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The potential of radiative cooling enhanced photovoltaic systems in China 辐射冷却增强型光伏系统在中国的潜力
IF 13 Q1 ENERGY & FUELS Pub Date : 2024-07-26 DOI: 10.1016/j.adapen.2024.100184

Soaring solar cell temperature hindered photovoltaic (PV) efficiency, but a novel radiative cooling (RC) cover developed in this study offered a cost-effective solution. Using a randomly particle-doping structure, the radiative cooling cover achieved a high “sky window” emissivity of 95.3% while maintaining a high solar transmittance of 94.8%. The RC-PV system reached a peak power output of 147.6 W/m2. A field study to explore its potential in various provinces in China revealed significant efficiency improvements, with yearly electricity outputs surpassing those of ordinary PV systems by a relative improvement of 2.78%–3.72%. The largest increases were observed under clear skies and in dry, cool climates, highlighting the potential of RC-PV systems under real weather and environmental conditions. This work provided the theoretical foundation for designing scalable radiative cooling films for PV systems, unlocking the full potential of solar energy.

太阳能电池温度的飙升阻碍了光伏(PV)效率的提高,但本研究开发的新型辐射冷却(RC)罩提供了一种经济有效的解决方案。辐射冷却罩采用随机颗粒掺杂结构,实现了 95.3% 的高 "天窗 "发射率,同时保持了 94.8% 的高太阳能透过率。RC-PV 系统的峰值功率输出为 147.6 W/m2。在中国各省进行的一项探索其潜力的实地研究表明,该系统的效率显著提高,年发电量超过了普通光伏系统,相对提高了 2.78% 至 3.72%。在晴朗的天气和干燥凉爽的气候条件下,效率提高幅度最大,凸显了 RC-PV 系统在实际天气和环境条件下的潜力。这项工作为设计可扩展的光伏系统辐射冷却薄膜提供了理论基础,从而释放了太阳能的全部潜力。
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引用次数: 0
Impact of forecasting on energy system optimization 预测对能源系统优化的影响
IF 13 Q1 ENERGY & FUELS Pub Date : 2024-07-14 DOI: 10.1016/j.adapen.2024.100181

Linear programs are frequently employed to optimize national energy system models, which are used to find a minimum-cost energy system. For the operation, they assume perfect forecasting of the weather and demands over the whole optimization horizon and can therefore perfectly fit the energy systems’ design and operation. Therefore, they will yield lower costs than any real energy system that only has partial forecasting available. We compare linear programming with a priority list, a heuristic operation strategy which uses no forecasting at all, in a model of a climate-neutral German energy system. We find a 28% more expensive energy system under the priority list. Optimizing the same energy system model with both strategies envelopes the cost and design of any energy system that has partial forecasting. We demonstrate this by incorporating some rudimentary forecasting into a modified priority list, which actually reduces the gap to 22%. This is thus an approach to find an upper bound for how much a linear program possibly underestimates the costs of a real energy system in Germany in regard to imperfect forecasting. We also find that the two approaches differ mainly in the dimensioning and operation of energy storage. The priority list yields 63% less batteries, 73% less thermal storage and 54% more hydrogen storage. The use of renewables and other components in the system is very similar.

线性程序经常被用于优化国家能源系统模型,以找到成本最低的能源系统。在运行过程中,它们假定在整个优化范围内对天气和需求都有完美的预测,因此可以完美地适应能源系统的设计和运行。因此,它们所产生的成本将低于任何只有部分预测功能的实际能源系统。在一个气候中和的德国能源系统模型中,我们比较了线性规划和优先列表(一种完全不使用预测的启发式运行策略)。我们发现,优先级列表下的能源系统成本要高出 28%。使用这两种策略对同一能源系统模型进行优化后,任何采用部分预测的能源系统的成本和设计都会大打折扣。我们通过在修改后的优先级列表中加入一些基本预测来证明这一点,这实际上将差距缩小到了 22%。因此,我们可以通过这种方法,找到线性规划在不完全预测的情况下可能低估德国实际能源系统成本的上限。我们还发现,这两种方法主要在储能的尺寸和操作方面存在差异。优先列表中的电池数量减少了 63%,热存储减少了 73%,氢存储增加了 54%。系统中可再生能源和其他组件的使用情况非常相似。
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引用次数: 0
Techno–Economic Modeling and Safe Operational Optimization of Multi-Network Constrained Integrated Community Energy Systems 多网络受限综合社区能源系统的技术经济建模与安全运行优化
IF 13 Q1 ENERGY & FUELS Pub Date : 2024-07-14 DOI: 10.1016/j.adapen.2024.100183

