Hasil untuk "Energy industries. Energy policy. Fuel trade"

Nader Marzban, Marios Psarianos, Christiane Herrmann et al.

Integrated biorefineries play a transformative role in sustainable development by converting biomass and biogenic residues into high-value products while minimizing waste, emissions, and resource inefficiencies. This review explores innovations in biorefinery processes, emphasizing the synergy between thermochemical, biochemical, and biological technologies such as pyrolysis, fermentation, anaerobic digestion, hydrothermal carbonization, and algae and insect systems. Recent advancements, including hydrothermal humification and fulvification, enhance nutrient recovery, carbon sequestration, and near-zero waste production by generating artificial humic substances. Smart integrated biorefineries and the sustainable and circular bioeconomy systems are introduced as frameworks that promote synergy, interconnectivity, and resource optimization. These concepts emphasize that biomass valorization should be maximized before its final use. Biochar plays a multifaceted role beyond carbon sequestration. Rather than premature burial, it can be derived from fermented residues for lactic acid production or used to enhance fermentation and methane yields in anaerobic digestion. Additionally, nutrient-loaded biochar serves as a slow-release fertilizer, mitigating runoff, and GHG emissions. Meanwhile, heat from biochar production can generate electricity, and CO₂ emissions can support algae cultivation. Bio-oil, another byproduct, can be upgraded into platform chemicals, forming a closed-loop system that optimizes biomass utilization and minimizes environmental impact. Conventional biomass treatment methods, such as incineration, combustion, and composting, waste valuable resources and contribute to environmental degradation. Instead, a closed-loop, self-optimizing approach ensures full biomass utilization while addressing planetary boundaries. By integrating machine learning, digital twins, and decision-support systems, smart integrated biorefineries enhance resource efficiency, adapt to market demands, and accelerate the transition to a low-carbon, resource-efficient future.

Omri Carmon, Na’ama Teschner, Shiri Zemah-Shamir et al.

This article addresses the limitations of traditional energy security assessment frameworks, such as the World Energy Trilemma Index (WETI) and Energy Transition Index (ETI), in capturing complexities introduced by the decarbonization, decentralization, and digitalization (D3) of national energy systems. The article proposes a refined, D3-compatible index that emphasizes four core elements challenging and expanding conventional notions of energy security: supply-demand variability, distributed system management, supply chain risks, and broader system vulnerabilities. The study employs a systematic three-stage methodology: a targeted literature review, identification of overlooked measurement aspects, and development of refined indicators. The resulting framework utilizes a dashboard approach, introducing adaptive indicators within four categories: “Security of supply and demand,” “Adequacy and stability,” “Operational resilience,” and “Societal resilience.” The German energy transition serves as an illustrative case that demonstrates the practical utility of the new framework. While the index's forward-looking design is fundamentally conceptual, its adaptive structure allows immediate operationalization of selected D3-compatible indicators, as demonstrated in the case study; others lay the groundwork for future empirical applications. This approach significantly advances existing energy security assessment methods, providing policymakers and researchers with a flexible, detailed tool for strategic analysis and informed decision-making in transitioning energy systems.

Cody Buehner, Sharaf K. Magableh, Oraib Dawaghreh et al.

This paper explores the solutions for minimizing renewable energy (RE) curtailment in the Texas Electric Reliability Council of Texas (ERCOT) grid. By utilizing current and future planning data from ERCOT and the System Advisor Model from the National Renewable Energy Laboratory, we examine how future renewable energy (RE) initiatives, combined with utility-scale energy storage, can reduce CO2 emissions while reshaping Texas's energy mix. The study projects the energy landscape from 2023 to 2033, considering the planned phase-out of fossil fuel plants and the integration of new wind/solar projects. By comparing emissions under different load scenarios, with and without storage, we demonstrate storage's role in optimizing RE utilization. The findings of this paper provide actionable guidance for energy stakeholders, underscoring the need to expand wind and solar projects with strategic storage solutions to maximize Texas's RE capacity and substantially reduce CO2 emissions.

Emmanuel Udo, OKEZIE IKEH, Abner Prince et al.

