Applied Energy最新文献

{"title":"A comparative state-of-the-art review of wave energy converters: point absorbers, linear absorbers, and hybrid systems","authors":"Alireza Abbasi ,&nbsp;Hassan Ghassemi","doi":"10.1016/j.apenergy.2026.127487","DOIUrl":"10.1016/j.apenergy.2026.127487","url":null,"abstract":"<div><div>Harnessing energy from ocean waves has gained increasing attention as a viable component of sustainable energy strategies due to its high power potential and predictable availability. This review provides a comprehensive assessment of three primary categories of wave energy conversion systems: point absorbers, linear absorbers, and hybrid systems. Drawing upon more than 200 scientific publications, the study examines the operational principles, structural characteristics, performance indicators, and control methodologies associated with each class. Special emphasis is given to recent technological advances, including adaptive power take-off architectures, and multifunctional offshore platforms that integrate wave, wind, and aquaculture. A comparative evaluation highlights performance trade-offs, techno-economic considerations, and environmental implications, while a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis is employed to identify both barriers and growth prospects. The review concludes by outlining future research directions, particularly the need for real-time control, large-scale demonstration, and integrated multi-use concepts. This study aims to serve as a reference for academics, engineers, and policymakers in shaping the next generation of marine renewable energy technologies.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127487"},"PeriodicalIF":11.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A 3D energy topology network modeling approach for cross-flow SOFCs: comprehensive evaluation of multi-physics field coupling and synergistic responses","authors":"Haobo Tang ,&nbsp;Zhe Wang ,&nbsp;Shilin Li ,&nbsp;Fei Long ,&nbsp;Yulong Ji ,&nbsp;Fenghui Han","doi":"10.1016/j.apenergy.2026.127446","DOIUrl":"10.1016/j.apenergy.2026.127446","url":null,"abstract":"<div><div>The cross-flow configuration of solid oxide fuel cell (SOFC) provides specific benefits related to inlet arrangement and performance. However, due to the complexity of flow direction, the use of high-dimensional models to replace labor-intensive experimental procedures has always been the preferred choice. The paper proposes a 3D energy topology network modeling method that provides a comprehensive understanding of heat transfer and temperature distribution along the X-Y-Z direction, and enables efficient calculation of electrical, thermal, and gas-related parameters within each segment. This study aims to statistically analyze and explore the trends of electro-thermal-gas energy flow distributions using a three-dimensional numerical model. The Comprehensive Response Coefficient (CRC) is introduced to quantify the interference effects among different energy flows under varying operating conditions. This approach enables both qualitative and quantitative assessments of multi-physics synergistic responses across different scales, representing a novel contribution of this work. At the cell scale, changes in load conditions yield the highest CRC value (0.17), while variations in the oxygen excess ratio correspond to the lowest CRC value (0.14). Under all tested conditions, the electrical response (61.08%–90.87%) consistently dominates the CRC distribution over the thermal (8.38%–37.85%) and gas responses (0.12%–1.28%), ultimately forming a characteristic sequence of “load-driven, heat-following, gas-feedback.” At the node scale, changes in electrical, thermal, and gas conditions lead to elevated CRC values in the central region of the cell, the inlet, and the outlet, respectively. The combination of 3D numerical modeling with a multi-scale synergistic response evaluation framework enables the investigation of internal node states in the SOFC, facilitates the detection of abnormal fluctuations in coupled behavior, and provides data support for coordinated multi-parameter optimization.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127446"},"PeriodicalIF":11.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sizing analysis and economic feasibility evaluations of offshore floating electric vessel charging stations for sustainable development","authors":"Sruthy V. ,&nbsp;Preetha P.K. ,&nbsp;Javier Rodríguez-García","doi":"10.1016/j.apenergy.2026.127447","DOIUrl":"10.1016/j.apenergy.2026.127447","url":null,"abstract":"<div><div>As the marine transportation sector gradually transitions towards sustainable electrified solutions, the significance of charging solutions has been receiving considerable attention. The study is a compendium covering the viability, analysis, and sizing of floating charging stations (FCS) for electric vessels, in offshore locations, including the North Sea, Europe, and the Arabian Sea, India. The comprehensive investigation used real-time renewable energy data to determine the optimal sizing of