Search Results (1,206)

Grid-Scale Battery Energy Storage Systems (GS-BESS) play a crucial role in modern power grids, addressing challenges related to integrating renewable energy sources (RESs), load balancing, peak shaving, voltage support, load shifting, frequency regulation, emergency response, and enhancing system stability. However, harnessing their full potential and lifetime requires intelligent operational strategies that balance technological performance, economic viability, and environmental sustainability. This systematic review examines how artificial intelligence (AI)-based intelligent optimization enhances GS-BESS performance, focusing on its techno-economic, environmental impacts, and policy and regulatory implications. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we review the evolution of GS-BESS, analyze its advancements, and assess state-of-the-art applications and emerging AI techniques for GS-BESS optimization. AI techniques, including machine learning (ML), predictive modeling, optimization algorithms, deep learning (DL), and reinforcement learning (RL), are examined for their ability to improve operational efficiency and control precision in GS-BESSs. Furthermore, the review discusses the benefits of advanced dispatch strategies, including economic efficiency, emissions reduction, and improved grid resilience. Despite significant progress, challenges persist in data availability, model generalization, high computational requirements, scalability, and regulatory gaps. We conclude by identifying emerging opportunities to guide the next generation of intelligent energy storage systems. This work serves as a foundational resource for researchers, engineers, and policymakers seeking to advance the deployment of AI-enhanced GS-BESS for sustainable, resilient power systems. By analyzing the latest developments in AI applications and BESS technologies, this review provides a comprehensive perspective on their synergistic potential to drive sustainability, cost-effectiveness, and energy systems reliability. Full article

It is crucial to understand the market structure and the formation of the electricity mix in the context of the increasingly widespread and global introduction of renewable energy sources as primary energy sources. Due to the cyclical nature of energy production from RES, a long-term plan for seasonal storage is mandatory for smooth and effective energy transition. The stability of the energy system remains a key requirement, especially due to the dynamic changes in the generation, consumption, and pricing of energy resources. This article aims to present the concept that, in the absence of a properly structured and balanced market, thermal power plants prove to be flexible and reliable power sources that can be quickly integrated into the energy system at critical moments when maintaining the grid balance is difficult (such as during peak hours of solar generation). Full article

This study critically examines the sectoral dynamics of renewable energy (RE) adoption across the EU-27 from 1990 to 2023, addressing the persistent gap between electricity generation and end-use sectors. Utilizing Eurostat energy balance data, the research employs a robust multi-methodological framework. We apply the Logarithmic Mean Divisia Index (LMDI) decomposition to isolate driving factors, and the Self-Organizing Maps (SOM) of Kohonen to cluster countries with similar transition structures. Furthermore, the Method of Moments Quantile Regression (MMQR) is used to estimate heterogeneous drivers across the distribution of RE shares. The empirical findings reveal a sharp dichotomy: while the share of renewables in the electricity generation mix (RES-E-Renewable Energy Share in Electricity) reached approximately 53.8% in leading member states, the aggregated share in the transport sector (RES-T) remains significantly lower at 9.1%. This distinction highlights that while power generation is decarbonizing rapidly, end-use electrification lags behind. The MMQR analysis indicates that economic growth drives renewable adoption more effectively in countries with already high renewable shares (upper quantiles) due to established market mechanisms and grid flexibility. Conversely, in lower-quantile countries, regulatory stability and direct infrastructure investment prove more critical than market-based incentives, highlighting the need for differentiated policy instruments. While EU policy milestones (RED I–III-) align with progress in power generation, they have failed to accelerate transitions in lagging sectors. This study concludes that achieving climate neutrality requires moving beyond aggregate targets to implement distinct, sector-specific interventions that address the unique structural barriers in transport and thermal applications. Full article

