Applied Energy最新文献_第2页

Risk prediction based preventive typhoon defending for semi-independent power system 基于风险预测的半独立电力系统防台风措施

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-09-30 DOI: 10.1016/j.apenergy.2024.124389

The regional power grid that relies on the coordinated operation of internal generators and feed-in power through transmission lines (TLs), referred to as a semi-independent power grid (SIPG), enjoys potential self-sustaining ability under TL outage events induced by typhoon disasters by self-regulating. However, due to the instantaneous power failure of TLs resulting from physical damage, which is far shorter than the adjustment time of internal generators, SIPG will presumably collapse facing sudden massive power shortage without a previous resource scheduling policy. To ensure the safe operation of SIPG under typhoon disasters, a preventive typhoon-defending scheme is proposed. First, derived from the idea of Few-Shot Class-Incremental Learning (FSCIL), a risk prediction model is constructed to fairly assess the outage probability of TLs in the absence of tower damage samples. Noteworthily, real features of towers are extracted. Thus, convincing decision-making is provided for the preventive scheduling strategy. Subsequently, a multistage stochastic optimizing strategy considering the outage probability of TLs is proposed. Specifically, generators and loads in SIPG are pre-scheduled to reduce the interactive power demand from the main grid, alleviating the instantaneous power shortage caused by potential TL outage events. The modified IEEE 24-reliability test system is used to validate the proposed scheme.

依赖于内部发电机协调运行和通过输电线路（TL）馈电的区域电网被称为半独立电网（SIPG），在台风灾害引起的 TL 停电事件中通过自我调节具有潜在的自持能力。然而，由于 TL 因物理损坏而瞬时断电，远远短于内部发电机的调节时间，在没有事先制定资源调度策略的情况下，SIPG 在面临突然的大规模电力短缺时很可能会崩溃。为确保 SIPG 在台风灾害下的安全运行，本文提出了一种预防性台风防御方案。首先，从 "少镜头分类增量学习"（FSCIL）的思想出发，构建了一个风险预测模型，以公平地评估在没有铁塔损坏样本的情况下 TL 的停电概率。值得注意的是，该模型提取了铁塔的真实特征。因此，为预防性调度策略提供了令人信服的决策依据。随后，提出了一种考虑风电机组停运概率的多阶段随机优化策略。具体来说，SIPG 中的发电机和负载通过预先调度来减少来自主电网的互动电力需求，从而缓解潜在 TL 停机事件造成的瞬时电力短缺。修改后的 IEEE 24 可靠性测试系统被用来验证所提出的方案。

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引用次数: 0

The design of fast charging strategy for lithium-ion batteries and intelligent application: A comprehensive review 锂离子电池快速充电策略的设计与智能应用：综述

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-09-28 DOI: 10.1016/j.apenergy.2024.124538

Lithium-ion batteries (LIBs) are essential components in the electric vehicle (EV) industry, providing the primary power source for these vehicles. The speed at which LIBs can be charged plays a crucial role in determining the charging efficiency and longevity of EVs. Consequently, the Multi-Stage constant current (MSCC) charging strategy is being adopted as a novel solution for EV charging. This strategy has shown potential in reducing charging times, enhancing efficiency, and prolonging the cycle life of LIBs. While the MSCC charging strategy offers various advantages, the complexity arises in the process of establishing the quantity of charging stages, defining the transition criteria between stages, and determining the suitable charging rates for each stage. These factors have a significant influence on the battery's charging efficiency and overall performance. Therefore, the primary objective of this paper is to conduct a thorough review of the research progress related to MSCC charging strategy, addressing technical issues in its application and proposing solutions. The article initially examines various common charging strategies, followed by an in-depth exploration of the effects of multi-level fast charging strategies on battery life, charging efficiency, charge capacity, charging speed, and temperature rise. Furthermore, the article explores the economic costs associated with the implementation of the MSCC charging strategy in the automotive industry. It also discusses the utilization of battery models within the context of batteries. This information can serve as a valuable reference for designing new fast charging strategies and developing power battery systems and charging facilities that are compatible with the MSCC charging strategy.