offshore charging stations. MATLAB simulations using Genetic Algorithm and the Interior-Point Fmincon algorithm optimized the FCS system for minimum life cycle cost with loss of power supply probability as a reliability criterion to evaluate the sizing analysis. The economic assessment produced a levelized cost of energy of 10.74 INR/kWh and a payback period of 5.1 years for the North Sea FCS and 12.92 INR/kWh and 6.2 years for the Arabian Sea FCS. Economic factors like net present value and profitability index as well as the sensitivity analysis with respect to discount rates confirmed the FCS project's feasibility at the selected locations. The study lays the groundwork for future research on offshore charging stations for the deployment of electric vessel transit services.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127447"},"PeriodicalIF":11.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy-optimized operation of a distributed data center infrastructure located in wind farms: a multi-agent system approach","authors":"Alexander Kilian,&nbsp;Michael Bettermann,&nbsp;Hermann de Meer","doi":"10.1016/j.apenergy.2026.127454","DOIUrl":"10.1016/j.apenergy.2026.127454","url":null,"abstract":"<div><div>This work proposes a multi-agent system aimed at increasing the computing sustainability of high-performance computing data centers that are distributed among several wind farms. The novel approach of wind turbines housing high-performance computing data centers seeks to maximize renewable energy usage by supplying the data centers with otherwise curtailed wind energy, thus increasing wind farm efficiency as well. To optimize data center operation in this unique environment, job execution should be prioritized during periods of high availability of renewable energy. When wind power generation is low, resource utilization should be continuously adjusted to minimize gray electricity consumption with high carbon intensity or high grid consumption costs. Furthermore, green service-level agreements are introduced allowing for more flexibility in terms of deadline compliance, thereby fostering energy-aware data center operation. The proposed multi-agent system realizes a moving-horizon, multi-objective optimization problem to find the best operational strategy, taking into account both sustainability and performance concerns, and is compared against a selection of baseline job scheduling strategies.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127454"},"PeriodicalIF":11.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy-efficient greenhouse climate control with diffusion reinforcement learning","authors":"Guodong Chen ,&nbsp;Fengqi You","doi":"10.1016/j.apenergy.2026.127437","DOIUrl":"10.1016/j.apenergy.2026.127437","url":null,"abstract":"<div><div>Greenhouse agriculture is vital for sustainable food production, yet its high energy demand and resource inefficiency pose significant challenges. Traditional climate control methods often rely on heuristic strategies or suboptimal rule-based systems, leading to excessive energy consumption and operational costs. To address this, we propose a diffusion reinforcement learning framework for resource-efficient greenhouse climate control, optimizing temperature, humidity, and CO₂ levels while minimizing energy use. Unlike conventional deep reinforcement learning, or stochastic policy methods, our diffusion-based approach enhances policy robustness by modeling stochastic environmental dynamics, enabling adaptive decision-making under uncertainty. We validate our method using real-world greenhouse data and simulations, demonstrating superior performance over conventional proportional–integral–derivative method. Simulation results show energy savings of 47.31% (±4.14%) in spring, 45.69% (±4.51%) in summer, 55.54% (±2.06%) in autumn, and 42.92% (±2.29%) in winter, compared to baseline methods while maintaining optimal crop growth conditions. This study advances intelligent control in precision agriculture by integrating denoising diffusion probabilistic models with reinforcement learning, offering a data-driven pathway toward energy-efficient and carbon-neutral greenhouse operations.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127437"},"PeriodicalIF":11.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146049139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards a sustainable workplace: a life cycle perspective on EV-charging-integrated HVAC systems","authors":"Ruiqi Wang,&nbsp;Janie Ling-Chin,&nbsp;Dibyendu Roy,&nbsp;Anthony Paul Roskilly","doi":"10.1016/j.apenergy.2026.127485","DOIUrl":"10.1016/j.apenergy.2026.127485","url":null,"abstract":"<div><div>Heating, ventilation, and air conditioning (HVAC) systems contribute substantially to energy consumption and carbon emissions in the building sector. This study evaluates the environmental impacts of solar-powered HVAC systems in the workplace in a city in Portugal (Sines) using a comprehensive life cycle assessment (LCA), compared to systems powered by national grid electricity. The LCA is performed using