Applied Energy最新文献_第3页

The future of offshore wind power production: Wake and climate impacts 海上风力发电的未来：尾流和气候影响

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-11-30 DOI: 10.1016/j.apenergy.2024.124956

Simon C. Warder, Matthew D. Piggott

Rapid deployment of offshore wind is expected within the coming decades to help meet climate goals. With offshore wind turbine lifetimes of 25–30 years, and new offshore leases spanning 60 years, it is vital to consider long-term changes in potential wind power resource at the farm planning stage. Such changes may arise from multiple sources, including climate change, and increasing wake-induced power losses. In this work, we investigate and compare these two sources of long-term change in wind power, for a case study consisting of 21 wind farms within the German Bight. Consistent with previous studies, we find a small but significant reduction in wind resource due to climate change by the end of the 21st century under the high-emission RCP8.5 scenario, compared with a historical period, with a mean power reduction (over an ensemble of seven climate models) of 2.1%. To assess the impact of wake-induced losses due to increasingly dense farm build-out, we model wakes within the German Bight region using an engineering wake model, under various stages of (planned) build-out corresponding to the years 2010–2027. By identifying clusters of wind farms, we decompose wake effects into long-range (inter-cluster), medium-range (intra-cluster) and short-range (intra-farm) effects. Inter-cluster wake-induced losses increase from 0 for the 2010 scenario to 2.5% for the 2027 scenario, with intra-cluster losses also increasing from 0 to 4.3%. Intra-farm losses are relatively constant, at around 13%. While the evolution of wake effects therefore outweighs the climate effect, and impacts over a shorter timescale, both factors are significant. We also find evidence of an interaction between the climate and wake effects. Both climate change and evolving wake effects must therefore be considered within resource assessment and wind farm planning.

预计在未来几十年内，海上风电的快速部署将有助于实现气候目标。由于海上风力涡轮机的使用寿命为25-30年，新的海上租约长达60年，因此在农场规划阶段考虑潜在风力资源的长期变化至关重要。这种变化可能有多种原因，包括气候变化和尾流引起的电力损失增加。在这项工作中，我们调查并比较了风力发电长期变化的这两种来源，以德国沿海地区21个风力发电场为例进行了研究。与以往的研究结果一致，我们发现在高排放RCP8.5情景下，到21世纪末，与历史时期相比，气候变化导致的风能资源减少幅度虽小，但却显著减少，平均功率减少2.1%（在7个气候模式的总体上）。为了评估由于日益密集的农场建设而导致的尾流损失的影响，我们使用工程尾流模型对德国沿海地区的尾流进行了建模，并在与2010-2027年相应的不同阶段（计划）扩建。通过识别风电场集群，我们将尾流效应分解为远程（集群间）、中程（集群内）和短程（电场内）效应。集群间尾流造成的损失从2010年情景的0增加到2027年情景的2.5%，集群内的损失也从0增加到4.3%。农场内部的损失相对稳定，在13%左右。因此，虽然尾流效应的演变超过了气候效应和在较短时间尺度上的影响，但这两个因素都很重要。我们还发现了气候和尾流效应之间相互作用的证据。因此，在资源评估和风电场规划中必须考虑气候变化和不断变化的尾流效应。

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引用次数: 0

Determination of trade-offs between 2G bioethanol production yields and pretreatment costs for industrially steam exploded woody biomass 测定2G生物乙醇产量和工业蒸汽爆炸木质生物质预处理成本之间的权衡

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-11-30 DOI: 10.1016/j.apenergy.2024.125028

Edwige Audibert , Juliette Floret , Adriana Quintero , Frédéric Martel , Caroline Rémond , Gabriel Paës

Lignocellulosic biomass, including wood, can be transformed into bioethanol using steam explosion as pretreatment to improve saccharification and fermentation steps. Pretreatment is however the most expensive part of the process in terms of CAPEX and OPEX and requires to be optimized. In order to evaluate the link between pretreatment efficiency and cost, three contrasted wood species (oak, poplar and spruce) were pretreated with continuous steam explosion at pilot-scale following full factorial designs. Response surfaces obtained were combined with an economic assessment to determine trade-offs aiming at maximizing both fermentable sugars released during the enzymatic hydrolysis step and bioethanol yield during the fermentation step as well as minimizing costs of pretreatment in an industrial context. Results showed that bioethanol yields were highly dependent on wood species and that high severities of pretreatment were not the most relevant to apply. Optimal conditions of pretreatment corresponding to 70 % and 48 % of bioethanol producible from oak and poplar, respectively, were defined. The desirability function that has been modelled thus helps designing adapted pretreatment conditions regarding bioethanol production and process cost.

