Search Results (293)

The article presents the possibilities of integrating artificial intelligence (through specific machine learning techniques) in the design and construction process of a battery in order to optimize its thermal management. The workflow starts from CFD thermal simulations (1C-rate) of a battery (16 Li-ion cells, type 18650, 4 × 4 arrangement), and based on the results, a complex thermal landscape is created through radial basis function (Rbf) interpolation. Furthermore, a robust neural network (NN) model is proposed and validated through the obtained performances, which is used further for the optimization of the design space (DSO) and multi-objective optimization (MOO) processes. The obtained results show that for DSO, a cell spacing of 1.37 mm is proposed for a maximum cell temperature of 25.53 °C, and in the case of MOO, a cell spacing of 2.64 mm (for minimum fan energy consumption). The main conclusion of the obtained results shows that the use of the NN model as a surrogate (the Digital Twin of a physical model) presents two great advantages in the process of designing a battery: running a CFD simulation for each point on the 2D grid would take hours, while the NN model can generate the entire map and find the optimum in less than 2 s, and moreover, thousands of additional points can be evaluated to find the thin limit of optimal models, effectively filtering out thousands of energy-consuming “suboptimal” configurations. Full article

This paper presents an optimization-based method to support distribution system operators (DSOs) in planning large-scale photovoltaic (PV) integration at the medium-voltage (MV) level. The PV hosting capacity (PV-HC) problem is formulated as a mixed-integer quadratically constrained program (MIQCP) without linearizing approximations to determine PV sizes and locations while enforcing operating limits and planning constraints, including candidate PV locations, per-unit PV capacity limits, active power exchange with the upstream grid, and PV power factor. Our method defines two HC solution classes: (i) sparse solutions, which allocate the PV capacity to a limited subset of candidate nodes, and (ii) non-sparse solutions, which are derived from locational hosting capacity (LHC) computations at all candidate nodes, and are then aggregated into conservative zonal HC values. The approach is implemented in a Hosting Capacity–Distribution Planning Tool (HC-DPT) composed of a Python–AMPL optimization environment and a Python–OpenDSS probabilistic evaluation environment. The worst-case operating conditions are obtained from probabilistic models of demand and solar irradiance, and Monte Carlo simulations quantify the performance under uncertainty over a representative daily window. To support integrated assessment, the index $G_{\exp }$ is introduced to jointly evaluate exported energy and changes in local distribution losses, enabling a system-level interpretation beyond loss variations alone. A strategy was also proposed to derive worst-case scenarios from zonal HC solutions to bound performance metrics across multiple PV integration schemes. Results from a real MV case study show that PV location policies, export constraints, and zonal HC definitions drive differences in losses, exported energy, and solution quality while maintaining computation times compatible with DSO planning workflows. Full article

Distributed energy systems (DES), such as photovoltaics (PV), heat pumps (HPs), and electric vehicles (EVs), are being rapidly integrated into low-voltage (LV) grids, while measurement coverage remains limited. This paper presents a concept for an LV grid digital twin designed to enable real-time state estimation (SE) and operation-oriented studies under constrained measurement availability. Based on this concept, an exemplary digital twin is developed and demonstrated for a test area with a high PV penetration. The proposed digital twin integrates a topology-aware grid model, realistic parameterization, standardized IEC 61850 data modeling, and a real-time estimation pipeline that processes heterogeneous measurement data, including PV inverter power and voltage as well as transformer and feeder measurements. The approach is demonstrated through software-in-the-loop (SIL) experiments using historical playback and accelerated simulations, as well as hardware-in-the-loop (HIL) tests for real-time operation. The SIL results show that the digital twin enables continuous grid monitoring, enhances transparency for distribution system operators (DSOs), and leverages existing infrastructure to increase the effective PV hosting capacity. Selective PV curtailment mitigates congestion and restores normal operation, indicating a potentially cost-effective alternative to grid reinforcement. The HIL experiments emphasize the importance of high-quality, standardized data. The achieved accuracy depends on data availability and synchronization, highlighting the need for improved data integration. Overall, the proposed approach provides a viable pathway toward data-driven planning and operation of LV grids with high DES penetration. Full article

