Applied Energy最新文献

{"title":"Study on two-phase transport and performance characterization in orientational structure proton exchange membrane fuel cells at high water content","authors":"Zi Rui Guo,&nbsp;Hao Chen,&nbsp;Hang Guo,&nbsp;Fang Ye","doi":"10.1016/j.apenergy.2025.125970","DOIUrl":"10.1016/j.apenergy.2025.125970","url":null,"abstract":"<div><div>With advances in catalyst technology, the power of commercial fuel cells has generally enhanced, imposing higher demands on water management. Clarifying the gas-liquid flow and performance characteristics under high water content is critical for improving the dynamic stability and lifetime of the fuel cell. In this study, the evolutions of gas-liquid flow and the performance of fuel cells at high water content are investigated using water injection. Differences in gas-liquid distribution and performance between the orientational and straight channels under high water content are discussed. Results show that the liquid water distribution of the orientational channels is more uniform compared to straight channels. The orientational plate has a blocking effect on the liquid droplets, and droplets first fill the channel and then flow downstream. Water mist dissipates faster in the orientational channels after switching to high voltages due to increased gas velocity induced by the orientational plate. In the cathode, the liquid water mainly forms film flow because of the low oxygen velocity, and the droplets in the orientational channels enable rapid movement driven by the upstream droplet pushing forces. Anode water injection improves membrane wettability and cell performance, suggesting anode water injection potential as a humidification method. The orientational channel demonstrates superior water retention, with the highest performance improvement observed during upstream water injection and achieving up to 95 % net power improvement. However, cathode water injection may degrade performance due to water flooding.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"392 ","pages":"Article 125970"},"PeriodicalIF":10.1,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874026","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":"Risk-informed resilience planning of transmission systems against ice storms","authors":"Chenxi Hu ,&nbsp;Yujia Li ,&nbsp;Yunhe Hou","doi":"10.1016/j.apenergy.2025.125801","DOIUrl":"10.1016/j.apenergy.2025.125801","url":null,"abstract":"<div><div>Ice storms, known for their severity and predictability, necessitate proactive resilience enhancement in power systems. Traditional approaches often overlook the endogenous uncertainties inherent in human decisions and underutilize predictive information like forecast accuracy and preparation time. To bridge these gaps, we proposed a two-stage risk-informed decision-dependent resilience planning (RIDDRP) model for transmission systems against ice storms. The model leverages predictive information to optimize resource allocation, considering decision-dependent line failure uncertainties introduced by planning decisions and exogenous ice storm-related uncertainties. We adopt a dual-objective approach to balance economic efficiency and system resilience across both normal and emergent conditions. The first stage of the RIDDIP model makes line hardening decisions, as well as the optimal siting and sizing of energy storage. The second stage evaluates the risk-informed operation costs, considering both pre-event preparation and emergency operations. Case studies demonstrate the model’s ability to leverage predictive information, leading to more judicious investment decisions and optimized utilization of dispatchable resources. We also quantified the impact of different properties of predictive information on resilience enhancement. The RIDDRP model provides grid operators and planners with valuable insights for making risk-informed infrastructure investments and operational strategy decisions, thereby improving preparedness and response to future extreme weather events.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"392 ","pages":"Article 125801"},"PeriodicalIF":10.1,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874027","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":"Optimal frequency support from virtual power plants: Minimal reserve and allocation","authors":"Xiang Zhu ,&nbsp;Guangchun Ruan ,&nbsp;Hua Geng","doi":"10.1016/j.apenergy.2025.125913","DOIUrl":"10.1016/j.apenergy.2025.125913","url":null,"abstract":"<div><div>This paper proposes a novel reserve-minimizing and allocation strategy for virtual power plants (VPPs) to deliver optimal frequency support. The proposed strategy enables VPPs, acting as aggregators for inverter-based resources (IBRs), to provide optimal frequency support economically. The proposed strategy captures time-varying active power injections, reducing the unnecessary redundancy compared to traditional fixed reserve schemes. Reserve requirements for the VPPs are determined based on system frequency response and safety constraints, ensuring efficient grid support. Furthermore, an energy-based allocation model decomposes power injections for each IBR, accounting for their specific limitations. Numerical experiments validate the feasibility of the proposed approach, highlighting significant financial gains for VPPs, especially as system inertia decreases due to higher renewable energy integration.