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.

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.

Digital substations, which replace traditional analog infrastructure, are essential to power grid operation but are facing growing vulnerability to cyber attacks. Existing anomaly detection in substation communication requires labeled datasets for supervised training and fails to incorporate temporal characteristics, which cannot detect unknown persistent attacks. Setting arbitrary thresholds for outlier detection leads to high false positives and low detection rates. This paper addresses cyber security challenges related to IEC 61850 Generic Object Oriented Substation Event (GOOSE) protocol within digital substations. We propose a novel unsupervised Transformer-based Distribution Fitting Anomaly Detection (TF-DiFAD) method for time series GOOSE frames with a robust thresholding technique. Deep packet inspection is used to extract features from GOOSE frames, which are processed through the proposed TF-DiFAD model. TF-DiFAD combines the deep learning transformer model with statistical distribution fitting techniques to accurately detect anomalous GOOSE frames. Specifically, reconstruction errors are generated using a state-of-the-art transformer model. A novel model-agnostic solution is applied for setting anomaly thresholds and calculating anomaly probabilities. The Kolmogorov-Smirnov test is employed to select the best-fitting distribution for these errors. TF-DiFAD is benchmarked against other state-of-the-art models using two distinct test datasets, demonstrating superior performance. The results indicate that TF-DiFAD detects anomalies with Receiver Operating Characteristics Area Under Curve (ROC AUC) scores of 96.84% and 95.73% respectively for both datasets.

The increasing digitalization of power grids has introduced cyber security vulnerabilities. One of the vulnerabilities is related to the IEC 61850 Generic Object Oriented Substation Event (GOOSE) protocol for time-critical communication between Intelligent Electronic Devices (IEDs). This protocol lacks built-in message integrity and authentication mechanisms, making it susceptible to cyber attacks, e.g., spoofing. To address these vulnerabilities, IEC 62351-6:2020 recommends the usage of a Hash-based Message Authentication Code (HMAC). However, implementing this security measure in existing brownfield digital substations is challenging due to the lack of compatible commercial devices and is economically expensive. Therefore, this research proposes and evaluates a cost-effective cyber security enhancement using commodity hardware, e.g., Raspberry Pi, to implement HMAC-based message authentication for ensuring GOOSE message integrity and authentication in brownfield digital substations with respect to stringent time requirements for the operation of protective relays. The proposed solution ensures message integrity and authentication while maintaining compliance with standard requirements. Validation is performed using real commercial IEDs in a real-time Hardware-in-the-Loop (HIL) architecture, demonstrating that the solution meets substation time requirements. This approach provides a feasible and immediate cyber security enhancement for brownfield digital substations without requiring significant infrastructure changes.

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.

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.

Cyber attacks on power grids are imminent and potentially have a severe impact, as evidenced by the cyber attacks in Ukraine in 2015, 2016, and 2022. In response to this challenge, machine learning-based Intrusion Detection Systems (IDS) have become more prevalent as a potential mitigation owing to their alignment with the latest advances in artificial intelligence. However, existing anomaly detection methods for power grid Operational Technology (OT) are often inadequate, as they primarily focus on detecting power grid physical anomalies at the later attack stages and suffer from the scarcity of available data for supervised machine learning. To address these limitations, we propose a novel semi-supervised IDS specifically for digital substations of the power system. The proposed detection method identifies the distinctive distance similarity of digital substation OT communication traffic using a Convolutional Neural Network and Chebyshev distance of packet payloads, and Kolmogorov-Smirnov of packets’ interarrival time using Fast Fourier Transform amplitude. Subsequently, these traffic features are combined into a vector and classified using a novel hybrid semi-supervised Self-Organizing Map (SOM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Results indicate that the proposed method can identify zero-day attacks and achieve accuracy and F1 above 95%.

The Digital Twins (DT) have emerged as the technology that provides capabilities to simulate and analyze cyber-physical systems’ behaviors using digital replicas. This is achieved through high-fidelity digital models, bi-directional communication and (near) real-time data exchange between physical real-world systems and DTs. Despite its capabilities of facilitating real-time monitoring, optimization, and predicting system performance, effectively leveraging DT for power system applications requires integrating data from heterogeneous sources and addressing various data related aspects. These include data modeling, exchange and interoperability. One promising concept to address these aspects is that of data federation which promotes interoperability, allowing DTs to operate autonomously, yet interact seamlessly. While various studies in literature have addressed DT applications, technologies, and challenges, a comprehensive review on the data federation aspects within power systems still needs to be investigated. This research seeks to bridge this gap by providing an in-depth review of DT practices in academia and industry, functional and non-functional requirements, and enabling technologies, with emphasis on data federation. Its role in enhancing system-wide interoperability in the power system, along with associated challenges are summarized and discussed.

