Energy最新文献

{"title":"Multi-faceted procurement with mixed integer linear programming for corporate 100 % renewable energy goal","authors":"Hsin-Wei Hsu ,&nbsp;Zhi-Wei Fan","doi":"10.1016/j.energy.2025.135144","DOIUrl":"10.1016/j.energy.2025.135144","url":null,"abstract":"<div><div>To achieve net-zero emissions, wind and solar power are projected to provide over half of global electricity by 2050. These sources, along with storage systems and Renewable Energy Certificates, are crucial for companies aiming for carbon neutrality. This study applies a mixed integer linear programming model to minimize procurement costs, considering regional cost differences and capacity factors. It focuses on the corporate sector, examining two cases based on different Renewable Energy 100 % Initiative accounting methods: the “Non-Circulation Case” and the “Circulation Case.” Each case includes “Aggressive” and “Normal” scenarios based on progress, outlining strategies, including capacity, electricity, storage, and cost. Key findings reveal that in the “Non-Circulation Case with Aggressive Scenario,” relying solely on a single energy source proves insufficient, necessitating investment in diverse sources such as secondary renewables (like wind), storage systems, and Renewable Energy Certificates. This strategy enhances system resilience but may pose financial challenges for smaller companies. In the “Non-Circulation Case with Normal Scenario,” companies can gradually invest in solar photovoltaics, supported by storage systems and Renewable Energy Certificates, to balance flexibility, efficiency, and affordability. The most significant contribution is demonstrated in the “Circulation Case with Aggressive Scenario,” where the model identifies a solar-dominated system emerges as the optimal strategy for achieving corporate 100 % renewable goals, driven by solar energy's superior cost-effectiveness and capacity factor in southern regions. In the “Circulation Case with Normal Scenario,” companies shift towards a solar-only energy system to leverage the high capacity factor in southern regions, ensuring a streamlined, scalable procurement strategy.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"320 ","pages":"Article 135144"},"PeriodicalIF":9.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519191","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-aspect evaluation and optimization of a tri-generation scheme integrating a geothermal power plant with a salinity-gradient solar pond","authors":"Siming Yu ,&nbsp;Rui Chen ,&nbsp;Zhilong Zhao ,&nbsp;Fang Wei","doi":"10.1016/j.energy.2025.135234","DOIUrl":"10.1016/j.energy.2025.135234","url":null,"abstract":"<div><div>The present study deals with the investigation of a tri-generation layout driven by geothermal and solar energy to produce electricity, cooling, and freshwater. The geothermal plant is composed of a steam flash cycle (SFC), a modified Kalina cycle (MKC), and a subsystem based on liquefied natural gas (LNG). In the configuration, the heat from a salinity-gradient solar pond (SGSP) is utilized to generate power in a trilateral cycle (TLC). The waste heat of the SFC, MKC, and TLC is recuperated by three thermoelectric generators (TEGs). The sum of electricity produced by the SFC and TLC is employed to desalinate seawater by a reverse osmosis system. Moreover, the output power of the Kalina-LNG cycle is considered the electricity production of the configuration. The favorable cooling is produced through the evaporator of the MKC. The system evaluation contains thermodynamic and exergy-economic analyses and three-objective optimization of the configuration utilizing two scenarios. The outcomes of the first optimization scenario reveal an exergy efficiency (<span><math><mrow><msub><mi>η</mi><mi>ex</mi></msub></mrow></math></span>) of 27.92 %, a total cost rate (<span><math><mrow><msub><mover><mi>C</mi><mo>˙</mo></mover><mi>tot</mi></msub></mrow></math></span>) of 267.5 <span><math><mrow><mo>$</mo><msup><mi>h</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span>, a payback period of 0.358 years, and a specific cost of tri-generation (<span><math><mrow><msub><mi>c</mi><mi>tri</mi></msub></mrow></math></span>) of 21.09 <span><math><mrow><mo>$</mo><msup><mi>GJ</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span> for the system. However, these values were calculated as 31.6 %, 278.4 <span><math><mrow><mo>$</mo><msup><mi>h</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span>, 0.408 years, and 19.46 <span><math><mrow><mo>$</mo><msup><mi>GJ</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></math></span>, respectively, for the second optimization scenario. These values suggest a proper thermodynamic performance with regard to the type of heat sources and a suitable economic performance since one of the system products is freshwater. <span><math><mrow><msub><mi>η</mi><mi>ex</mi></msub></mrow></math></span> and specific cost of products improve compared to the SGSP-based systems thanks to the combination of the SGSP system and the geothermal plant.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"320 ","pages":"Article 135234"},"PeriodicalIF":9.