This paper discusses the economic factors that affect and, hence, must be investigated for the subsequent stage of onshore power supply (OPS) (or cold ironing) applications. In order to be considered a viable alternative to the conventional but pollutant marine gas oil, the cost of electricity must be favorable when determining the optimal choice for vessels at the time of mooring. The financial burden of onshore power supply encompasses the expenses associated with its production, distribution, and the enhancement of port grids. Further research must be conducted on the potential for alternative approaches to recapture the authorized revenue for the DSO, along with the parameters that govern vessel types and the dimensions of ports equipped with operational cold ironing mechanisms. Full article

The electrification of transport is rapidly reshaping power distribution networks, introducing new technical, regulatory, and operational challenges for Distribution System Operators (DSOs). This article presents an international review of electromobility integration strategies, analyzing experiences from Europe, Canada, Australia, and Greece. It examines how DSOs address grid impacts through smart charging, vehicle-to-grid (V2G) services, and demand flexibility mechanisms, alongside evolving regulatory and market frameworks. European initiatives—such as Germany’s Energiewende and the UK’s Demand Flexibility Service—demonstrate how coordinated planning and interoperability standards can transform electric vehicles (EVs) into valuable distributed energy resources. Case studies from Canada and Greece highlight region-specific challenges, such as limited access in remote communities or island grid constraints, while Australia’s high PV penetration offers unique opportunities for PV–EV synergies. The findings emphasize that DSOs must evolve into active system operators supported by digitalization, flexible market design, and user engagement. The study concludes by outlining implementation barriers, policy implications, and a roadmap for DSOs. Full article

Traditional methods of asset management in electric power systems rely upon fixed schedules and reactive measurements, leading to challenges in the transparent prioritization of maintenance under evolving operating conditions and incomplete data. In this paper, we introduce a new, fully integrated artificial intelligence (AI)-driven approach for enhancing the resilience and reliability of open-source asset management tools to support improved performance and decisions in electric power system operations. This methodology addresses and overcomes several significant challenges, including data heterogeneity, algorithmic limitations, and inflexible decision-making, through a three-module workflow. The data fidelity module provides a domain-aware pipeline for identifying structural (missing) values from explicit missingness using sophisticated imputation methods, including Multiple Imputation Chain Equations (MICE) and Generative Adversarial Network (GAN)-based hybrids. The characterization module employs seven complementary weighting strategies, including PCA, Autoencoder, GA-based optimization, SHAP, Decision-Tree Importance, and Entropy Weighting, to achieve objective feature weight assignment, thereby eliminating the need for subjective manual rules. The optimization module enhanced the action space through multi-objective optimization, balancing reliability maximization and cost minimization. A synthetic dataset of 100 power transformers was used to validate that the MICE achieved better imputation than other methods. The optimized weighting framework successfully categorizes Health Index values into five condition levels, while the multi-objective maintenance policy optimization generates decisions that align with real-world asset management practices. The proposed framework provides the Transmission and Distribution System Operators (TSOs/DSOs) with an adaptable, industry-oriented decision-support workflow system for enhancing reliability, optimizing maintenance expenses, and improving asset management policies for critical power infrastructure. Full article

The article presents a blockchain-based architecture for decentralized electricity trading that tokenizes energy delivery rights and cash-flows. Energy Attribute Certificates (EACs) are implemented as NFTs, while buy/sell orders are encoded as ERC-1155 tokens whose tokenId packs a time slot and price, enabling precise matching across hours. A clearing smart contract (Matcher) burns filled orders, mints an NFT option, and issues two ERC-20 assets: PT, the right to consume kWh within a specified interval, and YT, the producer’s claim on revenue. We propose a simple, linearly increasing discounted buyback for YT within the slot and introduce an aggregating token, IndexYT, which accumulates YTs across slots, redeems them at par at maturity, and gradually builds on-chain reserves—turning IndexYT into a liquid, yield-bearing instrument. We outline the PT/YY lifecycle, oracle-driven policy controls for DSO (e.g., transfer/splitting constraints), and discuss transparency, resilience, and capital efficiency. The contribution is a Pendle-inspired split of electricity into Principal/Yield tokens combined with a time-stamped on-chain order book and IndexYT, forming a programmable market for short-term delivery rights and yield derivatives with deterministic settlement. Full article