This study explores the nexus between energy efficiency, renewable energy, and economic growth and its impact on CO2 emissions in the MINT countries of Mexico, Indonesia, Nigeria, and Turkey from 1990 to 2023. Despite the significance of energy efficiency in environmental policy formulation, the heavy reliance on fossil energy in these countries has led to significant environmental challenges due to concerns about climate change. Previous studies have predominantly used the symmetric model, arguing for a linear nexus and neglecting possible asymmetric contributions between renewable and nuclear energy on economic growth and urbanization as CO2 emission stimulators. This study adopted the asymmetric panel non-linear autoregressive distributed lag (PNARDL) model to argue for an asymmetric nexus. The key findings revealed an asymmetric nexus indicating that green energy sources reduce CO2 emissions and improve ecological quality through energy efficiency and renewable energy. The nexus between economic growth and CO2 emissions supports the Environmental Kuznets Curve (EKC) hypothesis, indicating that ecological quality deteriorates during the early phase of economic growth and improves as the economy evolves to prioritize environmental quality. The negative nexus between nuclear energy and CO2 emissions highlights a deficiency in nuclear energy generation to effectively mitigate CO2 emissions. Based on these findings, the study recommends prioritizing renewable energy policies, streamlining the regulatory approval process for nuclear energy projects, and providing incentives for investment in nuclear power infrastructure to achieve the 2030 Sustainable Development Goals (SDGs) for environmental quality and sustainability.

Kaizhi Chen, Hongyang Huang, Shitan Xu et al.

Abstract The implementation of sodium metal batteries (SMBs) is known for their low cost and high energy density. However, a major concern in SMBs is the formation of dendrites on the Na metal anode, which can potentially cause short circuits and compromise safety. Herein, to address this issue, we propose a novel approach to create a protective layer by decorating Na surface with NaI particles. This protective layer exhibits a high Young’s modulus and excellent sodium ion transference ability. As a result, the lifespan of the Na/NaI||Na/NaI cell is significantly extended to 850 h at 0.5 mA cm−2/1 mAh cm−2. Furthermore, when the Na/NaI anode is combined with a Na3V2(PO4)3 (NVP) cathode, the full cell retains 83 mAh g−1 (approximately 94% of its initial capacity) even after 1500 cycles at 5 C. Overall, this work presents a simple and effective method for establishing a protective layer on the Na surface, thereby enabling the realization of long lifespan and stable SMBs.

پریسا مهاجری, رضا طالبلو

احتمال معاملات آگاهانه (PIN)، یکی از معیارهای مهم ریزساختار بازار است که عموماً برای سنجش سطح عدم تقارن اطلاعات استفاده می‌شود. برآورد PIN به دلیل راه‌حل‌های مرزی، حداکثر محلی و استثناهای نقطه اعشار (FPE) می‌تواند مشکل‌ساز باشد. همچنین حاکم بودن فرض وجود فقط یک لایه اطلاعاتی در هر روز معاملاتی در PIN، با شواهد تجربی دنیای واقعی ناسازگار بوده و آن را در معرض اریب کم‌برآوردی قابل ملاحظه‌ای قرار می‌دهد. در مقاله حاضر با استفاده از مدل احتمال چندلایه معاملات آگاهانه (MPIN) که توسط قاچم و ارسان (2023) ارائه شده است، عدم تقارن اطلاعات برای 55 شرکت بورسی فعال در حوزه انرژی طی دوره 1Q:1396 تا 1Q:1402 برآورد شده است. یافته‌های مقاله حاضر حاکی از آن است که اولاً برای 67/2 درصد از 1200 مشاهده سهام/فصل، فرض وجود یک لایه اطلاعاتی برقرار است که دلالت بر ضرورت به‌کارگیری MPIN برای برآورد عدم تقارن اطلاعات دارد. ثانیاً به‌کارگیری PIN نه تنها با اریب کم‌برآوردی روبروست بلکه تصویر نادرستی از رتبه‌بندی شرکت‌ها از منظر عدم تقارن اطلاعات به دست می‌دهد. ثالثاً صنعت انرژی کشور به‌طور متوسط با عدم تقارن اطلاعات 4/34 درصدی روبروست و برآوردها نشان می‌دهد که وجود اطلاعات خصوصی در تابستان 1399 به اوج خود رسیده و بیش از 49 درصد بوده است. رابعاً نماد «بپیوند» از زیربخش برق، گاز و بخار و نماد «شپنا» از زیربخش «پالایشگاه» با عدم تقارن اطلاعات 75/64 و 9/18 درصدی به ترتیب در جایگاه بالاترین و کمترین عدم تقارن اطلاعات قرار می‌گیرند.