锂离子电池（LIB）是电动汽车（EV）行业的重要组成部分，为这些车辆提供主要动力源。锂离子电池的充电速度对电动汽车的充电效率和使用寿命起着至关重要的作用。因此，多级恒流（MSCC）充电策略被作为一种新型的电动汽车充电解决方案。这一策略在缩短充电时间、提高效率和延长锂电池的循环寿命方面显示出了潜力。虽然 MSCC 充电策略具有各种优势，但其复杂性在于确定充电阶段的数量、定义阶段之间的过渡标准以及确定每个阶段的合适充电率。这些因素对电池的充电效率和整体性能有重大影响。因此，本文的主要目的是全面回顾与 MSCC 充电策略相关的研究进展，解决其应用中的技术问题，并提出解决方案。文章首先研究了各种常见的充电策略，然后深入探讨了多级快速充电策略对电池寿命、充电效率、充电容量、充电速度和温升的影响。此外，文章还探讨了汽车行业实施 MSCC 充电策略的相关经济成本。文章还讨论了电池模型在电池方面的应用。这些信息可作为设计新的快速充电策略、开发与 MSCC 充电策略兼容的动力电池系统和充电设施的宝贵参考。

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引用次数: 0

Effects of carbon tax on energy transition, emissions and economy amid technological progress 碳税对技术进步中的能源转型、排放和经济的影响

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-09-28 DOI: 10.1016/j.apenergy.2024.124578

Although carbon tax has been widely deployed, few papers have simulated carbon tax from the perspective of technological progress in clean electricity generation. Different from existing studies, this paper imposes an improved recursive dynamic computable general equilibrium (DCGE) model with a carbon emission block. Moreover, a new scenario of technological progress in clean electricity generation has been added to analyze the effects of carbon tax on energy transition, emissions and the economy. The conclusions are as follows. First, the combination of carbon tax and technological progress in clean electricity generation is an important way to decarbonize the power sectors. With a carbon tax of 30 CNY/t-CO₂, the share of non-fossil energy consumption increases from 19.49 % in 2023 to 22.36 % in 2030 as technology progresses. Second, recycling carbon tax revenues to enterprises is more favorable for economic development than recycling to households. Third, technological progress in clean electricity generation effectively curbs the adverse impact of the carbon tax on the economy, turning the rate of change of GDP from negative to positive. It is recommended that policymakers initially implement a low-rate carbon tax, along with lower corporate and residential income taxes. Attention also needs to be paid to research and development of clean power technologies.

尽管碳税已被广泛征收，但很少有论文从清洁发电技术进步的角度模拟碳税。与现有研究不同的是，本文在改进的递归动态可计算一般均衡（DCGE）模型中加入了碳排放模块。此外，还增加了清洁发电技术进步的新情景，以分析碳税对能源转型、排放和经济的影响。结论如下。首先，碳税与清洁发电技术进步相结合是电力行业去碳化的重要途径。在碳税为 30 元人民币/吨-CO2 的情况下，随着技术进步，非化石能源消费比重将从 2023 年的 19.49% 提高到 2030 年的 22.36%。其次，碳税收入回收到企业比回收到家庭更有利于经济发展。第三，清洁发电技术的进步有效抑制了碳税对经济的不利影响，使 GDP 的变化率由负转正。建议政策制定者首先实施低税率的碳税，同时降低企业和居民所得税。还需要关注清洁能源技术的研发。

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引用次数: 0

What-if: A causal machine learning approach to control-oriented modelling for building thermal dynamics What-if：以控制为导向的建筑热动态建模的因果机器学习方法

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-09-28 DOI: 10.1016/j.apenergy.2024.124550

