IET Renewable Power Generation最新文献

The large-scale integration of renewable energy sources such as photovoltaic and wind farms presents significant challenges to voltage stability and fault-ride through capability, particularly in weak transmission networks characterized by low inertia and high impedance. Advancing carbon neutrality and energy sustainability demands flexible and adaptive control strategies capable of supporting diverse renewable technologies. This research introduces a cascaded control parameter optimization framework to enhance system stability and resilience in scenarios with high renewable energy penetration. Central to this framework is developing a novel dynamic resilience metric, which guided the optimization process by minimizing transient, extending permissible fault-clearing times, and strengthening post-recovery. An enhanced particle swarm optimization algorithm is employed to concurrently optimize parameters across plant models, electrical systems, and generator controllers, all in alignment with the PJM model development guidelines. This framework is validated on the Simplified 14 Generator Test System (Area 5), representative of Southeast Australia's grid, and verified for compliance with AEMC fault-clearing requirements and IEEE 1947-2003 standard. Case studies demonstrate its effectiveness and adaptability, with voltage overshoot reduced from 25%–40% to 6%–25%, and fault clearing times extended from 55–70 ms to 255–270 ms. These results confirm that the proposed approach offers a robust solution for integrating renewables into weak grids, enhancing reliability and supporting the shift toward a sustainable energy future.

With the increasing scale of offshore wind farms, the spatial-temporal correlation of wind turbines is commonly considered in predicting wind power generation. Meanwhile, the seasonal variation of offshore wind conditions necessitates the consideration of the spatial relationship of wind farms with dynamic changes. This paper proposes a new power prediction model for offshore wind farms, namely the feature attention graph convolutional neural network with temporal transformers (FAGTTN). Specifically, the feature attention module is utilised to extract important features from the offshore wind power supervisory control and data acquisition (SCADA) system data. Then, the adaptive graph convolutional neural network (AGCN) is employed to learn the embedding of multiple wind turbine nodes, uncovering the hidden spatial dependence in the data to express the dynamic spatial relationship of offshore wind farms. Besides, the temporal transformer is used to capture time dependence and temporal patterns in the time series. The proposed method is validated using the real-world data from the offshore wind farm at Donghai Bridge, demonstrating its validity and superiority. The results show that the proposed offshore wind turbine graph topology network can effectively utilise the geographic location information of wind turbines and outperform existing methods in terms of accuracy and interpretability for offshore wind turbine output prediction.

Generating a substantial set of long-term operational scenarios for wind power, photovoltaic (PV) power, and load sequences is the data foundation for planning high-penetration renewable energy power systems. The existing scenario generation methods (SGMs) have some defects, such as neural network–based approaches requiring a large amount of historical data and lack of preservation of the characteristics of extreme scenarios. In response to the above challenge, this paper proposes an SGM for wind/PV power outputs and load sequences, which is able to preserve the characteristics of extreme scenarios. Specifically, the method extracts extreme scenarios via an iterative procedure and generates conventional scenarios using a double-layer Markov chain model. By combining the iterative extraction process with the double-layer model, the proposed framework effectively incorporates extreme scenario characteristics into the scenario generation process. The results of the case study from a northern province of China demonstrate that the proposed method effectively preserves the statistical characteristics of the original data and extracts representative extreme scenarios, providing diverse scenarios for evaluating high-penetration renewable energy power systems.

This study presents a comprehensive analysis of system resilience and recovery in the face of cyber attacks on a hydrogen energy infrastructure, utilizing advanced modelling techniques and state-of-the-art cybersecurity strategies. We developed a robust optimization framework integrated with graph neural networks (GNNs) to detect and mitigate sophisticated cyber threats. The GNNs were trained on a dataset that included both normal operational data and simulated attack scenarios, enabling them to identify subtle patterns indicative of intrusions. Once an attack was detected, the system employed a stochastic dual dynamic programming (SDDP) approach to reconfigure operations dynamically, optimizing system performance while minimizing disruption. This dual-layer defence mechanism–comprising initial detection by the GNN and subsequent mitigation through robust optimization–was tested against various cyber threat scenarios, including data injection, denial of service (DoS) and control system hijacking. Our findings reveal that the automated adaptive recovery (AAR) Strategy, which integrates real-time monitoring and AI-driven adaptive response, significantly outperforms traditional methods. Specifically, the AAR Strategy restored system performance to 90 percent within 60 minutes post-attack, compared to only 70 percent recovery under conventional approaches. A 3D surface plot analysis further demonstrated that system performance declines sharply under prolonged high-load conditions, with potential performance drops to below 20 percent when the load exceeds 80 percent over a 100-min period. These results underscore the critical need for integrating adaptive and automated resilience strategies, like the AAR Strategy, into energy infrastructures. Our research contributes to the optimization of cybersecurity measures, offering a robust foundation for future advancements in the resilience of critical energy systems against evolving cyber threats.

This study develops a transparent MATLAB-based numerical model for simulating lead-free perovskite solar cells (PSCs), providing full equation-level control and reproducible device analysis. Unlike black-box tools such as SCAPS-1D, the framework offers open access to all physical parameters and faster computation. Validation against reported CsGeI₃/TiO₂/Cu₂O/Ni (∼25% PCE) and CsSnCl₃/MZO/C₆TBTAPH₂/Au (∼32% PCE) structures shows <5% deviation from benchmark results, confirming model accuracy. By combining SnF₂ passivation, MoO_x dipole contact, and a multi-layer anti-reflection coating, the optimised Pb-free design (Model C) achieves ∼35% efficiency—a 12.5% gain in PCE—with 3% higher Voc and 2% higher fill factor when compared to previous Sn-based PSC models. For Pb-free PSCs, this is the first MATLAB-based open-access modelling framework that combines optical and interfacial engineering, providing researchers and students with a scalable, instructive, and repeatable platform to investigate next-generation photovoltaic design.

