Search Results (3,719)

Optimal reactive power flow (ORPF) is a steady-state optimization problem used to determine reactive-power-related control settings in AC power systems while satisfying network operating constraints. In renewable-integrated transmission systems, explicitly representing the feasible reactive-power contribution of inverter-interfaced resources is essential, because wind, photovoltaic (PV), and battery energy storage system (BESS) units cannot operate as unlimited or overly flexible reactive-power sources. Their reactive capability depends on active-power output, apparent-power rating, voltage conditions, and equipment-level capability curves. This paper evaluates the impact of including these capability curves in the solution of the ORPF problem. A MATLAB-DIgSILENT PowerFactory co-simulation framework is implemented, in which MATLAB applies a hybrid particle swarm optimization-pattern search procedure and DIgSILENT PowerFactory performs repeated AC power-flow evaluations using detailed network models and predefined capability limits. The framework is tested on modified IEEE 39-bus and IEEE 118-bus systems with wind, PV, and BESS resources. The results show that neglecting capability curves can produce unrealistic reactive-power allocations for inverter-based units, whereas enforcing these limits shifts the ORPF solution toward operating points consistent with the modeled equipment capability. The study demonstrates the importance of capability-curve representation for obtaining physically meaningful steady-state ORPF results and supports clearer comparison of constrained and unconstrained dispatch cases. Full article

The rapid electrification of marine transportation has increased the number of hybrid marine microgrids with the addition of renewables and energy storage. The continuously varying propulsion loads, fluctuating sea states, and renewable intermittency introduce significant challenges in bidirectional power transfer and power quality enhancement in marine vessels. This work presents a power-oriented operational strategy for a hybrid Roll-on/Roll-off (Ro-Ro) ferry-based marine microgrid (FMG) integrating diesel generators (DGs), Solar Photovoltaic (PV) arrays, and battery energy storage systems as the primary power sources. The proposed FMG adopts a hybrid AC/DC bus configuration linked through a bidirectional voltage source interlinking converter (ILC). The ILC facilitates multiple functionalities, including effective load compensation, mitigation of Total Harmonic Distortion (THD), continuous power support through bidirectional energy exchange, maintenance of balanced sinusoidal currents, and unity power factor (UPF) operation, thereby providing an integrated solution for improved power quality and reliable microgrid performance. A supervisory control (SC) is devised to operate the FMG seamlessly under islanded modes depending on the availability of power sources. To achieve the above-mentioned objectives, a power-optimized Dual Power-based Instantaneous Power Theory (DP_IPT) is employed and it involves a Sequential Delay Signal Cancelation (SDSC)-based Phase-Locked Loop (PLL) for the effective extraction of sequence components, so as to address the unbalance and nonlinearities in an effective manner with reduced oscillations. The proposed control strategy reduces diesel generator utilization through the effective integration of Solar PV and battery support during anchoring operation. The integration of renewable energy sources substantially enhances clean energy utilization, resulting in the reduction of overall carbon emissions, accounting for a near-40% decrease in emissions compared with the conventional diesel generator (DG)-based operating mode. The proposed FMG and control framework are validated through the Hardware-in-the-Loop (HiL) approach employing an OPAL-RT (OP4512) real-time controller. The HiL investigations demonstrate the efficacious working of the proposed control in achieving less carbonized and enhanced power quality operation for next-generation electrified hybrid maritime microgrids. Full article

To mitigate oscillatory instability caused by pulsed-load impact currents in diesel-generator-based medium-voltage direct current (MVDC) integrated power systems, an adaptive virtual synchronous generator (AVSG)-based model predictive control (MPC) strategy is proposed. A mathematical model of the pulsed load and its power-supply circuit is established based on the MVDC system architecture, and the overall transfer function from the load side to the source side is derived. On this basis, the relationship between the pulsed-load impact currents and diesel-generator speed fluctuation is clarified, and the coupling between the disturbance frequency and the generator’s inherent frequency is revealed. In addition, the active compensation mechanism of a supercapacitor–lithium-battery hybrid energy storage system is analyzed. An AVSG control strategy suitable for MVDC systems is then developed, and adaptive tuning laws for the virtual inertia and damping coefficients are designed. MPC is incorporated into the current inner loop of the hybrid energy storage system to improve the dynamic response. Hardware-in-the-loop results obtained on the RT Box 3 platform verify the effectiveness of the proposed strategy. Full article

