Search Results (21,243)

Urban flexibility research is expanding across buildings, electric vehicles (EVs), distributed energy resources (DERs), storage, positive energy districts (PEDs), digital twins, and interoperability platforms. These strands are often reviewed separately, although urban distribution operators must manage their combined impacts on the same feeders. This paper presents a PRISMA 2020-aligned systematic review with evidence mapping and narrative synthesis of feeder-aware coordination in smart-city electricity systems. Searches of Scopus, Web of Science, IEEE Xplore, ScienceDirect, and citation chasing identified 312 records; 127 studies were included after screening and eligibility assessment, 101 entered the quantitative mapping sample, and 31 formed the deep-synthesis anchor core. Sparse contingency tables were analyzed with Monte-Carlo permutation chi-square tests and bootstrap confidence intervals for Cramér’s V, while ordinal variables were summarized with medians and interquartile ranges. Explicit feeder grounding was concentrated in grid-oriented and EV-oriented studies, whereas many AI/digital-twin and interoperability studies were less often validated against distribution-network operation. Economic and peak-flexibility indicators were reported far more often than interoperability, cybersecurity, or validation-maturity indicators in the anchor core. The synthesis also showed that deployment-oriented work depends on clearer treatment of standards, co-simulation workflows, regulatory instruments, and stakeholder roles. The evidence base is heterogeneous, English-only, and single-coded, so the quantitative results are descriptive rather than population-level. The review contributes a transparent three-layer corpus design (127 included/101 mapped/31 anchor), a domain-specific specialization of SGAM/IEEE 2030 for urban feeder orchestration, an operational digital-twin definition and validation ladder, a retrofittable benchmarking framework, and a practical roadmap for DSOs, municipalities, aggregators, EV operators, building managers, and ICT providers. Full article

Background: Blockchain and distributed ledger technologies are increasingly proposed to strengthen traceability, governance, visibility, and coordination in mining and mineral supply chains, but mining-specific evidence remains fragmented. Methods: We conducted a systematic mapping review of peer-reviewed articles indexed in Scopus and Web of Science to examine application contexts, functional roles, technical architectures, evidence types, and adoption constraints of blockchain-enabled systems in these settings. Results: The review shows that blockchain is used across five functional domains: traceability and provenance; governance and secure data control; operational monitoring and inspection; energy and market coordination; and sustainability and environmental surveillance. Permissioned and consortium-based architectures predominated and were commonly combined with sensors, external storage, identity mechanisms, and smart contracts. Evidence was strongest for technical feasibility under simulated, experimental, comparative, or bounded pilot conditions, whereas durable economic, social, and governance outcomes remained less substantiated. Conclusions: Blockchain is most credible in mining contexts when it supports controlled coordination, auditable recordkeeping, and process integrity. Its practical value depends on reliable physical-to-digital data capture, workable governance arrangements, interoperability, and validation under real institutional and operational conditions. Full article

Hybridizing concentrating solar power (CSP) with photovoltaics (PV) offers a pathway to combine low-cost daytime generation with dispatchable nighttime supply. This study compares two CSP–PV hybridization concepts for Midelt, Morocco, under a common tender-style design framework: (i) a co-located configuration in which PV and CSP interact at the grid level and (ii) an EH-integrated configuration in which an electric heater (EH) uses PV electricity to heat molten salt in a topping cycle. The main contribution of this study lies in the two-stage optimization workflow, in which leading candidates are selectively re-simulated at higher temporal resolution. This workflow is applied to a common design framework that compares EH-integrated and co-located concepts while considering multiple PV technologies and a broad set of interdependent sizing variables. A surrogate-assisted genetic algorithm evaluates more than 200,000 candidate designs across PV technology, inverter size, TES capacity, EH capacity, and battery energy storage system (BESS) size. The optimization minimizes the levelized cost of energy (LCOE) subject to a $200 {\mathrm{MW}}_{\mathrm{el}}$ export limit, a CAPEX ceiling, and a nighttime-delivery constraint of ${\mathrm{CF}}_{\mathrm{night}}\ge 39\%$. Candidate designs are screened at 600 s and selectively re-simulated at 120 s, showing that temporal refinement affects not only KPI values but also candidate feasibility, final ranking, and preferred component sizing. The lowest-LCOE solution is the EH-integrated bifacial configuration, achieving 64.5% overall capacity factor, ${\mathrm{CF}}_{\mathrm{night}}=39.1\%$, less than 0.1% curtailment, a specific CAPEX of $4698/kW, and an LCOE of 7.29 ¢/kWh. Pareto-front and parameter-trend analyses further show that stricter nighttime-delivery targets shift the dominant sizing levers and define a neighborhood of near-optimal solutions rather than a single fixed design. Full article