Olivia Vitale, Maha Haji

Maximizing the durability and reliability of offshore wind farms is essential for the clean energy transition. In this work, we demonstrate how wave energy converter (WEC) farms can shelter offshore wind farms from cyclic wave loading, resulting in significant reductions in turbine fatigue damage. Using experimentally validated hydrodynamic models, we show that WEC arrays can reduce wave-induced fatigue damage on the turbines by up to 25%, potentially lowering required monopile diameters and extending turbine lifetimes. This damage reduction propagates to the levelized cost of energy (LCOE) of the wind farm, targeting cost reductions in nearly 50% of the total system costs. Additionally, WEC farms can benefit from this co-location by sharing siting costs, operation and maintenance teams, and mooring and transmission cables with the offshore wind farm. This work supports resilient, cost-effective offshore renewables for global deployment.

Rui Xie, Yue Chen, Xi Weng

Data centers have become one of the major energy consumers, making their low-carbon operations critical to achieving global carbon neutrality. Although distributed data centers have the potential to reduce costs and emissions through cooperation, they are facing challenges due to uncertainties. This paper proposes an online approach to co-optimize the workload, energy, and temperature strategies across distributed data centers, targeting minimal total cost, controlled carbon emissions, and adherence to operational constraints. Lyapunov optimization technique is adopted to derive a parametric real-time strategy that accommodates uncertainties in workload demands, ambient temperature, electricity prices, and carbon intensities, without requiring prior knowledge of their distributions. A theoretical upper bound for the optimality gap is derived, based on which a linear programming problem is proposed to optimize the strategy parameters, enhancing performance while ensuring operational constraints. Case studies and method comparison validate the proposed method's effectiveness in reducing costs and carbon emissions.

Chao WANG, Qi CHEN, Xinmei GU et al.

[Introduction] Accurate estimation of the Li-ion batteries' State of Health (SoH) is essential for future intelligent battery management systems. To solve the problems of poor quality of data features and difficulties in adjusting model parameters, this study proposes a method for estimating the SoH of lithium batteries based on singular value fixed-order noise reduction and the sparrow search algorithm which can optimize the gated recurrent unit (GRU) neural network.[Method] Firstly, three indicators highly correlated with SoH decay were extracted from the battery charge and discharge data. Noise reduction was applied to the features using singular value decomposition techniques to improve their correlation with SoH. Next, using the sparrow search algorithm to optimize the model structure and parameters of the GRU neural network improve the accuracy of estimation of SoH. Finally, the battery data sets from Centre for Advanced Life Cycle Engineering (CALCE) were used to verify the validity of the proposed model. [Result] The experimental results show that the model proposed in this study applies to the battery SoH estimation, with a maximum root mean square error (RMSE) of only 0.018 4. After data noise reduction and algorithm optimization, the RMSE of the GRU model is reduced by 55.41% compared to the initial model. [Conclusion] The method proposed in this paper accurately estimates SoH and can be used as a reference for practical engineering applications.

D. Lerede, L. Savoldi

Electricity supply is one of the critical issues in the energy field. Due to the high shares of greenhouse gases emissions, the electricity sector is experiencing a transition towards a progressively wider use of low-carbon technologies. At the same time, electrification of end-use sectors is identified as one of the most suitable mitigation strategies, although requiring larger electricity production. This paper relates the historical development trends for installed capacity of electricity production technologies to the theory of the S-curves, building a method to depict plausible developments in the electricity sector. Projections are performed considering the existence of an upper limit for industrial capacity development, and according to a path envisaging a revolutionary, an evolutionary and a maturity phase for technologies showing considerable growth trends. Oppositely, stagnation is taken into account for those not showing any remarkable progress. The computed curves are used to perform forecasts about electricity generation potentials until 2050, showing how the projected growth trend of electricity generation technologies would result in a production sufficient to meet the expected global demand, even excluding the contribution of fossil fuels in some cases. In perspective, the presented method can be applied to retrieve maximum capacity constraints for energy system models.

Fang-rong Ren, Zhe Cui, Xue Ding et al.

The Yangtze River Economic Belt (YEB) and the Yellow River Ecological Economic Belt (YREB) surround the two biggest inland rivers and emit the greatest amount of carbon emissions in China. In order to implement China's dual carbon goal, this research applies a Meta-frontier DN-DEA model, including the carbon reduction factor (CRF), to assess the emission efficiency of energy, CO2, and atmospheric pollutants for a total of 19 regions in YEB and YREB from 2011 to 2017. The Synergistic climate risk control efficiency of atmospheric pollutants in the two economic belts are verified by tests for CO2 and three kinds of atmospheric pollutant emission elasticity coefficient and Kendall's coordination coefficient. The results are as follows: First, the overall efficiencies of the two economic belts are both improved by more than 30% when subject to CRF. YREB has great potential for carbon emission reduction, and the growth rate of its overall efficiency is higher than that of YEB. Second, YEB's input term efficiency performs better under the CRF constraint and has balanced development, while YREB's environmental pollution control input is insufficient. Third, the emission efficiencies of CO2 and atmospheric pollutants are significantly improved in YEB, while the environmental regulation of YREB is not obvious for the climate co-benefit of CO2 and atmospheric pollutants. Lastly, high level of climate co-benefit has revealed in YEB during 2011–2016, while in 2017, it plummets sharply. Conversely, YREB maintains a stable climate co-benefit.