The surging integration of volatile renewable energy severely exacerbates power grid frequency fluctuations, yet conventional frequency regulation (FR) market clearing mechanisms fail to efficiently coordinate heterogeneous energy storage systems (ESSs) due to the complete decoupling of multi-dimensional physical performance from economic dispatch. To resolve this critical industry bottleneck, this paper proposes a novel Stackelberg game-based clearing mechanism tailored for diverse ESS participation. A bi-level optimization framework is constructed to internalize physical FR characteristics into market economics; the upper level minimizes the system operator’s total procurement costs by transforming multi-dimensional physical metrics—including dynamic response rates, time delays, and control accuracy—into endogenous performance penalty factors. Concurrently, the lower level maximizes the individual revenues of heterogeneous ESS aggregators under a Gini coefficient-based fairness constraint to mitigate profit monopolization and promote a more sustainable market ecology. To address the computational challenges of high-dimensional non-convexity, an enhanced hybrid Genetic Algorithm and Quadratic Programming (GA-QP) solver is developed to secure robust convergence to the Stackelberg equilibrium. Comprehensive simulation results confirm that the proposed Stackelberg game-based clearing mechanism enables a highly rational, quality-driven allocation of frequency regulation capacity. By dynamically linking physical performance metrics with economic benefit factors, it successfully achieves an optimal balance of interests between heterogeneous energy storage aggregators and the overarching market. Crucially, compared to conventional purely economic models, this mechanism structurally prevents absolute technology monopoly—drastically reducing the market Gini coefficient from a hazardous 0.85 to a healthy 0.32—while sustaining multi-party equity at a negligible system cost increase of only 1.64%. Ultimately, this framework offers a highly feasible and resilient solution for the efficient clearing of multi-type energy storage in modern power systems. Full article

Building- and residential-scale energy systems are becoming increasingly complex due to the growing use of renewable energy sources, variable generation profiles, and uncertainties in user demand. This study comprehensively examines the role of electrical, thermal, and hydrogen-based energy storage technologies in building-scale hybrid energy systems and evaluates these systems alongside energy management strategies. In this context, lithium-ion batteries, supercapacitors, flywheel systems, thermal energy storage solutions, and hydrogen-/fuel cell-based architectures are discussed in terms of their technical characteristics, intended uses, limitations, and complementary aspects. The reviewed studies show that individual storage technologies remain limited in their ability to meet all operational requirements, whereas hybrid storage architectures offer significant advantages in terms of power quality, energy flexibility, energy storage system lifetime, renewable energy utilization, and long-duration energy supply security. Furthermore, energy management systems are shown to be critical not only for cost minimization but also for user comfort, grid interaction, forecasting accuracy, uncertainty management, and the coordination of storage units operating at different timescales. Consequently, achieving high efficiency, low-carbon operation, and energy autonomy in building- and residential-scale systems requires the integrated design of multilayered hybrid storage approaches that are supported by intelligent energy management. Full article

Water scarcity and increasing energy demand are critical challenges that often characterize Mediterranean regions, especially islands such as Crete. A sustainable solution for a combined water and energy supply lies in the domain of hybrid renewable energy systems. This research study evaluates a large-scale wind-driven desalination system with pumped-hydro energy storage for the Regional Unit of Rethymno, Crete, focusing on climate-driven demand and air temperature variation. The proposed system integrates wind energy production, seawater desalination, pumped-hydro storage, and water supply both for domestic and for irrigation purposes. Four scenarios, each with increasing air temperature, are examined in order to assess their effect on water demand and system performance. The analysis evaluates electricity allocation, the production of desalinated water, domestic and irrigation coverage, as well as the economic performance of the system. The results indicate that domestic water demand is almost fully covered in all four scenarios, reaching nearly 99.9%, while irrigation water coverage decreases from 82% under present conditions to 67% under higher-temperature scenarios. Wind-generated electricity is mainly used for water-related processes, with a constant share supplied to the grid. The economic assessment indicates that the system can operate under break-even conditions using realistic water and electricity prices. Full article