Full-waveform hyperspectral LiDAR offers a new approach for precise forest ecological monitoring by simultaneously acquiring the three-dimensional structure and continuous spectral information of targets. However, uncertainty in the backscattering cross-section and the inseparability of the reflectance coefficient lead to systematic underestimation of multi-echo reflectance retrieved using traditional methods. This limitation significantly hinders quantitative applications. The existing multi-echo reflectance correction using neighborhood single-echo reflectance (MCNS) method provides an effective solution by establishing proportional models between similar targets, laying an important foundation for the extraction of multi-echo reflectance. However, its applicability in complex forest scenes is limited due to its dependence on specific vegetation single-echo samples. To address this, an iterative correction method based on ground reflectance baseline, namely Bare-Ground-Echo-Based Iterative Correction of Multi-Echo Reflectance for Hyperspectral LiDAR (BGE-ICMER), is proposed. Using ground single-echo reflectance as a stable baseline, a multi-target energy distribution model is constructed based on energy conservation, and backscattering cross-section proportions for each echo are iteratively solved to recover true reflectance. Validation using a high-fidelity dataset generated by the Large-Scale remote sensing data and image Simulation framework (LESS) confirmed the effectiveness of the proposed method. This dataset encompasses three typical tree species with vegetation layers ranging from two to four, incorporates micro-topographic ground surfaces and ten spectral channels from 500 to 1000 nm, thereby capturing the structural and spectral complexity of real forests. The results showed that coefficients of determination (R²) between the corrected and true reflectance exceeded 0.9560, with an RMSE below 0.0418 and MAE below 0.0360. The average relative error was reduced from 26.66% to 10.07%, representing a 62.22% improvement in accuracy. Even in the most challenging scenarios with four-layer vegetation occlusion within this dataset, no significant error accumulation occurred. These results demonstrate the robustness and effectiveness of the proposed method for multi-echo reflectance extraction. This study lays a foundation for more accurate forest biochemical attribute assessment and enables the vertical characterization of multiple targets using high-resolution spectral reflectance. Full article

Reconfigurable battery modules can dynamically adjust the connection topology among battery cells, significantly improving the energy utilization efficiency of battery energy storage systems. However, existing state estimation methods focus primarily on individual battery cells. Frequent topology changes cause traditional State of Charge (SOC) estimation algorithms to accumulate large errors due to mismatches in equivalent capacity and internal resistance, making them ineffective for reconfigurable battery modules. To address this limitation, this paper proposes a Gated Recurrent Unit–Transformer architecture for precise SOC estimation in reconfigurable battery modules. The model uses a Gated Recurrent Unit to capture the temporal continuity of electrochemical evolution and employs the Transformer’s self-attention mechanism to analyze discrete topology changes. Experimental results show excellent estimation accuracy across different initial SOC levels, with a mean absolute error as low as 0.085% at full charge and 0.035% at 50% SOC. The architecture demonstrates strong topology self-identification capability and maintains high robustness even under abrupt voltage steps caused by reconfiguration. This method provides accurate and reliable state estimation for large-scale two-layer reconfigurable battery systems while reducing control complexity and improving operational efficiency. Full article

