Consumer phase identification in distribution grids using Graph Neural Networks based on synthetic and measured power profiles

Comparison of machine learning and MPC methods for control of home battery storage systems in distribution grids

Review of technical solutions addressing voltage and operational challenges in a distribution grid with high penetration of intermittent RES

A modified distribution flow-based quadratic programming with integer variables for co-optimization of on-site generation placement and network topology in power distribution grids

Photovoltaic hosting capacity evaluation of distribution grid integrated with two modes of battery swap stations

Safe reinforcement learning-based prescriptive maintenance of distribution grid using Bi-Mamba+ and constrained policy optimization: A Danish grid case study

Optimal scheduling of microgrid consortium and distribution grid hybrid game with shared energy storage

Energy storage devices are important components in portable electronics, electric vehicles, and the electrical distribution grid. Batteries and supercapacitors have achieved great success as the spearhead of electrochemical energy storage devices, but need to be further developed in order to meet the ever-increasing energy demands, especially attaining higher power and energy density, and longer cycling life. Rational design of electrode materials with particular structure and surface chemistry play a critical role in developing energy storage systems with higher performance. I will introduce some of our recent works about how to design and tune the surface chemistry of 2D materials and how to tailor the structure of 2D materials, with a purpose to improve the electrochemical performance. Specifically, the interlayer structure and the self-assembled unique structure will be discussed, and the use of various advanced characterization techniques (in situ/operando) to understand the materials and their energy storage mechanisms will be covered.

Abstract The short-term procurement of reactive power is becoming increasingly important, calling for more intensive coordination between grid operators due to local optimization challenges. The German congestion management instrument Redispatch 2.0 provides a platform for such coordination processes. This paper presents an approach and first field test results for extending Redispatch 2.0 by reactive power to provide it from the distribution grid at the point of common coupling to use it for voltage stability in the transmission grid. The results show the potential of expansion and at the same time the challenges that arise in terms of reliable reactive power provision.

Germany’s energy transition to a higher share of renewable energy sources (RESs) is characterized by decentralization, with citizens, cooperatives, SMEs, and municipalities playing a central role. As of early 2025, private individuals own a significant share of renewable energy installations, particularly PV panels, which corresponds to approximately half of the total installed PV power. This trend is driven by physical, technological, and societal factors. Technological advances in battery storage and sector coupling are expected to further decentralize the energy system. Thereby, the electrification of mobility, particularly through electric vehicles (EVs), offers significant storage potential and grid-balancing capabilities via bidirectional charging, although it also introduces challenges, especially for distribution grids during peak loads. Within this work we present a detailed digital twin of the entire distribution grid of the rural German municipality of Wüstenrot. Using grid operator data and transformer measurements, we evaluate strategic expansion scenarios for electromobility, PV and heat pumps based on existing infrastructure and predicted growth in both public and private sectors. A core focus is the intelligent integration of EV charging infrastructure to avoid local overloads and to optimise grid utilisation. Thereby municipally planned and privately driven expansion scenarios are compared, and grid bottlenecks are identified, proposing solutions through charge load management and targeted infrastructure upgrades. This study of Wüstenrot’s low-voltage grid reveals substantial capacity reserves for future integration of heat pumps, electric vehicles (EVs), and photovoltaic systems, supporting the shift to a sustainable energy system. While full-scale expansion would require significant infrastructure investment, mainly due to widespread EV adoption, simple measures like temporary charge load reduction could cut grid stress by up to 51%. Additionally, it is shown that bidirectional charging offers further relief and potential income for EV owners.

In this paper, virtual synchronous generator (VSG) technology is innovatively introduced into the distributor-unified power flow controller (D-UPFC) control to simulate the power generation characteristics of the synchronous generator. Concepts such as inertia and damping in the synchronous generator are introduced into power electronic equipment to provide voltage and frequency support for the system. The VSG control system, which specifically includes the virtual governor, the virtual excitation regulator, and the construction of the VSG model, is designed first. Then, the overall control combining the VSG and the series converter in D-UPFC is discussed. Finally, based on the influence of moment of inertia and damping coefficient on the response parameters, a VSG parameter adaptive control strategy based on refined fuzzy control was proposed. The simulation shows that this strategy can effectively reduce the active overshot and frequency deviation in the dynamic process of the system, eliminate secondary oscillations, and improve the dynamic response capability.

Dispersion, radiological dose assessment, risk evaluation, and emergency response of radioactive materials based on WRF-HYSPLIT modeling.

