Search Results (167)

The coordinated operation of interconnected multi-carrier energy hubs is a key enabler of cost-efficient and flexible energy management in modern smart cities. This paper develops a comprehensive optimization framework for the day-ahead scheduling of interconnected energy hubs in residential and commercial sectors. The problem is formulated as a mixed-integer linear programming (MILP) model that jointly manages electricity, natural gas, and thermal energy flows. To enhance operational flexibility, the proposed model incorporates demand response programs for both electrical and thermal loads, multiple energy storage technologies, and electric vehicles with vehicle-to-grid (V2G) capability. Six operating scenarios are defined to assess the impact of different resources and coordination levels, ranging from independent hub operation to fully integrated interconnected scheduling. Simulation results show that coordinated operation of the energy hubs, supported by flexible loads, storage systems, and electric vehicles, can significantly reduce total daily operating costs compared with conventional standalone configurations. The findings confirm that energy exchange among hubs, combined with demand-side flexibility and EV participation, improves both economic performance and system efficiency. The proposed framework offers a scalable scheduling approach for future integrated multi-energy systems. Full article

In a digital watermarking technology system, robustness, imperceptibility and payload capacity are the three core performance indicators that restrict one another. How to achieve the optimal balance between the three is still the key scientific challenge to be solved in this field. This paper proposes a digital watermarking algorithm based on the magnitude coefficient of Non-Subsampled Shearlet Transform Fast and Accurate Polar Harmonic Fourier Moments (NSST-FAPHFMs) and the Weibull–Power Cauchy (W-PC) statistical model. The algorithm consists of two stages: watermark embedding and detection. In the embedding phase, the original image is first decomposed by NSST multi-scale decomposition, and the high-frequency subbands are divided into non-overlapping blocks and partitioned. High-energy coefficient blocks are extracted to obtain NSST-FAPHFM magnitude coefficient features, which serve as robust carriers for watermark embedding. In the detection phase, the W-PC distribution is used to accurately statistically model the above magnitude coefficients to characterize their heavy-tailed characteristics and strong correlation structure. Maximum likelihood estimation (MLE) is employed to estimate the model parameters, and a blind watermark detection mechanism is further constructed by integrating the W-PC model with the Local Optimal Detector (LOD) under the Neyman–Pearson (N-P) criterion. Experimental results show that the proposed algorithm has good imperceptibility, and the area under the receiver operating characteristic curve (AUROC) can reach 0.9991 without attack. The algorithm maintains strong robustness against various attacks and can effectively realize the joint optimization of the three core performance indicators of the watermarking system. Full article

The energy management (EM) of smart multi-carrier energy hub (SMCEH) systems for cost and emission reduction remains a challenging problem due to the diversity of renewable energy resources (RERs), varying load demands, and the stochastic nature of these resources. This paper addresses the EM problem of SMCEHs to minimize operational costs and greenhouse gas (GHG) emissions using the particle swarm optimization (PSO) algorithm. The studied SMCEHs are designed to simultaneously supply electrical, cooling, and thermal demands. The hub system comprises wind turbines (WTs), photovoltaic (PV) panels, gas turbines (GT), electric chillers (EC), gas boilers (GBs), absorption chillers (AC), battery storage systems, and thermal storage units. To assess system performance and the impact of key technologies, three case studies are investigated: (i) EM of SMCEHs without RERs, (ii) EM of SMCEHs with RERs, and (iii) EM of SMCEHs with RERs and an integrated carbon capture unit (CCU). These scenarios enable a systematic evaluation of the role of renewable integration and carbon capture in enhancing system performance. The results demonstrate that incorporating RERs into SMCEHs leads to a substantial reduction in both operational costs and GHG emissions. Furthermore, the integration of a CCU provides additional emission reductions, underscoring its effectiveness in supporting the low-carbon operation of SMCEHs. The obtained results show that integrating RERs into SMCEH decreases the total cost and emissions by 64.12% and 7.95%, respectively, compared to the scenario without RERs. Furthermore, the integration of the CCU into SMCEHs provides a 39.36% reduction in total costs and a 72.57% decrease in CO₂ emissions. The suggested energy management solution promotes a sustainable and low-carbon emission system by maximum utilization of the RERs and CCU. Full article