Marko S. Đurović , Željko V. Despotović

Rad se odnosi na predlog rešenja solarnog tragača koji se može koristiti u sredinama bez stalno dostupnog konvencionalnog izvora električne energije, kao što je elektroenergetska mreža. Sam uređaj osmišljen je tako da bude veoma jednostavan za transport, montažu i demontažu. Suština rada je da se predstavi predlog rešenja koje treba da zadovolji potrebe za električnom energijom potrošača snage do 2 kW. U radu je predloženo rešenje koje omogućava lako i sigurno rukovanje i korišćenje. Takođe, dati su grafički prikazi, principske električne šeme uređaja solarnog tragača i predstavljeni su proračuni okreta uređaja. Na ovaj način u svakom trenutku dana paneli su u optimalnom položaju prema Suncu. Na osnovu prethodnog izvršen je izbor potrebnih mehaničkih i električnih komponenti. U okviru rada biće predstavljene mogućnosti instaliranja sistema za akumulaciju energije. Na ovaj način se omogućava autonomnost priključenog uređaja usled periodičnog nestanka Sunčevog zračenja. Kompletno rešenje omogućava dostupnost električne energije, a pri tome je mehanički pouzdano i ergonomski optimizovano. Rad se bazira prvenstveno na jednostavnosti same konstrukcije i prilagođavanju sistema akumulacije energije predloženoj konstrukciji. Buduća istraživanja biće usmerena u pravcu dobijanja jednostavnijeg rešenja mehaničkog sistema okreta.

Diana Neves, Patrícia Baptista, Ricardo Gomes et al.

Cities need to improve sustainability levels demanded by climate change mitigation efforts. The use of big data analytics is crucial for understanding its dynamics and deploying solid public policies. Nevertheless, data availability poses great challenges, being difficult to produce reliable analyses. Delivering trustable cross-sectorial energy datasets with high spatial and temporal resolution is thus critical to provide valuable insights for informed policymaking.This paper describes the MetaExplorer, a GIS-platform, which gathers trustable energy-related datasets, at municipal level for Portugal, providing a user-friendly georeferenced visualisation tool that can be used to derive statistical models, and support policymaking. Publicly available data was collected and cleaned, divided on five thematic areas: energy demand, buildings, mobility, waste management, and socio-economic, while a visualisation tool was developed to provide the possibility to further explore relations between indicators and support the energy transition at local level, delivering customised analyses with a global perception.

Raphael Fischer, Matthias Jakobs, Katharina Morik

Advances in artificial intelligence need to become more resource-aware and sustainable. This requires clear assessment and reporting of energy efficiency trade-offs, like sacrificing fast running time for higher predictive performance. While first methods for investigating efficiency have been proposed, we still lack comprehensive results for popular methods and data sets. In this work, we attempt to fill this information gap by providing empiric insights for popular AI benchmarks, with a total of 100 experiments. Our findings are evidence of how different data sets all have their own efficiency landscape, and show that methods can be more or less likely to act efficiently.

Chen Yin, Ran Ding, Haixiang Xu et al.

Abstract The increasing penetration of distributed energy resources (DERs) has led to increasing research interest in the cooperative control of multi‐prosumers in a transactive energy (TE) paradigm. While the existing literature shows that TE offers significant grid flexibility and economic benefits, few studies have addressed the incorporation of security constraints in TE. Herein, a market‐based control mechanism in real‐time markets is proposed to economically coordinate the TE among prosumers while ensuring secure system operation. Considering the dynamic characteristics of batteries and responsive demands, a model predictive control (MPC) method is used to handle the constraints between different time intervals and incorporate the following generation and consumption predictions. Owing to the computational burden and individual privacy issues, an efficient distributed algorithm is developed to solve the optimal power flow problem. The strong coupling between prosumers through power networks is removed by introducing auxiliary variables to acquire locational marginal prices (LMPs) covering energy, congestion, and loss components. Case studies based on the IEEE 33‐bus system demonstrated the efficiency and effectiveness of the proposed method and model.

