Electric-vehicle smart charging requires quick decision-making under uncertainty while enforcing strict electricity grid and user requirements. Mathematical optimization becomes too slow at scale, while online reinforcement learning struggles with sparse rewards and safety. This paper proposes GNN-DT, a topology-aware Decision Transformer that combines graph neural network embeddings with sequence modeling to learn charging policies from offline trajectories. The method operates over variable numbers of vehicles and chargers without retraining. Evaluated on realistic smart charging scenarios, GNN-DT achieves near-optimal performance, reaching rewards within 5 percent of an oracle solver while using up to 10× fewer training trajectories than baseline methods. It consistently outperforms online and offline reinforcement learning approaches and generalizes to unseen fleet sizes and network topologies. Inference runs in milliseconds, making the approach suitable for real-time deployment in large-scale charging systems.

Modern energy systems are increasingly characterized by large-scale renewable integration, deep digitalization, and tight coupling between physical infrastructure and cyber intelligence. These trends have significantly amplified the volume, heterogeneity, and complexity of data generated across energy generation, transmission, distribution, and consumption. Data fusion, which integrates multi-modal, multi-source, and multi-scale information, has therefore become a foundational enabler for prediction, optimization, security, and resilience in modern energy systems. This Special Issue, entitled “Data Fusion in Modern Energy Systems” , brings together ten original research articles that collectively advance the state of the art in fusion-driven energy intelligence. The accepted contributions are organized into three major research directions: (i) predictive intelligence via spatio-temporal and multi-modal data fusion, (ii) fusion-driven optimization and operational decision-making, and (iii) trustworthy and resilient energy systems through cross-domain data fusion. Together, these works illustrate how data fusion is evolving from a supporting data-processing technique into a central paradigm for intelligent, secure, and resilient energy systems.

With the growing integration of Modular Multilevel Converters (MMCs) in Multi-Terminal Direct Current (MTDC) transmission systems, there is a growing need for control strategies that balance economic efficiency with robust dynamic performance. This paper presents an enhanced Optimal Power Flow (OPF)-based framework for hybrid AC-MTDC systems, incorporating a novel droop control strategy that jointly coordinates DC-voltage and AC-frequency regulation. By embedding frequency control loops into the MMCs, the method enables system-wide coordination that enhances power sharing and improves resilience under disturbances. The proposed strategy dynamically adjusts converter operating points to minimize generation costs and DC-voltage deviations, balancing economic objectives with system stability. A modified Nordic test system integrated with a four-terminal MTDC grid is used to validate the approach. Optimization is performed using Julia, while the system's dynamic performance is evaluated through electromagnetic transient simulations with the EMTP software. Case studies across multiple scenarios demonstrate that the proposed droop control achieves markedly improved frequency and voltage robustness over active power control, while incurring lower generation costs than the adaptive droop benchmark. The results highlight the ability of the proposed strategy to deliver cost-effective operation without compromising performance, offering a promising solution for the coordinated control of future hybrid AC-DC transmission networks.

Centralized reinforcement learning-based voltage regulation in distribution networks is becoming increasingly difficult due to the growing penetration of distributed energy resources, high computational burden, repeated power flow calculations, and increasing privacy concerns. This paper proposes a physics-informed fully distributed reinforcement learning framework that enables autonomous voltage regulation using only local smart meter data. A Thevenin-equivalent-based local voltage estimation model and a hybrid correction mechanism are developed to support accurate local decision-making without synchronized global measurements or centralized power flow solvers. A lightweight coordination mechanism is further introduced to refine the actions of independently trained local agents. Case studies show that the proposed framework reduces voltage violations by approximately 80%, achieves performance close to that of power flow-based training environments, and achieves a training speedup of about 6×[jls-end-space/]. The results also indicate that the relaxation factors in the reward function and the coordination scaler are critical to voltage regulation efficiency, whereas the discount factor has a smaller impact. These findings demonstrate the practicality of the proposed framework for privacy-aware fully distributed voltage regulation.

