Applied Energy最新文献

{"title":"Speed-prediction-based hierarchical energy management and operating cost analysis for fuel cell hybrid logistic vehicles","authors":"Yang Zhou ,&nbsp;Yansiqi Guo ,&nbsp;Fan Yang ,&nbsp;Bo Chen ,&nbsp;Ruiqing Ma ,&nbsp;Rui Ma ,&nbsp;Wentao Jiang ,&nbsp;Hao Bai","doi":"10.1016/j.apenergy.2025.125843","DOIUrl":"10.1016/j.apenergy.2025.125843","url":null,"abstract":"<div><div>This paper devises a generalized two-layer predictive energy management strategy with a comprehensive operating cost analysis for fuel cell logistic vehicles under different application scenarios. In the upper layer, an improved speed predictor based on long-and-short-term memory neural network and fuzzy C-means clustering is proposed, which can recognize driving states in real time and select corresponding sub-models for speed forecasting. In the lower layer, a multi-objective cost function including hydrogen consumption cost and power-source degradation cost is established and the optimal control action is derived within each receding horizon using sequential quadratic programming. Moreover, the performance discrepancies caused by various factors such as optimization weighting coefficients, prediction horizon length, velocity prediction methods and solution method are analyzed. Compared with benchmark strategies, the proposed strategy could reduce vehicular total operating cost by 0.76 %–32.83 % and fuel cell aging cost by 0.75 %–16.04 % across all the cycles. In addition, the operating cost distribution law with respect to different logistic vehicle types and different component sizes are analyzed via a comparative study, which could be used as a guideline for prospective designers in control strategy development.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"390 ","pages":""},"PeriodicalIF":10.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807010","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 charging scheduling of an electric bus fleet with photovoltaic-storage-charging stations","authors":"Xiuyu Hu ,&nbsp;Hailong Li ,&nbsp;Chi Xie","doi":"10.1016/j.apenergy.2025.125714","DOIUrl":"10.1016/j.apenergy.2025.125714","url":null,"abstract":"<div><div>Replacing conventional diesel buses with widely acclaimed electric buses (EBs) for urban transit services can significantly reduce the operational costs and carbon emissions. However, if a bus fleet relies solely on the electricity grid as its energy supply, existing economic and environmental problems may not be fully overcome due to the grid’s overdependence on non-renewable energy sources such as fossil fuels. This study models and optimizes an emerging bus charging scenario where photovoltaic-storage-charging (PSC) stations and an electricity grid jointly supply electricity to an EB fleet. Each PSC station is equipped with photovoltaic (PV) panels to absorb solar power and a battery set to store electricity, which can either charge buses, supply electricity to the grid, or do both simultaneously when needed. Unlike previous studies, this research not only addresses when, where, and how much electricity each EB in the fleet should be charged but also determines the optimal internal allocation scheme of electricity within each PSC station that minimizes the total charging cost of the EB fleet in its daily operations. It introduces a mixed integer programming problem with time discretization across a time-expanded network. The charging cost of the fleet is calculated in terms of the sum of PV generation cost and time-of-use (TOU) electricity tariff minus the revenue of supplying electricity to the grid. To solve this problem, a Lagrangian relaxation procedure is designed, in which a dynamic programming algorithm implemented as a bi-criterion labeling procedure is developed for the decomposed single-bus charging scheduling subproblem. We collected relevant weather and operational data of an EB fleet operating in Jiading, Shanghai, to validate the model and algorithm and to gain managerial insights. A sensitivity analysis was conducted to examine how key model parameters such as charging demand and supply, PSC battery capacity, and electricity discharging price influence the charging schedule of the EB fleet. Finally, we compared our algorithm’s performance with a state-of-the-practice commercial solver, demonstrating that our algorithm achieves comparable solution optimality while significantly saving computing time.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"390 ","pages":"Article 125714"},"PeriodicalIF":10.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808757","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":"Modelling and simulation of H2-blended NG powered SOFC for heat and power generation applications","authors":"Seema Bharati ,&nbsp;B. Sai Mukesh Reddy ,&nbsp;Subodh Purohit ,&nbsp;Ibha Kalita ,&nbsp;Dadasaheb J. Shendage ,&nbsp;Pankaj Tiwari ,&nbsp;Senthilmurugan Subbiah","doi":"10.1016/j.apenergy.2025.125867","DOIUrl":"10.1016/j.apenergy.2025.125867","url":null,"abstract":"<div><div>This study presents a detailed modelling and simulation of a Solid Oxide Fuel Cell (SOFC) system powered by hydrogen (H₂) blended with natural gas (NG). The model, validated with experimental data from pure NG operation, predicts system performance with an average error of 1.46 %. Simulations were conducted for two cases of hydrogen blending: before and after the reformer. A 5 % by volume H₂ blend before the reformer increases power generation by 0.9 – 1 % and thermal output by 0.15 %, while post-reformer blending results in a 1.5 – 3 % increase in power generation and a 1.4 % rise in thermal output along with reduced CO₂ emission.