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Offshore renewable energy is widely recognised as a critical pathway for decarbonising electricity systems, but the integration of floating offshore wind turbines with wave energy converters remains technically challenging. This paper presents a structured literature review of hybrid wave–wind offshore energy platforms, drawing on 114 reviewed sources published between 2000 and 2026. The review classifies hybrid concepts using a three-axis framework based on floating platform type, wave energy converter (WEC) integration approach, and energy-dominance category. It then compares representative configurations, including point absorbers, oscillating water columns, flap-type devices, and heaving torus concepts, with emphasis on hydrodynamic response, energy contribution, structural complexity, mooring implications, validation status, and optimization suitability. The findings show that no single hybrid configuration can be ranked as universally superior because reported performance depends strongly on platform geometry, WEC scale, site wave climate, modelling assumptions, and validation maturity. Point absorber systems offer modularity and lower integration complexity, oscillating water column (OWC)-based systems provide protected power take-off (PTO) integration and moderate hydrodynamic interaction, flap-type systems can provide stronger motion-control potential but impose higher structural and mooring demands, and spar–torus concepts remain geometrically compatible with spar platforms but are generally wind-dominated. The review further shows that optimization method selection should depend on problem class: gradient-based methods are most suitable for local PTO tuning, evolutionary methods for non-convex multi-objective layout problems, surrogate-based methods for high-cost coupled simulations, and data-driven methods for adaptive control. The paper concludes that future progress requires standardized benchmark models, transparent evidence-level reporting, multi-physics co-optimization, techno-economic assessment, and systematic experimental or field validation before definitive concept ranking or commercial-readiness claims can be made. For decision-makers, industry stakeholders, and policymakers, the framework supports early-stage concept screening, identification of technology-specific risk factors, prioritisation of validation and investment pathways, and alignment of hybrid-platform development with site conditions, infrastructure constraints, and policy objectives. Full article

In multi-variety, small-batch electric oven manufacturing, sequence-dependent setup times (SDST) and worker skill heterogeneity jointly affect makespan, labor cost, and energy consumption. This study addresses a multi-objective hybrid flow shop scheduling problem with SDST and multi-skilled worker assignment (HFSP-SDST), in which symmetry and asymmetry coexist: the three objectives require balanced trade-offs, whereas sequence-dependent setups and skill–speed compatibility impose asymmetric constraints. A mixed-integer linear programming model is formulated to minimize the three objectives, embedding a skill–speed downward compatibility mechanism that couples worker assignment with processing time, power demand, and labor cost. To solve it, a hybrid algorithm integrating NSGA-II, variable neighborhood search (VNS), and multi-objective simulated annealing (MOSA) is designed on a four-matrix encoding with problem-specific crossover, neighborhood, and feasibility-repair operators. On 24 test instances of varied scale and structure, NSGA-II-VNS-MOSA attains the highest mean hypervolume (2.05) and the best average rank (2.07) against classical and recent Q-learning-guided algorithms, with its advantage growing as setup asymmetry intensifies; an ablation study shows that VNS and MOSA jointly increase hypervolume by 89.5% and reduce the inverted generational distance (IGD) by 45.2% relative to baseline NSGA-II. A real electric oven case confirms that the resulting Pareto set offers decision-makers actionable trade-offs among the three objectives. Full article

The baseline assessment analysis for total cost of ownership of the pilot demonstrations of the ESCALATE project was carried out for four different powertrain configurations, dealing with modular and scalable powertrains for various vehicle configurations in long-haul trucking. The baseline TCO methodology and results for battery electric trucks (BETs), fuel cell electric trucks (FCETs) and FC range-extending BETs are analysed based on the final designs of the demonstrator vehicles and their foreseen pilot use cases and operational scenarios. As real operation data is not yet available, the analysis relies on energy use and pilot mission analysis through simulation. Overall, the TCO analysis shows several key factors affecting the relative competitiveness of the different zero-emission powertrains and vehicles. Long-haul operations pose clear challenges to vehicle design and long-range vehicles on single charge or refill show increased curb weight, limiting allowable payload due to GVW limits. The best payload capacity is shown for opportunity charging BETs and FCETs. BETs are generally the closest competitor to conventional trucks, but a key factor is the relative energy price difference between diesel, electricity (private or public) and hydrogen. Energy sourcing will be an important factor for end users to enable competitive shift to zero-emission options. Access to cheap private electricity or local green hydrogen may facilitate a choice between the options. Full article

