The increase in extreme weather affects the distribution system, leading to system collapse and interruption power supply to loads. Island partition is a resilient solution when major faults happen in the distribution system. This paper proposes an island partition model considering load forecasting error. Specifically, a two-stage robust optimization island partition program is formulated to restore as many loads as possible, while satisfying operation and topology constraints. Ellipsoidal uncertainty set is built to describe the uncertainty of load forecasting error and take the temporal correlation of it into account. The two-stage robust optimization problem is transformed into a mixed integer second order cone problem to make it computationally tractable. The formed island can be more reliable and avoid secondary collapse when facing loads fluctuation, and uncertainty budget is set to control the degree of conservatism of the robust optimization result. Numerical results based on IEEE 33-node distribution system demonstrate the effectiveness of the developed model.

With the increasing integration of distributed energy resources (DERs) into the distribution system, an accurate aggregate model (AGM) that can describe the outer characteristics of the DER cluster is critical for unlocking their flexibility potential. Because the exact solution of the AGM is an NP-hard problem, existing research usually tries to seek an inner approximation by maximizing the feasible region of a prescribed parametric AGM. Although an AGM with the largest feasible region can be obtained by this method, it is usually not optimal for the distribution system because some parts of the feasible region of the AGM contribute little to the operational flexibility of the distribution system, i.e., the ineffective feasible region. In short, maximizing the feasible region is not consistent with maximizing operational flexibility. Considering this, we propose a novel objective-oriented aggregate model (OOAM) for the DERs that integrates the operational objective of the distribution system into the computation of the AGM to maximize the effective feasible region. First, we introduce the concept of the OOAM and formulate the mathematical model. Second, we propose a duality theory based solution method for the OOAM model. Particularly, the OOAM approach features a bi-level coordination framework: 1) At the distribution system level, the system operator uses a multi-scenario stochastic programming model to optimize its scheduling decisions and quantify its preference for the feasible region of the AGM; 2) At the DER level, the aggregator optimizes the parameters of the AGM based on the preference of the distribution system. Finally, case studies based on the IEEE 33-bus and IEEE 123-bus systems verify the effectiveness and superiority of the proposed approach.

Traditional machine learning prediction methods usually only predict input parameters through a single model, so the problem of low prediction accuracy is common. Different predictive models extract different information for input, and combining different predictive models can make as much use as possible of all the information contained in the inputs. Therefore, this paper improves the existing oil temperature prediction method of offshore wind turbine gearboxes, and for the actual prediction effect of Supervisory Control And Data Acquisition (SCADA) data in this paper, Bayesian-optimized Light Gradient Boosting Machine (LightGBM) and eXtreme Gradient Boosting(XGBoost) machine learning models are selected to be combined. A method based on the Induced Ordered Weighted Average (IOWA) operator combination prediction model is thus proposed, with simulation results showing that the proposed model improves the accuracy of gearbox condition monitoring. The innovation of this article lies in considering the various negative impacts faced by actual offshore wind turbines and adopting a combination prediction model to improve the accuracy of gearbox condition monitoring.

Reliability is a fundamental concept for power systems, and the optimal placement of switchable devices is a valuable tool for improvements in this area. The goal of this paper is to propose an optimal allocation method for circuit breakers and switches that can break the cost–reliability dilemma and simultaneously achieve reliability and economic improvement in terms of the distribution network. Moreover, in view of the fact that variations in the load level can affect the reliability of the distribution network, the variations of different load level scenarios are considered in this paper, where a mixed integer linear programming (MILP) model based on fictitious fault flows is established to derive the optimal allocation scheme that can adapt to the changes of multiple scenarios regarding the load. Meanwhile, due to the constraints of reliability indices, the post-fault reconfiguration scheme of a distribution network under different load level scenarios can also be obtained to enhance its overall reliability. Finally, the applicability and effectiveness of the proposed method are verified by numerical tests on a 54-node test system.

This letter first explores the solution uniqueness of the data-driven modeling of price-responsive flexible loads (PFL). The PFL on the demand side is critical in modern power systems. An accurate PFL model is fundamental for system operations. However, whether the PFL model can be uniquely and correctly identified from operational data remains unclear. To address this, we analyze the structural and practical identifiability of the PFL model, deriving the dataset condition that guarantees the solution uniqueness. Besides, we point out the practical implications of the results. Numerical tests validate this work.

The growing integration of DG enhances distribution network resilience, but existing centralized fault recovery strategies face critical limitations: excessive computational burdens on DCs and the insufficient utilization of local control capabilities, particularly with large-scale DG deployments. Current studies often fail to balance computational efficiency and dynamic recovery coordination between centralized and decentralized resources. To address the issue, a hierarchical control architecture is proposed that involves collaboration between the DC and LA energy gateways. By dynamically quantifying LA PSC through net power feasibility analysis, the framework optimizes network reconfiguration (DC level) and decentralized DG scheduling (gateway level). Validated on a modified IEEE 45-bus system, the strategy restored 7 MWh (4:00 fault) and 5 MWh (10:00 fault) of load, outperforming static methods by 26.9× in the mid-day case. While effective in urban grids, rural DG-sparse areas require future integration of mobile storage. The work balances centralized coordination and decentralized execution, offering a scalable resilience solution for modern networks.