</div><div>The study includes a techno-economic analysis examining the feasibility of blending hydrogen with natural gas in SOFC systems. This analysis assesses both blending strategies' cost implications and potential benefits, providing insights into the economic viability of integrating hydrogen into natural gas for SOFC use. According to the simulation results, to align the energy cost of an SOFC system using pure NG, the hydrogen price should be 0.096 USD/kg. With the SOFC system costing 6015 USD for a 1.5 kW setup, the price of NG per kg ranges from 0.14 to 1.20 USD, resulting in electricity costs between 0.11 and 0.36 USD per kWh. Given the anticipated NG market price in 2024 of 0.89 USD/kg, the estimated power cost is 0.37 USD per kWh, which remains higher than the electricity cost from a diesel generator.</div><div>In conclusion, this work highlights the potential of H₂ blending to improve SOFC performance and provides a framework for further research in sustainable energy technologies.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"390 ","pages":"Article 125867"},"PeriodicalIF":10.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815028","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":"An effective reserve capacity optimization method for power systems considering operational reliability with weather conditions","authors":"Wei Dai,&nbsp;Haoran Shen,&nbsp;Hui Liu,&nbsp;Bochen Shi","doi":"10.1016/j.apenergy.2025.125813","DOIUrl":"10.1016/j.apenergy.2025.125813","url":null,"abstract":"<div><div>A reasonable power system reserve is crucial for mitigating uncertain risks. However, determining an effective reserve that achieves both reliability and economics is challenging due to variable operating conditions and complex reliability calculations. This study proposes a reserve capacity optimization method that is embedded in operational reliability, considering multiple uncertainties. A set of operational reliability models for equipment is developed based on the operational status (current) and weather conditions (e.g., freezing rain, temperature, and wind speed). To reduce the computational complexity, a general analytical operational reliability method is proposed based on polynomial chaos expansion, considering the uncertainties of renewable energy, loads, and equipment failures. Using these analytical formulations, a two-stage reserve optimization model considering operational reliability is transformed into a single-stage optimization model, thereby enhancing the computational efficiency without compromising accuracy. Results demonstrate that the proposed method achieved reasonable reserve allocation with fast computation, balancing reliability and economics under variable operating conditions.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"390 ","pages":""},"PeriodicalIF":10.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807012","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":"Multi-objective optimization of operational strategy and capacity configuration for hybrid energy system combined with concentrated solar power plant","authors":"Wei Shuai ,&nbsp;Keqin Wang ,&nbsp;Tian Zhang ,&nbsp;Yibin He ,&nbsp;Haoran Xu ,&nbsp;Peiwang Zhu ,&nbsp;Gang Xiao","doi":"10.1016/j.apenergy.2025.125860","DOIUrl":"10.1016/j.apenergy.2025.125860","url":null,"abstract":"<div><div>The hybrid energy system (HES) integrated with concentrated solar power (CSP) offers a promising solution for stable power generation. To enhance reliability and cost-effectiveness of HES, this study investigates the operation of next-generation CSP system based on the solar Brayton cycle, within an HES framework. Three operational strategies (continuous operation, scheduled shutdown and predictive operation) are proposed for the HES which consists of a novel CSP plant, wind turbine, photovoltaic system, electric heater, and battery. Performance indicators including the loss of power supply probability (LPSP), the levelized cost of energy (LCOE) and the potential energy waste probability (PEWP) are evaluated to find optimal capacity configuration in different operational strategies. The results show that the predictive operational strategy achieves the lowest LCOE of 0.1866 USD/kWh with an LPSP of 0, which represents 12.6 % reduction compared to the continuous operational strategy when the PEWP constraint is not considered. However, incorporating the PEWP constraint in the multi-objective optimization increases LCOE across all strategies. Under strict LPSP and PEWP constraints, the scheduled shutdown strategy performs the best, with minimal impact from PEWP constraints when LPSP≤5 %. This work contributes to the improvement of integration and operational efficiency for next-generation renewable energy systems.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"390 ","pages":"Article 125860"},"PeriodicalIF":10.