Hydrogen (H₂) is regarded as a promising option for sustainable energy systems; however, its large-scale use in electricity supply remains limited. This study develops a stochastic network optimization model to examine the applicability of H₂-based electricity generation. The proposed Hydrogen Supply Chain (HSC) model evaluates cost and emission performance under uncertainty by considering disaster conditions, transmission losses, depreciation, and the time value of money. The Marmara Region of Türkiye is divided into 24 grid nodes, and a single-period model for 2023 is solved using Mixed-Integer Linear Programming (MILP). The HSC is allowed to meet 10–40% of electricity demand and to replace collapsed grid lines by supplying critical public centers (CPCs) during disasters. The results show that the HSC can meet 24.82% of demand, although at costs approximately 3.9 times higher than power grid (PG) electricity, while producing 3.44 MtCO₂/year compared to 65.96 MtCO₂/year from the PG. The model is then extended to a multi-period structure (2023–2053) and solved by Variable Neighborhood Search (VNS). Over time, H₂ costs decline, and their share rises from 19% to 35%, while electricity costs decrease from 408 USD/MWh to 170 USD/MWh. These findings suggest that H₂-based electricity supply can support long-term sustainability and resilience objectives in regional energy planning. Full article

Recent advances in chiplet architectures, heterogeneous integration, 2.5D/3D packaging, high-performance computing, and RF applications have increased the demand for high-density vertical interconnects and low-loss packaging platforms. Glass substrates have attracted considerable attention for next-generation advanced packaging because of their low dielectric loss, high dimensional stability, smooth surface, and compatibility with large-area panel-level processing. Through-glass vias (TGVs) are essential vertical interconnect structures that enable the electrical integration of glass substrates. This focused review summarizes TGV technologies for glass-based advanced packaging from the perspectives of via formation, seed layer deposition, metallization, Cu filling, defect formation, reliability, and plugging-based alternative architectures. Representative TGV formation methods, including laser drilling, selective laser etching, laser-induced deep etching, wet/dry etching, and photosensitive glass processing, are compared. Metallization approaches based on sputtering, electroless plating, ALD/CVD, and hybrid processes are discussed together with Cu electroplating strategies such as conformal plating, bottom-up filling, pulse or pulse-reverse plating, and engineered-geometry filling. Key defects, including voids, seams, pinch-off, seed discontinuity, Cu/glass interfacial delamination, glass cracking, and Cu protrusion, are reviewed in relation to thermomechanical reliability. Finally, polymer/dielectric plugging, plugging/re-drilling, conductive paste plugging, and hybrid Cu/plugging structures are discussed as application-specific alternatives for balancing electrical performance, reliability, manufacturability, yield, and cost. Full article

The growing penetration of renewable energy has intensified building load fluctuations, substantially increasing balancing costs. Electric vehicles (EVs) in building clusters often have considerable idle parking time beyond essential charging needs, enabling them to provide significant flexibility while meeting scheduled demands. This EV flexibility can balance intra-day load deviations and enable arbitrage in day-ahead electricity markets. However, conventional model-based approaches are fundamentally limited by their dependence on forecasting accuracy under high uncertainty from renewable generation and EV behavior. To address this, we propose a novel bi-level online optimization framework. The upper level employs a Lyapunov optimization-based algorithm that operates without predictions, making real-time decisions on total EV charging power to balance supply-demand mismatches. The lower level introduces novel flexibility metrics for individual EVs—encompassing temporal, volumetric, and cross-day dimensions—and optimizes power allocation by minimizing flexibility loss. Furthermore, we model EV flexibility as virtual queues and rigorously derive mathematical bounds on their limits, providing theoretical support for managing flexibility reserves. Rigorous analysis validates the framework’s feasibility, and comprehensive simulations demonstrate its superiority over benchmark algorithms, achieving significant cost reductions under various uncertainty scenarios. Full article