This study investigates the coupled quasi-static response and stable-state switching behavior of mechanically prestressed rhombic bistable composite laminates under internal pneumatic pressurization and external mechanical loading. A rhombic bistable composite laminate with embedded fluidic channels is proposed, where pneumatic pressurization is employed to reconfigure the deformation state and modulate the coupling between the laminate morphology and external actuation loads. An efficient reduced-order analytical model is developed to capture the interactions among geometric configuration, prestrain distribution, internal pressure, and external mechanical loading, enabling the rapid prediction of the deformation evolution and load–deflection response under coupled loading conditions. The main innovation of this work is integrating rhombic geometric tailoring, intrinsic pneumatic actuation, and multimode external loading into a unified analytical framework. The results demonstrate that the interior angle, prestrain distribution, and loading mode can effectively regulate equilibrium morphology, snap-through energy, and actuation efficiency. Parametric analyses reveal that the rhombic geometry introduces pronounced shear–bending coupling, providing an additional geometric degree of freedom for tailoring bistable configurations and energy barriers. In particular, a smaller interior angle generally reduces the snap-through energy barrier, whereas front-side prestrain increases the energy required for stable-state switching by enhancing the initial curvature. Comparisons among different loading modes further show that transverse point loading provides the highest energy conversion efficiency, in-plane loading requires the largest input energy, and pressure-assisted actuation exhibits intermediate efficiency. These findings provide fundamental insights and practical design guidelines for programmable morphing and load-efficient stable-state switching for rhombic composite laminates operating under coupled internal–external loading environments. Full article

With high renewable penetration, primary frequency regulation (PFR) in low-inertia power systems faces a critical challenge of balancing frequency security with sustainable energy storage system (ESS) utilization. Existing ESS-based PFR studies mainly focus on single-sided resource support or homogeneous modeling, which cannot address the cross-side coordination demands arising from heterogeneous ESS on the source, grid, and load sides. This paper extends ESS-based PFR to a synergistic scenario involving heterogeneous three-sided ESS. A unified modeling framework is established, explicitly incorporating differences in capacity, functional roles, participation priority, and security boundaries. Based on this, a hierarchical decoupling control structure is proposed, separating system-level frequency regulation from side-level energy coordination. The upper level uses a low-dimensional equivalent representation to reduce the optimization burden from modeling numerous ESS units, while the lower level achieves complementary advantages and orderly task allocation among the three sides through differentiated coordination. Simulations show that the method maintains system frequency performance while achieving rational PFR responsibility allocation across source-, grid-, and load-side ESS, effectively leveraging multi-sided heterogeneous ESS for synergistic regulation, and verifying the hierarchical decoupling framework as an effective approach for coordinating multi-side energy storage. Full article

To better meet the actual demand for local renewable energy consumption and accelerate the achievement of the “dual carbon” goals, this paper proposes a robust optimization model for a multi-microgrid integrated energy system that incorporates hydrogen storage characteristics and an energy-sharing station. First, a framework consisting of external energy networks, energy-sharing stations, and multi-microgrid systems is developed, and a specific system model is defined. Second, a multi-time-scale hydrogen energy storage model is designed to enhance renewable energy utilization and increase the seasonal supportive effect of electricity. Third, a typical scenario selection method is developed to capture short-term fluctuations, seasonal trends, and structural characteristics. This method combines the synchronous backward reduction method, the Quantity-Contour method, and the modified Ward method. Next, considering the uncertainty of renewable energy, a multi-scenario confidence gap decision model is constructed with the system operation cost as the optimization objective. Finally, a case study is conducted to demonstrate the effectiveness of the proposed model. The results show that the proposed approach can reduce the total annual operating cost of the system by 82.64% while increasing renewable energy utilization. This study provides a reference for the efficient use of renewable energy and cross-seasonal energy interaction in multi-microgrid integrated energy systems, thereby promoting low-carbon and sustainable social development. Full article

The accelerating generation of waste streams is observed globally. Spanning lignocellulosic biomass, plastic waste, sewage sludge, and industrial residues, this review presents both an urgent management challenge and a compelling materials opportunity. Carbon materials derived from these waste streams offer a sustainable route to functional carbons applicable in electrochemical energy storage, adsorption, heterogeneous catalysis, and high-temperature applications. Yet their rational design remains constrained by incomplete understanding of the relationships between feedstock composition, processing pathway, structural characteristics, and functional performance. This review provides an integrated analysis of waste-derived carbon materials from processing pathways to structure–property–function relationships. The principal feedstock categories are examined for their compositional characteristics and implications for carbon yield and structure. Five primary processing routes are assessed. The five routes examined are pyrolysis, hydrothermal carbonisation, physical and chemical activation, and microwave-assisted processing. They are assessed comparatively with emphasis on structural outcomes and governing parameters. The resulting structural characteristics are discussed. These are morphology, hierarchical pore architecture, surface chemistry, heteroatom doping, and crystallinity. They are discussed alongside their characterisation methods and known limitations as performance predictors. Structure–property relationships are examined quantitatively. Heteroatom-doped hierarchical porous carbons achieve 612 F/g specific capacitance. Turbostratic hard carbons deliver 450 mAh/g sodium storage with over 90% retention. Hierarchical porous carbons demonstrate CO₂ uptake of 5.0 mmol/g and dye adsorption exceeding 9000 mg/g under optimised laboratory conditions; these values reflect individual studies and are not directly comparable across systems. Biomass-derived sulfonated carbon catalysts sustain biodiesel yields above 90% over multiple cycles. Challenges of feedstock variability, process scalability, environmental compliance, and economic feasibility are addressed, and machine learning-guided design, standardised characterisation methodology, and circular economy policy frameworks are identified as key enablers for translating laboratory performance into industrial reality. Full article