This paper presents a pivotal stability analysis of the Dutch power system within the context of increased renewable energy integration, employing multiple future scenarios to navigate the inherent uncertainties. A large-scale synthetic model, utilizing ENTSOE-E reference data, uses time-domain simulations and eigenvalue analysis to assess the influence of systemic inertia and kinetic energy on the power system's dynamic frequency and angular stability. The study identifies specific inertia and kinetic energy projections that could undermine the stability of the Dutch power system and potentially affect the continental European power system. It also discusses potential enhancements, including supplementary damping control, to improve the primary control functions of power electronics interfaced generation. The results highlight the critical need for power system planners and operators to take proactive steps to prevent instabilities, ensuring that renewable energy integration strengthens rather than compromises power system reliability.

Digitalization is paving the way toward enhanced power grid operational capabilities and intelligence. The increased digitalization, however, also implies a greater risk of cyber vulnerabilities and threats. Therefore, various power systems facets such as transmission and distribution systems, digital substations, control centers, and wide-area communication networks are vulnerable to cyber-attacks. The most notable cyber-attacks on power grids are the twin attacks on the Ukrainian power grid in 2015 and 2016. These incidents clearly highlighted that cyber-attacks on power grids are an imminent threat that needs to be addressed. Keeping this in mind, this chapter provides essential knowledge of cyber-attack mitigation for cyber-physical power systems, i.e., secure communication protocols for operational technologies, penetration testing using cyber ranges and cyber-physical co-simulation, security controls, and intrusion detection and prevention systems. Among the wide-scope mitigation, artificial intelligence is highlighted as an emerging solution. This chapter presents how hybrid deep learning based on graph convolutional long short-term memory is used for anomaly detection in power system operational technology (OT) networks. Unlike traditional signature and supervised learning-based intrusion detection, the hybrid deep learning anomaly detection utilizes the OT traffic throughput. It takes advantage of the OT traffic’s deterministic and homogenous characteristics to provide a robust and flexible anomaly detection for a wide scope of cyber-attacks. The traffic anomalies are incorporated into an attack graph that aids power system operators identify and localize anomalies of active attacks on power systems in near real time. Cyber-attack case studies and cyber-physical co-simulation results are provided to demonstrate the efficiency of hybrid deep learning for anomaly detection.

The topology of low-voltage distribution networks (LVDNs) is crucial for system analysis, e.g., distributed energy resources (DERs) integration, network hosting capacity analysis, state estimation, and electric vehicle charging management. However, it is frequently unavailable or incomplete. This paper develops a data-driven topology identification approach for LVDNs with a high proportion of underground cables. The proposed approach exploits the fact that underground cables usually follow the street pattern, thus relying on open street map (OSM) and smart meter (SM) data. Three stages compose the proposed approach: In the first stage, a hierarchical minimum spanning tree algorithm is proposed to generate the initial topology with an accurate number of sub-branches from the pre-processed OSM data and peak demand. In the second stage, based on the limited SM data, the location of breakpoints in mesh topology caused by circle roads is verified and reconstructed to guarantee the radial structure of LVDNs. Finally, given multiple incomplete SM datasets, three data-driven optimization models based on a state estimation model are constructed to mitigate the error of cable length induced by OSM data. The feasibility of the proposed topology identification approach is verified on three actual LVDNs in The Netherlands and multiple incomplete SM datasets. Furthermore, the minimal amount of SM data needed to minimize the error of cable length is analyzed.

The increasing digitalization of Cyber-Physical Power Systems (CPPS) has enhanced power system operation and control but has also expanded the attack surface for cyber threats. Detection of early-stage attacks such as reconnaissance and Denial-of-Service (DoS) is critical to prevent power system-wide disruptions. Centralized Machine Learning (ML)-based techniques have been proposed for detecting cyber attacks. However, they struggle to ensure data privacy. Federated Learning (FL) can address this issue through collaborative model training without raw data sharing. Yet, FL’s performance degrades under non-Independent and Identically Distributed (non-IID) data, a common scenario in real-world CPPS environments. In this paper, we propose a cluster-based FL method using Bidirectional Long Short-Term Memory (BiLSTM) for attack detection at the early stages of the cyber kill chain. It uses unsupervised clustering of client model updates for aggregation robustness and model generalization across heterogeneous clients. By grouping clients based on similarity in model updates, our method mitigates the adverse effects of data heterogeneity while preserving data privacy. The UNSW-NB15 dataset is used for distributed training under non-IID conditions and evaluation of the proposed method. Experimental results demonstrate that our cluster-based FL method achieves over 95% detection accuracy, proving its effectiveness in distributed cyber attacks detection in power systems.