With the increasing integration of renewable energy sources into the grid, power quality at the point of common coupling (PCC)—particularly harmonic distortion introduced by power electronic converters—has become a critical concern. This paper presents a rigorous design and evaluation of a three-phase, fifteen-level cascaded H-bridge multilevel inverter (CHB MLI) with an LCL filter, selected for its superior harmonic attenuation, compact size, and cost-effectiveness compared to conventional passive filters. The proposed system employs Phase-Shifted Pulse Width Modulation (PS PWM) for balanced operation and low output distortion. A systematic, reproducible methodology is used to design the LCL filter, which is then tested across a wide range of switching frequencies (1–5 kHz) and grid impedance ratios (X/R = 2–9) in MATLAB/Simulink R2025a. Comprehensive simulations confirm that the filter effectively reduces both voltage and current total harmonic distortion (THD) to levels well below the 5% limit specified by IEEE 519, with optimal performance (0.53% current THD, 0.69% voltage THD) achieved at 3 kHz and X/R ≈ 5.6. The filter demonstrates robust performance regardless of grid conditions, making it a practical and scalable solution for modern renewable energy integration. These results, further supported by parametric validation and clear design guidelines, provide actionable insights for academic research and industrial deployment. Full article

The transition to renewable energy is generating numerous changes across different continents, some with greater impact than others, but the progress achieved is recognized and widely accepted. In particular, there are various solutions that include electric vehicles as elements that influence grid behavior when connected. Higher levels of electric vehicle penetration can present opportunities and solutions related to energy storage, V2G connections encompassing the distribution system, and long-term evaluation. High participation in V2G connections maintains the availability of the electrical system, while the high proportion of variable renewable energy sources forms the backbone of the overall electrical system. This study presents a systematic review of V2G systems in the Americas. The design of the Sustainable Mobility scenario and the high participation of V2G maintain the balance of the electrical system for most of the day, simplifying storage equipment requirements. Consequently, the influence of V2G systems on energy storage is an important outcome that must be considered in the energy transition and presents development opportunities for the various countries that make up the Americas. The stored electricity will not only serve as storage for future grid use, but V2G batteries will also act as a buffer between generation from diversified renewable sources and the end-use stage. This article shows that research on the design of V2G energy systems in scientific publications is relatively recent, but it has gained increasing attention in recent years. In total, 151 articles published since 1995 have been identified and analyzed. The overall result indicates that North American countries have developed the most V2G applications, and their deployment in the coming years will be significant. Meanwhile, in South and Central America, these systems are not yet being fully utilized due to the lack of growth in the electric vehicle market. Full article

The accelerating global transition to electric mobility demands data-driven infrastructure planning that balances technical, economic, and spatial considerations. This study develops a scenario-based demand and economic modeling framework to estimate electric vehicle (EV) charging infrastructure needs across Abu Dhabi’s urban and rural regions through 2050. Two adoption pathways, Progressive and Thriving, were constructed to capture contrasting policy and technological trajectories consistent with the UAE’s Net Zero 2050 targets. The model integrates regional travel behavior, energy consumption (0.23–0.26 kWh/km), and differentiated charging patterns to project EV penetration, charging demand, and economic feasibility. Results indicate that EV stocks may reach 750,000 (Progressive) and 1.1 million (Thriving) by 2050. The Thriving scenario, while demanding greater capital investment (≈108 million AED), yields higher utilization, improved spatial equity (Gini = 0.27), and stronger long-term returns compared to the Progressive case. Only 17.6% of communities currently meet infrastructure readiness thresholds, emphasizing the need for coordinated grid expansion and equitable deployment strategies. Findings provide a quantitative basis for balancing economic efficiency, spatial equity, and policy ambition in the design of sustainable EV charging networks for emerging low-carbon cities. Full article

The increasing penetration of variable renewable energy sources intensifies grid imbalance and challenges the reliability of small-scale power systems. This study addresses these challenges by developing and analyzing a fully integrated hybrid energy system that combines biogas upgrading to biomethane, photovoltaic (PV) generation, hydrogen production via alkaline electrolysis, hydrogen storage, and a gas-steam combined cycle (CCGT). The system is designed to supply uninterrupted electricity to a small municipality of approximately 4500 inhabitants under predominantly self-sufficient operating conditions. The methodology integrates high-resolution, full-year electricity demand and solar resource data with detailed process-based simulations performed using Aspen Plus, Aspen HYSYS, and PVGIS-SARAH3 meteorological inputs. Surplus PV electricity is converted into hydrogen and stored, while upgraded biomethane provides dispatchable backup during periods of low solar availability. The gas-steam combined cycle enables flexible and efficient electricity generation, with hydrogen blending supporting dynamic turbine operation and further reducing fossil fuel dependency. The results indicate that a 10 MW PV installation coupled with a 2.9 MW CCGT unit and a hydrogen storage capacity of 550 kg is sufficient to ensure year-round power balance. During winter months, system operation is sustained entirely by biomethane, while in high-solar periods hydrogen production and storage enhance operational flexibility. Compared to a conventional grid-based electricity supply, the proposed system enables near-complete elimination of operational CO₂ emissions, achieving an annual reduction of approximately 8800 tCO₂, corresponding to a reduction of about 93%. The key novelty of this work lies in the simultaneous and process-level integration of biogas, hydrogen, photovoltaic generation, energy storage, and a gas-steam combined cycle within a single operational framework, an approach that has not been comprehensively addressed in the recent literature. The findings demonstrate that such integrated hybrid systems can provide dispatchable, low-carbon electricity for small communities, offering a scalable pathway toward resilient and decentralized energy systems. Full article