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808758","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 study on natural gas hydrate production under different heat injection well patterns","authors":"Long-Hai Zhang ,&nbsp;Yi Wang ,&nbsp;Kun Wan ,&nbsp;Te Meng ,&nbsp;Xiao-Sen Li","doi":"10.1016/j.apenergy.2025.125870","DOIUrl":"10.1016/j.apenergy.2025.125870","url":null,"abstract":"<div><div>Natural gas hydrate is an alternative energy source with both reserve advantages and environmental friendliness. Thermal stimulation is a common method for extracting natural gas hydrate, and how to improve the extraction efficiency of natural gas hydrate by optimizing the layout of thermal injection well pattern is a subject worthy of in-depth exploration. This study conducted three sets of hydrate decomposition experiments in a pilot-scale hydrate simulator (PHS) with an effective volume of 117.8 L under different thermal injection well pattern conditions, including single-point heating, two-point heating, and four-point heating. The gas-water production characteristics and heat transfer processes of the three experimental groups were analyzed, and the real-time decomposition rate and energy efficiency ratio of hydrate decomposition under different extraction methods were quantitatively investigated. Experimental results show that under conditions of consistent total heating rate and hydrate saturation, the cumulative gas production of the experiments remained essentially the same, but increasing the density of heat source arrangements shortened the hydrate extraction time. Compared with two-point and single-point heating, four-point heating intensified heat diffusion, but the reservoir temperature gradient in the four-point heating system decreased compared to single-point and two-point modes. Increasing the density of heating points improved heat exchange efficiency, but the dispersed arrangement of heat sources also increased heat losses. The two-point configuration exhibited the highest gas production rate and energy efficiency ratio, followed by four-point, while single-point showed the lowest values. These results may hold implications for optimizing thermal injection well patterns in trial production projects and future commercial exploitation of natural gas hydrates.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"390 ","pages":"Article 125870"},"PeriodicalIF":10.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815027","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":"Physics-informed neural network for chiller plant optimal control with structure-type and trend-type prior knowledge","authors":"Xinbin Liang,&nbsp;Ying Liu,&nbsp;Siliang Chen,&nbsp;Xilin Li,&nbsp;Xinqiao Jin,&nbsp;Zhimin Du","doi":"10.1016/j.apenergy.2025.125857","DOIUrl":"10.1016/j.apenergy.2025.125857","url":null,"abstract":"<div><div>The development of advanced controller for heating, ventilation, and air conditioning (HVAC) system contributes significantly to building energy conservation. While the success of these optimal control technologies is highly relied on the accuracy of energy models. Existing energy models are mostly based on data-driven models, and their extrapolation/generalization ability is the major barrier for their real-world application. To solve this problem, this paper proposes a general framework of physics-informed neural network (PINN) to improve the extrapolation performance of energy models. The prior physics knowledge is divided into structure-type knowledge and trend-type knowledge, and they are embedded into neural network, forming the structure-type physics-informed neural network (S-PINN) and trend-type physics-informed neural network (T-PINN). The S-PINN aims at using known physics equation to guide the design of network architecture, while the T-PINN is to transform known trend relationship as physics loss function to ensure network output is consistent with physical trend. The overall idea of PINN is applied for the optimal control task of chiller plant in a real commercial building. The energy models of chilled water pump, cooling water pump, cooling tower and chiller are developed using both history data and physics knowledge. Comprehensive experiments are conducted to compare the extrapolation performance of gray-box model, pure data-driven model, and proposed PINN. The results demonstrate that both the structure-type knowledge and trend-type knowledge can significantly improve the model extrapolation performance. And the field experiments showed that the developed PINNs achieved 23.2 % improvement of energy efficiency by resetting system control setpoint.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"390 ","pages":""},"PeriodicalIF":10.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807011","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":"Demand response programs in decentralized hybrid local energy markets: Evaluating the impact of risk-adjusted behavior of market players and the integration of renewable energy sources, using a novel bi-level optimization framework","authors":"Mehran Moradi ,&nbsp;Hooman Farzaneh","doi":"10.1016/j.apenergy.2025.125806","DOIUrl":"10.1016/j.apenergy.2025.125806","url":null,"abstract":"<div><div>As renewable energy sources (RES) continue to rise, local energy markets (LEMs) play an increasingly vital role in enhancing system efficiency. This study introduces a comprehensive framework for assessing demand response programs (DRPs) in hybrid LEMs, where peers can trade across community-based and peer-to-peer markets, as well as the grid. To this aim, a novel bi-level optimization model is developed to minimize energy-sharing costs and maximize peer welfare by evaluating consumer and prosumer behaviors. The model considers dynamic temporal demand flexibility, enabling self- and cross-time interval adjustments over a 24-h period, allowing demand to shift, increase, or decrease in response to DRP price signals and influenced by customer risk preferences, hybrid market dynamics, and renewable energy availability. A Quality of Experience fairness index is introduced