AI Schedules Risky Grid Maintenance When Multiple Outages Strike at Once

Power grids are growing faster than the crews that keep them running. Renewable energy plants, electric vehicle charging networks, and power-hungry data centers have all expanded the size and complexity of modern electricity systems, and every new line, transformer, and substation eventually needs maintenance. For grid companies, this has turned maintenance planning from a routine task into a logistical headache, with hundreds of outage requests arriving in batches each spring and autumn. A research team at State Grid Tianfu New Area Electric Power Supply Company in Chengdu, China, now proposes a decision-making method that uses a large language model to schedule maintenance when several outages strike at once, published in Frontiers in Energy Research.

The problem the team tackles is subtle but consequential. Traditional maintenance plans are built on engineering experience and rules of thumb, and they work well when a single piece of equipment is taken out of service. In such cases the grid generally satisfies the N-1 criterion, meaning one outage does not interrupt the load supply. But when many outages overlap, the topology and operating mode of the system change considerably. Experience-based plans treat each outage in isolation and control risk only through time-window allocation and backup paths. They cannot capture the interaction between simultaneous outages, which may redistribute power flow locally, push equipment beyond its ratings, and eventually force load shedding.

Identifying weak links under multiple outage events is therefore essential for preventing voltage violations and equipment overload. Existing identification methods fall into three groups: those based on complex network theory, which reduce the grid to nodes and edges and examine topological metrics such as betweenness centrality; those based on the operating state, which build an index system from operational and equipment parameters; and comprehensive methods that mix subjective judgment with objective factors. Few of these methods, however, are designed for multiple-outage maintenance scenarios, and the risks that accumulate during such periods are rarely analyzed systematically.

The core of the new method is a multi-time-scale joint optimization framework in which forecast-informed weak-link evaluation, cumulative risk quantification, and schedule optimization are coupled. The large language model serves as an enabling tool inside this framework. Its forecasts make the vulnerability weights time-varying, which most existing risk-based scheduling studies do not consider. The first component is an adaptive load forecasting model in which frequency-domain features extracted by the Fast Fourier Transform and convolutional neural networks are fed into a pre-trained large language model.

Electricity load series contain periodic fluctuations at several time scales, such as daily and weekly periods, and extracting these features is important for predicting load variations during maintenance periods. The normalized load window, containing 168 hourly points, is divided into overlapping patches with a length of 24 and a stride of 12, yielding 13 patches that are transformed into the frequency domain. The frequency bins are grouped into three bands by k-means clustering, reflecting the daily fundamental component, the sub-daily harmonics, and the high-frequency stochastic fluctuations of load series. Convolutional neural networks with a kernel of size 3 and 32 filters then extract high-dimensional features from the three frequency bands, and linear layers fuse the periodic and random characteristics into a single feature map.

The forecasting backbone is a locally deployed GPT-2 model with 117 million parameters, chosen for its generalization capability. The fused features are converted back to the time domain by inverse Fourier transforms and packed into token vectors before entering the large language model, whose backbone consists of stacked multi-head self-attention and feed-forward network components. Rather than adjusting all of these parameters, which would increase training cost and compromise generalization, the team adopts Parameter Efficient Fine Tuning, fine-tuning only specific layers, primarily the Add & Norm and Embedding layers. This approach enhances the model’s accuracy in predicting electricity loads during maintenance periods, which can last from several hours to several days.

Based on the forecast load, the method then evaluates the weak links of the system through the electrical betweenness of lines and nodes. The weight of a generation node is its share of the total available supply capacity, and the weight of a load node is its share of the total forecast demand produced by the adaptive forecasting module. The greater the electrical betweenness of a line, the larger the transmission load it bears during the maintenance period, and the greater the impact if it is disconnected by a fault. On the 35-node test system, a 10 kV distribution network with a peak demand of about 60 MW, five nodes exceed the threshold of 0.7, sitting on the main transfer paths of the feeders, so maintenance events involving them are prioritized in the risk model.

To quantify the cumulative effect of simultaneous outages, the team establishes a Risk Overlapping Degree metric that measures how overlapping maintenance events reduce the supply redundancy of the same object. The risk overlapping degree is large when two overlapping events simultaneously increase the failure probability of one supply source and the dependency on that source. A multi-objective optimization model then minimizes load losses caused by maintenance while evenly distributing maintenance tasks across the month, avoiding concentration on a few days that would leave too little time for risk-prevention measures. The weights of the three objectives, determined through the analytic hierarchy process with dispatching engineers scoring pairwise importance, prioritize operational risk over economic loss, and a sensitivity analysis confirms the conclusion is robust to the weight selection.

The results are striking. The forecasting module achieves a mean absolute percentage error of 2.31%, outperforming ARIMA, LSTM, CNN, and standard Transformer baselines, with ablation tests showing that removing either the LLM backbone or the frequency-domain module degrades performance. The risk overlapping degree falls from 38.1 under the original schedule to 2.6 under the optimized plan, load loss decreases from 57.5 MWh to 49.2 MWh, and workload variance drops from 23.9 to 16.3. With 30 concurrent maintenance tasks, the proposed method keeps the risk overlapping degree at about 16, while the traditional method reaches 198. The day-ahead model then generates risk-prevention measures, combining load transfer through interconnection switches, upstream supply re-dispatch, and feeder topology reconfiguration, which together lower load loss by 28.1% and remove all remaining voltage violations, keeping every node within the range of 0.95 to 1.05 per unit.

Tests on IEEE standard systems up to 118 buses confirm that the method applies to larger grids, with computation times growing from 5.2 seconds to 245.3 seconds as system size increases, still acceptable for offline monthly planning on a standard workstation. The average solution time of the base case is 28.5 seconds, sufficient for practical use. The team notes that future work will extend the method to integrated energy systems, considering the coupling between different energy carriers during concurrent maintenance events and establishing risk-prevention and optimal decision-making models for such systems. For now, the proposed method provides a practical decision-making tool for maintenance scheduling under multiple power outage events, offering grid operators a way to plan hundreds of overlapping outages without gambling on the combined risk they create.

Subject of Research: Power grid maintenance scheduling

Article Title: Optimal decision-making method for power grid maintenance scheduling during multiple power outage events based on large language models

Article References: Lei, W., Chen, K., Su, C., Li, C., Hu, Q., Dai, C., Chen, J., & Li, Y. (2026). Optimal decision-making method for power grid maintenance scheduling during multiple power outage events based on large language models. Frontiers in Energy Research, 14, Article 1889135. https://doi.org/10.3389/fenrg.2026.1889135

Image Credits: AI Generated

DOI: 10.3389/fenrg.2026.1889135

Keywords: large language models, power grid maintenance, outage scheduling, load forecasting, risk overlapping degree, electrical betweenness, smart grids, GPT-2, Fourier transform, particle swarm optimization, IEEE test systems, risk prevention