Xiaoyan Bian

In offshore wind power development, offshore wind farm siting (OWFS) is a crucial yet complex decision process, often compromised by expert linguistic ambiguity, multidimensional expert evaluation and conflicting criteria. To tackle these challenges, this study proposes an integrated multicriteria group decision‐making (MCGDM) framework that integrates the entropy weight method (EWM) with the cubic q‐rung orthopair fuzzy extension of TOPSIS (Cq‐ROF‐TOPSIS). First, a hierarchical evaluation system consisting of six main criteria and 22 subcriteria is established, grounded in literature research. A dual‐layer weighting scheme is implemented to determine criteria weights. Specifically, main criteria weights are elicited from expert scoring, whereas subcriteria weights are objectively computed via EWM. Subsequently, expert evaluations are quantified as Cq‐ROF numbers to construct individual Cq‐ROF decision matrices, which are then aggregated into a comprehensive decision matrix using the power average operator. During this stage, a dynamic mechanism is employed to calculate expert weights based on their mutual support degrees, thereby effectively minimizing the negative impacts of extreme or biased assessments. Finally, the Cq‐ROF‐TOPSIS model ranks alternative sites based on their closeness coefficients to the positive ideal solution. A case study demonstrates the effectiveness of the proposed framework, while stability analysis across values confirms its robustness.

Wind speed measurements from fully assembled power meteorological devices (PMDs) are affected by structural flow disturbance caused by support columns, louvers and protective brackets, producing wind-speed-dependent nonlinear deviations that differ from conventional sensor offset or scale-factor errors. This study proposes a physics-guided Bagging ensemble calibration method (PG-Bagging) for device-level wind speed correction. Air-property-corrected Reynolds number, dynamic pressure and a local wind-speed dispersion index are integrated into a cascaded framework comprising a power-law baseline, a Bagging residual-correction network and an adaptive uncertainty indication interval (AUI) based on wind-speed-dependent noise, ensemble disagreement and out-of-bag scale calibration. Wind-tunnel tests on two same-model PMDs show that, within the interpolation region up to 40 m s ⁻¹ , the root mean square error (RMSE) decreases from 0.5892 to 0.1127 m s ⁻¹ and bias from −0.5374 to −0.0037 m s ⁻¹ . Cross-unit transferability was further assessed using 1, 413 routine calibration records from 64 additional same-model PMDs. The frozen model reduced RMSE from 1.0087 to 0.6482 m s ⁻¹ and the mean absolute error from 0.8528 to 0.4505 m s ⁻¹ , with lower device-level RMSE for 55 devices. Above 40 m s ⁻¹ , calibration error increases, indicating unreliable quantitative extrapolation. Accordingly, the AUI is used as an auxiliary indicator of reduced prediction confidence under out-of-distribution high-wind-speed conditions.

Future far-shore wind power development is expected to be characterized by large-scale and clustering deployment. However, wind curtailment is anticipated to become increasingly prominent because of the limits imposed by the reliability of the offshore wind power grid integration system device and the accommodation capability of the onshore power grid. To address this challenge, an optimal planning method of a grid integration network for a large-scale offshore wind power cluster (LSOWPC) incorporating interconnected lines is proposed. First, the network mode of grid integration for LSOWPCs incorporating interconnected lines is developed to enable reliable wind power grid integration and effective wind power accommodation of large-scale offshore wind power. Second, offshore wind accommodation and operational reliability indices under normal and fault scenarios are formulated for the LSOWPC grid integration network, incorporating interconnected lines. On this basis, a bi-level planning model of the grid integration network for LSOWPCs is established. The upper level minimizes the annual total cost by optimizing the siting and capacity of interconnected lines, transmission lines, and converter stations, etc., while the lower level meets the objective of minimizing the annual operating cost, incorporating wind power accommodation, and reliability indexes based on the upper-level planning result. Through iterative coordination between the two levels, the overall optimal planning scheme is obtained. Finally, a case study based on a practical offshore wind power cluster connected to an onshore power grid is conducted to verify the effectiveness and rationality of the proposed planning method.