The integrated community energy system (ICES) has emerged as a promising solution for enhancing the efficiency of the distribution system by effectively coordinating multiple energy sources. However, the concept and modeling of ICES still remain unclear, and operational optimization of ICES is hindered by the physical constraints of heterogeneous integrated energy networks. This paper, therefore, provides an overview of the state-of-the-art concepts for techno–economic modeling of ICES by establishing a Multi-Network Constrained ICES (MNC-ICES) model. The proposed model underscores the diverse energy devices at community and consumer levels and multiple networks for power, gas, and heat in a privacy-protection manner, providing a basis for practical network-constrained community operation tools. The corresponding operational optimization in the proposed model is formulated into a constrained Markov decision process (C-MDP) and solved by a Safe Reinforcement Learning (RL) approach. A novel Safe RL algorithm, Primal-Dual Twin Delayed Deep Deterministic Policy Gradient (PD-TD3), is developed to solve the C-MDP. By optimizing operations and maintaining network safety simultaneously, the proposed PD-TD3 method provides a solid backup for the ICESO and has great potential in real-world implementation. The non-convex modeling of MNC-ICES and the optimization performance of PD-TD3 is demonstrated in various scenarios. Compared with benchmark approaches, the proposed algorithm merits training speed, higher operational profits, and lower violations of multi-network constraints. Potential beneficiaries of this work include ICES operators and residents who could be benefited from improved ICES operation efficiency, as well as reinforcement learning researchers and practitioners who could be inspired for safe RL applications in real-world industry.

社区综合能源系统(ICES)是通过有效协调多种能源来提高配电系统效率的一种有前途的解决方案。然而,ICES 的概念和建模仍不清晰,异构综合能源网络的物理限制也阻碍了 ICES 的运行优化。因此,本文通过建立多网络约束 ICES(MNC-ICES)模型,概述了 ICES 技术经济建模的最新概念。该模型以保护隐私的方式强调了社区和消费者层面的各种能源设备以及电力、燃气和热力的多重网络,为实用的网络约束社区运营工具提供了基础。建议模型中相应的操作优化被表述为受限马尔可夫决策过程(C-MDP),并通过安全强化学习(RL)方法解决。为解决 C-MDP 问题,开发了一种新型安全 RL 算法--Primal-Dual Twin Delayed Deep Deterministic Policy Gradient (PD-TD3)。通过同时优化运营和维护网络安全,所提出的 PD-TD3 方法为 ICESO 提供了坚实的后盾,在实际应用中具有巨大潜力。PD-TD3 对 MNC-ICES 的非凸建模和优化性能在各种场景中进行了演示。与基准方法相比,所提出的算法具有训练速度快、运行利润高、违反多网络约束条件少等优点。这项工作的潜在受益者包括 ICES 运营商和居民,他们可以从 ICES 运营效率的提高中获益;也包括强化学习研究人员和从业人员,他们可以从实际工业中的安全 RL 应用中得到启发。
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引用次数: 0
Optimal scheduling of smart home energy systems: A user-friendly and adaptive home intelligent agent with self-learning capability 智能家居能源系统的优化调度:具有自学习能力的用户友好型自适应家庭智能代理
IF 13 Q1 ENERGY & FUELS Pub Date : 2024-07-11 DOI: 10.1016/j.adapen.2024.100182