Operational optimization of buildings can improve efficiency, and reduce costs and emissions. This optimization typically relies on a model of the building thermal dynamics, which is used by a predictive controller to obtain optimal schedules for heating, ventilation and air conditioning. This model has historically been constructed by domain experts using physical properties of the building. However, this approach scales poorly as more and more residential and commercial buildings need to be modelled and controlled. As a consequence, researchers and practitioners have turned to data-driven models, trained only on observational data. However, this alternative is no panacea: such models often fail to generalize to truly unseen conditions due to their inability to learn cause–effect relationships - i.e. they are not causal, rather they only learn correlational associations between input and target variables. In this paper, we demonstrate this problem using classical machine learning models trained on data from two different use cases (a simulated RC building and nine real-world Dutch buildings). Our results show that, unlike commonly used data-driven methods, causal machine learning (CML) algorithms trained on debiased data can produce accurate models necessary for control-oriented applications which outperform baseline models by over 40%, besides learning the correct causal associations which we verify using a custom testing environment as well as SHAP feature analysis. These results emphasize the need to move beyond simplistic data-driven methods if control-oriented applications are to be realized in a feasible manner.

建筑物的运行优化可以提高效率，降低成本和排放。这种优化通常依赖于建筑物热动态模型，预测控制器利用该模型来获得供暖、通风和空调的最佳时间表。该模型历来由领域专家利用建筑物的物理特性构建。然而，随着越来越多的住宅和商业建筑需要建模和控制，这种方法的扩展性越来越差。因此，研究人员和从业人员转而采用数据驱动模型，仅根据观测数据进行训练。然而，这种替代方法并不是万能的：由于无法学习因果关系，这类模型往往无法推广到真正未知的条件下，也就是说，它们并不是因果关系，而只是学习输入变量和目标变量之间的相关联系。在本文中，我们使用在两个不同使用案例（一个模拟 RC 建筑和九个真实世界中的荷兰建筑）的数据上训练的经典机器学习模型来证明这个问题。我们的结果表明，与常用的数据驱动方法不同，在去偏差数据上训练的因果机器学习（CML）算法可以生成控制型应用所需的精确模型，其性能比基线模型高出 40% 以上，此外还能学习正确的因果关联，我们使用自定义测试环境和 SHAP 特征分析验证了这一点。这些结果表明，如果要以可行的方式实现面向控制的应用，就必须超越简单的数据驱动方法。

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引用次数: 0

Unified carbon emissions and market prices forecasts of the power grid 电网碳排放和市场价格统一预测

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-09-28 DOI: 10.1016/j.apenergy.2024.124527

Carbon emissions and market prices forecasts of the power grid are of great importance for all electricity traders and consumers. Both forecasts enable flexible demand scheduling, ensuring sustainability and cost-efficiency. Many studies show the benefits of using both forecasts independently but not in combination, which remains an unexplored problem. The latest state-of-the-art techniques involve advanced statistical and machine learning algorithms leveraging seasonal patterns and exogenous forecasts. However, most of the reported studies deal only with problems of modeling and feature engineering, neglecting the forecast and model errors, which accumulate within the time-evolving power grid. This research aims to tackle these issues by introducing a versatile framework for short-term probabilistic forecasting of unified carbon emissions and market prices for electricity intra-day market participants. The approach utilizes the Hidden Markov Model for predictive estimation to determine the future energy mix of the country’s power grid. Furthermore, a novel optimization-based strategy, Moving Horizon Predictive Correction, is proposed to enhance the estimated energy mix performance, minimizing forecast and model errors. Subsequently, two separate recurrent neural networks are trained to provide probabilistic forecasts of carbon emissions and market prices, accounting for the stochastic dynamic of the power grid. A comparative analysis examines six case studies from various European countries and compares them with state-of-the-art forecasting methods. The results indicate that the proposed method can improve the qualitative measures by up to 53% for carbon emissions and up to 18% for market prices forecasts. Besides improving traditional point predictions, methods show significant increases in the quality of prediction intervals. Further application of the proposed forecasts is employed for a flexible 4-hour electricity consumption schedule. This showcases the usage of the proposed forecasts to find the best possible trade-off for low carbon emission, cost-effective electricity consumption time slots.