木质纤维素生物质，包括木材，可以转化为生物乙醇使用蒸汽爆炸预处理，以改善糖化和发酵步骤。然而，就资本支出和运营成本而言，预处理是工艺中最昂贵的部分，需要进行优化。为了评价预处理效率与成本之间的关系，采用全因子设计，在中试规模下对三种对比木材（橡木、杨树和云杉）进行连续蒸汽爆破预处理。获得的响应面与经济评估相结合，以确定旨在最大化酶解步骤中释放的可发酵糖和发酵步骤中的生物乙醇产量以及最小化工业环境中预处理成本的权衡。结果表明，生物乙醇产量对木材种类的依赖程度较高，预处理强度不适合应用。确定了橡树和杨树生物乙醇产率分别为70%和48%的最佳预处理条件。因此，已建模的可取性函数有助于设计有关生物乙醇生产和过程成本的适应预处理条件。

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引用次数: 0

Smart energy management: Process structure-based hybrid neural networks for optimal scheduling and economic predictive control in integrated systems 智能能源管理：集成系统中基于过程结构的混合神经网络优化调度和经济预测控制

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-11-29 DOI: 10.1016/j.apenergy.2024.124965

Long Wu , Xunyuan Yin , Lei Pan , Jinfeng Liu

Integrated energy systems (IESs) are complex prosumers consisting of diverse operating units spanning multiple domains. The tight integration of these units results in varied dynamic characteristics and intricate nonlinear process interactions, making detailed dynamic modeling and successful operational optimization challenging. To address these concerns, we propose a process structure-based hybrid time-series neural network (NN) surrogate to predict the dynamic performance of IESs across multiple time scales. This neural network-based modeling approach develops time-series multi-layer perceptrons (MLPs) for the operating units and integrates them with prior process knowledge about system structure and fundamental dynamics. This integration forms three hybrid NNs – long-term, slow, and fast MLPs – that predict the entire system dynamics across multiple time scales. Leveraging these MLPs, we design an NN-based scheduler and an NN-based economic model predictive control (NEMPC) framework to meet global operational requirements: rapid electrical power responsiveness to operators’ requests, adequate cooling supply to customers, and increased system profitability, while addressing the dynamic time-scale multiplicity present in IESs. The proposed day-ahead scheduler is formulated using the ReLU network-based MLP, which effectively represents IES performance under a broad range of conditions from a long-term perspective. The scheduler is then exactly recast into a mixed-integer linear programming problem for efficient evaluation. The real-time NEMPC, based on slow and fast MLPs, comprises two sequential distributed control agents: a slow NEMPC for the cooling-dominant subsystem with slower transient responses and a fast NEMPC for the power-dominant subsystem with faster responses. These agents collaborate in the decision-making process to achieve dynamic synergy in real time while reducing computational costs. Extensive simulations demonstrate that the developed scheduler and NEMPC schemes outperform their respective benchmark scheduler and controller by about 25% and 40%. Together, they enhance overall system performance by over 70% compared to benchmark approaches.

