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 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 contribution deals with the optimization of the frequency response of a multi-area, multi-energy HVDC-HVAC cyber-physical power system, representing a power electronic-dominated power system. The system consists of a three-area system, modified so that the areas are electromagnetically decoupled through MMC-based HVDC links, and different controllable energy sources, such as fully decoupled wind turbines type IV and proton exchange membrane electrolysers, are installed at various points of the system. The modified system exhibits three decoupled areas with different generation and demand mixes characterized by different inertia levels and increased controllability due to the converters’ capabilities. The outer controllers of the power electronic interfaced elements installed have been modified with the active power gradient control scheme to respond to frequency excursions and provide fast frequency support to the grid in case of commonly occurred active power-frequency imbalances. A problem formulation for coordinated optimization is presented, aiming at a coordinated tuning of the parameters of the frequency controllers of the synthetic inertia elements participating in the frequency regulation against critical commonly occurred active power-frequency imbalances. The formulations consider the minimization of the dynamic displacements of the areas’ speed following an active power imbalance. To effectively solve the optimization problem and enhance the frequency stability of the system, a powerful metaheuristic optimization algorithm, the mean-variance mapping optimization (MVMO) algorithm, has been utilized. The optimization results can effectively highlight the tuning strategy that achieves the best frequency response of the system under various commonly occurred active power frequency disturbances. It can also provide further insight on the proper utilization of various sources of synthetic inertia with respect to their response capabilities. Finally, the simulation results can also clarify the importance of the location of installation of converter-based elements providing fast frequency support with respect to the grid node the imbalance occurs.

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.

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.

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.

The modular multilevel converter (MMC) uses many power electronic components in the high voltage direct current (HVDC) application. One of the major concerns in half-bridge MMC is the fault in the converter submodules. It raises the question of whether the reliability and high-quality performance of the MMC can be increased significantly as the active device controls the power flow between the AC- and DC-sides. During the SM fault within the MMC leg, the unbalance is introduced in-side the MMC converter. The unbalanced voltage within the leg of the MMC will continuously introduce an AC-current component on the DC-side of the converter. Thus, the hybrid proportional-internal (PI) control and proportional-resonant control (PR) is introduced in controlling the power flow within the internal MMC to eliminate the AC-current component and ensure pure DC-current in the internal MMC. This study investigates the internal power flow control of a three-phase rectifier MMC with symmetric and asymmetric SM fault conditions. Compared with conventional control methods, the proposed control can tolerate SM faults and eliminate the AC-current component within the converter, increasing the converter's performance. Simulation results are included and discussed to verify the proposed control.

As the Netherlands moves toward climate neutrality by 2050, the national power system will rely heavily on variable renewable energy sources (VRES) such as offshore wind and solar photovoltaics. While previous studies have examined steady-state implications of overplanting and grid reinforcement, less attention has been given to assessing the effectiveness of flexibility resources during longer time periods. This paper presents an operational assessment of the 2050 Dutch transmission system using full-day optimal power flow simulations for typical summer and winter conditions.The synthetic model of the transmission system is developed in DIgSILENT PowerFactory and includes distributed and centralized supply, batteries, electrolyzers, and demand response mechanisms. Using the Mean-Variance Mapping Optimization (MVMO) algorithm with 15-minute resolution, system operation is optimized to minimize active power losses while respecting voltage and thermal limits. The results show that flexibility resources are essential to ensure demand coverage and reduce transmission congestion, especially during periods of high VRES generation. In winter, the centralized nature of offshore wind leads to regional overloads and higher losses, while summer benefits from decentralized PV generation and more balanced load matching. Batteries and hydrogen units show distinct operational patterns, emphasizing the importance of their strategic placement. These findings support the design of control strategies and infrastructure planning for high-VRES transmission systems.

Power grids are undergoing a fast-paced process of digitalization for enhanced monitoring and control capabilities and grid intelligence. However, the increased integration of digital technologies, such as the next generation of operational technologies (OTs) and digital substations, implies a new risk as information technology (IT)-OT systems are vulnerable to cyberattacks. Furthermore, the combination of heterogeneous, co-existing smart and legacy technologies generates significant vulnerabilities and security challenges. Examples of cybersecurity incidents related to power grids already exist around the world. On December 23, 2015, cyberattacks were conducted on the power grid in Ukraine that resulted in power outages, which affected 225,000 customers. More sophisticated cyberattacks on the Ukrainian power grid followed on December 17, 2016, resulting in a power outage in the distribution network where 200 MW of load was unsupplied. The complexity of cyberattacks on power systems is likely to increase. This chapter provides the state-of-the-art and essential knowledge of threats and cyberattacks on power systems. This chapter reviews major cyberattacks on power grids and industrial control systems. A detailed taxonomy of cyberattacks is provided. Power grid vulnerability to six main types of cyberattacks is discussed, that is, phishing, malware, network-based attacks, man-in-the-middle attacks, host-based attacks, and denial of service. The impact of cyberattacks on grid operation is analyzed in terms of loss of load, cascading effects, and equipment damage. A case study of a cyberattack scenario and simulation results are provided.