This paper proposed a user-friendly and adaptive home intelligent agent with self-learning capability for optimal scheduling of smart home energy systems. The intelligent agent autonomously identifies model parameters based on system operation data, eliminating the need for manual input and making it more user-friendly and practical to implement. It can also self-learn the latest energy consumption information from an updated dataset and adaptively adjust model parameters to accommodate changing conditions. Utilizing these determined models as input, the intelligent agent performs day-ahead optimal scheduling using the proposed many-objective integer nonlinear optimization model and automatically controls system operation. Experimental studies were conducted on a laboratory-based smart home energy system to verify the effectiveness of the developed intelligent agent in different scenarios. The results consistently demonstrate Mean Absolute Percentage Errors below -12.7 % across all three scenarios, indicating the accuracy of the intelligent agent. Furthermore, the optimal scheduling significantly enhances system performances. After optimization, daily operational costs, peak-valley differences, and CO2 emissions were reduced by 34.1 % to 81.6 %, 29.2 % to 36.7 %, and 19.6 % to 43.2 %, respectively. Moreover, the PV generation self-consumption rate and self-sufficiency rate improved by 29.6 % to 38.0 % and 40.5 % to 49.4 %, respectively. The proposed intelligent agent provides invaluable guidance for optimal dispatch of smart home energy systems in real-world settings.

本文针对智能家居能源系统的优化调度,提出了一种具有自学习能力的用户友好型自适应家庭智能代理。该智能代理可根据系统运行数据自主确定模型参数,无需人工输入,因而更加方便实用。它还能从更新的数据集中自我学习最新的能源消耗信息,并自适应地调整模型参数,以适应不断变化的条件。利用这些确定的模型作为输入,智能代理使用所提出的多目标整数非线性优化模型执行日前优化调度,并自动控制系统运行。在实验室智能家居能源系统上进行了实验研究,以验证所开发的智能代理在不同场景下的有效性。结果表明,在所有三个场景中,平均绝对百分比误差均低于-12.7%,这表明了智能代理的准确性。此外,优化调度大大提高了系统性能。优化后,日常运营成本、峰谷差和二氧化碳排放量分别降低了 34.1% 至 81.6%、29.2% 至 36.7%、19.6% 至 43.2%。此外,光伏发电的自消耗率和自给率分别提高了 29.6% 至 38.0%,以及 40.5% 至 49.4%。所提出的智能代理为现实世界中智能家居能源系统的优化调度提供了宝贵的指导。
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引用次数: 0
Risk-aware microgrid operation and participation in the day-ahead electricity market 具有风险意识的微电网运行和参与日前电力市场
IF 13 Q1 ENERGY & FUELS Pub Date : 2024-06-22 DOI: 10.1016/j.adapen.2024.100180
Robert Herding , Emma Ross , Wayne R. Jones , Elizabeth Endler , Vassilis M. Charitopoulos , Lazaros G. Papageorgiou

This work examines the daily bidding problem of a grid-connected microgrid with locally deployed resources for electricity generation, storage and its own electricity demand. Trading electricity in energy markets may offer economic incentives but exposes the microgrid to financial risk caused by market commitments. Hence, a multi-objective, two-stage stochastic mixed integer linear programming (MILP) model is formulated, extending prior work of a risk-neutral microgrid bidding approach. The multi-objective model minimises both expected total cost of day-ahead microgrid operations and financial risk from bidding measured by conditional value-at-risk (CVaR). Bidding curves derived as first stage decisions are always feasible under present market rules – including a limitation on the number of break points per submitted curve – while being near optimal for the microgrid’s day-ahead recourse schedule. The proposed optimisation model is embedded in a variant of the ɛ-constrained method to generate bidding curve candidates with different trade-offs between the two conflicting objectives. Moreover, scenario reduction is used to compromise accuracy of the uncertainty set for better computational performance. Particularly, the marginal relative probability distance between initial and reduced scenario set is suggested to make a decision on the extent of scenario reduction. The proposed solution procedure is tested in a computational study to demonstrate its applicability to generate optimal microgrid bidding curve candidates with different emphasis between total cost and CVaR in reasonable computational time.