综合能源系统是由跨越多个领域的不同操作单元组成的复杂产消系统。这些单元的紧密集成导致了不同的动态特性和复杂的非线性过程相互作用，使得详细的动态建模和成功的操作优化具有挑战性。为了解决这些问题，我们提出了一个基于过程结构的混合时间序列神经网络（NN）代理来预测跨多个时间尺度的ess动态性能。这种基于神经网络的建模方法为操作单元开发了时间序列多层感知器（mlp），并将其与系统结构和基本动力学的先验过程知识相结合。这种集成形成了三种混合神经网络——长期、慢速和快速mlp——预测跨多个时间尺度的整个系统动态。利用这些mlp，我们设计了一个基于神经网络的调度程序和一个基于神经网络的经济模型预测控制（NEMPC）框架，以满足全球运营要求：对运营商请求的快速电力响应，为客户提供充足的冷却供应，提高系统盈利能力，同时解决了ess中存在的动态时间尺度多样性。提出的日前调度程序是使用基于ReLU网络的MLP制定的，从长期角度来看，它有效地代表了IES在广泛条件下的性能。然后将调度程序精确地转换为混合整数线性规划问题，以便有效地进行计算。基于慢速和快速mlp的实时NEMPC包括两个顺序的分布式控制代理：用于具有较慢瞬态响应的冷却主导子系统的慢速NEMPC和用于具有较快响应的功率主导子系统的快速NEMPC。这些智能体在决策过程中相互协作，实现实时动态协同，同时降低计算成本。大量的仿真表明，所开发的调度程序和NEMPC方案比各自的基准调度程序和控制器分别高出25%和40%。与基准方法相比，它们将整体系统性能提高了70%以上。

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引用次数: 0

Lightweight energy harvesting backpack achieved with a slingshot-inspired flexible accelerator 轻型能量收集背包实现了弹弓灵感的柔性加速器

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-11-29 DOI: 10.1016/j.apenergy.2024.124993

Hongyuan Zhao, Kangqi Fan, Shizhong Zhao, Shuxin Wu, Xuan Zhang, Zehao Hou

The energy harvesting backpack (EHB) has been recognized as a sustainable power source for wearable electronics, but its daily applications have been hindered by the rigid and heavy frame and accelerator structure. Therefore, a flexible and lightweight design strategy for EHBs is proposed based on a slingshot-inspired flexible accelerator (FA). The proposed FA accomplishes the speed acceleration for harvester actuation by rapidly releasing accumulated elastic potential energy. Then, a 1.9 kg electromagnetic EHB with the FA (FA-EEHB) is modeled, tested, and applied to wearable electronics. Actuated by 3.0 Hz ultralow-frequency vibrations, the FA-EEHB with a 2.0 kg payload can generate 215.1 mW output power, which is over eight times the 26.6 mW output of an EHB without FA. With a small payload of 1.0 kg, the FA-EEHB can work well over a large traveling speed range from 4 km/h to 9 km/h. The application experiment of the FA-EEHB was conducted at 5 km/h traveling speed with a 2.0 kg payload to demonstrate the ability to continuously power a lamp, charge a smartphone, and sustain a tracking and positioning system. This study provides a distinctive strategy for the flexible and lightweight design of EHBs with small payloads.

能量收集背包（EHB）已被公认为可穿戴电子产品的可持续能源，但其日常应用一直受到刚性和沉重的框架和加速器结构的阻碍。因此，提出了一种基于弹弓式柔性加速器（FA）的EHBs柔性轻量化设计策略。该方法通过快速释放累积的弹性势能来实现收割机驱动的速度加速。然后，对一个1.9 kg的带FA的电磁ehhb （FA- eehb）进行了建模、测试并应用于可穿戴电子产品。由3.0 Hz的超低频振动驱动，FA- eehb具有2.0 kg的有效载荷，可以产生215.1 mW的输出功率，这是没有FA的EHB输出26.6 mW的8倍多。FA-EEHB的有效载荷为1.0公斤，可以在4公里/小时到9公里/小时的大速度范围内工作。FA-EEHB的应用实验以5公里/小时的行驶速度和2.0公斤的有效载荷进行，以验证其持续为灯供电、为智能手机充电以及维持跟踪和定位系统的能力。本研究为小载荷EHBs的柔性轻量化设计提供了一种独特的策略。

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引用次数: 0

Reversing climate policy stereotypes: Evidence from an energy-dependent region and China's major economic circles 扭转气候政策的刻板印象：来自能源依赖地区和中国主要经济圈的证据

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-11-29 DOI: 10.1016/j.apenergy.2024.125026

