Search Results (301)

This study develops a high-resolution techno-economic optimization framework to assess the feasibility of green hydrogen production in 100% renewable, off-grid systems. Utilizing 5-minute interval meteorological data aggregated to hourly resolution spanning 5 years across seven geographically diverse sites, this study co-optimizes the integration of hybrid wind–solar power generation, flexible electrolyzer operation, and a multi-timescale energy storage portfolio, incorporating short-duration, long-duration, and seasonal storage. On the generation side, a hybrid wind–solar configuration achieves the lowest levelized cost of hydrogen (LCOH). For energy storage, no single storage technology can economically address demand fluctuations across short-term, medium-term, long-term, and seasonal timescales. Instead, a coordinated multi-timescale storage strategy incorporating energy-to-energy mechanisms reduces the LCOH by up to 40%. Increasing hydrogen tank capacity and enabling flexible electrolyzer operation further lowers the LCOH. Significant regional resource variability leads to substantial cost disparities, with the most favorable region achieving a low LCOH of $2.45/kg. Several regions are projected to reach the $3/kg target by 2030, while areas with limited resources require large-scale hydrogen storage to ensure supply reliability. These results represent deterministic lower-bound estimates under perfect foresight; accounting for forecast uncertainty and real-world operational constraints would likely increase actual costs by approximately 5–15%. Full article

The rapid development of machine learning, large language models, and related technologies has greatly increased the demand for data storage capacity. Therefore, the role of distributed storage systems in such applications has become more prominent. However, it is inevitable that a single node fails in a distributed storage system during long-term use. Being able to repair failed nodes in a timely manner is extremely important for the stable operation of distributed storage systems, and a specific encoding scheme is required to meet the needs of efficiently repairing failed nodes. This research presents a novel family of binary locally repairable codes (LRCs) developed using multiple disjoint recovery sets constructed based on mutually orthogonal Latin squares (MOLS). The proposed constructions achieve distance optimality under the Singleton-like bound for LRCs with availability. Specifically, the codes are parameterized as $(n=r^2+tr,k=r^2,r,t)$ and $(n=rm+tm,k=rm,r,t)$, where n is the block length, k is the dimension, r is the locality, and t is the availability. These codes achieve minimum distance $d=t+1$, guaranteeing efficient recovery with t disjoint repair sets, each of size r. Compared to existing constructions, the proposed codes offer significant improvements in terms of code rate $\mathcal{R}={\displaystyle \frac{r}{r+t}}$, support for larger block lengths, and reduced finite field size requirements (field size $q=2$). Additionally, a method is introduced to improve the minimum distance of codes with even availability t, constructing codes with parameters $(n+1,k,r,t)$ and $d=t+2$, while preserving optimality. These properties make the proposed codes particularly suitable for distributed storage systems, where efficient and parallel repair of failed nodes is critical. Full article

The explosive growth of temporal graph data has led to significant training overheads for Dynamic Graph Neural Networks (DGNNs), a bottleneck primarily driven by massive data movement between host processors and storage arrays across conventional PCIe I/O buses. While near-data processing with Computational Storage Devices (CSDs) can alleviate this bottleneck, a single CSD is inherently incapable of meeting the terabyte-scale capacity requirements and complex sequence modeling demands of modern large-scale DGNNs. Horizontal scaling with multi-CSD clusters over standard PCIe topologies presents a viable, cost-effective solution, yet our in-depth profiling identifies two critical architectural bottlenecks in naive multi-CSD architectures: host-bounced memory copies significantly compromise inter-device communication efficiency, and sparse graph sampling frequently exceeds the capacity of the tightly constrained local DRAM of CSDs, resulting in excessive flash I/O and performance degradation. To address these interconnected bottlenecks, we propose M-DGNN, a hardware–software co-designed architecture optimized for standard PCIe interconnects. First, M-DGNN orchestrates direct peer-to-peer (P2P) DMA dataflows for inter-CSD hidden state exchange, completely bypassing host operating system intervention and reducing the context-switching overhead. Second, we design a host-assisted caching strategy with a Host-Pinned Memory Extension (HPME) mechanism, which leverages host-pinned memory as an asynchronous DMA extension pool to shield resource-constrained CSDs from high-latency flash I/O during structural subgraph sampling. Extensive experimental evaluations across seven large-scale dynamic graph datasets demonstrate that M-DGNN delivers up to a 6.2× end-to-end speedup over the state-of-the-art DGNN systems. This work establishes an efficient, scalable near-data computing paradigm for large-scale DGNN training. Full article