A unified framework for siting and sizing of distributed energy resources in power distribution grids using data-driven machine learning optimization

Research status and prospects of regional distribution grid resilience enhancement methods taking into account electrified transportation

A stochastic approach to estimate distribution grid state with confidence regions

Learning to Learn Topology and Parameters of Distribution Grid With Matrix Completion Under Partial Observability

Topology detection of distribution grid using wavelet-based Graph Attention Network

A virtual power plant integrates diverse energy sources and storage systems, functioning as producers within a network. Strategically determining its optimal placement and the size of its components leads to notable improvements in key economic and technical metrics of the network. This research builds a framework for sizing and positioning renewable virtual power plants, incorporating hydrogen storage systems as part of a broader multi-objective energy management strategy for smart grids. The proposed approach operates as a bi-level optimization model. In the upper-level model, it addresses technical, financial, and environmental objectives from the perspective of the distribution system operator. The aim is to minimize the total weighted operating costs, energy losses, and emissions within the distribution network. The model considers constraints such as the AC power flow equations for smart grids, alongside operational and voltage security restrictions. Meanwhile, the lower-level model focuses on the placement and sizing of renewable virtual power plants incorporating hydrogen storage systems. Here, the goal is to reduce planning costs, with constraints tied to the operation and flexibility models of renewable sources and storage systems. To integrate these models effectively, the Karush–Kuhn–Tucker (KKT) conditions are applied to create a unified single-level framework, while a Fuzzy decision-making technique facilitates the derivation of compromise solutions. This approach also accounts for uncertainties related to load demand, renewable energy outputs, and fluctuating energy prices using scenario-based stochastic optimization combined with the Kantorovich method and Roulette Wheel Mechanism. For solution optimization, Red Panda Optimization (RPO) is employed, demonstrating superior convergence speed and precision in comparison to other solvers. The research incorporates innovative elements by addressing optimal placement and sizing of flexible virtual power plants within active distribution grids, while factoring in bio-waste energy management and the role of hydrogen storage capacities in network operation and security. Evaluation through numerical case studies highlights significant enhancements in grid performance. In comparison to conventional load flow methods, the proposed solution optimizes operations by reducing network operating costs, improving voltage security, and mitigating environmental impacts. When adopting a compromise solution framework, the distances of network operation costs, energy losses, and emissions from their minimal achievable values are by 14%, 29%, and 21%, respectively. Moreover, RPO demonstrates remarkable precision with a final solution’s standard deviation of approximately 0.97%, effectively identifying optimal results with enhanced convergence efficiency. This study underscores the transformative potential of virtual power plants in improving energy management and distribution grid planning.

A hybrid energy system (HES) integrates various energy resources to attain synchronized energy output. However, HES faces significant challenges due to rising energy consumption, the expenses of using multiple sources, increased emissions due to non-renewable energy resources, etc. This study aims to develop an energy management strategy for distribution grids (DGs) by incorporating a hydrogen storage system (HSS) and demand-side management strategy (DSM), through the design of a multi-objective optimization technique. The primary focus is on optimizing operational costs and reducing pollution. These are approached as minimization problems, while also addressing the challenge of achieving a high penetration of renewable energy resources, framed as a maximization problem. The third objective function is introduced through the implementation of the demand-side management strategy, aiming to minimize the energy gap between initial demand and consumption. This DSM strategy is designed around consumers with three types of loads: sheddable loads, non-sheddable loads, and shiftable loads. To establish a bidirectional communication link between the grid and consumers by utilizing a distribution grid operator (DGO). Additionally, the uncertain behavior of wind, solar, and demand is modeled using probability distribution functions: Weibull for wind, PDF beta for solar, and Gaussian PDF for demand. To tackle this tri-objective optimization problem, this work proposes a hybrid approach that combines well-known techniques, namely, the non-dominated sorting genetic algorithm II and multi-objective particle swarm optimization (Hybrid-NSGA-II-MOPSO). Simulation results demonstrate the effectiveness of the proposed model in optimizing the tri-objective problem while considering various constraints.

Electric distribution networks are witnessing rapid growth in distributed generation units (DGs), with renewables forming the majority of new connections. To raise the maximum DG hosting capacity, operators increasingly consider transitioning from strictly radial feeders to selectively meshed arrangements, allowing improved sharing and balancing of power flows across circuits. When we mesh the network, prospective short-circuit currents rise. That makes protection coordination harder and pushes us to specify higher-rated transformers and primary gear. Superconducting fault current limiters (SCFCLs) present a compelling pathway to mitigate these elevated fault duties without compromising normal operation. This study examines a strategy that retrofits existing distribution topologies with meshed (looped) operation supported by SCFCLs to enable higher DG penetration. The operating concept leverages SCFCLs to permit rapid reconfiguration from meshed to radial topology when a fault occurs, thereby constraining fault current magnitudes. Because radial operation in the faulted state matches current practice, the approach preserves existing protection schemes and avoids changes to equipment ratings or layouts. In normal service, the network runs meshed to improve power-flow management; under faults, it reverts to radial behavior to contain short-circuit levels. This article tests that premise by evaluating whether SCFCL-assisted meshing (superconducting fault current limiter) can increase DG hosting capacity while remaining compatible with existing protection philosophies and equipment—minimizing or eliminating redesigns and upgrades.