Jinbo Zhang , Haoyun Meng , Liu Chen , Yang Zhang , Zhenghui Fu , Yulei Xie , Huaicheng Guo

Achieving decarbonization and industrial transformation in typical energy-dependent regions is a crucial step in mitigating regional climate change. This study constructed a factorial carbon policy equilibrium effect model (FCEE) that integrated input–output analysis, a computable general equilibrium model, factorial analysis, multi-regional input–output, and ecological network analysis. This work explored the internal impact of a composite carbon tax policy that considered the endogenous driving forces of the socioeconomic system on Shanxi Province, a typical energy-dependent region in China, as well as the cascading external effects on China's major economic circles. A significant interaction was found between carbon tax policy, production efficiency progress, and emission efficiency progress, confirming advantage of the composite policy in terms of the environmental–economic benefits. In contrast to common stereotypes, the composite climate policy will promote the clean transformation of the energy structure and gross domestic product growth, and water scarcity in Shanxi Province will be alleviated under the energy–water nexus mechanism. Relying on complex cross-regional networks, China's economic inequality will be indirectly eased. The economic driving forces of the Yangtze River, Chengdu-Chongqing economic circles, and Shanxi Province will be strengthened, promoting system stability and sustainability. It is recommended that Shanxi Province implement a moderate carbon tax policy while improving production efficiency; the intensity of emissions technology reform can be flexibly adjusted according to actual needs. This study provides evidence to support the implementation of climate policy, which is of great significance for other similar regions looking to solve the dilemma of sustainable development.

在典型能源依赖型地区实现脱碳和产业转型是减缓区域气候变化的关键步骤。本研究构建了一个集投入产出分析、可计算一般均衡模型、因子分析、多区域投入产出分析和生态网络分析于一体的因子碳政策均衡效应模型（FCEE）。本文探讨了考虑社会经济系统内生动力的复合碳税政策对中国典型能源依赖型省份山西的内在影响，以及对中国主要经济圈的级联外部效应。碳税政策与生产效率进步和排放效率进步之间存在显著的交互作用，证实了复合政策在环境经济效益方面的优势。与普遍的刻板印象不同，复合气候政策将促进能源结构的清洁转型和国内生产总值的增长，并在能源-水联系机制下缓解山西省的水资源短缺。依靠复杂的跨区域网络，中国的经济不平等将间接得到缓解。加强长江、成渝经济圈、山西经济驱动力，促进系统稳定性和可持续性。建议山西省在提高生产效率的同时，实施适度的碳税政策；排放技术改造力度可根据实际需要灵活调整。本研究为气候政策的实施提供了证据支持，对其他类似地区解决可持续发展困境具有重要意义。

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引用次数: 0

Metal-organic framework nanocomposites: Engineering and morphological advances for photocatalytic CO2 conversion into fuel 金属-有机框架纳米复合材料：光催化CO2转化为燃料的工程和形态学进展

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-11-29 DOI: 10.1016/j.apenergy.2024.124977

Manzoore Elahi M. Soudagar , Deepali Marghade , Sagar Shelare , D. Karunanidhi , Chander Prakash , T.M. Yunus Khan , Weiping Cao

Global industrialization and fossil fuel consumption have elevated CO₂ levels and caused global warming, causing catastrophes worldwide. Given this reality, removing CO₂ from the atmosphere and transforming acquired CO₂ into value-added resources like fuels and chemical feedstock is imperative to build a circular economy. The use of MOFs and their derivatives in CO₂ photocatalytic conversion is eco-friendly and could alleviate future fuel shortages. Modern photocatalysts MOFs, consist of hybrid organic ligand, and inorganic nodal metals with customizable morphology. MOF flexible rational design allows several active sites to be added to a framework, generating a complex photocatalytic system. The first half critically studied morphology, photocatalytic reduction mechanism, pristine MOF applications, and MOF modification/functionalization for photocatalytic reduction of CO₂. Their catalytic performance is currently insufficient for industrial usage because to poor light-harvesting and electron-hole separation. Researchers created MOF nanocomposites with many benefits by adding guest compounds. Morphological alterations, sensitization with polymers, plasmonic metals, and heterojunction generation with type I, type II, type III, and Z-scheme for MOF nanocomposites represent the current status of this large and significant research topic in the second half of paper. We presented the progress in photocatalytic CO₂ reduction into fuels and chemical feedstock using MOF nanocomposites. This review discussed MOF nanocomposites' photocatalytic merits and cons and stimulate the advancement of more efficient and broadly applicable photocatalysts. The broad range of work assembled in this review will be very valuable to a wide spectrum of research on worldwide CO₂ abatement.