Ahmed S. Alahmed, Lang Tong

The rapid growth of the behind-the-meter (BTM) distributed generation has led to initiatives to reform the net energy metering (NEM) policies to address pressing concerns of rising electricity bills, fairness of cost allocation, and the long-term growth of distributed energy resources. This article presents an analytical framework for the optimal prosumer consumption decision using an inclusive NEM X tariff model that covers existing and proposed NEM tariff designs. The structure of the optimal consumption policy lends itself to near closed-form optimal solutions suitable for practical energy management systems that are responsive to stochastic BTM generation and dynamic pricing. The short and long-run performance of NEM and feed-in tariffs (FiT) are considered under a sequential rate-setting decision process. Also presented are numerical results that characterize social welfare distributions, cross-subsidies, and long-run solar adoption performance for selected NEM and FiT policy designs.

Rabee Jibrin, Stuart Hillmansen, Clive Roberts

We optimize the operation of a fuel cell hybrid train using convex optimization. The main objective is to minimize hydrogen fuel consumption for a target journey time while considering battery thermal constraints. The state trajectories: train speed, energy management system, and battery temperature, are all optimized concurrently within a single optimization problem. A novel thermal model is proposed in order to include battery temperature yet maintain formulation convexity. Simulations show fuel savings and better thermal management when temperature is optimized concurrently with the other states rather than sequentially -- separately afterwards. The fuel reduction is caused by reduced cooling effort which is motivated by the formulation's awareness of active cooling energy consumption. The benefit is more pronounced for warmer ambient temperatures that require more cooling.

Shahid Rabbani

Combustion process can become more energy efficient and environment friendly if used with appropriate fuel additive. Discovery of fuel additive can be accelerated by applying hybrid approach of using of chemical kinetics and Machine Learning (ML). In this work, we present a framework that takes the robustness of Machine Learning and accuracy of chemical kinetics to predict the effect of fuel additive on autoignition process. We present a case of making predictions for Ignition Delay Time (IDT) of biofuel n-butanol ($C_4H_9OH$) with several fuel additives. The proposed framework was able to predict IDT of autoignition with high accuracy when used with unseen additives. This framework highlights the potential of ML to exploit chemical mechanisms in exploring and developing the fuel additives to obtain the desirable autoignition characteristics.

Chun Fu, Clayton Miller

In recent years, the availability of larger amounts of energy data and advanced machine learning algorithms has created a surge in building energy prediction research. However, one of the variables in energy prediction models, occupant behavior, is crucial for prediction performance but hard-to-measure or time-consuming to collect from each building. This study proposes an approach that utilizes the search volume of topics (e.g., education} or Microsoft Excel) on the Google Trends platform as a proxy of occupant behavior and use of buildings. Linear correlations were first examined to explore the relationship between energy meter data and Google Trends search terms to infer building occupancy. Prediction errors before and after the inclusion of the trends of these terms were compared and analyzed based on the ASHRAE Great Energy Predictor III (GEPIII) competition dataset. The results show that highly correlated Google Trends data can effectively reduce the overall RMSLE error for a subset of the buildings to the level of the GEPIII competition's top five winning teams' performance. In particular, the RMSLE error reduction during public holidays and days with site-specific schedules are respectively reduced by 20-30% and 2-5%. These results show the potential of using Google Trends to improve energy prediction for a portion of the building stock by automatically identifying site-specific and holiday schedules.

Mohamad Sayadi, Siab Mamipour, Hoda Talebi

Due to the increasing use of storage as one of the effective methods for peak demand management and increasing the reliability of the electricity network, prioritizing the use of storage is necessary. The purpose of this study was to conduct a multi-criteria decision making (MCDM) approach to prioritize selected sub-distributive substations of Tehran for peak shaving, curve leveling, and economic criteria using battery storage. Also, the Shannon entropy weighting method and SAW implementation method were implemented. After prioritizing the posts and identifying the priority posts, we determine the appropriate size of the storage and determine the delay time, and the amount of benefit from delaying the development of the post when using the electrical energy storage. In this study, we used real data obtained from Tehran Regional Electricity and the data used for the 63 to 20 kW substations “EKBATAN”, “AZADI”, “AZARBAIJAN”, “ABOUZAR”, “SINA”, “DEPO”, and “YAKHCHI-ABAD”. The results show that the maximum installed storage capacity calculated for the priority post (i.e. DEPO) is 119.66 MWh and the maximum storage capacity is 18 MW. The most suitable storage size for installing is 120 MWh. Using the storage at the selected post will delay the development of the post for 7 years and the economic benefit is 40% of the investment cost.