to evaluate the equity of energy distribution among consumers within the proposed market framework. A decentralized solution approach is proposed to facilitate participant negotiations, reflect individual preferences, address interactions between LEM and DRP pricing, and overcome challenges such as data aggregation, privacy concerns, and communication constraints, thereby eliminating the need for centralized optimization. The model's feasibility is validated through extensive simulations using real-time load and market price data from the Japan Electric Power Exchange in Tokyo. Results demonstrate rapid convergence, high scalability, and improved fairness. Furthermore, the availability of local RES decreases demand sensitivity to DRPs, with responsiveness, load factor, energy savings, and peak demand reduction shaped by the level of local generation and individual risk preferences. These results highlight the model's effectiveness in improving grid efficiency and maximizing benefits for participants.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"390 ","pages":"Article 125806"},"PeriodicalIF":10.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808814","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 large-scale ultra-high-resolution segmentation dataset augmentation framework for photovoltaic panels in photovoltaic power plants based on priori knowledge","authors":"Ruiqing Yang ,&nbsp;Guojin He ,&nbsp;Ranyu Yin ,&nbsp;Guizhou Wang ,&nbsp;Xueli Peng ,&nbsp;Zhaoming Zhang ,&nbsp;Tengfei Long ,&nbsp;Yan Peng ,&nbsp;Jianping Wang","doi":"10.1016/j.apenergy.2025.125879","DOIUrl":"10.1016/j.apenergy.2025.125879","url":null,"abstract":"<div><div>Most current efforts to improve model accuracy focus primarily on refining the model itself, often overlooking the critical role of dataset quality—particularly in the context of remote sensing big data. Many large-scale extraction studies of photovoltaics (PV) tend to focus on coarse delineation of PV plant boundaries, which limits the potential for more detailed downstream analysis. This paper presents a framework that targets the fine-grained extraction of PV panels within PV power plants, rather than merely capturing the external contours of the plants. By focusing on individual panel-level segmentation, this approach enables more accurate assessments for downstream applications, such as energy yield estimation and spatial optimization. The framework integrates prior knowledge to address challenges posed by land cover, imaging conditions, and background interference. An innovative label correction model reduces pixel-level labeling effort by 75 %, resulting in a more refined dataset. Experimental results show a significant accuracy improvement—from 78 % to 92 %—which is attributed not only to the model refinement but also to the enriched dataset. This dataset augmentation offers substantial advantages for PV mapping, enhancing the precision of energy-related analyses and facilitating more effective solar energy management.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"390 ","pages":"Article 125879"},"PeriodicalIF":10.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808745","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 the energy, economic, and environmental performance of next-generation photovoltaic technologies in residential buildings","authors":"Ju Won Lim ,&nbsp;Hyeonsoo Kim","doi":"10.1016/j.apenergy.2025.125895","DOIUrl":"10.1016/j.apenergy.2025.125895","url":null,"abstract":"<div><div>While silicon-cell photovoltaics have long dominated the solar power industry, emerging PV technologies now challenge their dominance through improvements in efficiency, cost-effectiveness, and sustainability. In this study, we compare three emerging solar cell materials—perovskite, chalcogenide, and organic—with conventional silicon-cell PV. We evaluate four different rooftop solar panels installed on a typical single-family residential building in Detroit, MI, examining their energy, economic, and environmental performance to determine which PV technology is best positioned to support the implementation of NZEBs by 2050. A five-parameter logistic (5PL) function was used to evaluate solar technologies by investigating the efficiency of PV devices and total investment costs over time. The results indicate that perovskite has the potential to outperform silicon-cell PV in terms of energy (energy reduction rate of 30.66 % for perovskite and 25.51 % for silicon-cell PV in 2050) and economic perspectives (cost savings of $443.71 USD/year for perovskite and $369.26 USD/year for silicon-cell PV in 2050), owing to its remarkable light absorption capabilities and low-cost manufacturing process. However, the high embedded CO₂ emissions of perovskite solar cells (1020 gCO₂/kWh) have resulted in this technology exhibiting the longest environmental payback period (i.e., 6.81 years in 2050) among the four solar cell materials covered in this study. Meanwhile, the performance of chalcogenide PV was found to be the best from an environmental standpoint. In conclusion, the significance of this paper lies in helping building engineers and PV technicians predict which solar cell materials have the market potential to replace the dominance of silicon-cell PV and become the “system of the future” in the solar power industry.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"390 ","pages":"Article 125895"},"PeriodicalIF":10.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808744","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}