The high energy demand driven by industrial development has transformed the power system from a single energy source to multiple energy systems (MESs). These systems, which involve thermal generators, combined heat-and-power (CHP) units, electrolyzers, fuel cells, etc., with realistic forecast uncertainty, are very operationally challenged. This paper proposes a Distributionally Robust Optimization (DRO) based on a Wasserstein-metric ambiguity set, which simultaneously optimizes the annual maintenance schedules and short-term operational dispatch across MESs. The ambiguity set is constructed using joint samples of forecast errors for the three carriers’ demand, allowing for a data-driven worst-case distribution approach that mitigates the excessive conservatism typically associated with conventional robust optimization (CRO). The penalties are explicitly enforced for load and renewable energy curtailments across each of the MESs with source-specific value-of-lost-load coefficients. The Wasserstein radius is improved by sensitivity analysis, obtaining a θ value of 0.20 as the cost reduction radius for a 40% RES penetration. Five RES penetration levels are implemented here on the IEEE 39-bus New England network, with CHP, electrolyzer, fuel cell, thermal storage, and hydrogen storage. The DRO reduces the total annual system cost by 56% compared to CRO, while reducing the unbalanced energy. Full article

Hydrogen is increasingly recognized as a key energy carrier for decarbonizing hard-to-electrify sectors, yet more than 95% of current global production remains fossil-derived. Biological hydrogen (biohydrogen) produced by dark fermentation (DF), photo-fermentation (PF), or microbial electrolysis cells (MEC) offers the dual advantage of valorizing organic wastes while delivering low-carbon H₂; however, none of these standalone technologies mobilizes more than 25–33% (DF), 40–70% (PF), or 40–60% (MEC) of feedstock organic carbon through H₂-producing oxidation pathways. Most existing reviews compare these pathways on hydrogen yield alone, a metric that conceals where the majority of feedstock carbon and electrons are actually lost and obscures the quantitative rationale for system integration. This review reframes the comparison around carbon and electron flow, explicitly tracking how much input carbon is mobilized through H₂-producing oxidation pathways, how much is retained in volatile fatty acids (VFAs), biomass, or unlinked CO₂, and what happens to the associated electrons. Stoichiometric, mechanistic, and reactor-level evidence is synthesized to show that DF channels only 25–33% of input organic carbon through H₂-yielding decarboxylation on real heterogeneous substrates, with 40–60% retained as residual VFAs and unhydrolyzed solids; PF can recover 60–80% of VFA carbon but is constrained by photon economics and nitrogenase sensitivity; and MEC achieves >85% COD removal only when coupled to an upstream acidogenic stage. Two-stage (DF–PF, DF–MEC) and three-stage (DF–PF–MEC, DF–MEC–AD) configurations are critically evaluated, with theoretical yields separated from experimentally demonstrated performance on real wastes and hidden energy inputs (pretreatment, inter-stage transfer, gas separation, and compression) explicitly accounted for. DF–MEC coupling is identified as the most near-term tractable configuration, achieving 55–70% H₂-pathway carbon mobilization and 80–92% COD removal at an electrical input of 0.9–1.5 kWh/m³ H₂, with levelized hydrogen costs of US$3–5.5/kg under favorable waste-tipping-fee conditions. Multi-stage systems push carbon recovery above 70% but carry unresolved capital, methanogenesis control, and scale-up penalties. This review closes by proposing a standardized ten-descriptor reporting framework including H₂-pathway carbon mobilization (%), cathodic hydrogen recovery (rCAT), net energy recovery (NEB), and LCA carbon intensity under both attributional and consequential boundaries, and demonstrates its backward compatibility by retrospective application to seven studies already in the literature. Research priorities tractable on a 5–10 year horizon are identified, centered on methanogen suppression at pilot scale, real-waste MEC performance, and renewable-electricity coupling. Full article