Under China’s “dual-carbon” strategic goals and the advancement of smart city development, the rapid adoption of electric vehicles (EVs) has deepened the spatiotemporal coupling between transportation networks and distribution grids, posing new challenges for integrated energy systems. To address this, we propose a collaborative optimization framework for power–transportation coupled networks that integrates multi-modal data with physical priors. The framework constructs a joint feature space from traffic flow, pedestrian density, charging behavior, and grid operating states, and employs hypergraph modeling—guided by power flow balance and traffic flow conservation principles—to capture high-order cross-domain coupling. For prediction, spatiotemporal graph convolution combined with physics-informed attention significantly improves the accuracy of EV charging load forecasting. For optimization, a hierarchical multi-agent strategy integrating federated learning and the Alternating Direction Method of Multipliers (ADMM) enables privacy-preserving, distributed charging load scheduling. Case studies conducted on a 69-node distribution network using real traffic and charging data demonstrate that the proposed method reduces the grid’s peak–valley difference by 20.16%, reduces system operating costs by approximately 25%, and outperforms mainstream baseline models in prediction accuracy, algorithm convergence speed, and long-term operational stability. This work provides a practical and scalable technical pathway for the deep integration of energy and transportation systems in future smart cities. Full article

Optical Fiber Composite Overhead Ground Wire (OPGW) cables serve dual functions in power systems, lightning protection and critical communication infrastructure for real-time grid monitoring. Accurate OPGW identification during UAV inspections is essential to prevent miscuts and maintain power-communication functionality. However, detecting small, twisted OPGW segments among visually similar ground wires is challenging, particularly given the computational and energy constraints of edge-based UAV platforms. We propose OPGW-DETR, a lightweight detector based on the D-FINE framework, optimized for low-power operation to enable reliable detection. The model incorporates two key innovations: multi-scale convolutional global average pooling (MC-GAP), which fuses spatial features across multiple receptive fields and integrates spectrally motivated features for enhanced fine-grained representation, and a hybrid gating mechanism that dynamically balances global and spatial features while preserving original information through residual connections. By enabling real-time inference with minimal energy consumption, OPGW-DETR addresses UAV battery and bandwidth limitations while ensuring continuous detection capability. Evaluated on a custom OPGW dataset, the S-scale model achieves 3.9% improvement in average precision (AP) and 2.5% improvement in AP₅₀ over the baseline. By mitigating misidentification risks, these gains improve communication reliability. As a result, uninterrupted grid monitoring becomes feasible in low-power UAV inspection scenarios, where accurate detection is essential to ensure communication integrity and safeguard the power grid. Full article