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488552","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":"Deep learning-driven estimation and multi-objective optimization of lithium-ion battery parameters for enhanced EV/HEV performance","authors":"Nima Khosravi ,&nbsp;Adel Oubelaid","doi":"10.1016/j.energy.2025.135147","DOIUrl":"10.1016/j.energy.2025.135147","url":null,"abstract":"<div><div>Accurate parameter identification of lithium-ion batteries (LIBs) is critical for the performance and safety of electric vehicles (EVs). This study introduces a hybrid approach to efficiently identify and forecast LIB parameters under dynamic operating conditions. The hybrid method combines the hierarchical deep learning neural network (HDLNN) with the walrus optimization (WO) technique. While HDLNN predicts the battery (BAT) components and greatly reduces the error between estimated and measured voltage, the WO method is used to determine optimal settings. The suggested method improves BAT modeling accuracy and ensures optimal performance under different load scenarios. Three complementary algorithms, the genetic algorithm (GA), the black hole algorithm (BHA), and harmony search (HS) have been used for comparison in order to confirm the efficacy of the WO approach. Results indicate the superiority of the WO method, achieving a faster computation time of 0.17 s and reducing voltage error by 4.65 mV, outperforming the alternative algorithms. The results demonstrate how the WO-HDLNN hybrid technique can reliably and steadily detect BAT characteristics in LIB applications for both EVs and hybrid EVs.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"320 ","pages":"Article 135147"},"PeriodicalIF":9.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479015","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":"The impact of extended energy balances on environmental policy stringency in climate modeling: Role of energy intensity in sustainable development","authors":"Shuang Wang ,&nbsp;Guixian Tian","doi":"10.1016/j.energy.2025.135208","DOIUrl":"10.1016/j.energy.2025.135208","url":null,"abstract":"<div><div>The world faces climate change as a central global issue needing immediate measures to defend both environmental systems and human communities. Strict environmental policy measures represent an important strategy for managing this challenge. This study aims to investigate the impact of extended energy balances on environmental policy stringency in the Organization of American States (OAS) countries from 1990 to 2023, focusing on the role of energy intensity in sustainable development. Empirical estimates are based on cross-sectional dependence, slope heterogeneity, Westerlund ECM panel cointegration, and Arellano–Bond dynamic panel-data estimation. The data analysis shows a negative association between environmental policy stringency index and extended energy balances, energy intensity, urbanization, and natural resources. This association suggests that higher levels of environmental policy stringency are associated with improved energy balances, reduced energy intensity, slower urbanization rates, and better management of natural resources. Understanding the associations between these variables and the environmental policy stringency index is essential for policymakers in designing and implementing effective environmental policies. By recognizing these relationships, policymakers can make informed decisions that balance economic development and environmental sustainability.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"320 ","pages":"Article 135208"},"PeriodicalIF":9.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508933","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":"Numerical investigation of hydrogen flame structure and NOx formation in a coaxial dual swirl burner","authors":"Zhengxin Lai,&nbsp;Wenyan Song,&nbsp;Qiuyin Wang,&nbsp;Sen Wang","doi":"10.1016/j.energy.2025.135048","DOIUrl":"10.1016/j.energy.2025.135048","url":null,"abstract":"<div><div>A numerical study of swirling hydrogen flame structure and nitrogen oxides (NO<span><math><msub><mrow></mrow><mrow><mi>x</mi></mrow></msub></math></span>) formation routes in a coaxial dual swirl burner is presented. Turbulent combustion modeling is performed by using partially stirred reactor (PaSR) model and flamelet generated manifold (FGM) model. NO prediction is conducted by transport model and chemistry tabulation methods, in combination with detailed chemical kinetic scheme. The numerical approaches are firstly validated against the experimental measurements, and overall good agreements are obtained by both PaSR and FGM for swirling velocity field and fundamental flame structure. The hydrogen flame takes a M-shape reaction layer consisting of two distinct flame branches with different stabilization regime. The shear reaction layer evolves in a relatively high velocity region between hydrogen and air streams, while the central reaction layer evolves in a relatively low velocity region and lies in central recirculation zone. FGM-NO model provides a fairly good prediction for NO concentration at combustor outlet with a relative error of 9.5%. The way the flame branches are stabilized has noticeable impacts on NO formation as the NO reactions in two flame branches are determined by different reaction routes. Thermal route dominates in central reaction region, whereas the N<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>O and NNH routes are found to be important for the preliminary conversion of NO in the vicinity of shear flame front.