这项研究探讨了一个并网微电网的日常竞标问题,该微电网拥有本地部署的发电、储能和自身电力需求资源。在能源市场上进行电力交易可能会带来经济激励,但也会使微电网面临市场承诺带来的财务风险。因此,我们制定了一个多目标、两阶段随机混合整数线性规划(MILP)模型,扩展了之前的风险中性微电网竞标方法。该多目标模型最大限度地降低了日前微电网运行的预期总成本和以条件风险值(CVaR)衡量的投标财务风险。根据目前的市场规则(包括对每条提交曲线的断点数量的限制),作为第一阶段决策得出的竞价曲线总是可行的,同时接近微电网日前追索计划的最优值。建议的优化模型被嵌入到ɛ-约束方法的变体中,以生成在两个相互冲突的目标之间进行不同权衡的投标曲线候选方案。此外,为了获得更好的计算性能,还采用了情景缩减法来降低不确定性集的精确度。特别是,建议使用初始情景集与缩减情景集之间的边际相对概率距离来决定情景缩减的程度。建议的求解程序在计算研究中进行了测试,以证明其适用于在合理的计算时间内生成总成本和 CVaR 之间不同侧重点的最佳微电网投标曲线候选方案。
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引用次数: 0
Life cycle assessment of ammonia co-firing power plants: A comprehensive review and analysis from a whole industrial chain perspective 氨联合燃烧发电厂的生命周期评估:从全产业链角度进行全面审查和分析
Q1 Energy Pub Date : 2024-05-20 DOI: 10.1016/j.adapen.2024.100178
Hui Kong , Yueqiao Sun , Hongsheng Wang , Jian Wang , Liping Sun , Jun Shen

Ammonia, a reliable low-carbon alternative fuel with energy storage capabilities, has garnered increasing attention for its application of co-firing in coal-fired power plants as a strategy to mitigate direct carbon emissions. However, various types of ammonia production technologies result in diverse economic feasibility and emission intensities. Simultaneously, each stage, spanning from upstream processes such as raw material extraction to downstream applications, contributes to carbon emissions, which cannot be ignored. It is crucial to select the appropriate assessment method to determine the transformation pathways for co-firing systems. To this end, this review presents a comprehensive life cycle assessment of ammonia co-firing systems from a whole industrial chain perspective, encompassing the entire gamut of processes from fuel production and transportation to co-firing. Studies of the industrial chain perspective and of life cycle assessment methodology that are uniquely tailored for co-firing systems are presented. A nuanced exploration of distinct technologies across the spectrum of system processes ensues, including the advantages, limitations, and trends in advancement, based on carbon emissions and economic criteria. Considering the diverse fuel production, especially ammonia, typologies and intricate processes have undergone comprehensive review. The combustion characteristics, emissions, and economic factors associated with the co-firing process are systematically summarized, drawing upon aspects such as dynamics, experiments, simulations, and demonstration projects. This study illuminates the progression and technology selection of co-firing systems across multiple stages of the whole industry chain, thereby furnishing insights relevant to the low-carbon transformation of ammonia co-firing with coal in power plants.

氨是一种可靠的低碳替代燃料,具有储能功能,在燃煤电厂中作为一种减少直接碳排放的战略,其联合燃烧的应用日益受到关注。然而,各种类型的合成氨生产技术导致了不同的经济可行性和排放强度。同时,从原材料提取等上游工艺到下游应用,每个阶段都会造成碳排放,这一点不容忽视。选择适当的评估方法来确定联合燃烧系统的转化途径至关重要。为此,本综述从整个产业链的角度对氨气联合燃烧系统进行了全面的生命周期评估,包括从燃料生产、运输到联合燃烧的整个过程。文中介绍了对产业链视角和生命周期评估方法的研究,这些都是为联合燃烧系统量身定制的。随后,根据碳排放和经济标准,对整个系统过程中的不同技术进行了细致的探讨,包括优势、局限性和发展趋势。考虑到燃料生产的多样性,特别是氨的生产,对类型和复杂工艺进行了全面审查。通过动态、实验、模拟和示范项目等方面,系统地总结了与联合燃烧工艺相关的燃烧特性、排放和经济因素。这项研究揭示了整个产业链多个阶段中联合燃烧系统的发展和技术选择,从而为电厂氨与煤联合燃烧的低碳转型提供了启示。
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引用次数: 0
Variational quantum circuit learning-enabled robust optimization for AI data center energy control and decarbonization 变式量子电路学习为人工智能数据中心能源控制和去碳化提供稳健优化
Q1 Energy Pub Date : 2024-05-11 DOI: 10.1016/j.adapen.2024.100179
Akshay Ajagekar , Fengqi You