As the global imperative for climate neutrality intensifies, hydrogen (H₂) from fossil fuels remains central to decarbonizing hard-to-abate sectors. Conventional production via steam methane reforming (SMR), however, is carbon-intensive and, even with carbon capture and storage (CCS), incurs energy penalties and long-term storage constraints. This review develops a harmonized well-to-gate, market-oriented framework to evaluate methane pyrolysis (MP) relative to SMR and autothermal reforming (ATR), with or without CCS, moving beyond reactor-focused assessments toward system-level commercialization analysis. MP decomposes methane into hydrogen and solid carbon, avoiding direct CO₂ formation and the need for CCS infrastructure. Integrating with the reverse water–gas shift (RWGS) reaction enables flexible syngas production with adjustable H₂:CO ratios for methanol and chemical synthesis. A central finding is the dominant role of the “carbon lever”: MP generates approximately 3 kg of solid carbon per kg of H₂, making the carbon market’s absorptive capacity the primary scalability constraint. While carbon monetization can reduce levelized hydrogen costs, large-scale deployment would rapidly saturate existing carbon black and specialty carbon markets. Techno-economic evidence indicates that carbon prices above $500/ton are required to achieve parity with gray hydrogen, whereas $150–200/ton enables competitiveness with blue hydrogen. Lifecycle assessments further show that climate superiority over SMR or ATR with CCS requires upstream methane leakage below 0.5% and very low-carbon electricity. Commercial readiness varies, with plasma MP at TRL 8–9 and thermal, catalytic, and molten-media pathways remaining at the pilot or demonstration stage. Parametric decision-space analysis under harmonized boundary assumptions shows that MP is not a universal substitute for reforming but a conditional pathway competitive only under aligned conditions of low-leakage gas supply, low-carbon electricity, credible carbon monetization, and supportive policy incentives. The review concludes with a roadmap that highlights standardized carbon certification, end-of-life accounting, and long-duration operational data as priorities for commercialization. Full article

To reduce renewable output volatility and improve system integration efficiency, this study constructs a coordinated wind–solar–storage–hydrogen framework. The proposed MILP model innovatively integrates electrolyzer power-dependent efficiency and start-up dynamics into a coupled capacity-sizing and dispatch framework and differs from existing MILP models in refined dynamic constraint construction, multi-energy flow coupling, and practical engineering logic constraints. Refined mathematical models are formulated for core components, including wind and photovoltaic units, battery energy storage systems (BESS), and electrolyzers with power-dependent hydrogen production efficiency and operational dynamics. The electrolyzer efficiency peak at 0.25 p.u. input power is calibrated by industrial test data, and the optimization results show strong robustness to the slight deviation of this peak point. Independent control strategies are designed for each electrolyzer, and a capacity optimization model is formulated to maximize system performance. Simulation tests using wind and solar profiles from Northwest China show that the optimized system achieves a renewable energy utilization rate of 96.7%, a BESS capacity of 7 MWh, and a hydrogen storage tank of 3500 kg. Adopting a time-of-use (TOU) electricity pricing mechanism combined with hydrogen sales significantly enhances system efficiency, while expanding power and hydrogen transmission capacities further improves renewable energy integration. These results demonstrate the practical potential of the proposed integrated system for large-scale renewable energy deployment. Full article