Hybrid renewable energy systems (HRESs) represent a promising strategy for reducing carbon emissions across multiple sectors by integrating complementary resources such as solar, wind, hydropower, and energy storage technologies. Identifying the most suitable location for a pilot installation requires a comprehensive evaluation that balances technical performance, environmental benefits, social considerations, and economic feasibility. This study employs an enhanced multi-criteria decision analysis (MCDA) framework, supported by machine learning (ML) techniques, to assess four pilot sites developed within the HY4RES project: a rural community, an aquaculture facility, a port installation, and an agriculture network. A comprehensive set of key performance indicators (KPIs) was established to capture technical, environmental, social, and economic dimensions. These include the degree of hybridization, carbon intensity, community benefit scores, net present value, levelized cost of energy, and payback period. After collecting and normalizing the site-specific data, ML EL-SVM, decision tree, and logistic regression models as computational surrogates designed to bypass the multi-step, matrix inversion mathematical requirements of the AHP when screening massive numbers of future scenario outputs supporting consistency checks and sensitivity exploration were used, along with criterion adjustments, to refine the relative importance of each KPI. The Analytical Hierarchy Process (AHP) was employed to assess potential factors and rank the sites, with the rural site achieving the highest overall score in the system, driven by its complex four-source hybrid configuration and strong community-level benefits. The agriculture scheme ranked second, demonstrating significant potential for carbon emission reductions. The port pilot placed third, distinguished by high technical innovation but more limited social impact. The aquaculture site ranked fourth, primarily due to environmental scores, despite its economic self-sufficiency. Full article

Assessing nitrate sensitivity to hydroclimatic variability is important for evaluating water-quality vulnerability under hydroclimatic variations in coupled surface-water and groundwater systems. This study applies a hybrid, scenario-based projection framework combining a Long Short-Term Memory (LSTM) model and Weighted Regression on Time, Discharge, and Season with Projections (WRTDS-P) to examine nitrate plus nitrite responses under idealized wet and dry conditions across seven U.S. river basins and three associated groundwater wells. LSTM projections evaluate conditional sensitivity to altered precipitation forcing, while WRTDS-P projections assess discharge-conditioned responses based on empirically derived concentration–discharge relationships. Results show strong basin-scale heterogeneity in nitrate sensitivity. Agriculturally dominated Midwestern and central U.S. basins generally exhibit higher nitrate concentrations under the idealized wet scenarios, whereas basins influenced by groundwater buffering, flow regulation, or managed hydrology show weak or slightly negative wet–dry responses. For the three evaluated wells, groundwater projections show site-specific damped or delayed responses relative to nearby surface-water systems, reflecting aquifer storage and legacy nitrogen effects, although the Illinois well shows a larger projected wet–dry amplitude than the associated river. Cross-framework comparison reveals moderate agreement in response direction but notable differences in magnitude, highlighting sensitivity to model structure and forcing assumptions. These findings emphasize the value of hybrid projection frameworks for cross-framework sensitivity analysis and for interpreting nitrate vulnerability under hydroclimatic variation while accounting for uncertainty across surface-water and groundwater systems. Full article

The absence of overhead catenary systems in contactless trams imposes stringent requirements on onboard energy efficiency and real-time power management. Hybrid energy storage systems combining batteries and supercapacitors provide an effective solution; however, conventional energy management strategies often suffer from limited global optimality under frequent traction–braking conditions. To address this issue, this paper proposes a variable-horizon model predictive control (MPC)-based energy management strategy for a battery–supercapacitor hybrid power supply system in contactless trams. A power-level-matching method is first adopted for capacity configuration, and the MPC prediction horizon is then dynamically adjusted to cover the entire traction phase, enabling global energy loss optimization while satisfying voltage, current, and SOC constraints. Simulation results obtained in MATLAB/Simulink demonstrate that the proposed strategy effectively suppresses excessive battery current and premature supercapacitor depletion. Compared with the conventional single-step MPC, the total energy loss is reduced by 9.88%, indicating improved energy efficiency and operational performance. Full article