全球工业化和化石燃料消耗导致二氧化碳浓度升高，导致全球变暖，给世界带来灾难。考虑到这一现实，从大气中去除二氧化碳并将获取的二氧化碳转化为燃料和化工原料等增值资源，是建立循环经济的必要条件。mof及其衍生物在CO2光催化转化中的使用是生态友好的，可以缓解未来的燃料短缺。现代光催化剂MOFs，由杂化有机配体和具有可定制形态的无机节点金属组成。MOF灵活合理的设计允许多个活性位点添加到框架中，生成复杂的光催化系统。前半部分重点研究了形貌、光催化还原机制、原始MOF应用以及MOF光催化还原CO2的改性/功能化。由于光捕获和电子-空穴分离性能差，目前它们的催化性能不足以用于工业应用。研究人员通过添加客体化合物创造了具有许多优点的MOF纳米复合材料。MOF纳米复合材料的形态改变、聚合物敏化、等离子体金属以及I型、II型、III型和z型异质结的产生代表了论文后半部分这个大型而重要的研究课题的现状。介绍了利用MOF纳米复合材料光催化CO2还原为燃料和化工原料的研究进展。本文综述了MOF纳米复合材料光催化性能的优缺点，并展望了开发更高效、更广泛应用的光催化剂的前景。这篇综述所汇集的广泛的工作将对全球范围内二氧化碳减排的广泛研究非常有价值。

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引用次数: 0

Modeling and energy management of hangar thermo-electrical microgrid for electric plane charging considering multiple zones and resources 考虑多区域多资源的电动飞机充电机库热电微网建模与能量管理

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-11-29 DOI: 10.1016/j.apenergy.2024.124951

Pablo Verdugo, Claudio Cañizares, Mehrdad Pirnia

Achieving net zero goals by 2050 is driving an energy transition towards clean electrical energy. Consequently, many initiatives have been proposed aiming to reduce carbon emissions in the building and transportation sectors, focusing, for instance, on the implementation of efficient heating and cooling systems based on heat pumps and the use of electric planes. Microgrids can effectively integrate thermal and electrical energy resources and loads to satisfy customer demands while providing technical, economic, and environmental benefits. Thus, this paper proposes the implementation of a model of a hangar microgrid and its Energy Management System to optimize the dispatch of resources of such thermo-electrical airport grid, using a Model Predictive Control approach to address uncertainties, and including a detailed building thermal model, heat pump modeling for the heating and cooling systems, and battery degradation. The proposed mathematical model of the Energy Management System is applied to a model of a microgrid being developed for a hangar at the Waterloo Wellington Flight Centre in Ontario, Canada, taking into account the specific characteristics of the microgrid’s components, the expected energy consumption of the equipment and the electric plane used for pilot training based on field measurements, and multi-room temperature control requirements, seeking to ensure a reliable and cost-effective operation, while considering the occupants’ comfort in different spaces. The results indicate that the proposed Energy Management System model, featuring multi-room temperature control through multiple thermal resources, can achieve significant savings in operational costs and CO2 emissions compared to a scenario where the microgrid is not deployed and another where a single-room building thermal model with a single heat pump is included.

