Iet Generation Transmission & Distribution最新文献

Accurate evaluation of typical industry customers’ demand response potential (DRP) is of great significance for promoting the electricity retail companies to achieve DR targets and supporting the balance regulation of the power system with a high penetration of renewable energy resources. Existing DRP evaluation methods ignore the differences in customers’ DR features and the correlation between DR features in different time periods. Moreover, the characterisation of DR willingness only considers the impact of electricity prices, which reduces the accuracy of DRP evaluation results. Given this background, a DRP evaluation method based on integrated empirical mode decomposition (IEMD) and the multi-head convolutional self-attention algorithm (MCSA) for typical industry customers is proposed in this paper. Firstly, an IEMD and DR willingness-based method for extracting DR features of industry customers is proposed. Then, an MCSA-based DRP evaluation method for typical industry customers, utilising the extracted DR features, is developed to realise accurate DRP evaluation by electricity retail companies. Finally, case studies on the industry customers in Zhejiang province, China, show that the proposed method can obtain higher accuracy in evaluating the typical industry customers’ DRP, thus providing technical support for the electricity retail companies to fully mobilise the flexible resources of the demand side.

Global energy transition drives large-scale renewable sources and diversified load integration into distribution networks, introducing significant uncertainty and challenging operational flexibility. Although AC/DC hybrid distribution networks offer advantages, existing methods inadequately model complex spatiotemporal correlations of source load uncertainties and lack systematic frameworks to quantify supply–demand flexibility imbalances under unique AC/DC interconnection modes. This study addresses these gaps by developing a Copula-Markov chain model for temporal correlations and an R-Vine Copula model for spatial correlations to generate renewable scenarios, formulating a daily-load-index-driven optimisation model for stochastic demand fluctuations, and establishing an electric vehicle (EV) charging model incorporating user behaviours. A novel flexibility assessment framework is proposed, defining supply–demand metrics and integrating security constraints via power flow verification. Case studies demonstrate the model's high fidelity, achieving a scenario coverage rate exceeding 0.99, a scenario width of approximately 0.002 and a slope similarity of around 0.06. The assessment reveals critical flexibility gaps; for instance, urban networks showed a moderate upward shortfall probability of 4.30%, whereas rural networks exhibited severe inadequacy with a shortfall probability of approximately 99.31%, underscoring the bottleneck effect of VSC capacity and resource availability.

Fault location is increasingly essential in inverter-based active distribution networks. This is due to the large number of branches and laterals in such networks, as well as the presence of inverter-based distributed generators (IBDGs). Several techniques are used for locating faults in distribution networks, including impedance-based approaches, traveling wave-based schemes, and artificial intelligence (AI)-based approaches. AI-based schemes are superior to others in terms of speed and accuracy, and they do not demand high-frequency devices. However, there is a lack of AI-based schemes that can effectively address scenarios involving a high number of branches, a limited number of measurement instruments, the presence of IBDGs, and high fault resistance. Accordingly, this paper introduces a modified one-dimensional convolutional neural network (1-D CNN) that combines residual connections with 1-D CNNs. The suggested approach includes two elements for fault location: (i) determining the fault distance and (ii) identifying the section of the network that is faulty. The results indicate that this approach effectively pinpoints faults with varying resistance levels at different locations, even in the presence of IBDGs. Ultimately, the proposed solution demonstrates enhanced accuracy in networks featuring multiple distributed generators, numerous sub-branches, unbalanced load conditions, and diverse fault scenarios.

Microgrids offer a viable solution to power outages by integrating distributed energy resources (DERs) to ensure a reliable, localised power supply. This paper presents a two-layered power management system (PMS) for a microgrid cluster, utilising a wide area measurement system (WAMS) to enhance operational reliability. In the proposed two-layer PMS, the first layer implements a centralized PMS for individual microgrid operation and the second layer employs a supervisory PMS for microgrid cluster management. The proposed PMS addresses the critical challenge of power imbalance arising from rapid load fluctuations and the stochastic nature of renewable-based DERs. The system is implemented on a modified IEEE 33-bus test system, configured as a microgrid cluster comprising residential and commercial microgrids. Two algorithms, a load prioritization algorithm (LPA) and a conventional load shedding algorithm (LSA), are developed and implemented within the phasor data concentrator (PDC) to manage power deficit and surplus. Both algorithms facilitate the power transactions by prioritising loads according to a priority factor and their performance is compared against each other. To ensure practical equivalence to real distribution systems, diverse load categories are incorporated. The system is validated through comprehensive case studies in MATLAB/Simulink and also demonstrated via real-time validation using an OPAL-RT (OP4510).

In this study, a sequence impedance model for a virtual synchronous machine (VSG) grid-connected inverter that incorporates fractional-order characteristics of real inductive and capacitive components was developed, utilising the harmonic linearisation method. The model's accuracy was validated through simulation. The impact of altering the sequence of the inductor and capacitor on the impedance characteristics was further examined, system's stability was assessed using the generalised Nyquist criterion. The outcomes of hardware-in-the-loop studies indicate that the fractional-order features of the components influence the stability of the VSG grid-connected system. In the interim, the parameter-optimised fractional-order model exhibited superior stability compared to the integer-order model.