The rapid adoption of electric vehicles (EVs) has intensified the demand for advanced energy storage technologies capable of delivering high energy density, high power density, enhanced safety, and extended service life. Although lithium-ion batteries remain the dominant energy storage technology for EVs, their limited power capability, thermal degradation, and accelerated aging under high transient loads constrain vehicle performance. Battery–supercapacitor hybrid energy storage systems (HESSs) have emerged as a promising solution by combining the high energy density of batteries with the high-power density and rapid charge–discharge capability of supercapacitors. However, the increasing complexity of HESS architecture requires intelligent energy management strategies to optimize power allocation, battery protection, thermal regulation, and overall system efficiency. Existing review papers primarily address individual aspects of HESS architecture, battery management, or artificial intelligence (AI)-based control, leaving a lack of a unified review integrating these topics. This paper addresses this gap by reviewing 181 publications published between 2020 and 2026, covering HESS architectures, conventional and AI-driven energy management strategies, machine learning, deep learning, reinforcement learning, battery state estimation, diagnostics, prognostics, thermal management, and fault diagnosis. The reviewed studies are critically analyzed to assess the impact of AI on battery lifetime, regenerative braking, charging performance, thermal behavior, and energy efficiency. The review further identifies emerging research directions, including explainable AI, digital twins, federated learning, edge intelligence, vehicle-to-grid integration, and cybersecurity-aware energy management. The findings indicate that AI-based approaches generally demonstrate greater adaptability, predictive capability, and battery protection than conventional methods under dynamic operating conditions, although challenges related to computational complexity, real-time implementation, data availability, explainability, cybersecurity, and standardization remain significant barriers to large-scale deployment. Full article

Supplying shore-side electricity to ships at berth, a practice known as cold ironing, removes the emissions of their auxiliary engines, yet the resulting electricity demand is large, highly seasonal and hard to serve economically from the grid at the small and medium-sized ports that make up most of the European network. This study asks how to meet that demand affordably and cleanly. It develops a smart-sizing and energy-management framework for a grid-connected hybrid renewable energy system that jointly optimizes solar photovoltaic and wind capacity together with a combined battery-and-hydrogen storage envelope. An energy-conserving stochastic reconstruction of the hourly cold-ironing demand is embedded within a genetic algorithm that minimizes the levelized cost of energy and the carbon footprint, and the system is operated by a transparent, priority-based controller. On a full year of real operational data from a Mediterranean port, the optimizer selects 380 kilowatts of photovoltaic capacity and a 2064 kilowatt-hour, battery-dominated storage envelope, reaching a renewable penetration equal to 76 percent of annual demand, with 57 percent of demand met without the grid. Relative to grid-only cold ironing it lowers the levelized cost of energy by about 10 percent on a screening basis, before life-cycle costs bring it to roughly grid parity, while cutting greenhouse-gas emissions by 45 percent; emissions fall 72 percent relative to auxiliary engines. Storage capacity, not oversized renewable generation, proves decisive for deep decarbonization, and battery storage dominates the cost-optimal design for this diurnal load. The framework gives port operators a transferable, data-driven decision-support tool. Full article

The energy transition and global decarbonization goals have accelerated the development of offshore renewable energy technologies, particularly in deep-water regions, where fixed foundations are limited by technical and economic constraints. Floating structures offer new opportunities for harnessing marine renewable resources, allowing them to be deployed in areas with favorable wind, wave, and oceanographic conditions. This paper presents a comprehensive analysis of European floating structures intended for hybrid renewable energy applications, combining environmental assessment, structural characteristics, hydrodynamic behavior, and energy integration aspects. Unlike previous analyses, which focused primarily on individual technologies, this study offers an integrated perspective on floating platform concepts—including spar, semi-submersible, tension-leg, barge, and FPSO-based solutions—as well as their potential for hybrid energy systems. The analysis shows that platform stability, motion response, and structural adaptability are critical factors affecting energy performance and operational reliability. Furthermore, the analysis highlights that hybrid configurations combining offshore wind, wave, and solar energy with energy storage technologies represent promising pathways toward more autonomous and sustainable offshore infrastructure. Key challenges related to design optimization, environmental loads, and system integration are also identified to support future developments in European offshore renewable energy. Full article

