Applied Energy最新文献_第5页

One-step thermal engineering of FTO substrate: unlocking higher-efficiency perovskite solar cells FTO衬底的一步热工程：解锁更高效率的钙钛矿太阳能电池

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-12 DOI: 10.1016/j.apenergy.2025.127230

Chuan Li , Yu Zhang , Weihui Bi , Zhaohui Ren , Gaorong Han , Likun Wang , Sainan Ma

Interfacial energy level alignment is crucial for minimizing efficiency loss in perovskite solar cells (PSCs), yet the energy level matching between transparent conductive oxide (TCO) electrode and electron or hole transport layer (ETL/HTL) has received limited attention. Here, rather than directly employing commercial fluorine-doped tin oxide (C-FTO) as the TCO electrode, a facile one-step thermal treatment strategy was applied prior to the fabrication of n-i-p PSCs. Notably, under thermal treatment at optimal 300 °C, the work function of C-FTO effectively reduced, enhancing the energy level alignment with the ETL. Moreover, the 300 °C-treated FTO (300-FTO) exhibits a smoother surface morphology, improved conductivity and reduced resistivity. These improvements contribute to superior interfacial compatibility with the ETL, facilitating the deposition of high-quality perovskite active layers and promoting more efficient charge transfer and collection. As a result, PSCs based on 300-FTO achieved an average power conversion efficiency (PCE) of 23.02 %, making a significant improvement of 2.7 % compared to devices utilizing untreated C-FTO. This work demonstrates the great efficacy of thermal treatment in modifying FTO, providing a simple and efficient approach for the development of higher-efficiency PSCs.

在钙钛矿太阳能电池（PSCs）中，界面能级对准是降低效率损失的关键，但透明导电氧化物（TCO）电极与电子或空穴传输层（ETL/HTL）之间的能级匹配一直受到较少的关注。在这里，不是直接使用商业氟掺杂氧化锡（C-FTO）作为TCO电极，而是在制造n-i-p psc之前采用简单的一步热处理策略。值得注意的是，在最优300°C的热处理下，C- fto的功函数有效降低，增强了与ETL的能级一致性。此外，300°c处理的FTO （300-FTO）表面形貌更光滑，电导率提高，电阻率降低。这些改进有助于提高与ETL的界面兼容性，促进高质量钙钛矿活性层的沉积，并促进更有效的电荷转移和收集。因此，基于300-FTO的psc实现了23.02%的平均功率转换效率（PCE），与使用未经处理的C-FTO的器件相比，显著提高了2.7%。这项工作证明了热处理在改性FTO方面的巨大功效，为开发更高效的psc提供了一种简单有效的方法。

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引用次数: 0

Catalyst design and machine learning for thermocatalytic CO2 methanation: A review 热催化CO2甲烷化的催化剂设计与机器学习研究进展

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-12 DOI: 10.1016/j.apenergy.2025.127221

Xiaoguo Zhang, Wei Lu, Ziyi He, Meng Yang, Shenfu Yuan

CO₂ methanation, as a cornerstone technology for a sustainable carbon economy, and its industrial deployment are currently severely constrained by catalyst performance. Although significant progress has been made through optimizing isolated components of the catalyst, the intrinsic complexity of these catalytic systems and their highly non-linear structure-activity relationships make it exceptionally challenging to optimize high-performance catalysts via conventional methods. Therefore, this review argues that future breakthroughs hinge on the synergistic integration of three fields: reaction mechanisms, catalyst design, and machine learning. A deep understanding of reaction mechanisms is the prerequisite for rational catalyst design, and that machine learning (ML) serves as the indispensable engine driving this transformation. To this end, this review systematically summarizes recent advances in active metals, supports, and interfacial engineering, aiming to outline the solid foundation laid by prior research in this field and analyze the inherent limitations of traditional trial-and-error approaches. Subsequently, we systematically introduce machine learning and its innovative applications in catalyst design. Finally, this review outlines key challenges and future research directions in CO₂ methanation catalyst design. By integrating these domains, this review aims to lay the groundwork for developing catalysts that combine high performance.