To address the output volatility of distributed photovoltaics, the low utilization efficiency of energy storage resources, and the challenge of optimal revenue for PV-storage virtual power plants (VPPs) in multi-market environments, this paper proposes a three-stage stochastic optimal operation strategy for PV-storage VPPs under multi-market synergy and develops a benefit allocation model based on the Nash–Harsanyi bargaining game. A Monte Carlo simulation was adopted to capture the uncertainties of market electricity prices and PV power output, and the stochastic dual-dynamic-programming (SDDP) algorithm was employed to solve the three-stage optimization framework consisting of day-ahead bidding, real-time optimization, and real-time frequency regulation. Bargaining power was quantified from four dimensions—the marginal contribution rate, PV prediction accuracy, energy storage capacity, and utilization rate—to establish a fair and reasonable internal benefit allocation mechanism. Case studies verified that the proposed method improved the single-day market revenue by up to 20.79% compared with traditional operation modes, achieved a near-zero curtailment rate for distributed PV, and maintained frequency regulation performance scores above 0.4 at all times. The benefits of all investment entities in the alliance increased by 3.36–99.43%, significantly enhancing the multi-market profitability of PV-storage VPPs and the stability of alliance cooperation. Full article

Sodium-ion batteries (SIBs) are emerging as a viable alternative to lithium-ion batteries (LIBs) due to their material sustainability and cost-effectiveness, helping address the high costs, supply limits, and environmental concerns associated with lithium. This paper reviews SIB materials, designs, and applications, and surveys their electrochemical performance, challenges, and future prospects. Recent advances in electrode materials (e.g., layered oxides, hard carbon composites, metallic alloys) are greatly improving SIB stability, conductivity, capacity, and cycle life. Improvements in both solid-state and liquid electrolytes have likewise enhanced ionic conductivity, capacity retention, thermal stability, and safety. Despite their lower energy density, SIBs tolerate wider temperature ranges and carry a significantly lower risk of thermal runaway compared to lithium-based systems, making them attractive for industrial, transportation, and large-scale power storage. Continuous progress in materials and cell engineering is narrowing the performance gap between SIBs and LIBs. Meanwhile, nascent battery recycling strategies for SIBs show promise for economic and environmental viability. Overall, SIBs represent a promising option for safer, more accessible, and more sustainable energy storage technology. Full article

As the energy and power sector transitions toward clean and low-carbon development, the installed capacity of renewable energy sources such as wind and photovoltaic power has been rapidly increasing. Wind–solar hydrogen production via water electrolysis can enhance renewable energy utilization and enable the supply of green hydrogen. Meanwhile, the $H_2/C{O}_2$ molar ratio in the syngas produced by conventional biomass gasification generally cannot directly meet the 2:1 stoichiometric requirement for methanol synthesis. To address this issue, this paper proposes an off-grid coordinated system integrating wind–solar hydrogen production and biomass gasification for methanol synthesis. The system incorporates multi-operating-condition constraints of electrolyzers, coordinated regulation between electrochemical energy storage and hydrogen storage, and coordinated matching between biomass gasification and the water–gas shift reaction. Based on the system energy and material balance, a mixed-integer linear programming (MILP) model is formulated with the objective of minimizing the annualized total cost and is solved using the Gurobi solver in the MATLAB environment. To highlight the roles of HES and the WGS reaction, four comparative scenarios are designed for validation. The results show that the system with an annual methanol production capacity of 100,000 tons achieves an annualized total cost of 318 million CNY, with a wind–solar utilization rate of 98.86%. The system is configured with 12 electrolyzers of 5 MW each. The biomass consumption per ton of methanol is 3.06, and the $C{O}_2$ emissions per ton of methanol are 2.37. Finally, a sensitivity analysis of the levelized methanol cost (LCOM) was conducted, providing guidance for cost reduction in green methanol production. Full article