This research presents a novel integrated optimization approach to enhance the performance of distribution systems. In this regard, a mathematical model based on mixed-integer nonlinear programming is introduced, which for the first time simultaneously addresses the problem of active power optimization and reactive power coordination of electric vehicles in the presence of distributed generations alongside distribution system reconfiguration. The proposed framework comprises a bi-objective programming structure implemented in two steps. In the first stage, the P of EVs is optimized to minimize the total load variations. In the second step, without relying on trigonometric functions or linearization approximations, the Q coordination of EVs alongside DSR is solved by utilizing the node-branch incidence matrix and the real and imaginary components of voltage and current. This model reduces computational complexity and ensures the attainment of the global optimal solution through the branch and bound algorithm in GAMS software, achieving objectives such as minimizing active power losses, reducing voltage deviation, and improving the voltage profile. Simulations conducted on 33-bus and 69-bus distribution systems demonstrate that the proposed method achieves a significant reduction in APL (96.21% and 97.77%) and notable improvement in voltage profile (with VD reduction of 99.55% and 99.60%) in these systems.

With the advancement of the energy revolution and the proposal of carbon peaking and carbon neutrality goals, the integrated energy system (IES) has received increasing attention from researchers. The efficient planning and control of IES cannot be separated from accurate multi-energy load forecasting, especially short-term load forecasting (STLF). Based on the above requirements, the transformer-based method is introduced, and an efficient information extracting informer (EI2) model is proposed to predict the electric, cooling, and heating loads in an IES. Firstly, the feature maps of electric, cold and heat loads are constructed from historical data, and then input to the parameter sharing encoder layer of the proposed STLF model. Secondly, to enable more efficient deep pattern information learning, we have added high-dimensional MLP layers to the feed forward layers in both the encoder and decoder parts of the joint prediction of electric, cold, and heat loads. As a result, the training model has been optimized. Finally, the predicted values for electric, cold, and heat loads are output through three independent decoders. The proposed EI2 STLF model effectively increases the prediction accuracy of multi-energy loads in an IES, as verified and compared with other models using actual examples.

Accurate wind speed prediction is essential for the safe and stable operation of the power system. Thus, an ultra-short-term prediction model of a convolutional memory network is proposed based on information aggregation of cluster space decoupling in this paper. Firstly, the influence of the wake effect of the cluster is analysed and the wake effect impact factor is embedded into cluster analysis to realise the space decoupling based on the wake correlation of the wind turbines. Then, the spatial correlation index is constructed. The representative wind turbine is selected from each decoupling cluster. And the spatial information domain is extended by combining temporal information similarity. Based on the aggregation information of the high-order spatial domain, the convolutional memory network is constructed to enhance the spatial characteristics and carry out ultra-short-term prediction of wind speed. Finally, the proposed model is applied to the wind speed prediction of an actual wind farm and the effectiveness and applicability of the model are verified through comparative analysis.

Intelligent fault diagnosis in wind energy systems requires accurate identification of faults since the annual maintenance cost can lead to substantial financial losses. Also, effective wind turbine fault diagnosis of critical fault types is essential, despite data discrepancies caused by unpredictable environmental conditions and human factors. This paper introduces a method combining deep learning with an optimized categorical boosting (CatBoost) model to improve fault classification using imbalanced SCADA data in wind energy systems. Our approach uniquely integrates t-distributed stochastic neighbour embedding (t-SNE) representations of the resampled SCADA data and its deep learning features extracted using a 1D physics-informed deep convolutional neural network (PDCNN) with combined loss functions, namely, standard categorical cross-entropy loss, deviation penalty loss and non-negativity loss. Additionally, we introduce a framework for optimizing a categorical boosting (CatBoost) classifier using adaptive elite particle swarm optimization (AEPSO). The effectiveness of the proposed framework is validated with multiple recently developed deep learning models using highly imbalanced SCADA datasets. Experimental results demonstrate superior diagnostic performance, achieving higher accuracy and robustness compared to existing methods. This study aims to contribute an advanced methodology for wind turbine fault diagnosis by introducing a comprehensive framework that combines advanced deep learning and gradient boosting techniques to handle the complexities of imbalanced data and improve diagnostic reliability.

Driven by the co-development of the hydrogen sector and new energy vehicles, integrated energy refueling stations (IERSs) that merge photovoltaic (PV) generation, battery energy storage systems (BESS), electric vehicle (EV) charging, fuel cell EV (FCEV) hydrogen refueling, and on-site electrolysis face intricate multi-energy allocation decisions under time-varying uncertainty. This paper formulates a multi-stage joint bidding model for electricity and ancillary service markets that embeds electric–hydrogen coupling, electrolyzer efficiency, and external hydrogen purchase costs. High-dimensional uncertainties in PV output, electricity prices, and charging/hydrogen demand are represented by Markov state transitions and reduced scenario sets. User waiting time is captured through a linear satisfaction-cost term, leading to a marginal-benefit game that allocates battery power between EV charging and electrolysis. Case studies with field data show that the proposed model enhances IERS profits and operational coordination across market stages. Sensitivity analyses reveal that raising the grid hydrogen price from 150 $/MWh to 350 $/MWh increases the contribution of on-site electrolysis from 6% to 91% under low waiting penalties and to 47% under moderate penalties, confirming the model's ability to quantify trade-off between hydrogen sourcing pathways.