Accurate probabilistic forecasting of photovoltaic (PV) power generation is crucial for grid scheduling and renewable energy integration. However, existing approaches often produce prediction intervals with limited calibration accuracy, and the interdependence among meteorological variables is frequently overlooked. This study proposes a probabilistic forecasting framework based on a Multi-scale Temporal–Spatial Attention Quantile Regression Network (MTSA-QRN) and an adaptive calibration mechanism to enhance uncertainty quantification and ensure statistically reliable prediction intervals. The framework employs a dual-pathway architecture: a temporal pathway combining Temporal Convolutional Networks (TCN) and multi-head self-attention to capture hierarchical temporal dependencies, and a spatial pathway based on Graph Attention Networks (GAT) to model nonlinear meteorological correlations. A learnable gated fusion mechanism adaptively integrates temporal–spatial representations, and weather-adaptive modules enhance robustness under diverse atmospheric conditions. Multi-quantile prediction intervals are calibrated using conformalized quantile regression to ensure reliable uncertainty coverage. Experiments on a real-world PV dataset (15 min resolution) demonstrate that the proposed method offers more accurate and sharper uncertainty estimates than competitive benchmarks, supporting risk-aware operational decision-making in power systems. Quantitative evaluation on a real-world 40 MW photovoltaic plant demonstrates that the proposed MTSA-QRN achieves a CRPS of 0.0400 before calibration, representing an improvement of over 55% compared with representative deep learning baselines such as Quantile-GRU, Quantile-LSTM, and Quantile-Transformer. After adaptive calibration, the proposed method attains a reliable empirical coverage close to the nominal level (PICP₉₀ = 0.9053), indicating effective uncertainty calibration. Although the calibrated prediction intervals become wider, the model maintains a competitive CRPS value (0.0453), striking a favorable balance between reliability and probabilistic accuracy. These results demonstrate the effectiveness of the proposed framework for reliable probabilistic photovoltaic power forecasting. Full article

The rapid integration of renewable energy sources and increasing complexity of modern power grids demand advanced forecasting tools capable of simultaneously predicting multiple interconnected variables. While time series foundation models (TSFMs) have demonstrated remarkable zero-shot forecasting capabilities across diverse domains, their application in power grid operations remains limited due to complex coupling relationships between load, price, emissions, and renewable generation. This paper proposes GridFM, a novel physics-informed foundation model specifically designed for multi-task energy forecasting in power systems. GridFM introduces four key innovations: (1) a FreqMixer adaptation layer that transforms pre-trained foundation model representations to power-grid-specific patterns through frequency domain mixing without modifying base weights; (2) a physics-informed constraint module embedding power balance equations and zonal grid topology using graph neural networks; (3) a multi-task learning framework enabling joint forecasting of load demand, locational-based marginal prices (LBMP), carbon emissions, and renewable generation with uncertainty-weighted loss functions; and (4) an explainability module utilizing SHAP values and attention visualization for interpretable predictions. We validate GridFM using over 10 years of real-time data from the New York Independent System Operator (NYISO) at 5 min resolution, comprising more than 10 million data points across 11 load zones. Comprehensive experiments demonstrate that GridFM achieves state-of-the-art performance with an 18.5% improvement in load forecasting MAPE (achieving 2.14%), a 23.2% improvement in price forecasting (achieving 7.8% MAPE), and a 21.7% improvement in emission prediction compared to existing TSFMs including Chronos, TimesFM, and Moirai-MoE. Ablation studies confirm the contribution of each proposed component. The physics-informed constraints reduce physically inconsistent predictions by 67%, while the multi-task framework improves individual task performance by exploiting inter-variable correlations. The proposed model provides interpretable predictions supporting the Climate Leadership and Community Protection Act (CLCPA) 2030/2040 compliance objectives, enabling grid operators to make informed decisions for sustainable energy transition and carbon reduction strategies. Full article

Accurate modeling and parameter estimation of photovoltaic (PV) systems are vital for advancing energy sustainability and achieving global decarbonization goals. Reliable PV models enable better integration of solar resources into smart grids, improve system efficiency, and reduce maintenance costs. This aligns with the vision of sustainable energy systems that combine intelligent optimization with environmental responsibility. The recently introduced Black-Winged Kite Algorithm (BWKA) has shown promise by emulating the predatory and migratory behaviors of black-winged kites; however, it still suffers from issues of slow convergence, limited population diversity, and imbalance between exploration and exploitation. To address these limitations, this paper proposes an Improved Black-Winged Kite Algorithm (IBWKA) that integrates two novel strategies: (i) a Soft-Rime Search (SRS) modulation in the attacking phase, which introduces a smoothly decaying nonlinear factor to adaptively balance global exploration and local exploitation, and (ii) a Quadratic Interpolation (QI) refinement mechanism, applied to a subset of elite individuals, that accelerates local search by fitting a parabola through representative candidate solutions and guiding the search toward promising minima. These dual enhancements reinforce both global diversity and local accuracy, preventing premature convergence and improving convergence speed. The effectiveness of the proposed IBWKA in contrast to the standard BWKA is validated through a comprehensive experimental study for accurate parameter identification of PV models, including single-, double-, and three-diode equivalents, using standard datasets (RTC France and STM6_40_36). The findings show that IBWKA delivers higher accuracy and faster convergence than existing methods, with its improvements confirmed through statistical analysis. Compared to BWKA and others, it proves to be more robust, reliable, and consistent. By combining adaptive exploration, strong diversity maintenance, and refined local search, IBWKA emerges as a versatile optimization tool. Full article

