IET Renewable Power Generation最新文献

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.

This paper proposes a two-layer, tri-objective optimization structure for the daily operation of integrated energy systems. The proposed structure integrates the water system into the electrical, thermal and cooling systems to model the energy-water nexus in modern energy systems. The first layer of the proposed model is formulated in MATLAB software and is responsible for modelling the uncertainty of renewable energies using a stochastic model. The second layer utilizes a hybrid classic weighted compromise programming to provide a sustainable and economic operation for the energy system. The second layer is solved using GAMS software to ensure optimality. The carbon capture, protection from underground sources and the cost of the system are the objective function. The main aim of the proposed model is to prevent the excess extraction of water from underground sources. To this end, the water storage tank and desalination systems are considered to meet the needed potable water. To show the effectiveness of the proposed model, it is tested on a general integrated energy system. The numerical results show that the proposed model improves water extraction and carbon emissions by 86.7% and 3.03%, respectively, while increasing the operating cost by 3.96%.

This paper presents an advanced and smart enhancement to the direct power control (DPC) strategy using grid voltage modulation for three-phase photovoltaic (PV) inverters. It introduces and evaluates three DC-link voltage control techniques: the proportional-integral (PI) controller, the fuzzy logic controller (FLC), and a novel M5-Pruned (M5P) decision tree–based algorithm. While PI-based DPC remains widely used, it is often constrained by its sensitivity to gain tuning, limited adaptability, suboptimal dynamic response, and not ideal decoupling of active and reactive powers. FLC offers greater flexibility and can handle nonlinearities more effectively, yet it still lacks precise control and structured scalability. To address these limitations, this study proposes the M5P-based control approach, a data-driven, self-adaptive strategy that combines model transparency with the ability to handle complex system behaviour efficiently. Simulation results show that the proposed M5P method significantly reduces total harmonic distortion to 0.20%, outperforming both PI (0.57%) and FLC (0.53%) controllers. Furthermore, it achieves complete decoupling of power components, enhances dynamic stability, and eliminates the need for manual gain tuning. The methodology is validated through extensive simulations in MATLAB/Simulink, highlighting its effectiveness under both steady-state and transient conditions. These results establish the M5P-based controller as a promising candidate for next-generation intelligent PV grid integration systems.

The interconnection of microgrids (MGs) to form a multi-microgrid (MMG) distribution system enables greater integration of distributed energy resources (DERs) into power grids. This paper proposes a framework for the optimal coordinated placement and sizing of DERs—including dispatchable and renewable distributed generation (DG) units and energy storage systems (ESSs)—in an MMG system to minimise total annual costs, covering both DER investment and operating costs. The model accounts for MMG operation under normal and emergency conditions, such as system faults or disconnection from the upstream grid, and includes the costs of interrupted energy. Additionally, a cost allocation scheme is introduced to divide the investment costs of newly installed DERs among MGs based on their earned benefits. The problem is formulated as a mixed-integer linear programming (MILP) model and solved using GAMS software. The framework is applied to a test MMG system, and the results show that coordinated planning reduces the total annual cost by 7.3% compared to uncoordinated planning, highlighting its potential for cost-effective and resilient operation in real-world systems.

This paper presents a bi-objective optimisation approach for grid-connected microgrids, aiming to minimise operational costs and voltage deviation at the connection nodes of distributed energy resources and loads. Existing research typically addresses these objectives separately, and the simultaneous consideration of economic performance and voltage deviation in grid-connected community microgrids with multiple generation resources remains in an early stage of development. To advance the research in this area, a novel mean-guided elite selection genetic algorithm (MGES-GA) is proposed to enhance the balance between convergence and diversity in multi-objective optimisation. The proposed algorithm enhances the selection process by re-evaluating low-performing individuals through gene mixing with elite solutions, thereby preserving diversity and avoiding premature convergence. Comparative analysis of the MGES-GA with the enhanced genetic algorithm, differential evolution with heuristic, and improved differential evolutionary optimisation algorithms demonstrates its superior performance in optimising the economic dispatch of a grid-connected microgrid. In a bi-objective comparison with state-of-the-art algorithms, tested on a modified IEEE European low-voltage test feeder and IEEE 33-bus network, MGES-GA demonstrates its effectiveness in balancing conflicting objectives by producing lower voltage deviations at comparable or lower costs.