To develop an optimal operational scheme for distribution networks capable of addressing asymmetric uncertainties associated with renewable energy and load demands, this paper presents a confidence level-based information gap decision theory (CL-IGDT) framework. Building on IGDT, the proposed framework utilizes the confidence level to capture the asymmetric characteristics of uncertainties and maximize the risk-averse capability of the solution in a probabilistic manner. To facilitate such probabilistic consideration, the imprecise Dirichlet model is employed to construct the ambiguity sets of uncertainties. Consequently, a two-stage robust optimal operation model for distribution networks using CL-IGDT is developed. An iterative method is proposed to solve the model and determine the upper and lower bounds of the objective function. Case study demonstrates that the proposed approach yields a more robust and statistically optimized solution with required accuracy compared to existing methods, contributing to a reduction in first-stage cost by 0.84%, second-stage average cost by 6.7%, and significantly increasing the reliability of the solution by 8%.

Electricity Consumption Profiles (ECPs) are crucial for operating and planning power distribution systems, especially with the increasing number of low-carbon technologies such as solar panels and electric vehicles. Traditional ECP modeling methods typically assume the availability of sufficient ECP data. However, in practice, the accessibility of ECP data is limited due to privacy issues or the absence of metering devices. Few-shot learning (FSL) has emerged as a promising solution for ECP modeling in data-scarce scenarios. Nevertheless, standard FSL methods, such as those used for images, are unsuitable for ECP modeling because (1) these methods usually assume several source domains with sufficient data and several target domains. However, in the context of ECP modeling, there may be thousands of source domains, e.g., households with a moderate amount of data, and thousands of target domains, e.g., households that ECP are required to be modeled. (2) Standard FSL methods usually involve cumbersome knowledge transfer mechanisms, such as pre-training and fine-tuning. To address these limitations, this paper proposes a novel FSL framework that integrates Transformers with Gaussian Mixture Models (GMMs) for ECP modeling. The proposed approach is fine-tuning-free, computationally efficient, and robust even with extremely limited data. Results show that our method can accurately restore the complex ECP distribution with a minimal amount of ECP data (e.g., only 1.6% of the complete domain dataset) and outperforms state-of-the-art time series modeling methods in the context of ECP modeling.

Addressing uncertainty is essential in power systems with high levels of renewable energy penetration. Distributed energy resources (DERs), due to their partially controllable nature, are a major source of uncertainty. However, due to their large numbers and complex correlations, their aggregated uncertainty is highly complex. This paper aims to track how the uncertainty is modeled from individual DER prediction errors to the aggregated-level. By enforcing a specified confidence level, the aggregated-level probabilistic flexibility boundary is formulated as a Minkowski sum under joint chance constraints (JCCs). Despite the inherent intractability of this problem, we establish an equivalent representation that allows for an effective approximation using the proposed quantile cube approximation method. An iterative algorithm is also developed to enhance computational efficiency in implementing the method. Numerical tests demonstrate that the proposed method effectively reduces the conservativeness of the aggregated confidence boundary and the computation time at the same time.

Rapidly expanding Electric Vehicle (EV) adoption necessitates robust, large-scale charging strategies to meet global decarbonization targets. Traditional methods, such as heuristics and mathematical programming-based approaches, struggle to scale effectively and adapt to EV dispatch’s complexity, uncertainty, and variability. Reinforcement Learning (RL) offers a promising alternative due to its ability to handle complex optimization problems, process substantial real-time data, and learn continuously without explicit retraining for every scenario. This study proposes a novel end-to-end RL framework that leverages the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, enhanced by Graph Neural Networks (GNN), to capture spatial interactions among charging nodes. A key contribution is integrating a safety layer designed to ensure grid stability, preserve EV charging requirements, and enforce power limits. The RL agent was trained and evaluated using real EV charging sessions, offering a realistic assessment of its performance. The results indicate that the proposed method can efficiently coordinate large fleets of EVs, ensuring stable power grid operation and fair distribution of charging resources.

The increasing penetration of Electric Vehicles (EVs) and renewable energy sources is placing significant stress on existing power grid infrastructure. This work investigates the application of vehicle-to-grid (V2G)-enabled smart charging in workplace environments from the perspective of EV aggregators, using real-world charging data from Dutch business parking lots. To address the limitations of conventional deep Reinforcement Learning (RL) methods in enforcing operational constraints, we propose a Safe RL method using the Constrained Variational Policy Optimization (CVPO) algorithm, specifically designed to reduce constraint violations and enhance reliability. Empirical results show that CVPO outperforms classic RL baselines and rule-based policies, closely approximating the performance of an optimal offline benchmark while exhibiting strong generalization to unseen scenarios.

