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

Laura Altstadt, Aileen Reichmann, Nora Weber et al.

Abstract Background Germany’s commitment to climate neutrality by 2045 poses significant challenges for its energy-intensive industries, especially in North Rhine-Westphalia, where green hydrogen is essential for the decarbonisation of basic industries. In this study, we investigate social acceptance of the hydrogen-driven industrial transition, focusing on public perspectives and the perspectives of stakeholders in industry, non-governmental organisations, trade unions, and political administration. Results The results indicate broad support for industrial green hydrogen use but also highlight acceptance issues along its value chain. Key challenges emerge in political, economic, social, and environmental dimensions, with notable public risk perception of hydrogen transport. Our analysis shows that increasing concerns tend to be accompanied by a willingness to protest, while knowledge is associated with acceptance of industrial hydrogen use. Conclusions Stakeholders must find ways to gather and address local public concerns. Moreover, the results indicate the need to assess green hydrogen along its entire value chain and on an international scale.

Ding Ding, Yang Li, Poh Ling Neo et al.

Accelerating the renewable energy transition requires informed decision-making that accounts for the diverse financial, technical, environmental, and social trade-offs across different renewable energy technologies. A critical step in this multi-criteria decision-making (MCDM) process is the determination of appropriate criteria weights. However, deriving these weights often solely involves either subjective assessment from decision-makers or objective weighting methods, each of which has limitations in terms of cognitive burden, potential bias, and insufficient contextual relevance. This study proposes the subjective-objective median-based importance technique (SOMIT), a novel hybrid approach for determining criteria weights in MCDM. By tailoring SOMIT to renewable energy evaluation, the method directly supports applied energy system planning, policy analysis, and technology prioritization under carbon neutrality goals. The practical utility of SOMIT is demonstrated through two MCDM case studies on renewable energy decision-making in India and Saudi Arabia. Using the derived weights from SOMIT, the TOPSIS method ranks the renewable energy alternatives, with solar power achieving the highest performance scores in both cases. The main contributions of this work are five-fold: 1) the proposed SOMIT reduces the number of required subjective comparisons from the conventional quadratic order to a linear order; 2) SOMIT is more robust to outliers in the alternatives-criteria matrix (ACM); 3) SOMIT balances subjective expert knowledge with objective data-driven insights, thereby mitigating bias; 4) SOMIT is inherently modular, allowing both its individual parts and the complete approach to be seamlessly coupled with a wide range of MCDM methods commonly applied in energy systems and policy analysis; 5) a dedicated Python library, pysomit, is developed for SOMIT.

Nebiyu Girgibo, Karita Luokkanen-Rabetino, Pekka Peura et al.

This article aims to map out a roadmap 2025–2029 for 4 African nations—Ethiopia, Kenya, Uganda, and Botswana—and associated policy recommendations. The method is to work on the project, long-term Joint Relationship Between European and African in Renewable Energy Research in Energy Village Concept in Africa (LEAP-RE: WP 14) and mapping polices and roadmaps from experience and literature. The significance and contribution is an example to African nations for developing and mapping out a Roadmap for 2025–2029 for Energy Village (EV) projects. The novelty is the African Energy Villages, which were identified to be unique and different from implementations in European nations. The EV concept identifies and analyses potential supplies of renewable energy (RE) and local consumption needs to help local communities become energy self-sufficient. Understanding policies and initiatives for self-sufficient RE villages in Africa under the LEAP-RE program is a crucial prerequisite in implementing EV concepts using clean and secured sources of RE such as biomass, small hydropower, solar, and wind for rural African people. The main conclusion is that such EVs are able to use more than 100% RE from local communities to overcome the energy shortage.

Jiayue Yu, Sebastian Galindo-Lopez, Matthew J. Cleary

A novel, coupled Eulerian–Lagrangian Large Eddy Simulation method is developed to model turbulent jet breakup, atomisation and droplet dispersion applicable to combusting sprays and other two-phase flows. The approach integrates an Eulerian single-fluid representation incorporating Explicit Volume Diffusion (EVD) subgrid closures for the continuous fluids, including the liquid core and interfacial region, which transitions to a two-fluid representation involving Lagrangian Particle Tracking (LPT) of inertial droplets. The Eulerian–Lagrangian transition utilises criteria based on liquid volume fraction thresholds and a critical droplet Weber number. The coupled model (EVD-LPT) is validated against new high-resolution Direct Numerical Simulation (DNS) data of a turbulent round liquid jet and existing experimental and numerical data for a turbulent jet in crossflow. Results demonstrate substantial improvements in droplet size prediction relative to Eulerian-only EVD simulations. In the round jet case, mesh convergence is achieved for droplets larger than 5μm, with low sensitivity to transition parameters. The crossflow simulations also agree closely with DNS and previous Large Eddy Simulation (LES) results, particularly in capturing inertial droplet behaviour. The study reveals that the Lagrangian representation significantly enhances the prediction of droplet size distributions, addressing known limitations of Eulerian-only models in regions dominated by aerodynamic inertial effects. Overall, the coupled EVD-LPT method provides a computationally efficient, accurate approach for atomisation predictions in complex spray systems, laying a foundation for future developments incorporating droplet secondary breakup, non-spherical droplet shapes, and droplet interaction models.