In accordance with the European common rules for the internal electricity market, suppliers offer end users contracts with dynamic energy prices. To reduce energy costs, prosumers must manage their installations with energy storage devices (ESSs). The authors propose a novel control strategy with a relatively simple simulation-based algorithm that effectively reduces daily energy costs by managing the ESS charging and discharging schedule under different types of dynamic energy tariffs. The algorithm operates in a running window mode to ensure ongoing control updates in response to the changing conditions of the prosumer’s installation operation and dynamically changing energy prices. A feature of the control system is its ability to regulate the power exchanged with the supply network in response to an external signal from a superior control system or a network operator. This feature allows the control system to participate in regulatory services provided by the prosumer to the DSO. The effectiveness of the proposed control algorithm was verified in the PSCAD V4 Professional environment and with the MS Excel SOLVER for Office 365 optimisation tool. The results showed good accuracy with respect to the cost reduction algorithm and confirmed that the additional regulatory service can be effectively implemented within the same prosumer ESS control system. Full article

This paper tackles the problem of noise analysis and identification in the gyroscope of the LSM06DSO32 inertial navigation sensor based on MEMS technology, under helium exposure. This study focuses on analyzing the bias and variance of the gyroscope noise, as well as identifying its model’s order using fractional-order calculus. The order was estimated using methods based on variance and correlation analysis of data collected from the sensor at various time intervals during helium exposure. This work extends previous research on analyzing and identifying inertial sensor noise under varying temperature conditions. Considering that helium exposure may significantly influence IMU measurements, this study presents a detailed investigation into the evolution of gyroscope noise under prolonged helium exposure, followed by an analysis of the sensor’s behavior after its removal from the helium environment. Full article

Variable renewable energy sources and cross-zonal trades stress transmission grids, pushing them toward thermal limits. This systematic review, reported in accordance with PRISMA 2020, examines how demand-side response (DSR) can provide relief at the transmission scale. We screened peer-reviewed literature and operator documentation, from 2010 to 2025, indexed in Web of Science, Scopus, and IEEE Xplore; organized remedial actions across supply, network, and demand/storage levers; and categorized operational attributes (time to effect, spatial targeting, activation lead times, telemetry, and measurement and verification). Few reviewed sources explicitly link DSR to transmission congestion relief, highlighting the gap between its mature use in frequency and adequacy services and its still-limited, location-specific application on the grid. We identify feasibility conditions, including assets downstream of the binding interface, minute-scale activation, and feeder-grade baselines with rebound accounting. This implies the following design requirements: TSO–DSO eligibility registries and conflict resolution, portfolio mapping to power-flow sensitivities, and co-optimization with redispatch, HVDC, topology control, and storage within a security-constrained optimal-power-flow framework. No full-text risk-of-bias assessment or meta-analysis was undertaken; the review used English-only title/abstract screening. Registration: none. Funding: none. Full article

This paper presents a data-driven decision-support framework for distribution system operations using logistic regression (LR) on the Voltage Margin Index (VMI). Treating VMI as the sole explanatory feature, the proposed two-stage workflow first fits an inferential LR model to establish statistical significance and perform valid statistical inference on the coefficients. Next, it trains a performance-optimized LR classifier with class-balanced sample weighting to produce calibrated violation probabilities. LR maps VMI to violation probability and analytically converts a calibrated probability threshold into an operator-ready VMI decision boundary. Applying 5-fold group cross-validation to 8816 node-level samples generated from a 22.9 kV Jeju Island model yields performance- and safety-oriented probability thresholds (θ_opt = 0.7891, θ_safe = 0.6880), which correspond to VMI decision boundaries VMI_DB,opt = 0.7893 and VMI_DB,safe = 0.8101. On an unseen 20% test set, the LR classifier achieves 99.94% accuracy (F₁ = 0.9977) under θ_opt and 100% recall under θ_safe. A random forest (RF) benchmark confirms comparable accuracy (=99.72%) but lacks analytical invertibility and transparency. This framework offers distribution system operators (DSOs) and virtual power plant (VPP) operators clear, evidence-based criteria for routine planning and risk-averse decision-making, and it can be applied directly to any distribution system with node-level voltage measurements and known regulation limits. Full article