Urban energy systems in Andean cities face growing pressure to accommodate rising electricity demand while progressing toward decarbonization and grid modernization. Residential rooftop photovoltaic (PV) generation offers a promising pathway to enhance transformer utilization, reduce emissions, and improve distribution network performance. However, most GIS-based rooftop solar assessments remain disconnected from operational constraints of urban electrical networks, limiting their applicability for distribution planning. This study examines the technical and environmental feasibility of integrating residential PV distributed generation into the urban distribution network of an Andean city by coupling high-resolution geospatial solar potential analysis with monthly aggregated electricity consumption (MEC) and transformer loadability (LD) information. A GIS-driven framework identifies suitable rooftops based on solar irradiation, orientation, slope, shading, and three-dimensional urban geometry, while MEC data are used to perform energy-balance and planning-level transformer LD assessments. Results indicate that approximately 1.16 MW of rooftop PV capacity could be integrated, increasing average transformer LD from 21.5% to 45.8% and yielding an annual PV generation of about 1.9 GWh. This contribution corresponds to an estimated avoidance of 1143 metric tons of CO₂ per year. At the same time, localized reverse power flow causes some transformers to reach or exceed nominal capacity, highlighting the need to explicitly consider network constraints when translating rooftop solar potential into deployable capacity. By explicitly linking rooftop solar resource availability with aggregated electricity consumption and transformer LD, the proposed framework provides a scalable and practical planning tool for distributed PV deployment in complex mountainous urban environments. Full article

This paper presents an Interoperable User-Centred Digital Twin (I-UCDT) framework for sustainable energy system management, addressing the growing complexity of energy generation, storage, demand, and grid interaction across industrial and community-scale systems. The proposed framework provides a unified environment for the visual representation and management of interconnected energy components, supporting informed decision-making among diverse stakeholder groups. The I-UCDT framework adopts a modular plug-and-play architecture based on the Functional Mock-up Interface (FMI) standard, enabling scalable and interoperable integration of heterogeneous energy models from platforms such as Modelica, MATLAB/Simulink, and EnergyPlus. A standardised data layer processes and structures raw model inputs, while an interactive visualisation layer translates complex energy flows into intuitive, user-accessible insights. By applying human–computer interaction principles, the framework reduces cognitive load and enables users with varying technical backgrounds to explore supply–demand balancing, decarbonisation pathways, and optimisation strategies. It supports the full lifecycle of energy system design, planning, and operation, offering flexibility for both industrial and community-scale applications. A case study demonstrates the framework’s potential to enhance transparency, usability, and energy efficiency. Overall, this work advances digital twin research for energy systems by combining technical interoperability with explicitly formalised user-centred design characteristics (C1–C10) to promote flexible and sustainable energy system management. Full article

To address the lack of inertia in full-power converter wind turbines and the inability of existing mechanical speed regulation technologies to achieve power smoothing without converters, this paper proposes a novel hybrid wind energy storage system integrating a Continuously Variable Transmission (CVT) and an electromechanical flywheel. This system establishes a cascaded topology featuring “CVT-based source-side speed regulation and electromechanical flywheel-based terminal power stabilization.” By utilizing the CVT for speed decoupling and introducing the flywheel via a planetary differential branch, the system retains physical inertia by eliminating large-capacity converters and overcomes the bottleneck of traditional mechanical transmissions, which struggle to balance constant frequency with stable power output. Simulation results demonstrate that the proposed system reduces the active power fluctuation range by 47.60% compared to the raw wind power capture. Moreover, the required capacity of the auxiliary motor is only about 15% of the rated power, reducing the reliance on power electronic converters by approximately 85% compared to full-power converter systems. Furthermore, during a grid voltage dip of 0.6 p.u., the system restricts rotor speed fluctuations to within 0.5%, significantly enhancing Low Voltage Ride-Through (LVRT) capability. Full article