrid optimization algorithm combining an improved gravitational field algorithm (IGFA) and second-order cone programming (SOCP) is introduced, incorporating a tent chaotic map and elite retention strategy. Simulation results show the method reduces network losses by 20.8% and increases carrying capacity by 15.9%. Compared with the GFA, the IGFA-SOCP improves convergence accuracy by 4.5% and reduces computation time by 42.4%, confirming the effectiveness of the proposed approach.

Modern distribution networks (DNs) widely permeate various kinds of distributed generators (DGs) and loads. Their output characteristics and complex spatiotemporal distribution characteristics have brought serious challenges to the safe and economic operation of DNs, especially for multi-voltage-level DNs. To enhance the flexibility and controllability of DN, the soft open point integrated with the energy storage system (E-SOP) has garnered significant attention, as it can facilitate the flexible regulation of energy distribution networks across blocks. However, such an arrangement also increases the difficulty of cooperative control for DNs with multi-voltage levels. To address this challenge, this paper constructs a form of flexible interconnected multi-voltage level DNs based on E-SOP and proposes a distributed control architecture suitable for multi-voltage level regional interconnection DN with E-SOP. Then, a target loosen-coupled self-adaptive method (TLCSAM) is developed to solve the regulation model. Finally, relevant cases are constructed in this paper to validate the effectiveness of the proposed dispatching strategy; the results demonstrate that the proposed distributed regulation strategy can significantly improve the performance of the multi-voltage level regional interconnected distribution network with E-SOP.

For active distribution networks with high penetration of distributed photovoltaic (PV) systems, research on short-circuit current calculation methods primarily focuses on single-point fault scenarios. However, studies addressing short-circuit calculations for two-point dissimilar-phase single-phase-to-ground faults remain limited. This paper proposes a steady-state short-circuit current calculation method for such faults in active distribution networks, based on the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm. By employing a general equivalent fault model of a distributed PV unit and constructing the fault sequence networks corresponding to two-point dissimilar-phase single-phase-to-ground faults, a system of equations is formulated using the sequence voltage vector at nodes as the unknown variable. A detailed solution approach utilizing the BFGS algorithm is then presented. The accuracy and effectiveness of the proposed method are validated through case studies and simulations conducted in PSCAD/EMTDC.

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.