Carlos Roberto Pinheiro Junior, João Luís Nunes Carvalho, Lucas Pecci Canisares et al.

ABSTRACT Sugarcane straw removal for bioenergy production—especially second‐generation ethanol—is shown to be a promising pathway for decarbonization. However, indiscriminate straw removal can negatively affect soil‐related ecosystem services (SES), compromising the sustainability of the associated bioenergy production. Here, a comprehensive literature review was conducted to select and quantify the changes in agronomic and environmental indicators affected by low (≤ 1/3), moderate (> 1/3 to ≤ 2/3), and high (> 2/3) straw removal levels and the consequential impacts on eight SES. A quali‐quantitative approach was developed to generate an impact matrix that provides the direction of the effects (negative, neutral, or positive) and the associated confidence levels. Overall, the lowest impact on SES occurs under low straw removal with a neutral effect on C storage, nutrient cycling, weed control, greenhouse gas (GHG) mitigation, and provision of food and bioenergy. Water regulation, erosion control, and maintenance of soil biodiversity were the SES most negatively affected by straw removal. Moderate and high levels of straw removal negatively impact the maintenance of SES and compromise the sustainability of sugarcane cultivation areas, except for pest control and soil GHG emission mitigation. Finally, it was also discussed how the negative impacts of straw removal on SES could be mitigated or even reversed through the adoption of best management practices, such as cover crops, organic amendments, biological products (e.g., use of phosphate‐solubilizing bacteria and mycorrhizal fungi), reduced tillage, and machinery traffic control. Ultimately, the results of this study can be useful to guide decision‐making by farmers, investors, stakeholders, and policymakers toward sustainable bioenergy production that contributes to a low‐carbon economy and climate change mitigation.

Olusola Bamisile, Yankai Xing, Caroline Acen et al.

Replacing fossil fuels with renewable energy sources, including wind and solar power, is a validated approach for mitigating carbon emissions. To maximize renewable energy potential and account for costs and impacts of low carbon energy transitions, research models or modelling tools must provide an accurate representation of the technological and economic capabilities of renewable energy technologies. Hence, this study assesses the electricity generation potential, and costs associated with onshore and offshore wind power, and solar photovoltaic (PV) system, in the Middle East and North Africa (MENA) region based on wind and solar atlas datasets. Unlike existing works in literature, the assessment of offshore wind energy potential and the calculation of the levelized cost of electricity (LCOE) in this study incorporates factors like water depth, distance from shore, and wind speed. Also, the potential of onshore wind energy and solar photovoltaic systems is evaluated by considering variables such as population density, availability of suitable land area, and geographical location, for every grid cell in the MENA region. We found that the average LCOE can be as low as US$40/MWh for solar PV and US$45/MWh for wind power, achieved in Saudi Arabia and Kuwait respectively. This work contributes to existing literature by providing reliable LCOE, capacity factor, and productivity data for solar and wind energy potential estimation in the MENA region.

Zheng Liu

Abstract The current farmland energy management and monitoring system still has problems, such as poor real-time data collection, low energy utilization efficiency, and insufficient intelligent decision-making. Focusing on digital energy management, this paper proposes a data collection and analysis based on edge computing and cloud collaboration architecture to improve the accuracy and real-time performance of farmland environmental monitoring. In terms of intelligent control, deep reinforcement learning is used to optimize irrigation decision-making, and adaptive algorithms are combined to improve the flexibility of agricultural equipment scheduling. Regarding energy management, a digital twin model of the photovoltaic energy storage system is constructed to achieve accurate prediction and optimization of energy flow. Edge-cloud collaborative architecture for real-time data collection/analysis, reducing network latency by 40% compared to traditional cloud-only models; deep reinforcement learning (DRL)-driven irrigation optimization, achieving 51% crop yield increase and 18% water efficiency improvement; digital twin modeling of photovoltaic-energy storage systems, enhancing energy flow prediction accuracy to 98.2% and reducing energy waste by 9.5%; game theory-based resource allocation to balance energy supply–demand, improving system economic benefits by 15%. The system stability reached 96.24%, and the maintenance cost was reduced by 21.0%. The utilization rate of irrigation water increased from 76.9% to 43.0% by 1.8 times, reaching 77.4%.