The role of distributed energy resources in distribution networks is evolving to support system operation, facilitated by their participation in local flexibility markets. Future scenarios envision a significant share of low-power resources providing ancillary services to efficiently manage network congestions, offering a competitive alternative to conventional grid reinforcement. Additionally, the interaction between distribution and transmission systems enables the provision of flexibility services at higher voltage levels for various applications. In such cases, the aggregated flexibility of low-power resources is typically represented as a capability envelope at the interface between the distribution and transmission network, constructed by accounting for distribution grid constraints and subsequently communicated to the transmission system operator. This paper revisits this concept and introduces a novel approach for envelope construction. The proposed method is based on a surrogate model composed of a limited set of standard power flow components—loads, generators, and storage units—enhancing the integration of distribution network flexibility into transmission-level optimization frameworks. Notably, this advantage can potentially be achieved without significant modifications to the optimization tools currently available to grid operators. The effectiveness of the approach is demonstrated through a case study in which the adoption of distribution network surrogate models within a coordinated framework between transmission and distribution operators enables the provision of ancillary services for transmission resilience support. This results in improved resilience indicators and lower control action costs compared to conventional shedding schemes. Full article

In recent years, SLAM, visual odometry, and structure-from-motion approaches have widely addressed the problems of 3D reconstruction and ego-motion estimation. Of the many input modalities that can be used to solve these ill-posed problems, the pure visual alternative using a single monocular RGB camera has attracted the attention of multiple researchers due to its low cost and widespread availability in handheld devices. One of the best proposals currently available is the Direct Sparse Odometry (DSO) system, which has demonstrated the ability to accurately recover trajectories and depth maps using monocular sequences as the only source of information. Given the impressive advances in single-image depth estimation using neural networks, this work proposes an extension of the DSO system, named DeepDSO. DeepDSO effectively integrates the state-of-the-art NeW CRF neural network as a depth estimation module, providing depth prior information for each candidate point. This reduces the point search interval over the epipolar line. This integration improves the DSO algorithm’s depth point initialization and allows each proposed point to converge faster to its true depth. Experimentation carried out in the TUM-Mono dataset demonstrated that adding the neural network depth estimation module to the DSO pipeline significantly reduced rotation, translation, scale, start-segment alignment, end-segment alignment, and RMSE errors. Full article

Peer-to-peer (P2P) energy trading has emerged as a novel approach to enhancing the coordination and utilization of distributed energy resources (DERs) within modern power distribution networks. This study presents a techno-economic analysis of different DER characteristics, focusing on the integration of photovoltaic (PV) systems and energy storage systems (ESS) within a community-based P2P energy trading framework in Aswan, Egypt, under a time-of-use (ToU) electricity tariff. Eight distinct cases are evaluated to assess the impact of different DER characteristics on P2P energy trading performance and an unbalanced low-voltage (LV) distribution network by varying the PV capacity, ESS capacity, and ESS charging power. To the best of the authors’ knowledge, this is the first study to comprehensively examine the effects of different DER characteristics on P2P energy trading and the associated impacts on an unbalanced distribution network. The findings demonstrate that integrating PV and ESS can substantially reduce operational costs—by 37.19% to 68.22% across the analyzed cases—while enabling more effective energy exchanges among peers and with the distribution system operator (DSO). Moreover, DER integration reduced grid energy imports by 30.09% to 63.21% and improved self-sufficiency, with 30.10% to 63.21% of energy demand covered by community DERs. However, the analysis also reveals that specific DER characteristics—particularly those with low PV capacity (1.5 kWp) and high ESS charging rates (e.g., ESS 13.5 kWh with 2.5 kW inverter)—can significantly increase transformer and line loading, reaching up to 19.90% and 58.91%, respectively, in Case 2. These setups also lead to voltage quality issues, such as increased voltage unbalance factors (VUFs), peaking at 1.261%, and notable phase voltage deviations, with the minimum Vb dropping to 0.972 pu and maximum Vb reaching 1.083 pu. These findings highlight the importance of optimal DER sizing and characteristics to balance economic benefits with technical constraints in P2P energy trading frameworks. Full article

Electrical energy supply services are characterised by unpredictable risks that affect both distribution network operators (DSOs) and electricity consumers. This paper presents an innovative cooperative game theory (GT) framework to enhance electric energy supply reliability and demand-side flexibility by aligning the interest of DSOs and consumers. The research investigates the performance of the proposed GT model under different distribution network (DN) topologies and fault intensities, explicitly considering outage durations and restoration times. A cooperation mechanism based on penalty compensation is introduced to simulate realistic interactions between DSOs and consumers. Simulation results confirm that adaptive cooperation under this framework yields significant reliability improvements of up to 70% in some DN configurations. The GT-based approach supports informed investment decisions, improved stakeholder satisfaction, and reduced risk of service disruptions. Findings suggest that integrated GT planning mechanisms can lead to more resilient and consumer-centred electricity distribution systems. Full article