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"320 ","pages":"Article 135048"},"PeriodicalIF":9.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143478766","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":"Insight into the whole from the part: Redefined state of health for lithium-ion batteries based on optimal charging fragment search","authors":"Zhen Zhang,&nbsp;Yuhao Zhu,&nbsp;Yichang Gong,&nbsp;Teng Wang,&nbsp;Naxin Cui,&nbsp;Yunlong Shang","doi":"10.1016/j.energy.2025.135220","DOIUrl":"10.1016/j.energy.2025.135220","url":null,"abstract":"<div><div>Supplemented with accurate state of health to evaluate the battery degradation level is an urgent and hot issue in the battery safety field. However, conventional state definitions and mainstream estimation methods are resistant to reliable implementation in complex real-world applications. In this work, we explore the possibility of redefining the state of health by using directly measurable charging data and accordingly validate its superior performance in identifying degradation. Over 51,600 cyclic samples from 63 cells with multiple charging protocols underpinned this investigation. Firstly, dedicated to alleviating the disparities between incomplete charging cycles in practice, this paper proposes the optimal charging fragment search method for feature screening based on the sliding window and data processing technologies. Subsequently, the distinct period feature parameters are fused to generate a redefined state of health. The validation results indicate that the redefined state of health effectively imitates the degradation trajectory of the original version, as evidenced by the correlation coefficients higher than 0.9. Thereafter, no modeling effort is involved in the proposed method, which has tremendous benefits in computational efficiency and robustness. The research findings highlight the promise of using directly measurable charging data to redefine the key state of batteries.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"320 ","pages":"Article 135220"},"PeriodicalIF":9.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143473977","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":"Stochastic velocity-prediction conscious energy management strategy based self-learning Markov algorithm for a fuel cell hybrid electric vehicle","authors":"Xinyou Lin,&nbsp;Yukun Ren,&nbsp;Xinhao Xu","doi":"10.1016/j.energy.2025.135167","DOIUrl":"10.1016/j.energy.2025.135167","url":null,"abstract":"<div><div>The stochasticity of vehicle velocity poses a significant challenge to enhancing fuel cell energy management strategy (EMS). Under these circumstances, a self-learning Markov algorithm-based EMS with stochastic velocity prediction capability is proposed. First, building upon the traditional offline-trained Markov model, a real-time self-learning Markov predictor (SLMP) is proposed, which collects historical data during the vehicle's driving process and continuously updates the state transition matrix on a rolling basis. It provides excellent prediction performance under stochastic driving cycles. and the impact of different prediction time-steps is analyzed. Subsequently, by employing sequential quadratic programming for optimal power allocation, the Stochastic Velocity-Prediction Conscious EMS for fuel cell hybrid electrical vehicle based on SLMP is constructed. Finally, the predictors and EMSs based on back-propagation neural network and offline-trained Markov are selected for performance comparison. The validation results indicate that the performance of SLMP improves as driving mileage accumulates. Meanwhile, the proposed Stochastic Velocity-Prediction Conscious EMS significantly improves economic performance in different driving cycles. Hardware-in-the-Loop experiments further validate the superior fuel cell efficiency and robustness of the proposed EMS. The key contribution lies in the real-time adaptability of the SLMP, which ensures improved prediction accuracy and economic performance as driving mileage accumulates.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"320 ","pages":"Article 135167"},"PeriodicalIF":9.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508836","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 and numerical study of the melting process of phase change materials with novel finned heat storage tank under non-steady state conditions","authors":"Yanglun Wang ,&nbsp;Qianjun Mao ,&nbsp;Yuan Zhao ,&nbsp;Yunlu Tan","doi":"10.1016/j.energy.2025.135230","DOIUrl":"10.1016/j.energy.2025.135230","url":null,"abstract":"<div><div>The efficient utilization of sustainable energy is one of the important studies in the world. Latent heat thermal energy storage (LHTES) technology can effectively utilize sustainable energy. However, sustainable energy such as solar energy has the challenge of supply-demand imbalance. To address this challenge, this paper performs a study on the effect of unsteady inlet temperature on the thermal storage characteristics of LHTES. The study uses a combination of experimental and simulation