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Deep Reinforcement Learning in Power Grids

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Dear Colleagues,

Modern power systems are rapidly evolving into highly dynamic cyber–physical infrastructures, driven by increasing penetration of renewable generation, distributed energy resources, electric vehicles, and advanced communication networks. Smart grids, smart energy systems, and smart homes with home energy management systems (HEMS) introduce unprecedented flexibility but also create complex decision-making problems across multiple time scales and layers. Classical optimization and control techniques often struggle with the nonlinear, stochastic, and high-dimensional nature of these tasks. Deep Reinforcement Learning (DRL) has emerged as a powerful paradigm to address these challenges by enabling agents to learn near-optimal control and management strategies directly from interaction with detailed grid models and real-world data.

This Special Issue aims to present and disseminate the most recent advances in DRL for planning, operation, control, and protection in future power grids, including transmission networks, distribution systems, and resilient microgrids. We especially welcome contributions that combine DRL with domain knowledge in power engineering and telecommunications, ensure safety and stability, and demonstrate practical implementations through simulations, hardware-in-the-loop experiments, or field trials.

Topics of interest for publication include, but are not limited to, the following:

Deep Reinforcement Learning for smart grids, smart energy systems, and resilient microgrids.
DRL for smart homes and HEMS.
DRL-based coordination of distributed energy resources and energy-on-demand services.
DRL combined with optimization methods for power grids and optimal power flow.
DRL for power flow control and power flow coloring.
Stability-aware DRL for power system stability and control.
DRL for demand response and flexible load management.
DRL for energy storage scheduling and energy balancing.
DRL for distributed sensing and control in large-scale networks.
Deep Reinforcement Learning and telecommunications for communication-aware grid control and edge/fog deployments.

Dr. Iacovos Ioannou
Dr. Saher Javaid
Guest Editors

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49 pages, 10129 KB

Open AccessArticle

PhysGTT: A Physics-Guided Self-Supervised Graph Temporal Transformer for Forecasting Electricity Inconsistencies in Mini-Grids

by Iacovos I. Ioannou, Saher Javaid, Minella Bezha, Yasuo Tan, Naoto Nagaoka and Vasos Vassiliou

Energies 2026, 19(10), 2262; https://doi.org/10.3390/en19102262 - 7 May 2026

Viewed by 205

Abstract

Electricity inconsistencies in mini-grids, stemming from meter drift, telemetry faults, topology misconfiguration, non-technical losses, phase imbalance or data manipulation, often emerge as weak, spatially distributed deviations that are difficult to anticipate, yet timely warning is important for future monitoring frameworks in rural electrification and island mini-grids. Existing approaches either apply post hoc threshold-based alarms to individual channels or employ deep learning models that treat metering points independently, ignoring the spatial coupling imposed by the electrical topology and lacking mechanisms to enforce physical feasibility under scarce labeled data. This paper introduces PhysGTT, a Physics-Guided Self-Supervised Graph Temporal Transformer that models the mini-grid as a topology-aware graph and combines a residual Graph Convolutional Network encoder with a temporal Transformer. PhysGTT employs self-supervised pretraining via masked multi-sensor reconstruction and contrastive regime alignment to exploit unlabeled operational data and incorporates gradient-coupled physics regularization through power-balance, voltage-bound and ramp-rate penalties applied to a learned reconstruction head, while producing constraint-level attributions that identify the dominant physical violation pattern for each forecast. PhysGTT is evaluated on a proxy benchmark derived from the UCI Individual Household Electric Power Consumption dataset and on the IEEE 13-node test feeder simulated in OpenDSS and it is compared under identical experimental protocols with eight baselines spanning recurrent, graph-temporal and unsupervised architectures. On the proxy benchmark, PhysGTT achieves an AUC-ROC of 0.8959, an F1-score of 0.8307 and a False Alarm Rate of 0.41%, improving the F1-score by 2.2% relative to the strongest recurrent baseline (GRU) and by up to 15.2% relative to the LSTM baseline, while reducing the False Alarm Rate by approximately 52% relative to the LSTM baseline. On the IEEE 13-node feeder, PhysGTT attains an AUC-ROC of 0.9016 and an F1-score of 0.8361. These results indicate that integrating topology-aware encoding, self-supervised pretraining and physics-guided learning provides a promising and interpretable framework for proactive inconsistency forecasting under synthetic and feeder-simulation benchmarks, although field validation on naturally occurring faults remains necessary. Full article

(This article belongs to the Special Issue Deep Reinforcement Learning in Power Grids)

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