Iet Generation Transmission & Distribution最新文献

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.

The increasing integration of wind power poses a challenge to power system transient stability. Conventional low-voltage ride-through (LVRT) control is hindered by passive triggering and delays, while synchronous generator tripping (SGT) incurs economic costs. This study proposes a coordinated strategy that enhances transient stability by combining active LVRT control of Wind Turbine Generators (WTGs) with SGT. The stability mechanism of active LVRT is analysed, and a model of an active LVRT doubly-fed induction generator (DFIG) is developed. An intelligent decision-making platform, employing the Proximal Policy Optimisation (PPO) deep reinforcement learning algorithm, dynamically optimises active LVRT parameters—start-stop timing, duration, and participating WTG count—based on real-time system states.

Furthermore, a disturbance severity-graded coordinated control strategy dynamically selects the appropriate control response level between active LVRT and SGT. Simulations confirm that the active LVRT strategy significantly improves transient stability. Its coordination with SGT optimises stability enhancement, demonstrating strong adaptability and robustness across diverse fault scenarios. This research presents a viable approach for ensuring the secure and stable operation of power grids with high penetration of renewable energy.

The primary goal of optimal power flow (OPF) is to optimise the operation of a power system while meeting the demand and adhering to operational constraints. This paper presents a new approach for AC OPF. First, the approach constructs a Voronoi diagram by distributing multiple sample points representing potential solutions throughout the search space. Then, it recursively adds new sample points, including a tentative optimal point from the continuous gradient-projection method, a point in the most sparsely populated region to ensure high fidelity and the connecting point, until the stopping criterion is met. The proposed approach is illustrated in detail using the IEEE 9-bus system and then validated on the IEEE 39-bus and 118-bus systems to verify the quality of the obtained solution.

Peer-to-peer (P2P) energy trading has emerged as a promising approach for managing energy produced by prosumers. However, the influence of diverse prosumer behaviours on P2P energy trading remains underexplored. This paper provides a comprehensive analysis of the various behavioural patterns shown by sellers in the energy trading market. Unlike previous studies that have examined only limited aspects of prosumer behaviour, this paper, in addition to behaviours such as competition and coalition, also examines the likelihood of strategic behaviours arising in sellers' decision‑making processes. To achieve this, game theory, a robust framework for modelling individual and collective behaviours, is employed. In the proposed model, buyers act as price proposers, while sellers serve as energy suppliers. The behaviour of sellers is modelled under different conditions: competition, coalition, and coalition suspension. The analysis reveals that coalition formation among sellers yields higher payoffs compared to competition behaviour. However, it is also demonstrated that coalitions can be suspended (violated) because prosumers can achieve greater gain by suspending their coalitions. Additionally, prosumers employ the grim trigger strategy to prevent the suspension of coalitions. Additionally, in another section of this article, a new bilateral negotiation mechanism is presented, which is designed to be implemented in a distributed manner within the structure of P2P energy trading. This market-clearing mechanism is designed to consider, in addition to economic constraints, technical and operational constraints and the matching of buyers and sellers. Moreover, the mechanism includes a constraint to prevent the emergence of market power and to address coalition and strategic behaviour by sellers. That constraint is applied so as, on the one hand, not to reduce sellers’ participation and, on the other, to remain effective. To evaluate the proposed approach, the performance of the bilateral negotiation mechanism, the economic aspects of the suggested method, the analysis of prosumer behaviour, the impact of coalition suspension and the effects of purposeful behaviours on P2P trading have been examined.

Accurate inertia estimation for grid-connected doubly fed induction generator (DFIG)-based wind farms is essential for providing inertia support and maintaining frequency stability. However, due to the diversity of operating conditions and control parameters among individual wind turbines, a DFIG-based wind farm cannot be represented as a single equivalent device. In this paper, a time-varying inertia estimation framework based on sensitivity-guided clustering and aggregation is proposed. First, a sensitivity analysis framework is proposed to analyse the impact factors affecting the time-varying inertia of individual DFIGs, using the extended Fourier amplitude sensitivity test. After that, the dominant factor, identified as the virtual inertia control parameter, is selected as the clustering indicator. Subsequently, DFIGs with similar dominant factors are clustered based on limited measurement data, utilizing the unscented Kalman filter to reduce the requirement for extensive measurement devices. The time-varying inertia of the wind farm is then estimated using streaming dynamic mode decomposition with information from each cluster. Simulation and experimental results demonstrate that the proposed framework achieves high-accuracy inertia estimation with limited measurements, reducing the relative error by nearly 10% compared with existing methods. Moreover, it exhibits strong robustness to noise and disturbances, confirming the effectiveness of the inertia estimation.

This study focuses on islanded AC microgrids and addresses the dynamic stability challenges caused by renewable energy variability and uncertain load demand. The work specifically targets the secondary control layer, which plays a critical role in restoring voltage and frequency while ensuring accurate power sharing among distributed generators (DG). The primary control relies on conventional voltage–current (V–I) droop characteristics to provide decentralized operation, but this approach alone leads to steady-state deviations and limited power-sharing accuracy. To overcome these limitations, we propose an advanced distributed secondary control strategy based on consensus algorithms. At this layer, the two fundamental parameters of the droop characteristic—its slope and offset—are dynamically tuned in a coordinated manner. Active power sharing is improved by adjusting both the slope and the offset of the d-axis droop, while reactive power control is refined through modifications to the q-axis slope. This dual-parameter adaptation ensures robust proportional power sharing, precise voltage regulation at the point of common coupling, and resilience against communication delays. The effectiveness of the proposed secondary control scheme is validated through detailed simulations in MATLAB/Simulink, demonstrating enhanced stability, faster transient recovery, and improved power quality under varying load conditions.