Lithium-ion battery energy storage systems (BESSs) face significant safety challenges arising from internal short-circuit (ISC) faults, which can ultimately trigger thermal runaway. To address this, this paper proposes an ISC fault diagnosis method based on multivariate information entropy (MIE). The proposed approach fuses voltage and temperature time series from battery cells to extract fault features via MIE. Furthermore, a hierarchical diagnosis framework incorporating statistical confidence intervals is developed to enable robust ISC fault diagnosis. Experiments were conducted on 180 Ah lithium iron phosphate batteries, utilizing external resistors to simulate ISC faults of varying severity. The method was further validated using real-world fault data from an electric vehicle accident. Results demonstrate that the proposed method effectively distinguishes between normal and faulty cells, with MIE values exhibiting a monotonic increase as fault severity intensifies. In the real-world dataset, the method identifies the faulty cell 240 s before a discernible voltage drop, demonstrating its capability for early ISC detection. Full article

The frequent occurrence of extreme climate events poses a severe challenge to the reliability of building energy systems. Hydrogen energy, with its long-term storage capacity, has become a key technology carrier for enhancing building resilience. This study constructs a resilience–environment–economy co-optimization framework that couples dynamic simulation and emergy analysis. Through a five-in-one approach of physical modeling, climate scenario generation, resilience quantification, emergy accounting, and multi-objective optimization, the resilience performance of building hydrogen energy systems under the scenario of extreme heat waves combined with grid failure is evaluated. The results show that the thermal time constant deviation of the electrolyzer is 4.06%, the correlation coefficient between the generated heat wave scenario sequence and the historical measured data is 0.94, the prediction deviation of the once-in-a-century extreme temperature is 0.5%, the environmental load rate is 4.33, the Pareto front contains 127 non-dominated solutions, and the comprehensive performance of the co-optimal solution is improved by 42% to 88%. Engineering suggestions: For public buildings in hot summer and cold winter regions, the hydrogen energy system should adopt a configuration of 50–60 kW electrolyzers and 50–70 kg hydrogen storage tanks, with a key load guarantee rate of no less than 95%, and the ecological cost is 35% lower than that of diesel backup. This study provides a quantitative decision-making tool for the resilience planning of building hydrogen energy systems under extreme climate conditions and can be extended to other high climate risk areas. Full article

Against the backdrop of China’s dual carbon goals, cross-regional low-carbon power transmission from large-scale clean energy bases is a pivotal direction for energy transition. Formulating their power delivery curves requires precise alignment with the load demand characteristics of receiving provinces and the coordinated operation of hydropower, wind power, photovoltaic (PV) power, and pumped-storage hydropower (PSH). To address the limitations of existing methods, such as the lack of linearized modeling for core operational constraints, low solution efficiency and inadequate integration of multi-energy coupling constraints, this paper proposes a tailored linearized optimization modeling approach. By adopting auxiliary variables, binary variables and the Big M method, core constraints including PSH pumping power supply, stepwise power delivery and multi-energy coordinated operation are linearized. A monthly rolling linear optimization model is constructed with triple objectives: minimizing the renewable curtailment rate and the absolute error between delivery and load curves, and maximizing delivered electricity volume. Multi-objective coordinated optimization is realized via the linear weighted summation method, and the model is solved with the Gurobi solver. Case validation on an integrated hydro–wind–solar clean energy base in Southwest China and its corresponding receiving provincial power grid shows that the proposed method effectively improves the curve matching degree, controls the wind–PV curtailment rate within around 12% (engineering tolerance), and strictly meets engineering safety constraints such as PSH operation and HVDC transmission requirements. Comprehensive optimization of the three objectives is achieved when the weight coefficients for curtailment rate, load matching error and delivered electricity volume are set to 0.3–0.8, 0.1–0.2 and 0.1–0.6, respectively. This method resolves the problems of traditional nonlinear models being disconnected from engineering practice and low solution efficiency, providing a reliable technical reference for the refined dispatching of cross-regional power transmission and scientific formulation of power delivery curves for clean energy bases. Full article