Driven by the global low-carbon transition and the rapid expansion of marine energy development, offshore integrated energy systems are emerging as a critical configuration for coupling offshore renewable resources, geothermal and subsurface thermal resources, oil and gas infrastructure, hydrogen pathways, multi-carrier networks, and offshore loads. Unlike onshore integrated energy systems, offshore systems are constrained by resource intermittency, harsh marine environments, platform space and weight limits, long-distance transmission, operation and maintenance accessibility, safety risks, and cross-regional governance mechanisms. Recent studies have advanced offshore wind-to-hydrogen systems, oil and gas platform electrification, offshore energy hubs, platform repurposing, and offshore geothermal utilization. However, these studies remain fragmented in terms of system boundaries, multi-energy coupling mechanisms, engineering constraints, and optimization methods. This paper reviews offshore integrated energy systems from the perspectives of system configuration, key integration technologies, optimization and assessment methods, and future research needs. Offshore integrated energy systems are first classified into offshore renewable-energy-dominated systems, offshore wind–hydrogen systems, oil and gas platform integrated systems, offshore energy hubs and multi-carrier networks, decommissioned-platform repurposing systems, and offshore geothermal and repurposed-well systems. Resource-side, conversion-side, storage-side, network-side, and load-side integration technologies are then summarized. Capacity configuration, operational scheduling, stochastic and robust optimization, multi-objective optimization, energy, exergy, economic, and environmental (4E) assessment, advanced exergy analysis, and energy-hub modelling are further reviewed. Finally, key research gaps are identified, including resource uncertainty, offshore engineering constraints, multi-carrier network coupling, insufficient demonstration data, and policy and economic uncertainty. This review provides a structured reference for the modelling, integration, optimization, and demonstration of offshore integrated energy systems for low-carbon transition. Full article

The growing demand for hydrogen as a low-carbon energy carrier has renewed interest in coal, an abundant and globally available resource, making it crucial to assess its suitability for efficient hydrogen production via in situ pyrolysis. The main objective of this study is to propose a new and robust hesitant fuzzy Multi-Criteria Decision-Making (MCDM) framework to the literature to determine the coal alternative that will provide the most efficient hydrogen production. For this purpose, five different coals with the largest reserves in the United States (Wyoming, Illinois, West Virginia, Kentucky, and Pennsylvania) were examined under eight criteria. To manage uncertainties in determining the weights of the criteria, the hesitant fuzzy set-based step-wise weight assessment ratio analysis (SWARA) method was used. The coal alternatives were ranked using the Alternative Ranking Technique based on Adaptive Standardized Intervals (ARTASI). The proposed framework uses expert evaluations and performance criteria as input parameters to determine criterion weights and rank the alternatives. The output consists of the calculated criterion weights, utility values, and the final ranking of the liquid hydrogen rocket oxidizer systems. The robustness of the obtained results is further verified through parametric analyses of the w and Ψ parameters and comparative other MCDM methods validation. The findings consistently demonstrate that Wyoming coal is the most suitable alternative for hydrogen production via in situ pyrolysis. This study provides a systematic decision support framework for evaluating coal resources and offers valuable guidance for future hydrogen production strategies based on in situ coal pyrolysis. Full article

Network functions virtualization (NFV) is an emerging technology that enables flexible service deployment for supporting the Beyond 5G/6G network. NFV transforms physical network devices into virtual network functions (VNF) over Edge Computing capabilities, thereby facilitating the agility of network services and reducing management costs. To effectively monitor Internet of Things (IoT) network resources, service function chaining (SFC) is used for its virtualizations to ensure the multi-service requirements are sufficiently in capability, scalability, and flexibility for computation workloads alignments. However, to satisfy the resource availability requirements and efficiency under several conditions, SFC reconfiguration methods face the challenges in meeting significant latency requirement of delay-sensitive applications while reaching the importance of energy saving on orchestration timespan. In this paper, we propose task management-aware SFC and orchestrating schemes, namely GNN-PPO. In this framework, we utilize the Graph Neural Network (GNN), which relies on the message-passing neural network (MPNN), to capture all the abstraction of physical resource nodes and link capabilities over MEC node states. In particularly, GNN is divided construction into two phrases: (1) GNN represents nodes for all the Mobile edge computing (MEC) nodes, which have a global view on resources of computation and communicational capabilities that could serve as carriers; (2) VNFs are transferred into graph networks by using feature-extraction MPNN to manage each VIM that seeks an optimal and reliable analysis of traffic fluctuations. Lastly, Deep Reinforcement Learning (DRL) is used to embrace the network determination in policy strategy, which utilizes a Proximal Policy Gradient (PPO). On the other hand, we propose a novel network architecture based on PPO to perform the design for the optimization of resource utilization and facilitate energy consumption on MEC servers under diverse setting scenarios, which enables continuous policy enforcement for our system. With the experimental results, we compare our proposed solution with reference schemes in terms of rewards with learning rate and batch size, average request acceptance, SFC success, packet delivery, throughput, and resource utilization ratio that confirm the scheme’s scalability and practical suitability for IoT network deployment. 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

