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.

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.

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.

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.

The transition to Electric Vehicles (EVs) introduces challenges for power grid integration, particularly due to the growing demand for charging infrastructure. To support research on smart charging strategies and bidirectional charging applications, this study presents an open-access dataset containing 142 EV charging profiles obtained in a laboratory environment. The dataset includes static charging and discharging scenarios alongside dynamic profiles where the charging power is varied over time. These scenarios are applied to eight commercially available EVs, three of which support bidirectional charging. It features tests in alternating current and direct current charging modes and includes high-resolution time series of grid and vehicle parameters at sub-second intervals. The dataset is technically validated by assessing charging efficiency, reactive power injection, harmonics, and its suitability for development of digital EV models. This dataset supports applications like model validation, grid integration simulations in the context of Vehicle-to-Grid (V2G), charging infrastructure planning, and smart charging strategy development.

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.

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.

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 deployment of voltage source converters (VSC) to facilitate flexible interconnections between the AC grid, renewable energy system (RES) and Multi-terminal DC (MTDC) grid is on the rise. However, significant challenges exist in exploiting coordinated operations for such AC/VSC-MTDC hybrid power systems. One of the most critical issues is how to achieve the optimal operation of such wide-area systems involving several power entities with as minimal communication burden as possible. To address this issue, an enhanced AC/DC optimal power flow (OPF) is specifically proposed. Firstly, a mixed-integer convex AC/DC OPF model is explicitly formulated to describe the optimal operation of such hybrid power systems. Subsequently, a nested distributed optimization method with double iteration loops is developed to offer optimal system-wide decision-making through a more “thorough” distributed communication architecture. In the outer iteration, the original AC/DC OPF problem is decomposed into several slave problems (SPs) associated with systems (including the AC grid and RESs) and one master problem (MP) associated with the integrated VSC-MTDC grid. Generalized Benders decomposition (GBD) serves to solve the master and slave problems iteratively. Techniques such as multi-cut generation and asynchronous updating are utilized to upgrade the GBD performance of computation efficiency and address communication delays. In the inner iteration, the master problem is continuously decomposed into multiple sub-MPs associated with individual VSCs. The alternating direction method of multipliers (ADMM) is employed to solve these sub-MPs iteratively. Proximal terms and heuristic approaches are embedded to enable parallel computation and handling of integer variables. Numerical experiment results finally validate the effectiveness of the proposed enhanced AC/DC OPF. The constructed AC/DC OPF model exhibits acceptable accuracy in terms of power flow calculation, and the developed nested distributed optimization method showcases decent convergence rate and solution optimality performances.

Accurate and efficient power flow (PF) analysis is crucial in modern electrical networks’ operation and planning. Therefore, there is a need for scalable algorithms that can provide accurate and fast solutions for both small and large scale power networks. As the power network can be interpreted as a graph, Graph Neural Networks (GNNs) have emerged as a promising approach for improving the accuracy and speed of PF approximations by exploiting information sharing via the underlying graph structure. In this study, we introduce PowerFlowNet, a novel GNN architecture for PF approximation that showcases similar performance with the traditional Newton–Raphson method but achieves it 4 times faster in the IEEE 14-bus system and 48 times faster in the realistic case of the French high voltage network (6470rte). Meanwhile, it significantly outperforms other traditional approximation methods, such as the DC power flow, in terms of performance and execution time; therefore, making PowerFlowNet a highly promising solution for real-world PF analysis. Furthermore, we verify the efficacy of our approach by conducting an in-depth experimental evaluation, thoroughly examining the performance, scalability, interpretability, and architectural dependability of PowerFlowNet. The evaluation provides insights into the behavior and potential applications of GNNs in power system analysis.

This paper investigates the impact of adaptive activation functions on deep learning-based power flow analysis. Specifically, it compares four adaptive activation functions with state-of-the-art activation functions, i.e., ReLU, LeakyReLU, Sigmoid, and Tanh, in terms of loss function error, convergence speed, and learning process stability, using a real-world distribution network dataset. Results indicate that the proposed adaptive activation functions improve learning capability over state-of-the-art activation functions. Notably, adaptive ReLU activation shows the most improved learning process, with convergence speed up to twice as fast as ReLU. Adaptive Sigmoid and Tanh activation functions exhibit superior performance in terms of loss function error, outperforming ReLU and LeakyReLU by up to two orders of magnitude. Furthermore, the proposed adaptive activation functions lead to smoother and more stable learning processes, especially during early training, improving convergence. The practical implications of this study include the potential application of these adaptive activation functions in distribution network modeling, forecasting, and control, leading to more accurate and reliable power system operation.