电网的碳排放和市场价格预测对所有电力交易商和消费者都非常重要。这两种预测都能实现灵活的需求调度，确保可持续性和成本效益。许多研究表明，单独使用这两种预测有好处，但不能结合使用，这仍然是一个尚未探索的问题。最新的先进技术包括利用季节模式和外生预测的先进统计和机器学习算法。然而，大多数已报道的研究仅涉及建模和特征工程问题，忽略了预测和模型误差，而这些误差会在随时间变化的电网中累积。本研究旨在通过引入一个多功能框架，对统一碳排放和当日电力市场参与者的市场价格进行短期概率预测，从而解决这些问题。该方法利用隐马尔可夫模型进行预测估计，以确定国家电网的未来能源组合。此外，还提出了一种基于优化的新策略--移动地平线预测修正，以提高能源组合的估计性能，最大限度地减少预测误差和模型误差。随后，对两个独立的递归神经网络进行了训练，以提供碳排放和市场价格的概率预测，同时考虑到电网的随机动态。对比分析研究了欧洲各国的六个案例，并将其与最先进的预测方法进行了比较。结果表明，所提出的方法在碳排放量和市场价格预测方面，分别可将定性指标提高 53% 和 18%。除了改进传统的点预测外，该方法还显著提高了预测区间的质量。建议的预测方法还可进一步应用于灵活的 4 小时用电计划。这展示了如何利用建议的预测来找到低碳排放、经济高效的最佳用电时段。

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引用次数: 0

Collective behavior information-based design approach to energy management strategy for large-scale population of HEVs 基于集体行为信息的大规模混合动力汽车能源管理策略设计方法

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-09-28 DOI: 10.1016/j.apenergy.2024.124530

Optimization-based energy management strategies (EMS) have raised the energy-saving potential for hybrid electric vehicles (HEV). Despite this, performance of most strategies highly rely on accurate predictions on future driving demand. These predictions for long preceding horizon are inherently challenging to made due to their dependency on many uncertain factors that are complex to model. To fill the gap, this paper presents a novel EMS design method utilizing collective behavior information of large-scale population (CBLP) of HEVs. Firstly, each HEV is modeled as a particle with identical stochastic dynamics, and CBLP of such individuals is proved to be traceable based on mean-field theory, which obeys the Fokker–Planck equation. Secondly Gaussian process regression methods are developed to predict the evolution of CBLP for long preceding horizons, leveraging historical and real-time traffic data. An EMS design problem is finally formulated to minimize the energy consumption over the obtained prediction result, where working mode switching control is adopted. Following the idea, an energy management framework is proposed to offer recommended real-time strategies for vehicles. It is shown that the EMSs, designed to be optimal with respect to CBLP, will also provide near-optimal performance for each members of the population in high probability. The effectiveness of the proposed method is evaluated through numerical validations conducted on the real-world trajectory datasets.

基于优化的能源管理策略（EMS）提高了混合动力电动汽车（HEV）的节能潜力。尽管如此，大多数策略的性能高度依赖于对未来驾驶需求的准确预测。由于这些预测依赖于许多不确定因素，而这些因素的建模又十分复杂，因此对长远的预测本身就具有挑战性。为了填补这一空白，本文提出了一种利用大规模 HEV 群体（CBLP）集体行为信息的新型 EMS 设计方法。首先，每个 HEV 都被建模为具有相同随机动态的粒子，并且基于均值场理论证明了这些个体的 CBLP 是可追溯的，它服从 Fokker-Planck 方程。其次，利用历史和实时交通数据，开发了高斯过程回归方法，以预测 CBLP 在较长前视域内的演变。最后，提出了一个 EMS 设计问题，以在预测结果的基础上最大限度地降低能耗，其中采用了工作模式切换控制。根据这一思路，提出了一个能源管理框架，为车辆提供推荐的实时策略。结果表明，根据 CBLP 设计的最优 EMS 也将为群体中的每个成员提供接近最优的性能。通过在真实世界轨迹数据集上进行数值验证，对所提方法的有效性进行了评估。