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.

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.

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.

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.

Increased electrification of energy end-usage can lead to network congestion during periods of high consumption. Flexibility of loads, such as aggregate smart charging of Electric Vehicles (EVs), is increasingly leveraged to manage grid congestion through various market-based mechanisms. Under such an arrangement, this paper quantifies the effect of lead time on the aggregate flexibility of EV fleets. Simulations using realworld charging transactions spanning over different categories of charging stations are performed for two flexibility products (redispatch and capacity limitations) when offered along with different business-as-usual (BAU) schedules. Results show that the variation of tradable flexibility depends mainly on the BAU schedules, the duration of the requested flexibility, and its start time. Further, the implication of these flexibility products on the average energy costs and emissions is also studied for different cases. Simulations show that bidirectional (V2G) charging outperforms unidirectional smart charging in all cases.

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.

This editorial introduces the Special Issue “Methodologies and Applications of Digital Twin for Renewable-Dominant Power Systems”, which highlights key research addressing the challenges associated with integrating renewable energy sources into power systems. Digital twins provide an advanced framework for modeling, simulation, and optimization of these complex systems, driven by the need to address the impact of power electronics and multi-source energies. The contributions featured in this issue focus on the application of DT technologies in renewable-dominant systems, offering solutions for enhancing system stability, optimization, and operational efficiency.

The exponential increase in the integration of Variable Renewable Energy Sources and responsive storage, compensation, and prosumers in electrical power systems raises many uncertainties that affect the operation, control, and planning across different time horizons. Dynamic stability refers to a system's ability to withstand and recover from disturbances while ensuring that systemic symptoms (e.g., voltages, currents, frequency, angular displacements) remain within acceptable limits under both normal and abnormal conditions. Unacceptable excursions in systemic symptoms can cause disruptions or blackouts. A suitably developed and calibrated digital model for dynamic simulations is a key tool for this purpose. This manuscript overviews the development of a digital synthetic model for in-depth analysis and identification of the occurrence and propagation of potential instability issues. The synthetic model is inspired by accessible data on the hypothetical future situation (e.g., year 2030) of the Dutch Power System. The model has been built on the basis of generic component models and parameters from the literature, and several disturbances are evaluated by time-domain simulations. Renewable power electronic-interfaced generators and remaining synchronous generators have implemented emerging methods to provide primary control for active and reactive power support in line with the state-of-the-art recommended practice. This model is proposed as the basis for studying different stability phenomena and challenges for controller design in future operating conditions of the Dutch system in light of the large-scale addition of renewable generation.

Achieving carbon neutrality in industrial ports demands a radical transformation of current energy systems. This paper presents a model-based optimization approach for the operation of a multi-energy cluster, considering a hypothetical evolution of a multi-energy industrial cluster in the Netherlands. The aim is to establish a new system operation strategy that supports the transition towards a carbon-neutral energy system. The synthetic model of the used multi-energy cluster integrates five energy carriers - electricity, natural gas, hydrogen, ammonia, and heat - using an energy hub approach to enable sector coupling and enhance flexibility. Physics-based modeling of electrical power flows is included to ensure technical feasibility in the power system. The model minimizes total cluster's cost while ensuring reliable energy supply. The optimization is implemented in Python by using the PyPSA toolbox and mixed-integer linear programming. A full-year, hourly-resolution simulation under three weather scenarios reveals optimal system operation strategies. Numerical results highlight the benefits of multi-energy cluster operation for managing renewable variability and identify ammonia as a key flexibility provider, supporting hydrogen and electricity systems through conversion and storage. The strategy emphasizes cross-sector economic optimization, dynamic dispatch, and enhanced flexibility, offering practical insights for decarbonizing industrial ports and informing future energy investment planning.

The optimal dispatch of energy storage systems (ESSs) in distribution networks poses significant challenges, primarily due to uncertainties of dynamic pricing, fluctuating demand, and the variability inherent in renewable energy sources. By exploiting the generalization capabilities of deep neural networks (DNNs), the deep reinforcement learning (DRL) algorithms can learn good-quality control models that adapt to the stochastic nature of distribution networks. Nevertheless, the practical deployment of DRL algorithms is often hampered by their limited capacity for satisfying operational constraints in real time, which is a crucial requirement for ensuring the reliability and feasibility of control actions during online operations. This paper introduces an innovative framework, named mixed-integer programming based deep reinforcement learning (MIP-DRL), to overcome these limitations. The proposed MIP-DRL framework can rigorously enforce operational constraints for the optimal dispatch of ESSs during the online execution. This framework involves training a Q-function with DNNs, which is subsequently represented in a mixed-integer programming (MIP) formulation. This unique combination allows for the seamless integration of operational constraints into the decision-making process. The effectiveness of the proposed MIP-DRL framework is validated through numerical simulations, demonstrating its superior capability to enforce all operational constraints and achieve high-quality dispatch decisions and showing its advantage over existing DRL algorithms.

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.