The power fluctuation of large-scale photovoltaic (PV) clusters is significantly affected by cloud movement. Aiming at the engineering reality that meteorological observation data are generally lacking for most power stations in wide-area PV clusters, as well as the problem that existing models overfit second-order high-frequency noise such as microscopic cloud deformation, this paper proposes a disturbance reconstruction and smoothing effect quantification method for PV clusters focusing on the first-order dominant meteorological component. First, a clear-sky model is introduced as a deterministic trend filter to extract the purely random disturbance sequence that induces grid-connection risks from the measured output power. Second, the dimensionality reduction modeling concept of “macro-advection dominance and microscopic deformation filtering” is established: the PV cluster is finely partitioned by fusing Dynamic Time Warping (DTW) and geographical distance, and a cross-space inversion of the macro-cloud velocity vector is realized, driven by pure power data using the Time-Lagged Cross-Correlation (TLCC) algorithm, thus constructing a disturbance power generation model that accounts for the phase misalignment of power output. Independent verification based on measured data in Jilin Province shows that the 95% confidence interval of the power reconstructed only by the first-order advection characteristics can cover 90.2% of the measured fluctuations, and the reconstruction error of the fluctuation standard deviation—an indicator that determines the system reserve demand—is merely 5.9%. This verifies that the macro-cloud displacement is the absolute dominant factor governing the extreme fluctuations of PV clusters. Finally, a normalized Smoothing Factor (SF) characterizing the “reserve capacity release ratio” is constructed, and combined with its statistical indicators, it is used to quantitatively evaluate the smoothing benefits provided by different spatial layout schemes. Under data-constrained conditions, the method proposed in this paper verifies the engineering rationality that microscopic meteorological noise can be safely neglected at the macro-PV cluster scale, providing a reliable quantitative basis for the safe grid expansion and peak-shaving energy storage capacity sizing of high-proportion PV bases. Full article

Renewable Energy Communities (RECs) are recognized as effective collective models to accelerate decarbonization through shared renewable generation, consumption, and local flexibility provision. However, their large-scale deployment remains constrained by the temporal mismatch between variable renewable generation and strongly time-dependent demand, particularly in buildings where heating and cooling dominate final energy use. This state-of-the-art review provides an integrated and comparative assessment of Thermal Energy Storage (TES) and Battery Energy Storage Systems (BESS) within RECs, with explicit focus on power-to-heat (PtH) pathways and phase change material (PCM)-based cooling storage. Based on a structured analysis of the peer-reviewed literature published between 2015 and 2025, the review shows that TES represents a cost-effective and durable complement to electrochemical storage in heating- and cooling-dominated communities. Reported results indicate that TES integration can reduce peak electrical demand by 20–35%, increase local renewable self-consumption by 15–40%, and significantly lower required battery capacity in hybrid configurations. While BESS remains indispensable for short-term electrical balancing and fast-response grid services, TES offers lower costs per kWh stored, longer operational lifetimes (often exceeding 25–40 years), and lower lifecycle greenhouse gas emissions, typically 70–85% lower than those of BESS when thermal energy is used directly. Among TES technologies, PCM-based systems demonstrate particular effectiveness in cooling-dominated RECs, enabling peak cooling power reductions of up to 30% through diurnal load shifting. Across climatic contexts, the literature converges on hybrid TES–BESS architectures as the most robust storage solution, with reported reductions in grid imports and renewable curtailment of up to 35–40%. In addition, TES uniquely enables seasonal energy shifting, for which no cost-competitive electrochemical alternative currently exists. Despite these advantages, the review identifies persistent gaps related to the limited availability of long-term operational data and the need for empirical validation of hybrid control strategies. Future research should prioritize multi-year field demonstrations, advanced data-driven energy management, and policy frameworks that explicitly recognize thermal flexibility and sector coupling within Renewable Energy Communities. Full article