Jovana Kovačević, Felix Langner, Erfan Tajalli-Ardekani et al.

Integrating flexible loads and storage systems into the residential sector contributes to the alignment of volatile renewable generation with demand. Besides batteries serving as a short-term storage solution, residential buildings can benefit from a Hydrogen (H2) storage system, allowing seasonal shifting of renewable energy. However, as the initial costs of H2 systems are high, coupling a Fuel Cell (FC) with a Heat Pump (HP) can contribute to the size reduction of the H2 system. The present study develops a Comfort-Oriented Energy Management System for Residential Buildings (ComEMS4Build) comprising Photovoltaics (PV), Battery Energy Storage System (BESS), and H2 storage, where FC and HP are envisioned as complementary technologies. The fuzzy-logic-based ComEMS4Build is designed and evaluated over a period of 12 weeks in winter for a family household building in Germany using a semi-synthetic modeling approach. The Rule-Based Control (RBC), which serves as a lower benchmark, is a scheduler designed to require minimal inputs for operation. The Model Predictive Control (MPC) is intended as a cost-optimal benchmark with an ideal forecast. The results show that ComEMS4Build, similar to MPC, does not violate the thermal comfort of occupants in 10 out of 12 weeks, while RBC has a slightly higher median discomfort of 0.68 Kh. The ComEMS4Build increases the weekly electricity costs by 12.06 EUR compared to MPC, while RBC increases the weekly costs by 30.14 EUR. The ComEMS4Build improves the Hybrid Energy Storage System (HESS) utilization and energy exchange with the main grid compared to the RBC. However, when it comes to the FC operation, the RBC has an advantage, as it reduces the toggling counts by 3.48% and working hours by 7.59% compared to MPC...

Lei Chen, Yuqi Jiang, Zekai Zhao et al.

Eric Evans Osei Opoku, Alex O. Acheampong, Kingsley E Dogah et al.

Achieving carbon neutrality by 2050 remains fundamental to limiting global warming to 1.5 °C this century and mitigating the catastrophic effects of climate change. Policymakers have indicated that the transition towards a renewable energy economy is the catalyst for achieving this. Transitioning towards a renewable energy economy requires substantial investment in renewable energy technologies. While most empirical studies have explored the linkage between investment in research and development (R&D) and carbon emissions, not much is known empirically about the effect of energy innovation R&D on renewable energy generation. This study, therefore, contributes to the literature by investigating the impact of energy innovation R&D on renewable energy generation using a comprehensive panel dataset of 26 OECD countries from 1974 to 2020. Using a battery of robust alternative estimation methods, the results indicate that energy innovation R&D generally does not increase total renewable energy generation in the panel of OECD countries. The results further show that energy innovation R&D has a heterogeneous effect on disaggregated renewable energy sources such as solar energy, wind energy, nuclear energy, and hydro energy generation.

刘泽洪, 孟婧, 张瑾轩 et al.

实现全球“碳中和”，核心是推动能源绿色低碳转型，重要举措是推动新能源大规模基地化开发和高效消纳。针对偏远、水电不足地区新能源基地开发面临的低碳调节手段不足、电网配置和消纳能力有限、“风光火”开发模式降碳难等问题与挑战，提出了基于电-氢-碳协同的新能源基地发展思路和开发模式，将氢基产业发展与新能源开发消纳、煤电灵活低碳转型深度融合，同时结合绿氢、绿氨、绿色甲醇等氢基产品制取工艺的技术发展和调节能力提升，量化分析绿电与绿色氢/氨/甲醇在终端消费市场的竞争力及经济效益，研判不同时期新能源基地开发与氢基产业协同发展的适用模式及应用时序，展望新能源基地电-氢-碳协同开发模式在中国三北地区以及北非等海外地区的应用前景。