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引用次数: 0

Multiobjective energy management of multi-source offshore parks assisted with hybrid battery and hydrogen/fuel-cell energy storage systems 利用混合电池和氢气/燃料电池储能系统辅助多资源离岸园区的多目标能源管理

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-09-28 DOI: 10.1016/j.apenergy.2024.124529

With the recent advancements in the development of hybrid offshore parks and the expected large-scale implementation of them in the near future, it becomes paramount to investigate proper energy management strategies to improve the integrability of these parks into the power systems. This paper addresses a multiobjective energy management approach using a hybrid energy storage system comprising batteries and hydrogen/fuel-cell systems applied to multi-source wind-wave and wind-solar offshore parks to maximize the delivered energy while minimizing the variations of the power output. To find the solution of the optimization problem defined for energy management, a strategy is proposed based on the examination of a set of weighting factors to form the Pareto front while the problem associated with each of them is assessed in a mixed-integer linear programming framework. Subsequently, fuzzy decision making is applied to select the final solution among the ones existing in the Pareto front. The studies are implemented in different locations considering scenarios for electrical system limitation and the place of the storage units. According to the results, applying the proposed multiobjective framework successfully addresses the enhancement of energy delivery and the decrease in power output fluctuations in the hybrid offshore parks across all scenarios of electrical system limitation and combinational storage locations. Based on the results, in addition to the increase in delivered energy, a decrease in power variations by around 40 % up to over 80 % is observed in the studied cases.

随着近来在开发混合型离岸园区方面取得的进展，以及预计在不久的将来将大规模实施这些园区，研究适当的能源管理策略以提高这些园区与电力系统的可集成性变得至关重要。本文探讨了一种多目标能源管理方法，该方法使用由电池和氢/燃料电池系统组成的混合储能系统，应用于多源风力-波浪和风力-太阳能离岸园区，以在最大限度地提高输送能量的同时，最大限度地减少电力输出的变化。为了找到为能源管理定义的优化问题的解决方案，提出了一种战略，该战略基于对一组加权因素的检查，以形成帕累托前沿，同时在混合整数线性规划框架中评估与每个加权因素相关的问题。随后，应用模糊决策在帕累托前沿中选择最终解决方案。考虑到电力系统的限制和存储单元的位置，研究在不同地点进行。结果表明，应用所提出的多目标框架成功地解决了在电力系统限制和组合存储位置的所有情况下，提高混合海上电站的能量输送和降低电力输出波动的问题。根据研究结果，在所研究的案例中，除了增加能量输送外，还观察到功率波动减少了约 40%，最高超过 80%。

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引用次数: 0

Strategic joint bidding and pricing of load aggregators in day-ahead demand response market 日前需求响应市场中负荷聚合者的战略性联合投标和定价

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-09-28 DOI: 10.1016/j.apenergy.2024.124552

Load aggregators (LAs) purchase demand response (DR) capacity from users by incentive compensation and then resell them by bidding in the day-ahead DR market to gain profits. Existing related research fails to adequately integrate bidding and pricing decision-makings and lacks sufficient consideration of uncertain user response behaviors, resulting in unstable trading returns. To this end, we propose a novel joint optimization method for bidding and pricing of the LA. First, a two-stage joint optimization framework is constructed to coordinate the temporally coupled bidding and pricing decisions, where these two strategies are simultaneously optimized to form the DA bidding strategy at first, and the pricing adjustment is then introduced after market-clearing to develop the formal user incentive scheme. Second, to account for the risks from uncertain response behaviors, the first stage incorporates DR settlement rules and the hedging effect of the aggregated response uncertainty to develop a relatively aggressive bidding strategy. After that, a user selection mechanism is then introduced to filter out users with both low response costs and reliable responses for participation. Numerical studies are presented to validate the proposed methodology in formulating optimal trading strategies that effectively tradeoff transaction revenue and risks.