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"392 ","pages":"Article 125913"},"PeriodicalIF":10.1,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869964","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":"Online decoupling feature framework for optimal probabilistic load forecasting in concept drift environments","authors":"Chaojin Cao ,&nbsp;Yaoyao He ,&nbsp;Xiaodong Yang","doi":"10.1016/j.apenergy.2025.125952","DOIUrl":"10.1016/j.apenergy.2025.125952","url":null,"abstract":"<div><div>Probabilistic load forecasting (PLF) is crucial for optimizing power production and distribution in energy management systems (EMS), enhancing grid stability. However, the issue of concept drift has become increasingly prevalent due to the high sensitivity of electric loads to external features, such as weather and holidays, which cause shifts in the distribution characteristics of load data over time. The current study suffers from the following limitations: (1) Current probabilistic models that handle concept drift often overlook the coupling between external features. (2) There is a notable lack of research exploring the impact of concept drift on quantile and interval predictions, particularly concerning quantile crossing issues in a concept drift setting. To address these challenges, we propose an online probabilistic decoupling feature (OPDF) framework. It captures the coupling relationships among high-impact factors using a decoupling feature structure model based on least absolute shrinkage and selection operator. In the framework, a quantile reconstruction strategy is developed to address the quantile crossover problem in concept drift environments. The quantile reconstruction coefficients are adaptively determined based on the degree of concept drift impact on the model, obtaining optimal probabilistic predictions in terms of sharpness and resolution. Furthermore, the framework employs online caching and adapting schemes to track elusive data patterns in real time and adjust the model learning strategy to accommodate various data distributions in concept drift environments. The proposed framework is validated using real-world load data from three regions in the United States with varying concept drift frequencies (high, moderate, and low) and further demonstrated on the public building load dataset from Suzhou, China, encompassing over 700 users. The analysis demonstrates that our method yields higher quality probabilistic forecasts compared to other online learning approaches and exhibits greater robustness to concept drift than offline probabilistic models.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"392 ","pages":"Article 125952"},"PeriodicalIF":10.1,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874025","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":"Off-grid green hydrogen production and liquefaction system driven by renewable energy and LNG cold energy: A comprehensive 4E analysis and optimization","authors":"Manfeng Li ,&nbsp;Juncheng Yang ,&nbsp;Mehdi Mehrpooya ,&nbsp;Zhanjun Guo ,&nbsp;Tianbiao He","doi":"10.1016/j.apenergy.2025.125882","DOIUrl":"10.1016/j.apenergy.2025.125882","url":null,"abstract":"<div><div>To address the growing demand for sustainable hydrogen production and reduce the carbon footprint of hydrogen liquefaction, an off-grid system integrating renewable energy, liquefied natural gas cold energy and organic Rankine cycle is proposed. The renewable energy generation and proton exchange membrane electrolyzer hydrogen production processes are modeled in TRNSYS, while the hydrogen liquefaction and the organic Rankine cycle are simulated using ASPEN HYSYS. The Particle Swarm Optimization algorithm is used to optimize the hydrogen liquefaction process by evaluating various configurations based on energy efficiency, environmental impact, exergy efficiency, and economic feasibility. The optimization results show that the system achieves a reduction in specific energy consumption from 7.948 <span><math><mi>kWh</mi><mo>/</mo><msub><mi>kg</mi><msub><mi>LH</mi><mn>2</mn></msub></msub></math></span> to 6.937 <span><math><mi>kWh</mi><mo>/</mo><msub><mi>kg</mi><msub><mi>LH</mi><mn>2</mn></msub></msub></math></span>. Within the 4E analytical framework, Case 10 achieves the highest energy efficiency at 23.55 %, whereas Case 1 demonstrates the most significant pollutant reduction, decreasing emissions by 2.901 % relative to the reference system. Case 11 exhibits the best exergy efficiency at 26.61 %, while Case 8 optimizes economic viability with the lowest initial investment, featuring a dynamic payback period of 3.56 years and a levelized hydrogen production cost of 1.1 $/<span><math><msub><mi>kg</mi><msub><mi>LH</mi><mn>2</mn></msub></msub></math></span>. From a dual-criterion perspective, Case 8 outperforms others in life cycle cost and emission reduction performance, while Case 10 maintains superior energy and exergy efficiency. Significantly, Case 5 emerges as the Pareto-optimal solution under equally weighted multi-criteria evaluation, balancing all performance indices with minimal trade-off compromise. This integrated system provides a promising solution for utilizing offshore renewable energy in hydrogen production, offering a low-emission and sustainable fuel pathway.