Residential Load Profile (RLP) generation is critical for the operation and planning of distribution networks, especially as diverse low-carbon technologies (e.g., photovoltaic and electric vehicles) are increasingly adopted. This paper introduces a novel flow-based generative model, termed Full Convolutional Profile Flow (FCPFlow), uniquely designed for conditional and unconditional RLP generation. By introducing two new layers – the invertible linear layer and the invertible normalization layer – the proposed FCPFlow architecture shows three main advantages compared to traditional statistical and contemporary deep generative models: (1) it is well-suited for RLP generation under continuous conditions, such as varying weather and annual electricity consumption, (2) it demonstrates superior scalability in different datasets compared to traditional statistical models, and (3) it also demonstrates better modeling capabilities in capturing the complex correlation of RLPs compared with deep generative models.

Industrial electrification plays a crucial role in reducing carbon dioxide emissions, and ensuring power reliability is important in this process. Reliability and techno-economic evaluations are fundamental to designing, operating, and managing power systems, ensuring that electricity is delivered continuously and securely under various conditions. In particular, maintaining a reliable power supply to industrial loads is critical, especially when renewable sources are present, as these introduce greater variability and uncertainty into the operation of industrial systems. Therefore, this document aims to use a cost-effective storage approach to ensure the reliable operation of sustainable industrial multi-energy systems. In addition, three storage mitigation strategies against random operation are formulated based on financial, technical, practical, and other aspects. A synthetic industrial model consisting of generic component representations in DIgSILENT PowerFactory 2024 is taken as a case study. The structure and parameters of the synthetic model are inspired by data from the literature and a hypothetical projection of a future evolution of a 500 MW sustainable industrial multi-energy system in Rotterdam by 2035. Numerical results provide insight into the flexible and cost-effective operation of sustainable industrial multi-energy systems within the context of decarbonised future Dutch energy systems.

Distribution system operators (DSOs) often lack high-quality data on low-voltage distribution networks (LVDNs), including the topology and the phase connection of residential customers. The phase connection is essential for phase balancing assessment and distributed energy resources (DERs) integration. The existing load profiles-based approaches rely on stepwise subtraction of the identified customers in a step-by-step identification procedure, while the accuracy of each step is not guaranteed. This paper introduces a siamese neural network model to identify single-phase connections without requiring stepwise subtraction. It comprises self-taught learning (STT) and a phase-label identification strategy. The introduced self-taught learning enables DSOs to train a recurrent neural network-based Siamese network (RSN) only relying on an unlabelled dataset. Besides, the siamese network (SN) is robust to noise and fluctuations in the data to a certain extent, making the proposed method robust to measurement errors. A Kendall correlation-based phase modification strategy is introduced to modified phase labels with lower confidence, aiming to mitigate the accuracy loss induced by the limited generalization of SN. The proposed approach is tested on the IEEE European low voltage test feeder and a residential network in the Netherlands Simulation results illustrate the feasibility and robustness of the proposed approach on incomplete datasets. The accuracy exceeded 83% and 90%, respectively, when using datasets of less than 20 days with and without measurement errors.

Rising energy expenses, the shift towards renewable sources, and grid congestion considerably affect the operations of container terminals. To tackle these challenges, it is necessary to implement energy-aware integrated operational planning which considers related uncertainties. This work proposes a two-stage stochastic mixed integer programming model to optimize container terminal operations planning and demand-responsive energy management. To this end, energy consumption is shifted whenever operationally possible and economically beneficial. We solve the proposed model by developing a dedicated progressive hedging algorithm. Operations considered in this model include vessel scheduling at berths, temperature control of refrigerated containers, and allocation of handling capacity of quay cranes, yard cranes, and automated guided vehicles to serve each vessel. Various scenarios for vessel arrival times and electricity prices are explored representing the uncertainty of energy demand and supply, respectively, based on a case study of the Altenwerder container terminal in Hamburg. Our results suggest potential cost savings of 5.9 per cent on average with a single energy price based on a long-term contract and 13.2 per cent when applying varying real-time electricity prices based on wholesale market rates. These findings underscore the substantial potential of demand response strategies for (electrified) container terminal operations.