负荷聚合器（LA）通过激励补偿从用户处购买需求响应（DR）容量，然后在日前需求响应市场上通过竞价转售获得利润。现有的相关研究未能充分整合投标和定价决策，也没有充分考虑不确定的用户响应行为，导致交易收益不稳定。为此，我们提出了一种新型的 LA 竞价和定价联合优化方法。首先，我们构建了一个两阶段联合优化框架来协调时间耦合的竞价和定价决策，首先对这两种策略同时进行优化，形成DA竞价策略，然后在市场出清后引入定价调整，制定正式的用户激励方案。其次，为考虑不确定响应行为带来的风险，第一阶段结合 DR 结算规则和总响应不确定性的对冲效应，制定相对激进的投标策略。之后，引入用户选择机制，筛选出响应成本低且响应可靠的用户参与。通过数值研究，验证了所提出的方法可以制定出有效权衡交易收益和风险的最优交易策略。

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引用次数: 0

Waste-to-energy: Repurposing flexible polyurethane waste for triboelectric nanogenerator applications 废物变能源：将柔性聚氨酯废料重新用于三电纳米发电机

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-09-28 DOI: 10.1016/j.apenergy.2024.124579

Triboelectric Nanogenerators (TENG) is a promising approach for clean energy harvesting. Light weight, flexible, cheap and environmentally-friendly materials are being explored as potential components in the TENG device in order to increase its efficiency. This paper reports the first effort to successfully utilize rebonded flexible polyurethane (RFPU) waste as a tribopositive material in a TENG device. A batch moulding technique was used to create two different densities (60 and 70 kg/m³) of flexible polyurethane (FPU) scraps, including customer waste and slabstock foam production waste. The current study investigates the effect of density on the compression strengths of the RFPU materials, as well as the impact on the output voltage of the TENG. Additionally, the effect of various RFPU sheet thicknesses (2, 4, 6, and 8 mm) as well as the effect of different applied forces and frequencies on the TENG's output voltage were investigated. The findings showed that the compressive strength increases with higher RFPU density. The output voltage values of the TENG device were recorded both with and without pre-charging. The results, without pre-charging, revealed that the highest output voltage of the TENG was obtained using an RFPU sheet with a density of 60 kg/m³. Furthermore, output voltage was shown to decrease with increasing RFPU sheet thickness and to increase with applied frequency. Pre-charging showed a similar trend, but yielded better results compared to the RFPU samples that were not pre-charged. The power density peaked at 0.085 mW/cm², at a load resistance of 5 MΩ and a force of 4.7 N. The RFPU-based TENG successfully powered four white LEDs connected in series. Analysis of the embodied energy associated with using PU foam waste instead of virgin PU foam was conducted and demonstrated that utilizing PU foam waste provides environmental benefits due to the significant contribution of raw materials to the overall embodied energy in PU foam that is saved when reusing foam waste. Additionally, assessing the embodied energy of the components of the TENG device was also conducted and shows that the generated energy can partially offset the embodied energy of the TENG device. As a result, the prepared RFPU material not only helps to safeguard the environment but also shows great promise for use in developing more efficient and affordable TENGs in the future.