Oskar Vågerö, Tor Håkon Jackson Inderberg, Marianne Zeyringer

What constitutes socially just or unjust energy systems or transitions can be derived from philosophy and theories of justice. Assessments of distributive justice and utilising them in modelling lead to great differences based on which justice principles are applied. From the limited research so far published in the intersection between energy systems modelling and justice, we find that comparisons between the two principles of utilitarianism and egalitarianism dominate in assessments of distributive justice, with the latter most often considered representing a ‘just energy system’. The lack of recognition of alternative and equally valid principles of justice, resting on e.g. capabilities, responsibilities and/or opportunities, leads to a narrow understanding of justice that fails to align with the views of different individuals, stakeholders and societies. More importantly, it can lead to the unjust design of future energy systems and energy systems analysis. In this work, we contribute to the growing amount of research on distributive justice in energy systems modelling by assessing the implications of different philosophical views on justice on modelling results. Through a modelling exercise with a power system model for Europe (highRES), we explore different designs of a future (2050) net-zero European electricity system, and its distributional implications based on the application of different justice principles. In addition to the utilitarian and egalitarian approach, we include, among others, principles of ‘polluters pay’ and ‘ability-to-pay’, which take historical contributions of greenhouse gas emissions and the socio-economic conditions of a region into account. We find that fair distributions of electricity generating infrastructure look significantly different depending on the justice principles applied. The results may stimulate a greater discussion among researchers and policymakers on the implications of different constructions of justice in modelling, expansion of approaches, and demonstrate the importance of transparency and assumptions when communicating such results.

Iñigo Muñoz, Patxi Hernández, Estibaliz Pérez-Iribarren et al.

Following the example of national pledges and strategies to tackle climate change, cities are mobilising themselves towards decarbonisation, playing a key role in the achievement of those commitments due to their relevance within national energy systems. However, despite cities ambitions, there is a need for coordinating the efforts from national and local scales in order to ensure the effective fulfilment of energy and climate goals at both levels. In this paper a method for the transposition of national energy planning to the local level is proposed based on the downscaling, adaptation, and allocation of specific targets and energy measures from the national plan to the city scale. The further modelling of downscaled national energy measures allows to quantify the reach of their impacts, thus supporting the establishment of realistic goals aligned with national ones and achieving the effective contribution of urban areas towards higher climate targets. The methodology is demonstrated through the downscaling and comparison of the measures from the Spanish national energy strategy with the ones included in the energy plan of the Spanish city of Valencia. A mismatch between the two is evidenced with some local measures outperforming the national plan, while others proving themselves insufficient. These results show that urban energy planners should consider the real capacities and competences of the city when setting energy measures and goals in accordance with national ones. A correct downscaling and modelling of the former are key in this work.

崔一澜*, 孙成

当前产业园区能效管理平台建设过程主要采用“烟囱式”的体系架构，存在建设成本高、重复开发、技术能力难以积累，数据不共享、可扩展性差等弊端。为此，以科技型产业园区低碳转型为切入点，通过引入数据中台概念对园区多源异构大数据进行汇聚和存储，为前端业务提供可共享复用、可快速构建的数据应用服务，探索智慧园区能效精细化管理方式。以上海市某科技型产业园区为实证，基于数据中台构建能效管理平台，依托平台积累的数据、特征算子及模型资产，驱动用电异常检测、用电时序预测等数据应用的快速构建，为园区实现节能减排、绿色发展提供坚实的数据服务支撑。

Felix Lanfermann, Qiqi Liu, Yaochu Jin et al.

Implementing resource efficient energy management systems in facilities and buildings becomes increasingly important in the transformation to a sustainable society. However, selecting a suitable configuration based on multiple, typically conflicting objectives, such as cost, robustness with respect to uncertainty of grid operation, or renewable energy utilization, is a difficult multi-criteria decision making problem. The recently developed concept identification technique can facilitate a decision maker by sorting configuration options into semantically meaningful groups (concepts). In this process, the partitioning of the objectives and design parameters into different sets (called description spaces) is a very important step. In this study we focus on utilizing the concept identification technique for finding relevant and viable energy management configurations from a very large data set of Pareto-optimal solutions. The data set consists of 20000 realistic Pareto-optimal building energy management configurations generated by a many-objective evolutionary optimization of a high quality Digital Twin energy management simulator. We analyze how the choice of description spaces, i.e., the partitioning of the objectives and parameters, impacts the type of information that can be extracted. We show that the decision maker can introduce constraints and biases into that process to meet expectations and preferences. The iterative approach presented in this work allows for the generation of valuable insights into trade-offs between specific objectives, and constitutes a powerful and flexible tool to support the decision making process when designing large and complex energy management systems.