Background: Interest in green hydrogen (GH) is increasing, as it can act both as an energy carrier and as an industrial feedstock to decarbonise applications that currently rely on fossil-based (grey) hydrogen. Hard-to-abate industries, such as steelmaking, face complex and multi-dimensional uncertainties when assessing conversion to GH and the associated supply chain redesign. Materials and Methods: We propose an enterprise-oriented decision-modelling framework that structures conversion drivers into six decision-relevant dimensions (socio-economic, infrastructure, technology, market, supply chain, and enterprise). The framework is refined through a two-round expert elicitation process and operationalised through a scenario planning workflow based on discrete key-factor projections and an elicited interdependency network. Building on this dependency structure, we propose a transparent consistency-based reduction approach that integrates pairwise projection compatibility and graph-guided screening to identify internally coherent and decision-relevant scenarios. The procedure is further demonstrated through an illustrative steelmaking conversion case. Results: The expert-supported workflow identifies 14 external key factors and their decision-relevant projections, together with an elicited interdependency structure among them. The illustrative application shows how an initial scenario space of 6561 configurations, based on eight selected key factors, can be screened to 1335 internally admissible configurations and consolidated into four representative scenarios. These scenarios capture distinct decision contexts, including coordinated acceleration, demand-led but infrastructure-constrained transition, technology and policy push with limited market pull, and fragmented, delayed transition. Conclusions: The approach enhances methodological transparency in scenario-based decision support and offers hard-to-abate industries a structured basis for evaluating green hydrogen conversion under systemic interdependencies and deep uncertainty. The illustrative application further demonstrates how the framework can transform combinatorial uncertainty into a compact and interpretable set of scenarios supporting stakeholder discussion and strategic decision-making. Full article

Against the backdrop of climate change, the volatility of energy supply and demand in integrated energy systems (IESs) has intensified, resulting in heightened scheduling challenges. Electric–hydrogen coupling has emerged as a pivotal approach to fostering multi-energy complementarity while enhancing the flexibility and stability of IES. Rational planning of an electric–hydrogen coupled integrated energy system (EH-IES) can further strengthen energy interconnection and mutual support. First, the architecture and diverse coupling modes of the EH-IES are outlined based on key technologies and coupling mechanisms. Accurate modeling serves as the “cornerstone” of planning, with electric power and hydrogen energy equipment acting as the foundational “carriers” and electric–hydrogen coupling devices as the critical “link.” By examining application scenarios across the transportation, building, and industrial sectors, the study analyzes EH-IES planning scenarios, objectives, and modeling methodologies. Sector-specific planning primarily focuses on equipment configuration and layout, evaluated from economic and/or environmental perspectives. Finally, future research directions for EH-IES planning are proposed, addressing multiple uncertainties, energy demand dynamics, and market mechanisms. These insights aim to provide a reference for subsequent studies. Full article