the Environmental Footprint (EF) 3.1 method with a cradle-to-grave system boundary. Electric vehicle chargers are included to temporarily store excess electricity generated from a solar photovoltaic system. Results from the LCA indicate that the manufacturing and operation phases of the product system powered by grid electricity contribute significantly to most of the environmental impacts, while switching to a solar-powered system could reduce the climate change impact by 87.28%. The sensitivity analysis demonstrates that heating and cooling set point temperatures of HVAC systems critically affect the environmental burdens arising from the product system. Substituting reclaimed refrigerants for newly produced refrigerants can reduce global warming potential by 5.8% during the manufacturing phase of HVAC systems. Enhancing HVAC energy efficiency and adapting heating and cooling operation are essential for reducing GHG emissions. Furthermore, the scope of the study is expanded by evaluating the environmental impacts of workplace HVAC systems in three more different European cities, i.e., Paris, London, and Oslo, representing warm and cold climate zones. Future increases in renewable electricity capacity lead to reduced environmental impacts but increase mineral and metal resource use. A broader consideration of environmental impacts through LCA offers insights into the future development and application of sustainable energy systems.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127485"},"PeriodicalIF":11.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiobjective mixed-integer Bayesian optimization for the implementation of hydrogen as maritime fuel through liquid organic hydrogen carriers (LOHCs)","authors":"Carlos Prieto ,&nbsp;Antonio Sánchez ,&nbsp;Ehecatl Antonio del Rio-Chanona ,&nbsp;Mariano Martín","doi":"10.1016/j.apenergy.2026.127493","DOIUrl":"10.1016/j.apenergy.2026.127493","url":null,"abstract":"<div><div>Hydrogen is proposed as a fuel for maritime decarbonization, but its storage is challenging. Liquid Organic Hydrogen Carriers (LOHCs) offer a solution for storing hydrogen under ambient conditions enabling to reuse oil infrastructure. The dehydrogenation module, coupled with a Solid Oxide Fuel Cell, is modeled using a first-principles model. Detailed three-phase reactor modeling accounts for heat, mass and momentum and represents relevant phenomena in LOHCs reactors. Two different stages are set: process design and operation. In the first stage, unit design parameters and operating conditions are considered and several objectives are proposed. Additionally, several reaction technologies are suggested (mixed-integer formulation). With equipment defined, operation is optimized. Expensive high-complexity models hinder optimization. Hence, Machine Learning algorithms and specifically Bayesian Optimization (BO) can be leveraged for LOHCs optimization. Process design involves the optimization of a mixed-integer, single (SO) and multiobjective (MO) constrained formulation. Then, SO and MO constrained optimization are carried out for operation. To handle integers, adapted kernels and multiple acquisition function optimizers are used. These strategies perform comparably or better than approaches ignoring integers. Then, these are encouraged for an effective mixed-integer optimization. Design stage reached a trade-off with 1990 $/kW<span><math><mrow><msub><mspace></mspace><mrow><mi>el</mi></mrow></msub></mrow></math></span> and 5.95 l/kW<span><math><mrow><msub><mspace></mspace><mrow><mi>el</mi></mrow></msub></mrow></math></span> equipment investment and volume respectively and 37.49% efficiency. Detailed reactor models differ significantly from simplified baselines showing the need to accurately represent these units. Then, operation MO was carried out. Pareto Front was built with several operational points. Thus, the use of LOHCs for hydrogen storage in maritime sector is explored and promoted.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127493"},"PeriodicalIF":11.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two-stage optimization scheduling strategy for virtual power plants considering the correlated uncertainty of distributed energy storage in extreme scenarios","authors":"Runxin Chen ,&nbsp;Liqing Liao ,&nbsp;Dongran Song ,&nbsp;Chuxi Wei ,&nbsp;Jian Yang ,&nbsp;M. Talaat ,&nbsp;M.H. Elkholy","doi":"10.1016/j.apenergy.2026.127477","DOIUrl":"10.1016/j.apenergy.2026.127477","url":null,"abstract":"<div><div>Under extreme scenarios, virtual power plants (VPPs) are subjected to multiple impacts such as abrupt changes in source-load power and mismatched coordinated operation of distributed energy storage (DES) clusters, which significantly increases the difficulty of balancing scheduling security and economic efficiency. Considering the correlated uncertainty of DES, this article proposes a two-stage optimization scheduling strategy for VPPs to address the issue of enhancing system resilience in extreme disaster events. Firstly, K-means clustering is performed on the wind and load data, and