Integrating electric vehicles (EVs) into the power grid can revolutionize energy management strategies, offering both challenges and opportunities for creating a more sustainable and resilient grid. In this context, model predictive control (MPC) emerges as a powerful tool for addressing the complexities of Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) enabled demand response management. By leveraging advanced optimization techniques, MPC algorithms can anticipate future grid conditions and dynamically adjust EV charging and discharging schedules. However, no standard tools exist to evaluate novel energy management strategies based on MPC approaches. This work focuses on harnessing the potential of MPC in G2V and V2G applications by providing open-source algorithms that allow the maximization of EV flexibility and support demand response initiatives while mitigating the impact on EV battery health. Through extensive simulation and analysis, we demonstrate the efficacy of our approach in maximizing the benefits of G2V and V2G while assessing the impact on the longevity and reliability of EV batteries. Specifically, the proposed methods enable the optimization of EV charging and discharging schedules in real-time, taking into account fluctuating energy prices, grid constraints, and EV user preferences.

The power consumption flexibility of distributed energy resources (DERs) must be aggregated to enable effective interaction with power systems. However, model heterogeneity, geographical dispersion, and the large number pose significant challenges to aggregation. This paper first models DER flexibility by explicitly incorporating heterogeneity in both state variables and available time periods, represented through polytopes of heterogeneous dimensions. The aggregation of DERs is then formulated as a standard projection maximal inner approximation (MIA) problem. To efficiently and accurately solve this problem, a novel linear programming (LP)-based algorithm is developed. Furthermore, a hierarchical framework is introduced to enable large-scale aggregation, within which a Minkowski-closed family is proven, allowing accurate and efficient secondary aggregation through vector addition. In addition, generalized operating envelopes (OEs) are proposed for distribution system operators (DSOs) to establish and communicate network constraints, enabling integration into the aggregation process without disclosing sensitive network information. Numerical experiments validate the proposed formulations and demonstrate superior accuracy and scalability of the proposed method while maintaining high computational efficiency.

We introduce a physics-informed neural network for power flow (PINN4PF) that effectively captures the nonlinear dynamics of large-scale modern power systems. The proposed neural network (NN) architecture consists of two important advancements in the training pipeline: (A) a double-head feed-forward NN that aligns with power flow (PF), including an activation function that adjusts to the net active and reactive power injections patterns, and (B) a physics-based loss function that partially incorporates power system topology information through a novel hidden function. The effectiveness of the proposed architecture is illustrated through 4-bus, 15-bus, 290-bus, and 2224-bus test systems and is evaluated against two baselines: a linear regression model (LR) and a black-box NN (MLP). The comparison is based on (i) generalization ability, (ii) robustness, (iii) impact of training dataset size on generalization ability, (iv) accuracy in approximating derived PF quantities (specifically line current, line active power, and line reactive power), and (v) scalability. Results demonstrate that PINN4PF outperforms both baselines across all test systems by up to two orders of magnitude, not only in terms of direct criteria, e.g., generalization ability, but also in terms of derived physical quantities.

This article presents the theoretical and practical foundation of a spherical lower dimensional representation for daily medium voltage load profiles, based on principal component analysis. The objective is to unify and simplify the tasks for (i) clustering visualisation, (ii) outlier detection and (iii) generative profile modelling under one concept. The lower dimensional projection of standardised MV load profiles unveils a latent distribution in a three-dimensional sphere. This spherical structure allows us to detect outliers by fitting probability distribution models in the spherical coordinate system, identifying measurements that deviate from the spherical shape. The same latent distribution exhibits an arc shape, suggesting an underlying order among load profiles. We develop a principal curve technique to uncover this order based on similarity, offering new advantages over conventional clustering techniques. This finding reveals that energy consumption in a wide region can be seen as a continuously changing process. Furthermore, we combined the principal curve with a von Mises-Fisher distribution to create a model capable of generating profiles with continuous mixtures between clusters. The presence of the spherical distribution is validated with data from four municipalities in the Netherlands. The uncovered spherical structure implies the possibility of employing new mathematical tools from directional statistics and differential geometry for load profile modelling.

As the adoption of electric vehicles (EVs) accelerates, addressing the challenges of large-scale, city-wide optimization becomes critical in ensuring efficient use of charging infrastructure and maintaining electrical grid stability. This study introduces EV-GNN, a novel graph-based solution that addresses scalability challenges and captures uncertainties in EV behavior from a Charging Point Operator’s (CPO) perspective. We prove that EV-GNN enhances classic Reinforcement Learning (RL) algorithms’ scalability and sample efficiency by combining an end-to-end Graph Neural Network (GNN) architecture with RL and employing a branch pruning technique. We further demonstrate that the proposed architecture’s flexibility allows it to be combined with most state-of-the-art deep RL algorithms to solve a wide range of problems, including those with continuous, multi-discrete, and discrete action spaces. Extensive experimental evaluations show that EV-GNN significantly outperforms state-of-the-art RL algorithms in scalability and generalization across diverse EV charging scenarios, delivering notable improvements in both small- and large-scale problems.