approaches. Four different inlet temperature modes are selected, respectively steady state, sinusoidal unsteady state, cosine unsteady state, and segmented. The segmented temperature mode has two different cases. The results of the study show that the sinusoidal unsteady inlet temperature has the advantage of energy storage. The melting time of the sinusoidal unsteady state is optimized by 5.3 % compared to the steady state inlet temperature and by 10.0 % compared to the cosine unsteady state. Segmented inlet temperature modes do not have a significant optimization effect. Also, the sinusoidal unsteady inlet temperature has the largest average Nusselt number, with a number of 8.2, and the convective heat transfer inside it is more intensive. The results of the study can provide guidance for the efficient utilization of sustainable energy and can give a reference value for the enhancement of phase change heat storage technology.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"320 ","pages":"Article 135230"},"PeriodicalIF":9.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143478760","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":"Impacts of shadow conditions on solar PV array performance: A full-scale experimental and empirical study","authors":"Zihao Song ,&nbsp;Lin Huang ,&nbsp;Qichang Dong ,&nbsp;Guomin Zhang ,&nbsp;Michael Yit Lin Chew ,&nbsp;Sujeeva Setunge ,&nbsp;Long Shi","doi":"10.1016/j.energy.2025.135219","DOIUrl":"10.1016/j.energy.2025.135219","url":null,"abstract":"<div><div>Shadow is an important hurdle to the power generation efficiency of solar photovoltaic (PV) modules. So far, most previous studies on this aspect have focused on simulation, lacking full-scale experimental study, not to mention the relevant quantitative experimental analysis. Therefore, this study conducted a full-scale outdoor experimental and empirical study on the PV modules under different shadow conditions. Experimental results revealed that the power generation capacity of a single-string PV module decreases by approximately 90 % when a specific solar cell is entirely obstructed. When a cell is shadowed, the short-circuit current drops by 20–25 %. The open-circuit voltage (<em>V</em>_<em>oc</em>) drops by 25–30 % when 2/3 of the PV modules are shadowed. The short-circuit current (<em>I</em>_<em>sc</em>) has a linear relationship with a smaller shadow less than a solar cell, and the <em>V</em>_<em>oc</em> has a linear relationship with a shadow larger than a solar cell. However, the power generation efficiency exhibits a nonlinear relationship with the shadow ratio of the cells when they are subjected to shading. Based on the full-scale experimental tests, this study developed an empirical model, for the first time, to address the relationship between shadow ratio and power generation efficiency, where the power generation efficiency is negatively related to the 3/2 power of the shadow area. The obtained research outcome, together with the empirical model, can pave the way for future large-scale (<em>e.g.</em>, global scale) study on addressing the impact of shadow conditions (<em>e.g.</em>, bird droppings, dark clouds, gravel, and dust) on the power generation of solar PV systems.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"320 ","pages":"Article 135219"},"PeriodicalIF":9.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488553","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":"Deploying a rooftop PV panels in the southern regions of Kazakhstan","authors":"Saulesh Minazhova ,&nbsp;Michael Kurrat ,&nbsp;Bulbul Ongar ,&nbsp;Aleksandar Georgiev","doi":"10.1016/j.energy.2025.135205","DOIUrl":"10.1016/j.energy.2025.135205","url":null,"abstract":"<div><div>Kazakhstan's solar energy sector is rapidly advancing due to its vast territory and high solar radiation levels in its regions. The progress achieved to date has been primarily driven by large-scale solar power plants. However, further growth in solar energy also requires the development of small-scale projects. Therefore, this study explores the feasibility of deploying rooftop PV panels in the country. It analyses the current situation of solar sector and examines the impact of solar radiation intensity on panel temperature. The analysis is based on field measurements and simulations using PVsyst software. The innovation of the research is in assessing solar activity in the selected region, analysing the effect of solar radiation intensity on the efficiency of rooftop PV installations and studying the influence of panel characteristics on heating. Results show that in summer, solar irradiance reaches 786 W/m², and ambient temperature rises to 33 °C, causing panel surfaces to heat up to 46 °C. Total energy loss is 17 %, with 7 % attributed to overheating. System efficiency decreases by 5–10 % due to high radiation. The study concludes the need for panel cooling technologies, higher-efficiency <span>PV</span> modules, and highlights the importance of policy and technological advancements to support small-scale solar installations.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"320 ","pages":"Article 135205"},"PeriodicalIF":9.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488561","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}