To improve the flexibility and carbon reduction performance of coal-fired power plants, a solar-aided power generation (SAPG) system integrated with parabolic trough collectors and thermal energy storage (TES) was proposed and investigated using a combined Aspen Plus and System Advisor Model (SAM) framework. Two different integration schemes, namely SAPG-1 and SAPG-2, were evaluated under 100%, 75%, and 50% load conditions with a solar multiple of 2 and a TES duration of 6 h. The thermodynamic, economic, and environmental performances of the systems were comprehensively analyzed. The results show that TES significantly improves solar energy utilization, annual solar contribution, and system dispatchability. Compared with SAPG-2, SAPG-1 demonstrates superior thermodynamic and economic performance due to its lower boiler heat demand and more effective feedwater integration. At full load, the solar contribution of SAPG-1 with TES reaches 16.04%, while the annual solar energy production increases to 190.35 GWh with a capacity factor of 21.75%. In addition, TES integration effectively reduces the levelized cost of electricity and shortens the payback period under both CO₂ pricing and non-CO₂ pricing scenarios. The proposed SAPG framework demonstrates considerable potential for enhancing renewable energy utilization, operational flexibility, and economic feasibility in large-scale solar–coal hybrid power generation systems. Full article

This paper addresses the current development status of a innovative direct high-pressure electrolyser (DHPEL, operating up to 700 bar) and its integration into a microgrid system in which solar energy constitutes the primary energy source and a hybrid energy storage system, comprising a battery and hydrogen, is employed. The DHPEL under development enables the direct production and storage of hydrogen at high pressures, thereby obviating the need for intermediate mechanical compression. In combination with standardized pressure vessels (300–350 bar) or the increasingly widespread use of CFRP-based high-pressure storage tanks (up to 700 bar), the DHPEL concept represents a technically and economically attractive option for microgrids with hybrid energy storage. The hybrid storage concept is based on functional differentiation between the storage media: the battery is intended to act predominantly as a buffer or short-term storage unit, and the hydrogen is designated for long-term energy storage. In principle, this configuration facilitates an autonomous energy supply relying exclusively on renewable energy sources; this is achieved by enabling the surplus solar energy generated in summer to be converted into hydrogen and subsequently utilized in winter. A rule-based energy-management algorithm is presented, prioritizing hydrogen production from surplus energy during the summer period and aiming to minimize interaction with the public electricity grid. This is particularly relevant for high-latitude regions, such as Germany, where solar irradiation is significantly lower in winter than in summer. A quasi-optimal sizing of all components in the microgrid, along with a realistic techno-economic assessment of the overall system, is performed using an energy-management model implemented in Simulink and utilised with realistic boundary conditions. A case study utilizing realistic solar generation and empirically derived electrical load profiles demonstrates the technical and economic viability of seasonal energy shifting from summer to winter (resulting in an autarky degree exceeding 1) within an economically acceptable cost range. Full article

The growing penetration of electric vehicles (EVs) and distributed photovoltaic (PV) generation is increasing operational uncertainty in distribution networks and intensifying long-standing challenges such as higher power losses, rising peak demand, and voltage instability. To address these issues, this paper proposes a multi-objective optimization framework for the strategic placement of smart meters equipped with demand response (DR) capability in radial distribution systems. Unlike conventional placement approaches that mainly focus on monitoring or reducing non-technical losses, the proposed method integrates active load control into the planning stage and explicitly considers the stochastic behavior of loads, PV generation, and electric vehicle charging stations (EVCSs). The problem is formulated with four objectives: minimizing total power losses, substation peak demand, voltage deviation penalty, and installation cost. A scenario-based stochastic model is employed to represent operational variability across the network. The resulting nonlinear mixed discrete optimization problem is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), an evolutionary multi-objective optimization technique that generates a set of Pareto-optimal solutions representing trade-offs among conflicting objectives. Smart meters are allowed to curtail a portion of controllable demand during critical loading conditions, which helps reduce feeder loading and improve voltage profiles. The proposed approach is evaluated on the IEEE 33-bus and IEEE 69-bus distribution systems. Simulation results demonstrate significant reductions in power losses and peak demand, with the IEEE 33-bus system achieving up to a 26.2% reduction in power losses and 52.5% reduction in substation peak demand compared with existing metaheuristic approaches. The results also indicate improved voltage stability and effective performance in the IEEE 69-bus system, confirming the importance of topology-aware DR-enabled planning. Overall, the findings show that embedding demand response capability within smart meter allocation can significantly enhance the resilience and operational efficiency of modern distribution networks with high EV and PV penetration. Full article