二氧化碳甲烷化作为可持续碳经济的基石技术，其工业部署目前受到催化剂性能的严重制约。尽管通过优化催化剂的分离组分已经取得了重大进展，但这些催化系统的内在复杂性及其高度非线性的构效关系使得通过传统方法优化高性能催化剂非常具有挑战性。因此，本文认为未来的突破取决于三个领域的协同整合：反应机制、催化剂设计和机器学习。对反应机制的深入理解是合理设计催化剂的先决条件，而机器学习（ML）是推动这一转变不可或缺的引擎。为此，本文系统地总结了活性金属、支撑体和界面工程的最新进展，旨在概述该领域先前研究奠定的坚实基础，并分析传统试错方法的固有局限性。随后，我们系统地介绍了机器学习及其在催化剂设计中的创新应用。最后，综述了二氧化碳甲烷化催化剂设计面临的主要挑战和未来的研究方向。通过对这些领域的整合，为开发高性能催化剂奠定基础。

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引用次数: 0

Hybrid game-based dispatch for 5G BS-BSC synergy in distribution networks 5G配电网BS-BSC协同的混合博弈调度

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-11 DOI: 10.1016/j.apenergy.2025.127153

Qi Qi , Yujie Li , Lingyi Ma , Xiangjun Liu , Zhe Bao , Lu Zhang , Canbing Li

The development of 5G base stations (5G BSs) and the presence of electric scooter battery swapping cabinets (BSCs) provide substantial dispatch resources for distribution networks (DNs). Due to the fact of their frequent co-deployment, this paper proposes an optimal dispatch method for DNs considering the cooperative operation of 5G BSs and BSCs based on hybrid game theory. Firstly, the dispatch capabilities of 5G BSs and BSCs are analyzed considering the communication service quality and battery-swapping demands respectively. The joint dispatch capability of 5G BS and BSC is then modeled by comparing their independent and cooperative operations. Next, an optimal dispatch model utilizing hybrid game theory is developed for the joint participation of 5G BS-BSC in DN operations, where a leader-follower game is set up between the DN and the 5G BS-BSC alliance, while a cooperative game is established within the alliance. To efficiently solve this model, a Double Agent-assisted Bi-level Evolutionary Algorithm (DA-BLEA) is proposed to enhance the speed and accuracy of the leader-follower game process. A Priority-based Weighted Ranking (PWR) method is also proposed to identify the optimal equilibrium solution in the leader-follower game. This method facilitates the determination of operational costs for both 5G BS and BSC by transferring the follower's cost from the equilibrium solution to the cooperative game model, ensuring a seamless integration of costs and strategies between the two game frameworks. Through conducting multi-scenario simulations, effectiveness of the optimal dispatch model is validated, along with the superiority of the joint dispatch of 5G BS and BSC over their parallel independent operations.

5G基站（5G BSs）的发展和电动滑板车电池交换柜（bsc）的出现为配电网络（dn）提供了大量的调度资源。针对5G基站与bsc协同部署频繁的特点，本文基于混合博弈论，提出了一种考虑5G基站与bsc协同运行的DNs最优调度方法。首先，分别从通信服务质量和换电池需求两方面分析了5G基站和bsc的调度能力。通过对5G分站和平衡分站的独立运行和协同运行进行比较，建立了5G分站和平衡分站的联合调度能力模型。其次，利用混合博弈论建立5G BS-BSC联合参与DN运营的最优调度模型，在DN与5G BS-BSC联盟之间建立领导-追随者博弈，在联盟内部建立合作博弈。为了有效地求解该模型，提出了一种双智能体辅助双级进化算法（DA-BLEA），以提高领导-追随者博弈过程的速度和准确性。提出了一种基于优先级的加权排序（PWR）方法来识别领导-随从博弈的最优均衡解。该方法通过将跟随者的成本从均衡解转移到合作博弈模型，促进了5G BS和平衡计分卡运营成本的确定，保证了两种博弈框架之间成本和策略的无缝集成。通过多场景仿真，验证了优化调度模型的有效性，以及5G基站和BSC联合调度相对于其并行独立运行的优越性。

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引用次数: 0

A hierarchical resilient economic dispatch strategy for multi-energy system under DoS attacks DoS攻击下多能系统的分层弹性经济调度策略

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-11 DOI: 10.1016/j.apenergy.2025.127204

Pengbo Du , Bonan Huang , Fanghui Li , Rui Wang , Chaoyu Dong , Qiuye Sun , Tianyi Li , Yushuai Li