With the high proportion of renewable energy connected to the power grid, it has become more and more difficult to regulate residential and commercial loads, and industrial loads are urgently needed to participate in the regulation. Industrial loads face strict constraints, making their participation in grid regulation challenging. To this end, this paper analyses the tunable resource characteristics of the fused magnesite production process, and establishes a mathematical model to describe the strong constraints of its production work. Based on this model, a two-layer optimisation model is developed for wind and photovoltaic power absorption and fused magnesite load regulation. The upper layer aims to maximise the system's new energy consumption, while the lower layer optimises the economic operation of thermal power units using the total power and renewable energy output determined by the upper model. Finally, the effectiveness of the optimisation model is verified by comparing different fused magnesite load adjustment ratios, considering the energy consumption and cost, using the CPLEX solver and comparing the simulation of typical cases. The results show that the proposed optimisation model increases the new energy consumption rate by 93.89%, reduces the system operating cost by USD 537.29, and provides strong support for the construction of new power systems.

The threat of compound temperature–precipitation events (CTPEs) under global climate change to the resilience of the distribution network with high-penetration new energy (DN-HNE) is far more serious than that of single meteorological events. Therefore, this paper proposes a co-planning method of distribution line and storage for resilience improvement of DN-HNE under CTPEs. First, a CTPEs identification method is proposed, and a CTPEs-sources/load prediction model is constructed based on Sparrow Search Algorithm–Random Forest Regression prediction algorithm and coupling analysis of meteorological–electrical. Second, a double-layer model of line-storage co-planning for resilience improvement under CTPEs is established. The co-planning scheme of line and storage is optimised in the upper layer. In the lower layer, the output of each resource is decided by optimal operation simulation. Finally, the modified IEEE 33-node distribution system is tested. The result shows that the proposed method can significantly improve the resilience and economy of DN-HNE.

Due to long supply lines, dispersed consumers, and a high proportion of inductive motor loads, severe line loss issues may occur in rural low-voltage distribution networks (LVDNs). Passive capacitors offer a cost-effective solution for reactive power compensation. Existing literature has proposed numerous line loss compensation strategies based on passive capacitors, yet most involve complex calculations that hinder widespread adoption and large-scale implementation. In practice, distribution network operators often face limited theoretical expertise, constrained budgets, and a vast number of lines requiring compensation. Thus, a critical challenge lies in determining the placement of capacitors in a simple and effective manner. To address this gap, the paper proposes a practical capacitor placement strategy specifically for line loss reduction in LVDNs. Leveraging real-time data from consumer metering systems, it calculates active and reactive power distributions under various capacitor placement scenarios. An optimisation problem is then formulated and solved under two input modes with the objective of minimising total line loss, ultimately identifying the optimal set of capacitor installation locations. The proposed strategy is computationally efficient, low-cost, and practical for implementation. Case validation conducted on a real fish and crab farming distribution network demonstrates significant line loss reduction, confirming the strategy's effectiveness.

With the growing demand for scalable and resilient high-voltage direct current infrastructure driven by offshore renewables and international power exchange, multi-terminal DC (MTDC) systems are emerging as a key solution. However, their deployment is hindered by the lack of effective DC fault protection. Conventional single-layer architectures assign detection, limitation, and interruption to a single breaker, resulting in over-engineered designs and high system complexity. This study proposes a multi-layer protection approach that decouples fault current management into two dedicated functions: a thyristor-based fault current limiter for initial current suppression and an active resonance circuit breaker for selective fault isolation. A comparative simulation using the CIGRE B4.57 MTDC benchmark in PSCAD/EMTDC evaluates the proposed scheme against a widely adopted hybrid DC circuit breaker baseline. The results demonstrate reduced peak fault current, lower surge-arrester stress, and a significant decrease in semiconductor requirements. Together, these outcomes confirm that the proposed multi-layer architecture provides comparable interruption performance while offering practical implementation advantages and improved scalability for future MTDC systems.

Deep electrification of energy systems and massive integration of distributed energy resources (DERs) push the growth of radial distribution networks (RDNs), requiring innovative methods to deal with the increased complexity and enhance current operational tasks. Network reconfiguration (NR) is one of the tools researched and developed during the last 50+ years for large distribution sizes for more robust operations. NR is vital for reliable operation and demands swift, accurate decision-making, prompting distribution utilities to adopt faster solution methods. In this sense, a method is proposed in this paper with the objective of minimising the execution time of reconfigured large RDNs. A T-Model feeder reduction method, a mapping artificial neural network (ANN) method to map feeder demand and power injections of DERs to T-Model parameters, a reduced RDNs representation using T-Models of feeders, and a network reconfiguration method leveraging this reduced network are proposed. The proposed T-Model reduces the number of nodes in RDNs considering connected loads and DERs by around 55%, which leads to significantly reduced network representation and thereby execution time when reconfiguring the RDNs. The proposed method is tested on several systems, including the IEEE 123-Bus network. The execution time is reduced by up to 74.53% while providing the accuracy of at least 97.17%. This method scales well and performs better for active larger RDNs.