As the demand for artificial intelligence (AI) models and applications continues to grow, data centers that handle AI workloads are experiencing a rise in energy consumption and associated carbon footprint. This work proposes a variational quantum computing-based robust optimization (VQC-RO) framework for control and energy management in large-scale data centers to address the computational challenges and overcome limitations of conventional model-based and model-free strategies. The VQC-RO framework integrates variational quantum circuits (VQCs) with classical optimization to enable efficient and uncertainty-aware control of energy systems in AI data centers. Quantum algorithms executed on noisy intermediate-scale quantum (NISQ) devices are used for value function estimation trained with Q-learning, leading to the formulation of a robust optimization problem with uncertain coefficients. The quantum computing-based robust control strategy is designed to address uncertainties associated with weather conditions and renewable energy generation while optimizing energy consumption in AI data centers. This work also outlines the computational experiments conducted at various AI data center locations in the United States to analyze the reduction in power consumption and carbon emission levels associated with the proposed quantum computing-based robust control framework. This work contributes a novel approach to energy-efficient and sustainable data center operation, promising to reduce carbon emissions and energy consumption in large-scale data centers handling AI workloads by 9.8 % and 12.5 %, respectively.

随着对人工智能(AI)模型和应用的需求不断增长,处理 AI 工作负载的数据中心正经历着能耗和相关碳足迹的上升。本研究提出了一种基于变量子计算的鲁棒优化(VQC-RO)框架,用于大规模数据中心的控制和能源管理,以应对计算挑战并克服传统的基于模型和无模型策略的局限性。VQC-RO 框架将变分量子电路 (VQC) 与经典优化相结合,实现了对人工智能数据中心能源系统的高效和不确定性感知控制。在噪声中量子(NISQ)设备上执行的量子算法被用于通过 Q-learning 训练的值函数估计,从而提出了一个具有不确定系数的鲁棒优化问题。基于量子计算的稳健控制策略旨在解决与天气条件和可再生能源发电相关的不确定性问题,同时优化人工智能数据中心的能源消耗。这项工作还概述了在美国多个人工智能数据中心地点进行的计算实验,以分析与拟议的基于量子计算的鲁棒控制框架相关的电力消耗和碳排放水平的降低情况。这项工作为高能效和可持续的数据中心运营提供了一种新方法,有望将处理人工智能工作负载的大型数据中心的碳排放和能耗分别降低 9.8% 和 12.5%。
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引用次数: 0
Advances in model predictive control for large-scale wind power integration in power systems 电力系统中大规模风电集成的模型预测控制进展:全面回顾
Q1 Energy Pub Date : 2024-04-20 DOI: 10.1016/j.adapen.2024.100177
Peng Lu , Ning Zhang , Lin Ye , Ershun Du , Chongqing Kang

Wind power exhibits low controllability and is situated in dispersed geographical locations, presenting complex coupling and aggregation characteristics in both temporal and spatial dimensions. When large-scale wind power is integrated into the power grid, it will bring a significant technical challenge: the highly variable nature of wind power poses a threat to the safe and stable control of the power, frequency, and voltage in the power system. Simultaneously, the model predictive control (MPC) technology provides more opportunities for investigating control issues related to large-scale wind power integration in power systems. This paper provides a timely and systematic overview of the applications of MPC in the field of wind power for the first time, innovatively embedding MPC technology into multi-level (e.g., wind turbines, wind farms, wind power cluster, and power grids) and multi-objective (e.g., power, frequency, and voltage) control. Firstly, the basic concept and classification criteria of MPC are developed, and the available modeling methods in wind power are carefully compared. Secondly, the application scenarios of MPC in multi-level and multi-objective wind power control are summarized. Finally, how to use a variety of optimization algorithms to solve these models is discussed. Based on the broad review above, we summarize several key scientific issues related to predictive control and discuss the challenges and future development directions in detail. This paper details the role of MPC technology in multi-level and multi-objective control within the wind power sector, aiming to help engineers and scientists understand its substantial potential in wind power integration in power systems.