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"392 ","pages":"Article 125882"},"PeriodicalIF":10.1,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874024","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 data-physics hybrid-driven layout optimization framework for large-scale wind farms","authors":"Peiyi Li ,&nbsp;Yanbo Che ,&nbsp;Anran Hua ,&nbsp;Lei Wang ,&nbsp;Mengxiang Zheng ,&nbsp;Xiaojiang Guo","doi":"10.1016/j.apenergy.2025.125908","DOIUrl":"10.1016/j.apenergy.2025.125908","url":null,"abstract":"<div><div>The global trend of wind energy utilization moves towards building large-scale and remotely-located wind farms, while strategic layout optimization is crucial to improving the power generation of wind farms. However, large-scale wind farm layout optimization (WFLO) faces challenges due to complicated calculations involving the high-dimensional decision variables and the need to trade-off between wake model accuracy and efficiency. To address these issues, this paper proposes a novel data-physics hybrid-driven framework for layout optimization of large-scale wind farms. This framework attempts to integrate physical equations with variable parameters to guide the modeling of wake effects and further facilitate the layout optimization process. Specifically, the physics-informed dual neural network (PIDNN) model is proposed to estimate the wind velocity. This model incorporates a variable thrust coefficient into the Navier–Stokes equations through dual neural networks. Moreover, the gene-targeted differential evolution (GTDE) algorithm is employed to optimize the wind farm layout, which is particularly designed for large-scale optimization problems. Simulation results demonstrate that the proposed PIDNN can estimate wake velocity effectively. Furthermore, the proposed optimization framework outperforms competing methods, achieving the highest power generation.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"392 ","pages":"Article 125908"},"PeriodicalIF":10.1,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869963","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":"Accelerating device characterization in perovskite solar cells via neural network approach","authors":"Xinhai Zhao ,&nbsp;Chaopeng Huang ,&nbsp;Erik Birgersson ,&nbsp;Nikita Suprun ,&nbsp;Hu Quee Tan ,&nbsp;Yurou Zhang ,&nbsp;Yuxia Jiang ,&nbsp;Chunhui Shou ,&nbsp;Jingsong Sun ,&nbsp;Jun Peng ,&nbsp;Hansong Xue","doi":"10.1016/j.apenergy.2025.125922","DOIUrl":"10.1016/j.apenergy.2025.125922","url":null,"abstract":"<div><div>Perovskite solar cells are promising candidates for next-generation high-efficiency photovoltaic devices, especially as top cells in tandem applications. Using a physical-based optoelectronic model, we collect big data of one hundred thousand sample size to train neural network models for efficient prediction of device performance and recombination losses. Latin hypercube sampling, Bayesian regularization, and Bayesian optimization are adopted for data preparation, model training, and optimization of the neural networks, respectively. The best neural network models achieved mean squared errors below <span><math><mn>4</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>4</mn></mrow></msup></math></span> on a reserved testing dataset. The computational speed of the neural network is more than one thousand times faster than traditional optoelectronic models. As a result, fast device calibration can be conducted in twenty-four seconds. The reduced computational cost allows for efficient device characterization, parametric studies, sensitivity analysis, loss analysis, and optimization. After optimizing interface recombination in our in-house fabricated devices, we observed an experimental improvement of approximately 2 % in power conversion efficiency. Additionally, we predict theoretical power conversion efficiencies of 28.9 % and 25.5 % for perovskite solar cells with band gaps of 1.56 eV and 1.63 eV, respectively.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"392 ","pages":"Article 125922"},"PeriodicalIF":10.1,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865203","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":"Data-driven parametric optimization for pre-calibration of internal combustion engine controls","authors":"Matteo Meli ,&nbsp;Zezhou Wang ,&nbsp;Stefan Sterlepper ,&nbsp;Mario Picerno ,&nbsp;Stefan Pischinger","doi":"10.1016/j.apenergy.2025.125893","DOIUrl":"10.1016/j.apenergy.2025.125893","url":null,"abstract":"<div><div>This paper presents an efficient pre-calibration method for combustion engine controls. In particular, it focuses on the initial shaping of multiple Lookup Tables (LUTs) within LUT-based Multiple-Input Single-Output (MISO) engine control systems. The approach addresses the increasing complexity of engine software, the rising number of calibration variables, and the time pressures prevalent in automotive development. Employing a white-box Model-in-the-Loop (MiL) optimization reduces the demands on hardware reliance and optimization time compared to conventional engine calibration techniques. The white-box model enables the pre-calibration of LUTs using known system inputs, expected system outputs, and the control system model structure. To optimize the white-box control system model, LUTs are parametrized through Rational Bézier Regression (RBR), facilitating Sequential Quadratic Programming (SQP) for optimization. RBR, which includes both Rational Bézier Curve Regression (RBCR) and Rational Bézier Surface Regression (RBSR), allows for flexible and smooth shaping of 1D and 2D LUTs with a unified and few number of parameters. The pre-calibration process is further improved using historical calibration data from various vehicle variants stored in a relational database. This ensures that the final outputs of the LUT-based MISO control system closely approximate the expected target outputs with high shape similarity. The proposed method is exemplified using an oil temperature control model from a state-of-the-art hybrid powertrain with an internal combustion engine. The results demonstrate Pearson Correlation Coefficients (PCCs) exceeding 0.8 between target and pre-calibrated LUTs, indicative of high shape similarity. Additionally, the system outputs of pre-calibrated control system models closely match expected system outputs with an <span><math><msup><mi>R</mi><mn>2</mn></msup></math></span> value of <span><math><mn>0.9385</mn></math></span>. This underscores the practical applicability of the proposed pre-calibration method for internal combustion engine controls.