到2050年实现净零排放目标将推动能源向清洁电力能源转型。因此，提出了许多旨在减少建筑和运输部门碳排放的倡议，例如，侧重于实施基于热泵和使用电动飞机的有效加热和冷却系统。微电网可以有效整合热电能源资源和负荷，满足用户需求，同时提供技术、经济和环境效益。因此，本文提出了一个机库微电网模型及其能源管理系统的实现，以优化这种热电机场电网的资源调度，使用模型预测控制方法来解决不确定性，包括详细的建筑热模型，加热和冷却系统的热泵建模，以及电池退化。能源管理系统的拟议数学模型应用于正在为加拿大安大略省滑铁卢惠灵顿飞行中心的机库开发的微电网模型，考虑到微电网组件的具体特性、设备的预期能耗和基于现场测量的用于飞行员培训的电动飞机，以及多室温度控制要求。寻求确保可靠和经济高效的运行，同时考虑居住者在不同空间的舒适度。结果表明，与不部署微电网和包含单个热泵的单室建筑热模型的情况相比，所提出的能源管理系统模型通过多种热资源实现多室温度控制，可以显著节省运营成本和二氧化碳排放。

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引用次数: 0

Distributed voltage control for multi-feeder distribution networks considering transmission network voltage fluctuation based on robust deep reinforcement learning 基于鲁棒深度强化学习的考虑输电网电压波动的多馈线配电网分布式电压控制

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-11-29 DOI: 10.1016/j.apenergy.2024.124984

Zhi Wu , Yiqi Li , Xiao Zhang , Shu Zheng , Jingtao Zhao

In the multi-feeder distribution network, the power balance between photovoltaics generations and load demands across regions is more complex. To solve the above problems, this paper proposes a multi-agent distributed voltage control strategy based on robust deep reinforcement learning to reduce voltage deviation. The whole multi-feeder distribution network is divided into a main agent and several sub-agents, and a multi-agent distributed voltage control model considering the transmission network voltage fluctuations and the corresponding power fluctuations is established. Based on the information uploaded by sub-agents, the main agent models the uncertainty of the transmission network voltage fluctuations and the corresponding power fluctuations as a disturbance to the state, and a RDRL method is employed to determine the tap position of on-load tap changer. Furtherly, each sub-agent uses the second-order cone relaxation technique to adjust the reactive power outputs of the inverters on each feeder. The effectiveness of the proposed method has been verified in two real-world multi-feeder systems. The results show that the proposed method can achieve millisecond-level decision-making, with a voltage deviation only 1.28 % higher than the global optimal results, achieving near-optimal control. The proposed method also demonstrates robustness in handling transmission network uncertainties and partial measurement loss.

在多馈线配电网中，光伏发电机组之间的功率平衡和跨区域的负荷需求更为复杂。针对上述问题，本文提出了一种基于鲁棒深度强化学习的多智能体分布式电压控制策略，以减小电压偏差。将整个多馈线配电网划分为一个主agent和几个子agent，建立了考虑输电网电压波动和相应功率波动的多agent分布式电压控制模型。基于子agent上传的信息，主agent将输电网电压波动和相应功率波动的不确定性作为状态扰动进行建模，并采用RDRL方法确定有载分接开关的分接位置。此外，每个子智能体使用二阶锥松弛技术来调节每个馈线上逆变器的无功输出。在两个实际的多馈线系统中验证了该方法的有效性。结果表明，该方法可以实现毫秒级的决策，电压偏差仅比全局最优结果高1.28%，实现了近最优控制。该方法在处理传输网络不确定性和部分测量损失方面具有鲁棒性。

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引用次数: 0

Multi-objective hierarchical energy management strategy for fuel cell/battery hybrid power ships 燃料电池/电池混合动力船舶多目标分层能量管理策略

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-11-29 DOI: 10.1016/j.apenergy.2024.124981

Hanyou Liu , Ailong Fan , Yongping Li , Richard Bucknall , Nikola Vladimir

The energy management strategy and the local controller in the ship energy management system are interconnected, impacting the performance of the hybrid propulsion system. To achieve the efficient operation of the hydrogen fuel cell (FC) and battery hybrid power system, based on the modelling and analysis of the hybrid power system, a nonlinear model predictive control (NMPC) based energy management strategy is proposed, and a dynamic virtual impedance droop controller and a classical proportional-integral (PI) controller are designed as local controllers. By simulating the designed random load conditions, pulse load conditions, and actual sailing conditions using hardware-in-the-loop (HiLs) technology, six different energy management strategies and their comprehensive performance with local controllers are compared and analysed. Comparing performance in terms of energy consumption, operating pressure, control accuracy, real-time performance, and robustness, it has been proven that the energy management strategy based on NMPC, coupled with a PI controller, is superior to other strategies overall. It can balance hydrogen consumption and the stable operation of the hybrid power system. Compared to existing energy management strategies, the proposed NMPC+PI strategy can reduce hydrogen consumption by 7.00 % and 40.29 %, and FC operating pressure by 44.96 % and 49.88 %, respectively, under both designed navigation conditions and actual navigation conditions.