风力发电的可控性低,地理位置分散,在时间和空间维度上都具有复杂的耦合和聚集特性。当大规模风电并入电网时,将带来巨大的技术挑战:风电的高可变性对电力系统中功率、频率和电压的安全稳定控制构成威胁。与此同时,模型预测控制(MPC)技术为研究与大规模风电并入电力系统相关的控制问题提供了更多机会。本文首次对 MPC 在风电领域的应用进行了及时而系统的概述,创新性地将 MPC 技术嵌入到多层次(如风力涡轮机、风电场、风电集群和电网)和多目标(如功率、频率和电压)控制中。首先,提出了 MPC 的基本概念和分类标准,并仔细比较了现有的风电建模方法。其次,总结了 MPC 在多级多目标风电控制中的应用场景。最后,讨论了如何使用各种优化算法来求解这些模型。在上述综述的基础上,我们总结了与预测控制相关的几个关键科学问题,并详细讨论了所面临的挑战和未来的发展方向。本文详细介绍了 MPC 技术在风电领域的多级和多目标控制中的作用,旨在帮助工程师和科学家了解其在电力系统风电集成中的巨大潜力。
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引用次数: 0
Introducing sodium lignosulfonate as an effective promoter for CO2 sequestration as hydrates targeting gaseous and liquid CO2 将木质素磺酸钠作为针对气态和液态二氧化碳的水合物进行二氧化碳封存的有效促进剂
Q1 Energy Pub Date : 2024-04-16 DOI: 10.1016/j.adapen.2024.100175
Hailin Huang , Xuejian Liu , Hongfeng Lu , Chenlu Xu , Jianzhong Zhao , Yan Li , Yuhang Gu , Zhenyuan Yin

Hydrate-based CO2 sequestration (HBCS) emerges as a promising solution to sequestrate CO2 as solid hydrates for the benefit of reducing CO2 concentration in the atmosphere. The natural conditions of high-pressure and low-temperature in marine seabed provide an ideal reservoir for CO2 hydrate, enabling long-term sequestration. A significant challenge in the application of HBCS is the identification of an environmental-friendly promoter to enhance or tune CO2 hydrate kinetics, which is intrinsically sluggish. In addition, the promoter identified should be effective in all CO2 sequestration conditions, covering CO2 injection as gas or liquid. In this study, we introduced sodium lignosulfonate (SL), a by-product from the papermaking industry, as an eco-friendly kinetic promoter for CO2 hydrate formation. The impact of SL (0–3.0 wt.%) on the kinetics of CO2 hydrate formation from gaseous and liquid CO2 was systematically investigated. CO2 hydrate morphology images were acquired for both gaseous and liquid CO2 in the presence of SL for the explanation of the observed promotion effect. The promotion effect of SL on CO2 hydrate formation is optimal at 1.0 wt.% with induction time reduced to 5.3 min and 21.1 min for gaseous and liquid CO2, respectively. Moreover, CO2 storage capacity increases by around two times at 1.0 wt.% SL, reaching 85.1 v/v and 57.1 v/v for gaseous and liquid CO2, respectively. The applicability of SL as an effective kinetic promoter for both gaseous and liquid CO2 was first demonstrated. A mechanism explaining how SL promotes CO2 hydrate formation was formulated with additional nucleation sites by SL micelles and the extended contact surface offered by generated gas bubbles or liquid droplets with SL. The study demonstrates that SL as an effective promoter for CO2 hydrate kinetics is possible for adoption in large-scale HBCS projects both nearshore and offshore.