Concrete arch dams, which account for about 4% of large dams worldwide, are distinguished by their efficient geometry, economy, effective load distribution, and high storage capacity. Under thermal loads, they are susceptible to unusual behavior in terms of deflection and stresses due to geometrical peculiarities, construction methodology, and restraints, which in turn may cause potential failure. This paper analyzes the behavior of a 50-year-old double-curvature, high, thin concrete arch dam under extreme thermal loading and fluctuating water levels, using 3D linear elastic FEM analyses and monitoring data. It rigorously evaluates structural response—deflections and stresses—at salient locations and interaction zones under large temperature fluctuations, a key yet underexplored risk for thin concrete arch dams in tropical and equatorial regions. Using real monitoring data, the research also examines the effectiveness of rehabilitation measures designed to mitigate thermal impacts. Results indicate that the dam deflection reverses at extreme temperature drops and rises when the reservoir is at higher or lower levels, respectively, which is not unusual for thin concrete double-curvature arch dams. Long-term exposure to high extreme temperatures with low reservoir water levels may become a concern, as it can cause higher tensile stresses at salient points and significant dam deflections towards upstream. Full article

The large-scale integration of photovoltaic systems into modern distribution networks requires advanced forecasting and optimisation tools to address variability, uncertainty, and increasingly complex operational conditions. This review examines 160 peer-reviewed studies published primarily between 2018 and 2026 and provides a unified, system-level perspective that links photovoltaic power forecasting, photovoltaic optimisation, and energy storage system management within the broader context of Smart Grid operation. The analysis covers forecasting techniques across all temporal horizons, compares deterministic, stochastic, metaheuristic, and hybrid optimisation approaches, and reviews siting, sizing, and operational strategies for both PV units and Energy Storage Systems, including their effects on hosting capacity, reactive power control, and network flexibility. A key contribution of this work is the consolidation of planning- and operation-oriented methods into a coherent framework that clarifies how forecasting accuracy influences Distributed Energy Resources optimisation and system-level performance. The review also highlights emerging trends, such as reinforcement learning for real-time Energy Storage Systems control, surrogate-assisted multi-objective optimisation, data-driven hosting capacity evaluation, and explainable AI for grid transparency, as essential enablers for flexible, resilient, and sustainable distribution networks. Open challenges include uncertainty modelling, real-world validation of optimisation tools, interoperability with flexibility markets, and the development of scalable and adaptive optimisation frameworks for next-generation smart grids. Full article

This study develops a conceptual framework for characterizing reservoir architecture in multi-component, discrete systems using pressure transient analysis (PTA), aimed at calibrating inflow geometry prior to full-field dynamic simulation for subsurface gas storage applications such as CO₂ and hydrogen. A secondary objective is to identify variations in permeability over time by analyzing flow capacity trends and evaluating the dynamic influence of faults and fractures. The analysis is based on a gas-condensate field comprising seven wells and four zones (A, B, C, D), using integrated dynamic datasets including extended well tests (EWTs), mud loss, production logs, and production data. Detailed interpretation of PX-1’s EWT indicated delayed re-pressurization and persistent under-pressure, suggesting a compartmentalized or transient system with limited gas-in-place connectivity. Four reservoir architecture concepts were developed: (1) lithology-dominated inflow, (2) structurally controlled inflow, (3) discrete, weakly connected compartments, and (4) transient-dominated systems with tight matrix GIIP. These concepts informed four reservoir models: matrix-only (M), areal heterogeneity (A), sparse bodies (B), and sparse networks (S). Application of these models across other wells revealed consistent localized KH (permeability–thickness product) behavior, with all models fitting short-duration data comparably. However, only sparse drainage models (B/S) adequately matched PX-1’s EWT response. PTA results confirm that well tests constrain KH locally but provide limited insight into large-scale reservoir architecture. EWTs may reach ~1 km, while shorter tests are confined to ~200–400 m, typically within one to two simulation grid blocks. This study demonstrates how integrating PTA with multi-scale data improves characterization of naturally fractured, tight carbonate reservoirs and supports reservoir simulation and history matching for hydrogen storage evaluation. Based on reservoir simulations, this study concluded that naturally fractured carbonate gas reservoirs can provide significant storage and injection capacities for underground hydrogen storage. This study exemplifies how to characterize the naturally fractured tight carbonate reservoirs by integrating multi-scale and multi-dimensional data such as PTA. Furthermore, this study assists in gridding for full-field reservoir models, for history matching and quantifying the potential of hydrogen storage in these complex reservoirs. The proposed workflow provides an uncertainty-bounded reservoir characterization framework and should not be interpreted as a complete field-design methodology for hydrogen storage. The modeling does not explicitly couple geomechanical fracture growth, hydrogen diffusion, long-term geochemical reactions, or caprock integrity degradation. Therefore, the presented storage scenarios represent technically feasible cases under defined assumptions. Comprehensive site-specific geomechanical and containment assessments are required prior to field-scale implementation. Full article