With the rapid development of the Industrial Internet of Things, data-driven deep learning has achieved remarkable success in bearing fault diagnosis. However, traditional static models suffer from catastrophic forgetting when facing continuously emerging fault categories and limited edge storage. Existing class-incremental learning frameworks expose critical limitations when applied to 1D vibration signals on micro edge devices, including feature space oscillation, difficulty in anchoring lightweight classifiers, and prototype drift over long incremental cycles. To address these challenges, this paper proposes a novel end-to-end class-incremental fault diagnosis method based on lightweighting and feature calibration tailored for severe memory-constrained conditions. Specifically, a lightweight feature extraction mechanism based on an $L_2$ constraint is introduced to replace computationally expensive similarity distillation, effectively suppressing feature space oscillations and providing stable spatial coordinates for old knowledge. Moreover, a mandatory balanced center–margin hybrid replay (CAHM) strategy is designed to balance class representation while proportionally retaining class center prototypes and marginal hard examples, balancing the anchor accuracy of the Nearest Class Mean (NCM) classifier and the discriminability of the decision boundary. Furthermore, an ultra-low-cost linear prototype calibration module is constructed using a learnable affine transformation to actively redirect shifted old class prototypes with negligible inference latency. Extensive long-tail incremental experiments on the CWRU bearing dataset demonstrate that the proposed method forms a highly synergistic anti-forgetting closed loop. Under an extremely limited memory budget ($K=40$), the proposed framework achieves an outstanding final average accuracy of 98.92% after five incremental stages, significantly outperforming mainstream baselines such as iCaRL, PRIL, and SCKD and exhibiting exceptional robustness for continuous online monitoring on industrial edge devices. Full article

Cover song recognition typically relies on computationally expensive raw audio analysis, which limits applicability in resource-constrained or privacy-preserving scenarios. Existing audio-free alternatives use metadata or lyrics to augment rather than replace audio analysis, and the performance level achievable from compact pre-computed audio descriptors alone has not been systematically established. This paper is an empirical study of that constrained regime: how far pre-computed audio descriptors go without raw audio, and which engineering choices matter. We propose a lightweight approach based on a Siamese retrieval model operating on Million Song Dataset features and evaluated on the SecondHandSongs benchmark. The method combines temporal slope features extracted from pitch and timbre time series, confidence-weighted feature multiplication, and a hybrid delta-gradient optimization framework designed for low-dimensional feature spaces. Each component contributes measurably: temporal slope features raise the unweighted baseline from 0.370 to 0.400 MAP@10, normalization to 0.420, heuristic feature weighting to 0.520, and delta-gradient refinement to the final 0.553, to our knowledge, the best result reported under this constraint. This remains below audio-based state-of-the-art systems, which exploit fine-grained spectral detail that compact descriptors discard; in exchange, the proposed system requires much less storage and computation, enabling privacy-preserving, bandwidth-constrained, and audio-unavailable deployment scenarios. Full article

The present study investigates the combined influence of manufacturing route and material configuration on the mechanical, elastic, viscoelastic, and impact behavior of Kevlar–carbon hybrid laminates. Three eight-ply laminate configurations (V1, V2, and V3) were manufactured through distinct technological routes: fully prepreg-based hot pressing, Kevlar-prepreg/dry-carbon hand lay-up followed by vacuum curing, and multi-stage hybrid consolidation combining repeated hot pressing with subsequent vacuum curing. The experimental characterization included tensile tests according to ASTM D3039, compression tests according to ASTM D695, determination of Young’s modulus from extensometer measurements and Poisson’s ratio using strain-gauge instrumentation, dynamic mechanical analysis (DMA), and low-velocity impact tests under controlled energy conditions. The novelty of this work consists in the integrated process–configuration–property comparison of these Kevlar–carbon hybrid routes within the same experimental framework, rather than in a generic demonstration that manufacturing affects composite laminates. The V1 laminate exhibited the highest strength-related performance, reaching an average tensile strength of 335.88 MPa and a compressive strength of 165.85 MPa, and it also showed the highest DMA storage modulus at 30 °C, E’ = 53.42 GPa. The V2 laminate presented lower tensile performance but the most pronounced damping response, with the highest tanδ peak value. The Young’s modulus determined from the extensometer measurements was 29.26 ± 1.45 GPa for V1, 26.10 ± 0.22 GPa for V2, and 29.52 ± 1.27 GPa for V3, indicating comparable longitudinal stiffness for the V1 and V3 laminates. The results indicate that the measured behavior is governed by the combined effects of reinforcement form, matrix/resin arrangement, consolidation route, and laminate architecture. Direct quantification of laminate compaction, fiber volume fraction, and void content was outside the scope of the present experimental campaign and is identified as a necessary step for future validation. Full article