Green hydrogen is increasingly discussed as an energy carrier that can link electricity, gas, heat, and transport sectors. However, many existing reviews address this topic from separate viewpoints, such as hydrogen production technologies, Artificial Intelligence (AI) applications, or system integration, with less attention to how policy and market conditions affect deployment. This review brings these related aspects together in one structured discussion. The paper first reviews the hydrogen supply chain, including production, storage, transport, and utilization. It then discusses an integrated multi-energy architecture in which hydrogen interacts with electricity, natural gas, heat, and cooling networks. Policy instruments in five major economies, including the European Union, the United States, China, Japan, and India, are compared. The review also summarizes the main barriers to large-scale deployment, including high production costs, limited infrastructure, technological challenges, regulatory uncertainty, and supply-chain constraints. In addition, the current market structure and selected large-scale hydrogen projects planned in the United States are reviewed. The paper also examines the role of artificial intelligence in green hydrogen systems. AI applications are grouped into four main stages of the hydrogen value chain: forecasting renewable energy generation, improving electrolyzer design and operation, optimizing storage and distribution, and supporting system-level techno-economic assessment. Recent Machine Learning (ML) studies are compared based on their methods and their contributions to operation and planning. Overall, this review highlights the role of AI in enabling green hydrogen integration within multi-energy systems. Full article

The escalating global demand for renewable energy has positioned solar photovoltaics (PV) as a critical technology for achieving net-zero emissions. However, PV efficiency is strictly limited by thermal degradation, where elevated operating temperatures significantly reduce power output and accelerate material aging. This review systematically evaluates the integration of advanced phase change materials (PCMs) as a passive thermal management solution. We analyze the transition from material-level innovations—including nano-enhanced PCMs, 3D conductive frameworks, and shape-stabilization—to system-level hybrid architectures such as liquid—PCM, heat pipe-fin, and thermoelectric generator (TEG) integrations. Synthesis of recent empirical data (2024–2026) demonstrates that optimized PCM composites can achieve PV temperature reductions of up to 32 °C and electrical efficiency enhancements exceeding 19%. Furthermore, techno-economic assessments reveal that these systems can reduce the levelized cost of energy (LCOE) by 5–15% and achieve energy payback times as short as 1.5 years. Finally, this paper identifies critical research gaps in long-term outdoor durability, AI-driven predictive modeling, and sustainable bio-based encapsulation, providing a strategic roadmap for the commercialization of next-generation solar thermal management systems. Full article

Deep excavation dewatering is an energy-intensive groundwater control process in underground engineering, especially under strong recharge and heterogeneous hydrogeological conditions. Conventional dewatering designs often rely on conservative pumping schemes to ensure the required drawdown, which may generate redundant groundwater extraction, unnecessary electricity consumption, additional carbon emissions, and excessive drawdown-induced settlement. To address this problem, this study develops a coupled improved genetic algorithm and MODFLOW optimization model, termed IGA-M, for dewatering well-group operation under engineering safety constraints. The purpose of the proposed model is not to reduce pumping arbitrarily, but to identify and eliminate redundant pumping while satisfying prescribed requirements for target water levels, settlement control, and hydraulic-gradient safety. Through the FloPy interface, the Improved Genetic Algorithm is dynamically linked with MODFLOW to establish a closed-loop simulation-optimization framework. In each optimization iteration, candidate well operation schemes are automatically transferred to MODFLOW, and the simulated hydraulic heads and settlement responses are returned to evaluate the objective function and safety constraints. In this framework, groundwater extraction, electricity consumption, carbon emissions, and land subsidence are treated as physically linked performance indicators of the optimized dewatering scheme. Validation using an idealized case shows that, under the same safety requirements, the IGA-M model reduces redundant hydraulic loading compared with the traditional uniformly distributed pumping method. By removing redundant pumping beyond the safety requirement, the optimized scheme reduced groundwater extraction by 62.7%, which was accompanied by a 44.9% decrease in both carbon emissions and comprehensive costs, as well as a 57.7% reduction in settlement at observation points. In a practical high-permeability deep excavation adjacent to the Yellow River, the model achieved well-group flow regulation under strong recharge conditions. Compared with the traditional scheme, it eliminated approximately 661,000 m³ of redundant groundwater extraction, corresponding to a 17.7% decrease, and consequently saved 26,800 kWh of electricity and reduced CO₂ emissions by nearly 16,000 kg during the dewatering period. These results demonstrate that the proposed IGA-M framework can transform MODFLOW from a post-design verification tool into an active optimization engine for dewatering design. It provides a physically based decision-support method for reducing redundant pumping and improving energy efficiency while maintaining engineering safety. Full article