Battery-electric residential storage systems can increase the self-consumption of photovoltaic (PV) generation; however, economical sizing typically requires a high-resolution time series of PV production and household load behind the meter. In practice, such data are often unavailable. This work therefore presents a simulation model for determining the economically optimal residential storage capacity based exclusively on smart-meter data at the point of common coupling (PCC), i.e., hourly import and export time series. Economic performance is assessed using net present value (NPV) over a multi-year evaluation horizon. In addition, technical constraints (SoC limits, power limits, charging/discharging efficiencies) as well as capacity degradation are considered via a semi-empirical aging model. For validation, a reproducible reference scenario is constructed using PVGIS generation data and the standard load profile H23, enabling a direct comparison between the conventional approach (consumption/generation) and the PCC approach (import/export). The results show that the capacity optimum can be reproduced consistently using PCC data, even under smart-meter-like integer kWh quantization. At the same time, large parts of the investigated parameter space indicate that, under the assumed scenarios, foregoing a storage system is often not economically sensible. Sensitivity analyses further highlight the strong impact of load shifting, in particular due to the charging time of electric vehicles. A case study using real PCC measurement data, together with a two-week-window analysis, demonstrates practical applicability and robustness under limited measurement durations. Full article

Taking a metal structural part blanking workshop as the application background, this study addresses the challenges of high material variety, long crane feeding travel caused by heterogeneous line-side storage layouts, and frequent machine stoppages due to the limited feeding capacity of a single overhead crane. To this end, an integrated machine–crane dual-resource scheduling model is developed by explicitly considering line-side storage locations. The objective is to minimize the maximum waiting time among all machine tools. Under constraints of material assignment, processing sequence, and the crane’s single-task execution and travel requirements, the storage positions of materials in line-side buffers are jointly optimized. To solve the problem, a genetic algorithm with fitness-value-based crossover is proposed, and a simulated-annealing acceptance criterion is embedded to suppress premature convergence and enhance the ability to escape local optima. Comparative experiments on randomly generated instances show that the proposed algorithm can significantly reduce the maximum waiting time and yield more stable results for medium- and large-scale cases. Furthermore, a simulation based on real production data from an industrial enterprise verifies that, under limited feeding capacity, the proposed method effectively shortens material-waiting time, improves equipment utilization, and enhances production efficiency, demonstrating its effectiveness. Full article

