Power grid congestion has become a recurring issue due to system uncertainties caused by the increasing integration of renewable energy sources. This study proposes a data-driven approach for intrahour congestion forecasting and proactive relief in power grids. By leveraging historical congestion data, a histogram-based gradient tree boosting (HGTB) model is trained to predict real-time congestion severity and probability using grid measurements. This enables operators to anticipate and prevent congestion events. The proposed framework integrates a proactive congestion relief strategy, incorporating a multitime-scale marking method. It facilitates coordinated collaboration among optimization control schemes and harnesses the benefits of adjustable resources with varying response characteristics. Case studies on the IEEE 118-bus power system and the China Central Region power system validate the effectiveness of the approach in accurately forecasting and mitigating congestion events. The results demonstrate precise congestion forecasts and timely preventive actions, leading to cost reduction.

With the rapid development of distribution networks and increasing demand for electricity, the pressure of power supply for medium- and low-voltage distribution networks (M&LVDNs) is increasingly significant, especially considering the large scale of customers at the low-voltage (LV) level. In this paper, an outage sequence optimization method for low-voltage distribution networks (LVDNs) that considers the importance of users is proposed. The method aims to develop an optimal outage sequence strategy for LV customers in case of medium-voltage (MV) failure events. First, a multi-dimensional importance indicator system for LV users is constructed, and the customers are ranked using a modified Analytic Hierarchy Process–Entropy Weight (AHP-EW) method to determine their priorities during outages. Then, an elastic net regression-based method is used to identify the topology of the LV network. Finally, an outage sequence optimization model based on the user importance is proposed to reduce the load-shedding level. Extensive case studies are conducted in the modified LV distribution network. The results show that the proposed method results in fewer outage losses throughout the restoration periods than traditional methods and effectively improves the reliability of the power supply to LV users.

The increasing uncertainty of distributed energy resources (DERs) challenges the secure and resilient operation of low-voltage distribution networks (LVDNs). Flexible interconnection via power-electronic devices enables controllable links among LVDAs, supporting capacity expansion, reliability, load balancing, and renewable integration. This paper proposes a two-stage robust optimization framework for flexible interconnection planning in LVDNs. The first stage determines investment decisions on siting and sizing of interconnection lines, while the second stage schedules short-term operations under worst-case wind, solar, and load uncertainties. The bi-level problem is reformulated into a master–subproblem structure and solved using a column-and-constraint generation (CCG) algorithm combined with a distributed iterative method. Case studies on typical scenarios and a modified IEEE 33-bus system show that the proposed approach mitigates overloads and cross-area imbalances, improves voltage stability, and maintains high DER utilization. Although the robust plan incurs slightly higher costs, its advantages in reliability and renewable accommodation confirm its practical value for uncertainty-aware interconnection planning in future LVDNs. Case studies on typical scenarios and a modified IEEE 33-bus system demonstrate that under the highest uncertainty the proposed method reduces the voltage fluctuation index from 0.0093 to 0.0079, lowers the autonomy index from 0.0075 to 0.0019, and eliminates all overload events compared with stochastic planning. Even under the most adverse conditions, DER utilization remains above 84%. Although the robust plan increases daily operating costs by about $70, this moderate premium yields significant gains in reliability and renewable accommodation. In addition, the decomposition-based algorithm converges within only 39 s, confirming the practical efficiency of the proposed framework for uncertainty-aware interconnection planning in future LVDNs.

The reliability of the power supply in low-voltage distribution networks plays a crucial role in efficient power system operation. Faced with the growing demand for electricity and the diverse usage patterns of users, existing management approaches struggle to meet the varying needs of different groups. This paper proposes a reliability assessment model that is based on user demands and integrates the Delphi method and gray relational analysis to provide an innovative approach for low-voltage distribution network tiered classification management. The study focuses on the distribution network of a certain area in China. In terms of reliability assessment methods, this study creatively introduces the equivalent series method to simplify the reliability evaluation, enabling a more efficient and intuitive reliability analysis. Through actual substation case studies, this research not only assesses low-voltage distribution network reliability but also identifies weak links within the system and the key factors affecting power supply reliability via a chain tracing method, providing a scientific basis for future management strategies.

The frequency of extreme weather events has become higher, and electricity consumption has also become more complex. These changes increase the risk of overload in distribution transformers (DTs), and this risk threatens the stability and reliability of the power grid. Existing methods have significant limitations. Traditional static threshold methods (based on DGA gas ratios and electrical signal thresholds) fail to consider temporal changes and complex links between factors, while modern machine learning models lack cause–effect relationships over time and clear ways to describe uncertainty. With such motivations, this paper proposes a causal-enhanced hybrid framework, which combines Long Short-Term Memory (LSTM) networks and Random Forest (RF) algorithms. The framework uses causal Seasonal Trend decomposition using Loess (STL) to reveal load patterns at different time scales. The mutual information index and spatiotemporal graph convolutional network (ST-GCN) are used to explore nonlinear relations and reveal how temperature affects load changes. The LSTM model captures time dependence in load series, and the Bayesian optimized Random Forest is used to solve the problem of data imbalance and quantify uncertainty. In addition, the framework constructs an early warning system that combines data from many sources in real time. Test results show that the proposed algorithm exhibits excellent performance in multi-source data environments.

The optimal scheduling of networked microgrids considering the coupled trading of energy and carbon emission allowance (CEA) has been extensively studied. Notably, the scheduling is performed on a daily basis, whereas the CEA is usually checked and determined once a year. The temporal mismatch between the daily scheduling and the yearly CEA should be addressed to realize the dynamic valuation of CEA. In this paper, the optimal scheduling of networked microgrids considering the temporal equilibrium allocation of annual CEA is investigated. Firstly, a CEA decomposition model is developed, which allocates allowance to individual microgrids and further decomposes them temporally using the entropy method. Secondly, a Lyapunov optimization-based low-carbon scheduling model is introduced to manage carbon emissions within each dispatch interval, ensuring annual CEA compliance and daily economic efficiency. Thirdly, a Stackelberg game-based energy–carbon coupling trading model is presented, which considers the uncertainties caused by fluctuations in external electricity and carbon prices to optimize trading prices and strategies of the microgrids. Finally, a test system is used to demonstrate the significant effects of emission reduction and the economic benefits of the proposed methods.