Residential load profiles (RLPs) play an increasingly important role in the optimal operation and planning of distribution systems, particularly with the rising integration of low-carbon energy resources such as PV systems, electric vehicles, small-scale batteries, etc. Despite the prevalence of various data-driven models for generating consumption profiles, there is a lack of clear conclusions about their relative strengths and weaknesses. This study undertakes a comprehensive comparison of frequently used data-driven models in recent research, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAE), Wasserstein GANs (WGAN), WGANs with Gradient Penalty (WGANGP), Gaussian Mixture Models (GMMs), and Gaussian Mixture Copulas (GMC). The presented comparison explores the effectiveness of the above-mentioned models on transformer- and consumer-level consumption profiles, as well as for different time resolutions (15-min, 30-min, and 60-min). The objective of this research is to elucidate the respective advantages and drawbacks of these models, thereby providing valuable insights for subsequent research in this field.

The energy sector's digital transformation brings mutually dependent communication and energy infrastructure, tightening the relationship between the physical and the digital world. Digital twins (DT) are the key concept for this. This paper initially discusses the evolution of the DT concept across various engineering applications before narrowing its focus to the power systems domain. By reviewing different definitions and applications, the authors present a new definition of DTs specifically tailored to power systems. Based on the proposed definition and extensive deliberations and consultations with distribution system operators, energy traders, and municipalities, the authors introduce a vision of a standard DT ecosystem architecture that offers services beyond real-time updates and can seamlessly integrate with existing transmission and distribution system operators' processes while reconciling with concepts such as microgrids and local energy communities based on a system-of-systems view. The authors also discuss their vision related to the integration of power system DTs into various phases of the system's life cycle, such as long-term planning, emphasising challenges that remain to be addressed, such as managing measurement and model errors, and uncertainty propagation. Finally, the authors present their vision of how artificial intelligence and machine learning can enhance several power systems DT modules established in the proposed architecture.

By 2050, there will be more than 1 billion electric vehicles (EVs) on the road. This presents an opportunity for Europe to rapidly expand its share of variable renewable energy (VRE) generation, which is in line with major climate policies at European Union (EU) level. Despite that promising outlook, the rapid growth of electric mobility will pose major technical challenges and infrastructural risks to Europe’s power system. One way to address this is through widespread implementation of smart bidirectional EV charging, which can potentially provide the power system with the flexibility to accommodate higher shares of VREs. The implementation scenarios for this technology are collectively known as vehicle-to-everything, or V2X, including vehicle-to grid (V2G), vehicle-to-home (V2H), and vehicle-to-building (V2B). This paper summarizes recent developments in the V2X arena, doing it in three stages. Firstly, it focuses on taxonomy aspects, performing a bibliometric analysis and studying technical term conventions. Secondly, it compiles relevant barriers to implementation and maps key issues addressed in scientific literature. And thirdly, it surveys past and existing projects to retrieve updated insights on its technical and market orientations. This review is produced as part of the EU-funded DriVe2X project, which develops new expert knowledge and technical solutions to help cope with the forthcoming mass EV deployment in Europe.

Under new EU regulation, as of 2035 all new cars and vans registered in the EU are set to be zero-emission. This ambitious target will be an important driver for a large-scale rollout of e-mobility across European cities. To ensure the successful planning of the energy infrastructure and optimized operation of the expanding e-mobility systems, smart and robust tools will be in high demand for various stakeholders, such as industry, city planners, or researchers. As part of the EU-funded research and innovation project DriVe2X, an interactive open-source prediction tool for Vehicle-to-Grid (V2G) operation and planning has been conceptualized and is under development. The main functions of this tool are the prediction of energy balance and flexibility at the public charging station level for a day-ahead operation and providing useful customized analytics for long-term planning purposes.