Heavy-duty vehicles dominate global freight movement and primarily rely on fossil-derived diesel fuel. However, fluctuations in crude oil prices and evolving emissions regulations have prompted interest in alternative powertrains to enhance fleet energy resiliency. This study paired real-world operational data from a large commercial fleet with high-fidelity vehicle models to evaluate the potential for replacing diesel internal combustion engine (ICE) trucks with alternative powertrain architectures. The baseline vehicle for this analysis is a diesel-powered ICE truck. Alternatives include ICE trucks fueled by bio- and renewable diesel, compressed natural gas (CNG) or hydrogen (H₂), as well as plug-in hybrid (PHEV), fuel cell electric (FCEV), and battery electric vehicles (BEV). While most alternative powertrains resulted in some payload capacity loss, the overall fleetwide impact was negligible due to underutilized payload capacity for the specific fleet considered in this study. For sleeper cab trucks, CNG-powered trucks achieved the highest replacement potential, covering 85% of the fleet. In contrast, H₂ and BEV architectures could replace fewer than 10% and 1% of trucks, respectively. Day cab trucks, with shorter daily routes, showed higher replacement potential: 98% for CNG, 78% for H₂, and 34% for BEVs. However, achieving full fleet replacement would still require significant operational changes such as route reassignment and enroute refueling, along with considerable improvements to onboard energy storage capacity. Additionally, the higher total cost of ownership (TCO) for alternative powertrains remains a key challenge. This study also evaluated lifecycle impacts across various fuel sources, both fossil and bio-derived. Bio-derived synthetic diesel fuels emerged as a practical option for diesel displacement without disrupting operations. Conversely, H₂ and electrified powertrains provide limited lifecycle impacts under the current energy scenario. This analysis highlights the complexity of replacing diesel ICE trucks with admissible alternatives while balancing fleet resiliency, operational demands, and emissions goals. These results reflect a US-based fleet’s duty cycles, payloads, GVWR allowances, and an assumption of depot-only refueling/recharging. Applicability to other fleets and regions may differ based on differing routing practices or technical features such as battery swapping. Full article

Standalone photovoltaic systems play an important role in providing reliable renewable energy in remote areas. These systems depend heavily on battery energy storage, especially lithium iron phosphate batteries, which are known for their safety and long cycle life. However, battery degradation remains a major challenge, as high charging currents, temperature variations, and wide state-of-charge fluctuations introduce electro-thermal stress that reduces the useful lifetime of the storage system. To address this issue, this paper presents a Flexible Power Point Tracking (FPPT) strategy supported by a fuzzy-logic-based controller. In this context, battery stress refers to the combined electrochemical and thermal stress induced by high charging currents, elevated operating temperatures, and large state-of-charge (SOC) excursions, which are known to accelerate ageing mechanisms and capacity fade. Based on a review of the existing literature, most FPPT and lifetime-oriented control studies have focused on lithium-ion batteries such as NMC or LCO chemistries, while limited attention has been given to lithium iron phosphate (LiFePO₄) batteries. The goal is to limit battery stress by reducing current peaks, mitigating temperature rise, and smoothing state-of-charge variations, thereby improving battery lifetime without compromising the stability of the standalone PV system. A complete PV–battery model is developed in PLECS and tested using one-year irradiance, temperature, and load data from Perth, Australia. The results show that the FPPT–Fuzzy controller reduces current peaks, stabilises the state of charge, and lowers the thermal impact on the battery when compared with traditional MPPT. As a result, overall degradation decreases and the battery lifetime is extended by approximately 7%. These findings demonstrate that FPPT is a promising method for improving the long-term performance of renewable energy systems based on lithium iron phosphate battery storage. Full article

The integration of a user-side energy storage system (ESS) faces notable economic challenges, including high upfront investment, uncertainty in quantifying battery degradation, and fragmented ancillary service revenue streams, which hinder large-scale deployment. Conventional configuration studies often handle capacity planning and operational scheduling at different stages, complicating consistent life-cycle valuation under degradation and multi-service participation. This paper proposes a life-cycle multi-service co-optimization model (LC-MSCOM) to jointly determine ESS power–energy ratings and operating strategies. A unified revenue framework quantifies stacked revenues from time-of-use arbitrage, demand charge management, demand response, and renewable energy accommodation, while depth of discharge (DoD)-related lifetime loss is converted into an equivalent degradation cost and embedded in the optimization. The model is validated on a modified IEEE benchmark system using real generation and load data. Results show that LC-MSCOM increases net present value (NPV) by 26.8% and reduces discounted payback period (DPP) by 12.7% relative to conventional benchmarks, and sensitivity analyses confirm robustness under discount-rate, inflation-rate, and tariff uncertainties. By coordinating ESS dispatch with distribution network operating limits (nodal power balance, voltage bounds, and branch ampacity constraints), the framework provides practical, investment-oriented decision support for user-side ESS deployment. Full article