based on typical scenario screening, an extreme scenario optimization selection mechanism is proposed with the principle of maximizing scenario dissimilarity. Secondly, based on cross correlation analysis, the correlated uncertainty of DES power changes in extreme scenarios is characterized, and a correlated matrix is constructed. Then, the two-stage optimization scheduling strategy is proposed. In the first stage, the total cost of purchasing electricity and operation and maintenance expenses is minimized. In the second stage, based on the output of each DES obtained in the first stage, the correlated uncertainty between DES and their own response deviation are reduced. Finally, the effectiveness of the strategy is verified through numerical analysis. The results show that compared to existing methods, the proposed strategy can reduce 30.91% of the power gap and 68.77% of the total cost in extreme scenarios of the distribution network. Therefore, the proposed method effectively balances the risk resistance ability and scheduling economy in extreme scenarios, providing a new technical path for the safe and efficient operation of VPP under extreme working conditions.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127477"},"PeriodicalIF":11.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A self-adaptive modeling and control framework for HVAC systems considering user stochasticity","authors":"Zelin Guo ,&nbsp;Yiyan Li ,&nbsp;Jian Ping ,&nbsp;Jiayi Zhu ,&nbsp;Zheng Yan ,&nbsp;Mo-Yuen Chow","doi":"10.1016/j.apenergy.2026.127512","DOIUrl":"10.1016/j.apenergy.2026.127512","url":null,"abstract":"<div><div>The modeling and control of HVAC systems are critical for energy savings and demand response programs. However, as part of the HVAC thermal system, user activities such as opening/closing windows and using appliances will impact the system thermal dynamics, which are stochastic in nature and cause modeling errors. Meanwhile, user's thermal preferences vary with time, weather, clothing, etc., which are also stochastic and make it difficult to design control strategies that can accurately optimize the energy costs and user comforts. In this paper, to better accommodate user stochasticity, a self-adaptive modeling and control framework is proposed. In the modeling part, a hybrid model integrating classical Equivalent Thermal Parameter (ETP) model with an advanced neural network TimesNet is proposed to capture both static building properties and dynamic user disturbances. In the control part, a two-stage control strategy combining Model Predictive Control (MPC) and Deep Q-Network (DQN) is designed to balance the control optimality and flexibility. Simulation results based on real-world building dataset demonstrate that the proposed hybrid model can achieve less than 1% of modeling error. The control strategy can achieve on average 13% energy savings while maintaining the temperature deviation within 0.5 °C from user preferences.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127512"},"PeriodicalIF":11.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Collaborative risk-resistant distributionally robust dispatch and benefit allocation scheme for interconnected distribution systems","authors":"Jialin Du ,&nbsp;Di Cao ,&nbsp;Weihao Hu ,&nbsp;Sen Zhang ,&nbsp;Wen Liu ,&nbsp;Zhenyuan Zhang ,&nbsp;Daojuan Wang ,&nbsp;Zhe Chen","doi":"10.1016/j.apenergy.2026.127458","DOIUrl":"10.1016/j.apenergy.2026.127458","url":null,"abstract":"<div><div>To further exploit the potential of interconnected active distribution networks in reducing overall operational risks through coordinated scheduling under source-load uncertainties, this paper proposes a distributionally robust dispatch and benefit allocation scheme. First, a collaborative risk-resistant distributionally robust optimization model based on the conditional value at risk measure is established to minimize the whole risk cost associated with the heavy-tailed distribution of uncertain variables, trading a small loss in expected economic performance for a significant gain in tail robustness. Then, by employing piecewise linearization techniques and duality theory, the introduction of integer variables is avoided, and the original semi-infinite programming problem is equivalently transformed into an efficiently solvable semidefinite programming problem. Finally, an asymmetric Nash bargaining model that accounts for risk-reduction contributions is employed to ensure a fair and equitable allocation of cooperative benefits. Simulation results based on the modified 119-bus system demonstrate that the proposed method can effectively improve the economic and robust operation of interconnected active distribution networks under source-load uncertainties. Compared with the non-cooperative distributionally robust optimization model, the day-ahead risk and intraday cost of the interconnected microgrids are reduced by 27.48% and 14.34%, respectively.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127458"},"PeriodicalIF":11.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}