以水合物为基础的二氧化碳封存(HBCS)是以固体水合物形式封存二氧化碳以降低大气中二氧化碳浓度的一种前景广阔的解决方案。海洋海底高压低温的自然条件为二氧化碳水合物提供了理想的储层,可实现长期封存。HBCS 应用中的一个重大挑战是找到一种环境友好型促进剂,以增强或调整二氧化碳水合物动力学,因为二氧化碳水合物动力学本质上是缓慢的。此外,确定的促进剂应在所有二氧化碳封存条件下都有效,包括以气体或液体形式注入二氧化碳。在本研究中,我们引入了造纸工业的副产品木质素磺酸钠(SL)作为二氧化碳水合物形成的环保型动力学促进剂。我们系统地研究了 SL(0-3.0 wt.%)对气态和液态 CO2 形成 CO2 水合物动力学的影响。为了解释所观察到的促进作用,在 SL 存在的情况下采集了气态和液态 CO2 的 CO2 水合物形态图像。SL 对 CO2 水合物形成的促进作用在 1.0 wt.% 时达到最佳,气态 CO2 和液态 CO2 的诱导时间分别缩短至 5.3 分钟和 21.1 分钟。此外,在 1.0 wt.% SL 条件下,二氧化碳的储存能力提高了约两倍,气态和液态二氧化碳的储存能力分别达到 85.1 v/v 和 57.1 v/v。SL 作为一种有效的动力学促进剂对气态和液态 CO2 的适用性首次得到了证实。通过 SL 胶束的额外成核位点以及生成的气泡或液滴与 SL 的扩展接触面,提出了 SL 如何促进二氧化碳水合物形成的机理。研究表明,SL 作为二氧化碳水合物动力学的有效促进剂,可用于近岸和离岸的大型 HBCS 项目。
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引用次数: 0
Assessing the conditions for economic viability of dynamic electricity retail tariffs for households 评估家庭动态电力零售价的经济可行性条件
Q1 Energy Pub Date : 2024-04-16 DOI: 10.1016/j.adapen.2024.100174
Judith Stute , Sabine Pelka , Matthias Kühnbach , Marian Klobasa

The success of the energy transition relies on effectively utilizing flexibility in the power system. Dynamic tariffs are a highly discussed and promising innovation for incentivizing the use of residential flexibility. However, their full potential can only be realized if households achieve significant benefits. This paper specifically addresses this topic. We examine the leverage of household flexibility and the financial benefits of using dynamic tariffs, considering household heterogeneity, the costs of home energy management systems and smart meters, the impact of higher electricity prices and price spreads and the differences between types of prosumers. To comprehensively address this topic, we use the EVaTar-building model, a simulation framework that includes embedded optimization designed to simulate household electricity consumption patterns under the influence of a home energy management system or in response to dynamic tariffs. The study's main finding is that households can achieve significant cost savings and increase flexibility utilization by using a home energy management system and dynamic electricity tariffs, provided that electricity prices and price spreads reach higher levels. When comparing price levels in a low and high electricity price scenario, with an increase of the average electricity price by 15.2 €ct/kWh (67 % higher than the average for the year 2019) and an increase of the price spread by 8.9 €ct/kWh (494 % higher), the percentage of households achieving cost savings increases from 3.9 % to 62.5 %. Households with both an electric vehicle and a heat pump observed the highest cost benefits. Sufficiently high price incentives or sufficiently low costs for home energy management systems and metering point operation are required to enable households to mitigate rising electricity costs and ensure residential flexibility for the energy system through electric vehicles and heat pumps.

能源转型的成功有赖于有效利用电力系统的灵活性。动态电价是一项备受讨论且前景广阔的创新措施,用于激励居民使用灵活性。然而,只有当家庭获得显著收益时,才能充分发挥其潜力。本文专门讨论了这一主题。考虑到家庭的异质性、家庭能源管理系统和智能电表的成本、较高电价和价差的影响以及不同类型消费者之间的差异,我们研究了家庭灵活性的杠杆作用以及使用动态电价的经济效益。为了全面探讨这一主题,我们使用了 EVaTar 建筑模型,这是一个包含嵌入式优化的模拟框架,旨在模拟家庭能源管理系统影响下或响应动态电价时的家庭用电模式。研究的主要发现是,只要电价和价差达到较高水平,家庭就能通过使用家庭能源管理系统和动态电价显著节约成本并提高灵活性利用率。在比较低电价和高电价情景下的价格水平时,如果平均电价上涨 15.2 欧元/千瓦时(比 2019 年的平均电价高出 67%),价差上涨 8.9 欧元/千瓦时(高出 494%),则实现成本节约的家庭比例将从 3.9% 增加到 62.5%。同时拥有电动汽车和热泵的家庭成本效益最高。家庭能源管理系统和计量点的运行需要足够高的价格激励或足够低的成本,才能使家庭通过电动汽车和热泵降低不断上涨的电费,并确保住宅能源系统的灵活性。
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Advances in Applied Energy
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