The wide application of liquid hydrogen as a key energy carrier is severely limited by the reliability of high-pressure and low-temperature pumps. The traditional research on liquid hydrogen pumps relies on empirical analysis of isolated components, but fails to reveal the fundamental failure mechanism of these pumps. This review argues for a paradigm shift in the understanding and design of liquid hydrogen pumps. We systematically decomposed the failure of the liquid hydrogen pump into a thermodynamic-driven, cross-scale cascading process rather than the failure of isolated components. At the molecular level, the extreme thermal physical properties of liquid hydrogen (ultra-low latent heat and surface tension) can lead to widespread nucleation under slight thermal disturbances. At the mesoscopic scale, the initial perturbation is significantly amplified through the nonlinear dynamics of bubble clusters. This amplification is characterized by intense collapse and strong energy concentration due to the low density and low viscosity of liquid hydrogen. At the component level, this enhanced destructive energy will cause faults similar to phase transitions; namely, the liquid lubrication in the bearings will disappear, the seals will shift from viscous blockage to gas diffusion, and at the same time, the damage caused by low-temperature hydrogen cavitation and corrosion to the materials will also occur simultaneously. At the system level, the strong dynamic coupling among the subsystems has led to a nonlinear performance collapse. This cross-scale failure chain reveals the flaws in the classical cavitation theory, which is based on the assumptions of isothermal and inertia dominance. We have expounded the thermodynamic-dominated cavitation state in liquid hydrogen. This state is quantified by the Σ parameter and governs the multimodal behavior of low-temperature cavitation phenomena. To address this complexity, we have proposed a comprehensive framework that integrates multi-scale collaborative simulation and digital twin, combining molecular dynamics, CFD, system dynamics, and targeted experiments. This review proposes a candidate physical framework for addressing the reliability challenges of liquid hydrogen pumps. It also provides a clear roadmap for the next generation of inherently robust cryogenic fluid machinery, and offers a reference for the design of energy systems under other extreme conditions. 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

As modern power systems undergo a paradigm shift toward decentralization, driven by substantial investments in Distributed Energy Resources (DERs), Virtual Power Plants (VPPs) have emerged as the primary mechanism for their effective technical and commercial integration. This paper provides a seminal and comprehensive literature review, dissecting the VPP ecosystem through operational, infrastructural, and coordination strategy perspectives. By categorizing VPPs into distinct technical and commercial frameworks, this study critically evaluates their role in optimizing smart grid components, including demand response, multifaceted market structures, cooperative game-theoretic behaviors, and multi-carrier energy systems. The analysis transcends basic infrastructure, focusing on the resolution of fundamental challenges: mitigating carbon emissions and energy costs, characterizing generation uncertainty and asynchrony, and maintaining the dynamic equilibrium between supply and demand. Furthermore, the review explores advanced strategies for incentivizing prosumer engagement, enhancing market pricing transparency, and ensuring transaction integrity within rigorous operational constraints. A significant methodological evolution is identified, highlighting the transition toward advanced mathematical frameworks and data-driven optimization techniques designed to enhance system resilience and operational stability under multifaceted uncertainties. The synthesis reveals that VPP-led sector coupling integrating electricity, thermal, and hydrogen vectors provides a robust pathway for minimizing grid imbalances and diminishing the overall carbon footprint. By evaluating the subject through a multidimensional lens (technical, economic, environmental, and regulatory) this study serves as a critical reference and strategic roadmap for researchers, planners, and policymakers aiming to navigate the complexities of future smart grids and build a sustainable energy ecosystem. Full article

The global network of pipelines constitutes a strategic backbone for the world economy, enabling safe and efficient transportation of energy products. These pipelines serve distinct functions in the energy supply chain: gas pipelines support emerging cleaner energy carriers; multi-product pipelines provide versatility in transporting refined liquid fuels; and oil pipelines remain dominant for crude oil delivery. Energy management across the pipeline value chain emphasizes efficiency optimization, cost reduction, and sustainability through real-time monitoring, data analytics, integrated systems, and technological innovations spanning operations, maintenance, and emission control. Despite their critical role, petroleum depots remain relatively understudied, particularly in developing and Sub-Saharan African contexts. This review synthesizes insights from over 100 studies on energy-efficient pumping, predictive control, digitalization, and socio-technical energy management in depots. Analysis of these studies highlights recurring operational and infrastructural issues that constrain energy efficiency in depots. The challenges include irregular truck-loading schedules, frequent pump cycling, aging equipment, power-supply instability, manual operator interventions, and policy-driven constraints. The reviewed studies demonstrate that anticipatory, multi-layer control strategies integrating short-horizon flow forecasting, hybrid model predictive control, and cyber–physical–human–institutional system representations outperform reactive approaches in mitigating energy losses and operational variability. Site-specific calibration and phased deployment emerge as pragmatic pathways for implementing advanced energy optimization under the constrained conditions typical of real-world petroleum depots. Full article