Large-scale offshore wind energy development is crucial for energy transition. At present, there is no consideration of onshore grid accommodation capacity when integrating and planning offshore wind farm systems, which severely constrains the deployment of giga-scale projects, i.e., super-large offshore wind farms (SLOWFs). To achieve rational accommodation of wind power, we propose a transmission system planning method for partitioned SLOWFs by considering the accommodation capacity of the onshore power grid. First, based on the constraints of the onshore partition grid’s accommodation capacity, we apply the density peak clustering algorithm to partition the SLOWF. Second, a double-layer planning model is established for the transmission system of the partitioned SLOWF; here, the upper-layer model focuses on layout optimizing of the offshore converter stations based on the partitioned SLOWF, while the lower-layer model addresses the planning of DC submarine cables and onshore converter stations. The overall economic optimization of the system is achieved by iteratively solving the double-layer model. Finally, the simulations are validated using the links of a SLOWF integrated with an actual coastal grid. The results demonstrate that the proposed model and solution method significantly reduce planning costs while improving the efficiency of convergence.

Nonintrusive load monitoring (NILM), also known as energy disaggregation, infers the energy consumption of individual appliances from household metered electricity data. Recently, NILM has garnered significant attention as it can assist households in reducing energy usage and improving their electricity behaviors. In this paper, we propose a two‐subnetwork model consisting of a regression subnetwork and a seq2point‐based classification subnetwork for NILM. In the regression subnetwork, stacked dilated convolutions are utilized to extract multiscale features. Subsequently, a self‐attention mechanism is applied to the multiscale features to obtain their contextual representations. The proposed model, compared to existing load disaggregation models, has a larger receptive field and can capture crucial information within the data. The study utilizes the low‐frequency UK‐DALE dataset, released in 2015, containing timestamps, power of various appliances, and device state labels. House1 and House5 are employed as the training set, while House2 data is reserved for testing. The proposed model achieves lower errors for all appliances compared to other algorithms. Specifically, the proposed model shows a 13.85% improvement in mean absolute error (MAE), a 21.27% improvement in signal aggregate error (SAE), and a 26.15% improvement in F1 score over existing algorithms. Our proposed approach evidently exhibits superior disaggregation accuracy compared to existing methods.

During extreme meteorological events, the output of wind farms may undergo noticeable fluctuations within short timeframes, leading to wind power ramp events (WPREs), which can result in severe disruptions to the power system, potentially causing widespread outages. However, limited studies have dedicated to addressing the adverse impacts of WPREs on the power system. Moreover, few articles consider the influence of power correlations among multiple wind farms on WPREs. This paper proposes an improved multi‐parameter segmentation algorithm for detecting WPREs based on the generation of wind farm output scenarios with different correlation coefficients. And a bi‐level optimization model is developed for the allocation of electrochemical energy storage and thermal power units to address offshore WPREs, considering the correlations among wind farms. The effectiveness of the proposed approach is validated through case study based on the modified IEEE RTS‐24 node system. Results demonstrate that stronger correlation among wind farms lead to fewer WPREs and lower requirements for thermal power units and electrochemical energy storage capacity, thereby reducing both investment and operational costs.

In recent years, compound dry‐hot events have significantly impacted human society, particularly affecting the source and load sides of the power system. With the increasing penetration of renewable energy, these events pose growing challenges to power supply‐demand balances. Therefore, this paper proposes the concept of ‘power drought’ for the first time to quantify the severity of supply‐demand imbalances and identify their spatial‐temporal evolution under compound dry‐hot events. The analysis begins by examining the coupling between meteorological parameters, renewable energy output and load demand under compound dry‐hot events. Specifically, the concept of power drought is defined, followed by the formulation of relevant evaluation metrics. Then, a spatial‐temporal clustering algorithm and a centroid migration model are applied to analyse the evolution characteristics of power drought events. Finally, the validity and practicality of the proposed method are demonstrated using practical data from a certain region to analyse the evolution of power drought over the past decade. Case studies reveal a south‐westward migration of power drought centroids, with 66.49% of grids showing positive correlation between the standardised compound event index and the power drought index.