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"392 ","pages":"Article 125893"},"PeriodicalIF":10.1,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865162","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":"Predicting and publishing accurate imbalance prices using Monte Carlo Tree Search","authors":"Fabio Pavirani ,&nbsp;Jonas Van Gompel ,&nbsp;Seyed Soroush Karimi Madahi ,&nbsp;Bert Claessens ,&nbsp;Chris Develder","doi":"10.1016/j.apenergy.2025.125944","DOIUrl":"10.1016/j.apenergy.2025.125944","url":null,"abstract":"<div><div>The growing reliance on renewable energy sources, particularly solar and wind, has introduced challenges due to their uncontrollable production. This complicates maintaining the electrical grid balance, prompting some transmission system operators in Western Europe to implement imbalance tariffs that penalize unsustainable power deviations. These tariffs create an implicit demand response framework to mitigate grid instability. Yet, several challenges limit active participation. In Belgium, for example, imbalance prices are only calculated at the end of each 15-minute settlement period, creating high risk due to price uncertainty. This risk is further amplified by the inherent volatility of imbalance prices, discouraging participation. Although transmission system operators provide minute-based price predictions, the system imbalance volatility makes accurate price predictions challenging to obtain and requires sophisticated techniques. Moreover, publishing price estimates can prompt participants to adjust their schedules, potentially affecting the system balance and the final price, adding further complexity. To address these challenges, we propose a Monte Carlo Tree Search method that publishes accurate imbalance prices while accounting for potential response actions. Our approach models the system dynamics using a neural network forecaster and a cluster of virtual batteries controlled by reinforcement learning agents. Compared to Belgium’s current publication method, our technique improves price accuracy by 20.4 % under ideal conditions and by 12.8 % in more realistic scenarios. This research addresses an unexplored, yet crucial problem, positioning this paper as a pioneering work in analyzing the potential of more advanced imbalance price publishing techniques.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"392 ","pages":"Article 125944"},"PeriodicalIF":10.1,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870078","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":"Experimental validation of a hybrid electric organic Rankine vapor compression cooling system","authors":"Bennett Platt,&nbsp;Derek Young,&nbsp;Todd Bandhauer","doi":"10.1016/j.apenergy.2025.125956","DOIUrl":"10.1016/j.apenergy.2025.125956","url":null,"abstract":"<div><div>Thermally activated chillers, such as absorption chillers and Organic Rankine Vapor Compression (ORVC) systems, offer promising solutions for improving efficiency and reducing emissions in heating, ventilation, and air conditioning (HVAC) applications. However, their adoption in the U.S. has been limited due to performance challenges with variable heat input. Integrating electric input into ORVC systems has been proposed as a solution to variable heat input performance degradation, but the concept has not experimentally validated. This study presents results from a 263 kW_th hybrid ORVC test facility operating in electric, thermal, and hybrid cooling modes under HVAC-relevant conditions. In hybrid cooling mode, compression in the vapor compression cycle was provided by an electric compressor and a thermally driven compressor. Three configurations were evaluated: parallel compressors, series compressors with the thermally driven compressor first, and series compressors with the electric compressor first. The optimal configuration (thermally driven compressor first in series) was tested with heat inputs ranging from fully thermal to fully electric operation. Testing was conducted with cooling duty at 175 kW, heat input at 91 °C, heat rejection at 30 °C, and cooling delivered at 9 °C. At low heat input (113 kW), the system achieved high thermal COP (1.02) and low electric COP (3.80), while at high heat input, the thermal COP was 0.54 and the electric COP was 8.76 at 331 kW. Performance surpassed purely thermal (COP_th = 0.42) and purely electric (COPe = 4.55) modes with heat input above 180 kW. Turbomachinery analysis identified compressor limitations, suggesting optimized selection could further enhance efficiency. This study establishes hybrid ORVC performance for HVAC applications.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"392 ","pages":"Article 125956"},"PeriodicalIF":10.1,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869962","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}