三电纳米发电机（TENG）是一种前景广阔的清洁能源采集方法。为了提高 TENG 设备的效率，人们正在探索将轻质、柔性、廉价和环保材料作为 TENG 设备的潜在组件。本文首次成功地将再粘合柔性聚氨酯（RFPU）废料用作 TENG 设备中的摩擦正极材料。采用批量模塑技术制造了两种不同密度（60 和 70 kg/m3）的软质聚氨酯（FPU）废料，包括客户废料和板坯泡沫生产废料。本研究调查了密度对 RFPU 材料压缩强度的影响，以及对 TENG 输出电压的影响。此外，还研究了不同 RFPU 板材厚度（2、4、6 和 8 毫米）的影响，以及不同作用力和频率对 TENG 输出电压的影响。研究结果表明，抗压强度随着 RFPU 密度的增加而增加。在预充电和不预充电的情况下，都记录了 TENG 设备的输出电压值。结果显示，在没有预充电的情况下，密度为 60 kg/m3 的 RFPU 片材获得的 TENG 输出电压最高。此外，输出电压随 RFPU 片厚度的增加而降低，并随应用频率的增加而升高。预充电显示了类似的趋势，但与未预充电的 RFPU 样品相比，结果更好。在负载电阻为 5 MΩ 和力为 4.7 N 时，功率密度达到峰值 0.085 mW/cm2。对使用聚氨酯泡沫废料而不是原始聚氨酯泡沫所产生的内含能源进行了分析，结果表明，利用聚氨酯泡沫废料具有环境效益，因为重复使用泡沫废料时，原材料对聚氨酯泡沫的总体内含能源贡献很大，从而节省了能源。此外，还对 TENG 设备组件的体现能源进行了评估，结果表明所产生的能源可部分抵消 TENG 设备的体现能源。因此，所制备的 RFPU 材料不仅有助于保护环境，而且在未来开发更高效、更经济的腾博会登录_腾博会官网手机版_腾博会官网_腾博会手机版登录入口中大有可为。

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引用次数: 0

Wind curtailment powered flexible direct air capture 以灵活的直接空气捕获为动力的风力削减

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-09-27 DOI: 10.1016/j.apenergy.2024.124402

Direct air capture (DAC) has emerged as a pivotal negative emission technology for mitigating climate change. A DAC system integrated with renewable electricity can maximize its carbon capture efficiency; however, the existing DAC systems are incompatible with the intermittency and fluctuations of renewable electricity. This study develops an optimal model aiming to enable flexible operation of the DAC, thereby enhancing the utilization of wind electricity. Transferable and curtailable loads are applied to develop a new operational paradigm for adsorption-DAC systems. A linear programming optimization model is developed to enhance the adaptability of the DAC, ensuring complete utilization of curtailed electricity from wind farms. The case analysis pinpoints the requisite DAC deployment capacity, enabling the nullification of wind electricity curtailment within an actual power profile. Deploying DAC facilities with an annual CO₂ capture capacity of 43,500 tons is likely to eliminate the electricity curtailment of a wind farm of 400 MW in the optimal scenario. Techno-economic analysis indicates that deploying DAC facilities with an annual CO₂ capture of nearly 30,000 tons, along with an impressive 68 % curtailed electricity utilization rate, is the optimal financial choice for the wind farm. This study confirms that the feasibility of flexible DAC operation is essential for coupling with renewable electricity.

直接空气捕集（DAC）已成为减缓气候变化的关键负排放技术。与可再生能源电力相结合的直接空气捕集系统可以最大限度地提高碳捕集效率；然而，现有的直接空气捕集系统与可再生能源电力的间歇性和波动性不兼容。本研究开发了一个优化模型，旨在实现 DAC 的灵活运行，从而提高风电的利用率。应用可转移和可缩减负荷，为吸附式 DAC 系统开发了一种新的运行模式。开发了一个线性规划优化模型，以提高 DAC 的适应性，确保完全利用风电场削减的电力。案例分析精确定位了所需的 DAC 部署能力，从而使风电削减在实际功率曲线内无效。在最佳情况下，部署年二氧化碳捕集量为 43,500 吨的 DAC 设施有可能消除 400 兆瓦风电场的电力削减。技术经济分析表明，部署年二氧化碳捕集量近 3 万吨的 DAC 设施，以及令人印象深刻的 68% 的削减电力利用率，是风电场的最佳财务选择。这项研究证实，灵活的 DAC 运行可行性对于与可再生能源电力耦合至关重要。

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引用次数: 0