Florian Meier, Hayata Yamasaki

Energy consumption in solving computational problems has been gaining growing attention as one of the key performance measures for computers. Quantum computation is known to offer advantages over classical computation in terms of various computational resources; however, proving its energy-consumption advantage has been challenging due to the lack of a theoretical foundation linking the physical concept of energy with the computer-scientific notion of complexity for quantum computation. To bridge this gap, we introduce a general framework for studying the energy consumption of quantum and classical computation, based on a computational model conventionally used for studying query complexity in computational complexity theory. Within this framework, we derive an upper bound for the achievable energy consumption of quantum computation, accounting for imperfections in implementation appearing in practice. As part of this analysis, we construct a protocol for Landauer erasure with finite precision in a finite number of steps, which constitutes a contribution of independent interest. Additionally, we develop techniques for proving a nonzero lower bound of energy consumption of classical computation, based on the energy-conservation law and Landauer's principle. Using these general bounds, we rigorously prove that quantum computation achieves an exponential energy-consumption advantage over classical computation for solving a paradigmatic computational problem -- Simon's problem. Furthermore, we propose explicit criteria for experimentally demonstrating this energy-consumption advantage of quantum computation, analogous to the experimental demonstrations of quantum computational supremacy. These results establish a foundational framework and techniques to explore the energy consumption of computation, opening an alternative way to study the advantages of quantum computation.

Dongwei Zhao, Vladimir Dvorkin, Stefanos Delikaraoglou et al.

This work proposes an uncertainty-informed bid adjustment framework for integrating variable renewable energy sources (VRES) into electricity markets. This framework adopts a bilevel model to compute the optimal VRES day-ahead bids. It aims to minimize the expected system cost across day-ahead and real-time stages and approximate the cost efficiency of the stochastic market design. However, solving the bilevel optimization problem is computationally challenging for large-scale systems. To overcome this challenge, we introduce a novel technique based on strong duality and McCormick envelopes, which relaxes the problem to a linear program, enabling large-scale applications. The proposed bilevel framework is applied to the 1576-bus NYISO system and benchmarked against a myopic strategy, where the VRES bid is the mean value of the probabilistic power forecast. Results demonstrate that, under high VRES penetration levels (e.g., 40%), our framework can significantly reduce system costs and market-price volatility, by optimizing VRES quantities efficiently in the day-ahead market. Furthermore, we find that when transmission capacity increases, the proposed bilevel model will still reduce the system cost, whereas the myopic strategy may incur a much higher cost due to over-scheduling of VRES in the day-ahead market and the lack of flexible conventional generators in real time.

Junzhe Shi, Ulf Jakob Flø Aarsnes, Shengyu Tao et al.

Fuel cell (FC)/battery hybrid systems have attracted substantial attention for achieving zero-emissions buses, trucks, ships, and planes. An online energy management system (EMS) is essential for these hybrid systems, it controls energy flow and ensures optimal system performance. Key aspects include fuel efficiency and mitigating FC and battery degradation. This paper proposes a health-aware EMS for FC and battery hybrid systems with multiple FC stacks. The proposed EMS employs mixed integer quadratic programming (MIQP) to control each FC stack in the hybrid system independently, i.e., MIQP-based individual stack control (ISC), with significant fuel cost reductions, FC and battery degradations. The proposed method is compared with classical dynamic programming (DP), with a 2243 times faster computational speed than the DP method while maintaining nearoptimal performance. The case study results show that ISC achieves a 64.68 % total cost reduction compared to CSC in the examined scenario, with substantial reductions across key metrics including battery degradation (4 %), hydrogen fuel consumption (22 %), fuel cell idling loss (99 %), and fuel cell load-change loss (41 %)

Łukasz Chmielewski

David H. Hurley, Anter El-Azab, Matthew S. Bryan et al.

To efficiently capture the energy of the nuclear bond, advanced nuclear reactor concepts seek solid fuels that must withstand unprecedented temperature and radiation extremes. In these advanced fuels, thermal energy transport under irradiation is directly related to reactor performance as well as reactor safety. The science of thermal transport in nuclear fuel is a grand challenge due to both computational and experimental complexities. Here, we provide a comprehensive review of thermal transport research on two actinide oxides: one currently in use in commercial nuclear reactors, uranium dioxide (UO2), and one advanced fuel candidate material, thorium dioxide (ThO2). In both materials, heat is carried by lattice waves or phonons. Crystalline defects caused by fission events effectively scatter phonons and lead to a degradation in fuel performance over time. Bolstered by new computational and experimental tools, researchers are now developing the foundational work necessary to accurately model and ultimately control thermal transport in advanced nuclear fuel. We begin by reviewing research aimed at understanding thermal transport in perfect single crystals. The absence of defects enables studies that focus on the fundamental aspects of phonon transport. Next, we review research that targets defect generation and evolution. Here, the focus is on ion irradiation studies used as surrogates for damage caused by fission products. We end this review with a discussion of modeling and experimental efforts directed at predicting and validating mesoscale thermal transport in the presence of irradiation defects. While efforts into these research areas have been robust, challenging work remains in developing holistic tools to capture and predict thermal energy transport across widely varying environmental conditions.