With the increasing availability of smart meter (SM) data and the frequent lack of accurate network topology information, model-free power flow (PF) calculation has gained traction, often leveraging artificial neural networks (ANNs). However, training such models typically requires large volumes of SM data, raising significant privacy concerns for households in distribution networks. To address this challenge, we propose a privacy-preserving PF calculation framework that incorporates two local privacy-enhancing mechanisms: a Local Randomisation Strategy (LRS) and a Zero-Knowledge Proof (ZKP)-based data collection strategy. The LRS provides irreversible transformation of power data, ensuring strong privacy protection while preserving data utility. In parallel, the ZKP-based strategy enables secure and trustworthy voltage data collection, allowing smart meters to interact with distribution system operators without disclosing actual voltage magnitudes. To address performance degradation caused by seasonal variations in load profiles, we further integrate an incremental learning strategy into the online application. Extensive evaluations across three datasets demonstrate that the proposed framework can efficiently collect one month of SM data within one hour while maintaining most voltage magnitude estimation errors lower than 0.01 p.u. under varying measurement noise and seasonal conditions.

The widespread use of modular multilevel converters (MMCs) in the evolution of complex power grids presents new challenges for grid stability. MMCs have highly nonlinear impedance characteristics due to their complex internal dynamics and intricate control architectures. Due to practical constraints, physics-based models cannot accurately compute these impedances, and the use of closed-box measurement techniques is time-consuming, resulting in a limited amount of data available for impedance characterization. Thus, using current methods to estimate impedances over a wide range of operating points can be unreliable. This paper presents a transfer learning-based framework for MMC impedance characterization using system-level parameters as operating point variables. The proposed approach predicts both AC and DC side impedances simultaneously by extrapolating impedances derived using state-space modeling approaches to real-time electromagnetic transient (EMT) simulations. Finally, the method is evaluated on a practical converter from the CIGRE B4 DC grid test system for various types of controllers and scenarios involving unknown parameters.

This paper proposes a novel Direct Current (DC)aware building Energy Management System (EMS) platform. The proposed EMS is a comprehensive ecosystem that includes both the necessary hardware and software components to facilitate the transition of buildings toward compatibility with future intelligent power grids and DC Technologies. A key advantage of this platform is its hybrid topology, which enables simultaneous interfacing with and supply of both Alternating Current (AC) and DC loads. The platform integrates real-time monitoring, optimization, and solar power generation forecasting and demand forecasting units. The focus of this paper is the experimental realization of the proposed solution and an evaluation of the hardware and software performance during their synergetic operation.

Deep Reinforcement Learning (DRL) presents a promising avenue for optimizing Energy Storage Systems (ESSs) dispatch in distribution networks. This paper introduces RL-ADN, an innovative open-source library specifically designed for solving the optimal ESSs dispatch in active distribution networks. RL-ADN offers unparalleled flexibility in modeling distribution networks, and ESSs, accommodating a wide range of research goals. A standout feature of RL-ADN is its data augmentation module, based on Gaussian Mixture Model and Copula (GMC) functions, which elevates the performance ceiling of DRL agents, achieving an average performance improvement of 21.43%, 1.08%, 2.76%, by augmenting five-year, one-year and three-month data, respectively. Additionally, RL-ADN incorporates the Tensor Power Flow solver, significantly reducing the computational burden of power flow calculations during training without sacrificing accuracy, maintaining voltage magnitude with an average error not exceeding 0.0001%. The effectiveness of RL-ADN is demonstrated using distribution networks with size varying, showing marked performance improvements in the adaptability of DRL algorithms for ESS dispatch tasks. Furthermore, RL-ADN achieves a tenfold increase in computational efficiency during training, making it highly suitable for large-scale network applications. The library sets a new benchmark in DRL-based ESSs dispatch in distribution networks and it is poised to advance DRL applications in distribution network operations significantly. RL-ADN is available at: https://github.com/ShengrenHou/RL-ADN and https://github.com/distributionnetworksTUDelft/RL-ADN.