This paper explores the potential application of quantum and hybrid quantum–classical neural networks in power flow analysis. Experiments are conducted using two datasets based on 4-bus and 33-bus test systems. A systematic performance comparison is also conducted among quantum, hybrid quantum–classical, and classical neural networks. The comparison is based on (i) generalization ability, (ii) robustness, (iii) training dataset size needed, (iv) training error, and (v) training process stability. The results show that the developed hybrid quantum–classical neural network outperforms both quantum and classical neural networks, and hence can improve deep learning-based power flow analysis in the noisy-intermediate-scale quantum (NISQ) and fault-tolerant quantum (FTQ) era.

This work seeks to reduce the severity of congestion in the medium voltage (MV) cyber-physical systems (CPES) by optimising the network topology in line with seasonal variations in the supply and demand of electricity. To this aim a two-stage reconfiguration algorithm is proposed. In the first stage, the positions of the normally open switches within the network are optimised in to adjust the power flow therein. These optimised positions are subsequently used in the second stage to calculate the network variables. The first stage is implemented as a mixed-integer linear program (MILP) optimisation in Python, whereas the second stage consists of a Newton-Raphson calculation in DIgSILENT PowerFactory software package. The benefit of network reconfiguration is that it is a short-term and low-cost solution which can be implemented by a distribution system operator (DSO) without relying on other external parties. The presented case study addresses the need of seasonal reconfigurations to accommodate for the manual operation of the switches present within a synthetic model of MV CPES, which is implemented inspired from CPES in the Netherlands. The application of the algorithm significantly reduces the frequency by which reconfiguration actions can be performed. Furthermore, the algorithm is able to consistently reduce congestion within the analysed synthetic CPES, completely removing it or reducing its severity. It also outperforms two alternative optimisation options implemented in PowerFactory with regard to the objective function value. Those being an iterative exploration of meshes and a genetic algorithm.

We investigate the performance of different annealers for power flow analysis using adiabatic computing. The annealers include D-Wave's simulated annealer Neal, D-Wave's quantum-classical hybrid annealer, D-Wave's Advantages system (QA), Fujitsu's classical simulated annealer, and Fujitsu's digital annealer V3 (DA). We implement Quadratic Unconstrained Binary Optimization (QUBO) and Ising model formulations, with the latter offering finer control over complex voltage adjustments. Different test systems are experimented with to systematically evaluate the annealers. The evaluation is based on the accuracy, the annealer's capability to handle the decision variables, and the computational time needed. QA and DA show superior performance over classical annealers for our application. DA effectively manages larger test systems, whereas QA encounters difficulties embedding the problem graph onto the hardware graph because of the limited qubit connectivity. This constraint confines QA to the 14-bus system with the QUBO formulation and the 4-bus system with the Ising model formulation. The best performance is associated with different annealers across different test systems, which suggests that adjusting the threshold can improve precision if the compiler and annealer are capable of handling the number of variables involved.

Low-voltage distribution networks (LVDNs) topology is significant for distributed energy resources (DERs) integration, and network operation management, among others. However, topology identification is a difficult task due to the outdated recordings of networks, the uncertainty of DERs and data privacy. To address this issue, a data-driven topology generation approach is proposed based on open GIS and voltage magnitude data. The proposed approach aims to generate a topology with an accurate number of main feeders and sub-branches for adjacent substations. The boundaries between adjacent substations are first identified by using hierarchical clustering (HC) to cluster normalized voltage magnitude. Given the boundaries and the location of LV transformers, a hierarchical minimum spanning tree algorithm (HMST) is adopted to generate graph topologies using GIS data, which simultaneously verifies the number of cables under the streets. Finally, the endpoints of each feeder are estimated by clustering the transformed Pearson correlation coefficient of voltage magnitude. The feasibility of the proposed approach is evaluated on two real LVDNs in the Netherlands.

Optimizing operational set points for modular multilevel converters (MMCs) in Multi-Terminal Direct Current (MTDC) transmission systems is crucial for ensuring efficient power distribution and control. This paper presents an enhanced Optimal Power Flow (OPF) model for MMC-MTDC systems, integrating a novel adaptive voltage droop control strategy. The strategy aims to minimize generation costs and DC voltage deviations while ensuring the stable operation of the MTDC grid by dynamically adjusting the system operation points. The modified Nordic 32 test system with an embedded 4-terminal DC grid is modeled in Julia and the proposed control strategy is applied to the power model. The results demonstrate the feasibility and effectiveness of the proposed droop control strategy, affirming its potential value in enhancing the performance and reliability of hybrid AC-DC power systems.