This study presents an innovative approach to risk-aware decision-making in water resource management. We focus on a case study in the Netherlands, where risk awareness is key to water system design and policy-making. Recognizing the limitations of deterministic methods in the face of weather, energy system, and market uncertainties, we propose a scalable stochastic Model Predictive Control (MPC) framework that integrates probabilistic forecasting, scenario generation, and stochastic optimal control. We utilize Combined Quantile Regression Deep Neural Networks and Non-parametric Bayesian Networks to generate probabilistic scenarios that capture realistic temporal dependencies. The energy distance metric is applied to optimize scenario selection and generate scenario trees, ensuring computational feasibility without compromising decision quality. A key feature of our approach is the introduction of Exceedance Risk (ER) constraints, inspired by Conditional-Value-at-Risk (CVaR), to enable more nuanced and risk-aware decision-making while maintaining computational efficiency. In this work, we enable the Noordzeekanaal–Amsterdam-Rijnkanaal (NZK-ARK) system to participate in Demand Response (DR) services by dynamically scheduling pumps to align with low hourly electricity prices on the Day Ahead and Intraday markets. Through historical simulations using real water system and electricity price data, we demonstrate that incorporating uncertainty can significantly reduce operational costs—by up to 44 percentage points compared to a deterministic approach—while maintaining safe water levels. The modular nature of the framework also makes it adaptable to a wide range of applications, including hydropower and battery storage systems.

As a part of energy transition, the shift from internal combustion engines (ICEs) to electric vehicles (EVs) has accelerated the development of the EV charging infrastructure (EVCI). EVCI rely heavily on information and communication technologies (ICTs) and the Internet of Things (IoT). As a result, the susceptibility to cyber attacks increases. However, although EVCI are strongly intertwined with cyber-physical power systems (CPPSs), the consequences of such a cyber attack on the power grid are not widely researched. In this paper, we present a comprehensive cyber-physical system architecture of the EV charging infrastructure based on the industry practice in The Netherlands, which is applicable to European distribution systems. We present a survey of work on EVCI cyber security and CPPS resilience. We combine unique industrial insights with the academic state-of-the-art. We show that although cyber security of EVCI is researched, the state-of-the-art inadequately covers the consequences for CPPSs, especially distribution networks. We survey the current work on CPPS resilience and conclude that while cyber attacks are often recognized as high impact low probability (HILP) disturbances of CPPSs, the resilience-related research on cyber attacks on EVCI is lacking. Therefore, we present a novel method to model the stochastic EV charging behaviour based on probability density functions (PDFs). We validate the method using PowerFactory models of distribution networks supplied by a Dutch distribution system operator (DSO). We demonstrate the effects of cyber attacks on EVCI on distribution networks voltages. Under the investigated operational scenario, the impact is not significant. However the results do underline the importance of researching cyber attacks on EVCI from a CPPS resilience perspective. Research into future scenarios of energy transition is essential for future resilient operation of power grids.

The integration of distributed energy resources (DERs) has escalated the challenge of voltage magnitude regulation in distribution networks. Model-based approaches, which rely on complex sequential mathematical formulations, cannot meet the real-time demand. Deep reinforcement learning (DRL) offers an alternative by utilizing offline training with distribution network simulators and then executing online without computation. However, DRL algorithms fail to enforce voltage magnitude constraints during training and testing, potentially leading to serious operational violations. To tackle these challenges, we introduce a novel safe-guaranteed reinforcement learning algorithm, the DistFlow safe reinforcement learning (DF-SRL), designed specifically for real-time voltage magnitude regulation in distribution networks. The DF-SRL algorithm incorporates a DistFlow linearization to construct an expert-knowledge-based safety layer. Subsequently, the DF-SRL algorithm overlays this safety layer on top of the agent policy, recalibrating unsafe actions to safe domains through a quadratic programming formulation. Simulation results show the DF-SRL algorithm consistently ensures voltage magnitude constraints during training and real-time operation (test) phases, achieving faster convergence and higher performance, which differentiates it apart from (safe) DRL benchmark algorithms.

Short-term load prediction (STLP) is critical for modern power distribution system operations, particularly as demand and generation uncertainties grow with the integration of low-carbon technologies, such as electric vehicles and photovoltaics. In this study, we evaluate the zero-shot prediction capabilities of five Time-Series Foundation Models (TSFMs) - a new approach for STLP where models perform predictions without task-specific training - against two classical models, Gaussian Process (GP) and Support Vector Regression (SVR), which are trained on task-specific datasets. Our findings indicate that even without training, TSFMs like Chronos, TimesFM, and TimeGPT can surpass the performance of GP and SVR. This finding highlights the potential of TSFMs in STLP.