船舶能量管理系统中的能量管理策略与局部控制器相互关联，影响着混合动力推进系统的性能。为了实现氢燃料电池与电池混合动力系统的高效运行，在对混合动力系统建模分析的基础上，提出了一种基于非线性模型预测控制（NMPC）的能量管理策略，并设计了动态虚拟阻抗下降控制器和经典比例积分（PI）控制器作为局部控制器。采用硬件在环（HiLs）技术对设计的随机负载工况、脉冲负载工况和实际航行工况进行仿真，比较分析了六种不同的能量管理策略及其在局部控制器下的综合性能。从能耗、操作压力、控制精度、实时性和鲁棒性等方面进行了性能比较，证明了基于NMPC和PI控制器的能量管理策略总体上优于其他策略。它可以平衡氢消耗和混合动力系统的稳定运行。与现有能源管理策略相比，所提出的NMPC+PI策略在设计航行工况和实际航行工况下，氢耗分别降低7.00%和40.29%，FC操作压力分别降低44.96%和49.88%。

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引用次数: 0

A cellular automata modelling approach for grain growth topological evolution process of ternary cathode materials combined with deep neural networks 结合深度神经网络的三元正极材料晶粒生长拓扑演化过程元胞自动机建模方法

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-11-29 DOI: 10.1016/j.apenergy.2024.124980

Tianyi Li , Ning Chen , Chunhua Yang , Hongzhen Liu , Biao Qi , Weihua Gui , Zhixing Wang , Jiexi Wang

Ternary cathode materials are pivotal in high-performance battery technologies, with grain size influencing their electrochemical performance. However, the absence of real-time grain size inspection during material preparation poses challenges in maintaining the consistent quality of ternary cathode materials. To address this, this paper proposes a novel method employing cell automata to model the topological evolution of grain growth in these materials, integrated with deep neural networks (DNN). The grain growth process is divided into two stages: heating and constant temperature. In the heating stage, varying heating rates and plateau temperatures serve as DNN inputs, yielding the primary grain distribution for the cell automata model in the constant temperature stage. Based on cell automata, the grain growth model links the grain growth rate to the grain size distribution in the constant temperature stage. A surface energy constraint rule, based on local curvature and grain boundary surface tension, governs growth rates. The grain boundary growth ratio is also used to create a grain ID transition variable, dictating grain ID conversion in the model. This approach accurately simulates the dynamic evolution of polycrystalline grain size and morphology. Simulation results show that this method effectively models grain growth in ternary cathode materials, offering insights for optimising the sintering process and improving material quality.

三元正极材料是高性能电池技术的关键，晶粒尺寸影响其电化学性能。然而，在材料制备过程中缺乏实时粒度检测，这给保持三元正极材料的一致质量带来了挑战。为了解决这个问题，本文提出了一种新的方法，利用细胞自动机来模拟这些材料中晶粒生长的拓扑进化，并结合深度神经网络（DNN）。晶粒生长过程分为加热和恒温两个阶段。在加热阶段，不同的加热速率和高原温度作为DNN输入，产生恒温阶段细胞自动机模型的初级颗粒分布。基于细胞自动机的晶粒生长模型将晶粒生长速率与恒温阶段晶粒尺寸分布联系起来。基于局部曲率和晶界表面张力的表面能约束规则控制生长速率。晶界生长比还用于创建晶粒ID转换变量，该变量指示模型中的晶粒ID转换。该方法准确地模拟了多晶晶粒尺寸和形貌的动态演变。仿真结果表明，该方法有效地模拟了三元正极材料的晶粒生长，为优化烧结工艺和提高材料质量提供了参考。

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引用次数: 0