Cities preparing Sustainable Urban Mobility Plans (SUMPs) increasingly require practical tools capable of merging diverse mobility datasets and transforming them into planning-relevant indicators. This article introduces PRIORITY (Platform for the tRansition to sustaInable zerO-caRbon mobilITY), a prototype platform designed to support mobility analysis and decision-making in urban contexts. The platform integrates Floating Car Data, GTFS feeds describing public transport supply, and detailed land-use and zoning information. By relying on these heterogeneous data streams, PRIORITY generates indicators such as travel and stop times, trip distances, trip volumes, energy consumption, pollutant emissions, external costs, and electric-vehicle charging behavior. The platform is organized into two main components: a back end and a front end. The back end, which constitutes the operational core, manages all collected data and ensures their structured storage in a shared database capable of handling large volumes of information on urban form, individual mobility patterns, public transport services, and modeling outcomes. The front end provides an intuitive and versatile interface that dynamically presents the outputs generated by the platform’s analytical and modeling processes. A case application for the Metropolitan City of Rome (Italy) illustrates the operational use of the prototype and shows how PRIORITY can support transparent and reproducible evaluations during the preparation and monitoring of SUMPs. The demonstrated workflow highlights the prototype’s value for public authorities and planners seeking data-informed approaches to urban mobility assessment and decarbonization strategies. Full article

The volatility of renewable energy generation and nodal electricity prices provides an arbitrage opportunity for Mobile Battery Energy Storage Systems (MBESS) leveraging both temporal and spatial advantages. However, the inherent high complexity and strong randomness of both power and transportation systems lead to complex risks for MBESS control. Existing works mainly consider the market price risk and ignore the transportation system risk caused by traffic congestion. Specifically, they are constrained by two critical limitations: (1) decisions can only be made upon arrival at a destination, making the agent unresponsive on the road, and (2) traffic congestion risk is neither quantified nor controlled, leading to suboptimal routing strategies. To address these limitations, the MBESS needs more flexible “on the road” decision making and multiple risk management capabilities. Guided by this objective, a flexible deep reinforcement learning-based MBESS control framework is proposed, considering both market and traffic congestion risk. First, dynamic routing ability is integrated with the MBESS agent to provide more flexibility in making decisions, regardless of whether the agent has reached the designated location or not. Second, two risk metrics are proposed to quantitatively assess the traffic congestion risk based on moving time, and then the agent can make decisions considering both market and traffic congestion risk. Finally, considering the inefficiency of learning caused by introducing multiple risks, a risk curriculum learning method is proposed to improve the training efficiency and reduce learning costs. These components are unified in the Multiple Risk Estimation SDDPG (MRE-SDDPG) algorithm, which jointly maximizes profitability while controlling electricity price and traffic congestion risk. Simulations in the IEEE 30 bus environment show that the proposed framework can increase profit by 8.6% while reducing the traffic time by 15.8% on average, demonstrating the superiority of our design in considering traffic congestion risk. Full article

In electricity–hydrogen coupling systems (EHCSs), the uncertainty of renewable energy generation (REG) tends to impact electrolyzers (ELs) in the following ways: (1) input powers of ELs are prone to fluctuations; (2) ELs are forced to operate under variable load states. Consequently, both impacts will reduce the service life of ELs. In this paper, considering the start–stop characteristics and combined operation modes of multiple ELs, a two-stage multi-time-scale energy allocation strategy (MSEAS) is proposed to mitigate the impacts of REG uncertainty and optimize the energy allocation for EHCSs. First, five refined operating states of ELs, such as shutdown, cold standby, low-load, variable-load and overload, are formulated as mixed-integer constraints and embedded into the system-level energy optimization model. Second, to mitigate power fluctuations caused by REG, a day-ahead optimization is employed to plan the power allocations of ELs, lithium batteries, fuel cells, and the grid with a 1 h time step; and then an intra-day rolling optimization is employed to adjust the operating states and power outputs of the above units with a 4 h window and 15 min step. Third, by enabling multiple ELs to flexibly operate in a combined mode, power-sharing mode and switching mode, the proposed MSEAS can refine the operation powers of ELs and reduce their start-up frequency. Comparative case studies are conducted in the off-grid and grid-connected operation tests, and the relevant results verify that the proposed MSEAS can effectively prevent the frequent start–stop of ELs, which contributes to extending the service life of ELs and reducing the system operating cost. Full article