The economic dispatch problem (EDP) plays a critical role in achieving efficient and low-carbon operation of multi-energy systems (MES). Distributed optimization algorithms have been widely adopted to solve this problem. However, their reliance on communication networks exposes them to cybersecurity threats, particularly denial-of-service (DoS) attacks, which can disrupt information exchange and degrade system performance. To address this, this paper proposes a resilient distributed optimization algorithm for MES that operates effectively under adverse communication environments. In the proposed method, a cloud-edge-device hierarchical architecture is designed, which integrates multiple independently controlled regional microgrids. With the proposed framework, participants can conduct edge computing through edge intelligent terminals (EITs) to collaboratively optimize operational costs. This framework ensures that distributed optimization algorithms can collaborate seamlessly, even when the system is exposed to DoS attacks. By explicitly considering the coexistence of event-triggered communication and DoS attacks, the framework redefines secure and attack intervals and incorporates switching protocols with second-order information. These features enable the algorithm to maintain convergence and reduce communication burdens, ensuring system performance without relying on initialization. The Lyapunov stability-based theoretical analysis proves that the algorithm achieves exponential convergence to the global optimum under bounded attack frequency and duration, while avoiding Zeno behavior. Simulation results further validate the effectiveness and robustness of the proposed method in securing EDP under adversarial communication environments.

经济调度问题是实现多能源系统高效低碳运行的关键问题。分布式优化算法被广泛采用来解决这一问题。然而，他们对通信网络的依赖使他们面临网络安全威胁，特别是拒绝服务（DoS）攻击，这会破坏信息交换并降低系统性能。为了解决这个问题，本文提出了一种弹性分布式优化算法，用于MES在不利的通信环境下有效运行。在该方法中，设计了一种云边缘设备分层架构，该架构集成了多个独立控制的区域微电网。利用所提出的框架，参与者可以通过边缘智能终端（EITs）进行边缘计算，以协同优化运营成本。该框架确保分布式优化算法可以无缝协作，即使系统暴露于DoS攻击。通过明确考虑事件触发通信和DoS攻击的共存，该框架重新定义了安全和攻击间隔，并将交换协议与二阶信息结合起来。这些特性使算法保持收敛性，减少通信负担，保证系统性能，而不依赖于初始化。基于Lyapunov稳定性的理论分析证明了该算法在有界攻击频率和持续时间下实现了指数收敛到全局最优，同时避免了zno行为。仿真结果进一步验证了该方法在对抗通信环境下保护电子数据传输的有效性和鲁棒性。

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引用次数: 0

The flexibility dilemma―managing cost and emissions in a fossil fuel-dominated power sector under increasing penetration of variable renewable energy sources 灵活性困境——在可变可再生能源日益普及的情况下，以化石燃料为主导的电力部门的成本和排放管理

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-11 DOI: 10.1016/j.apenergy.2025.127199

Subhadip Bhattacharya , Rangan Banerjee , Venkatasailanathan Ramadesigan , Ariel Liebman , Roger Dargaville

The limited flexibility of thermal power plants (TPP) is crucial when assessing the transition to higher penetration of variable renewable energy resources (VRES). This study develops an enhanced TIMES-based power-sector optimisation framework that integrates unit-level technical and operational constraints into a multi-stage capacity expansion model with hourly dispatch resolution. This approach endogenises TPP-specific dynamics—including minimum load, ramp rates, start-up/shut-down behaviour, minimum on/off duration, and part-load efficiency variation—features that are typically simplified or omitted in conventional long-term energy system models. The cost-minimising model is applied to India's power sector, beginning from 2020 until 2032, to assess the trade-off between system cost, CO₂ emissions, and flexibility under alternative coal retirement and retrofitting scenarios.

The results show that disregarding these constraints leads to overestimating the potential of TPPs to supply peak demand and underestimating the role of energy storage. Extreme strategies, such as a complete phase-down of coal TPPs or not retiring old TPPs altogether, might not be either economically or environmentally beneficial. Achieving higher flexibility for the present Indian coal TPP fleet would offset the requirement for energy storage and reduce renewable energy curtailment, but would marginally increase CO₂ emissions. It further highlights that meeting India's existing NDC will require around USD 127 billion in annual spending. The proposed unified modelling framework will provide a replicable tool for integrating dispatch realism into long-term energy system planning, enabling system planners in other similar fossil-dominated regions to investigate future cost-effective and emission mitigation pathways for the power sector.

在评估向可变可再生能源（VRES）的更高渗透率过渡时，火力发电厂（TPP）有限的灵活性至关重要。本研究开发了一个增强的基于时间的电力部门优化框架，该框架将单元级技术和操作约束集成到具有小时调度分辨率的多阶段容量扩展模型中。这种方法内化了tpp特定的动态，包括最小负荷、斜坡速率、启动/关闭行为、最小开/关持续时间和部分负荷效率变化，这些特征在传统的长期能源系统模型中通常被简化或省略。从2020年到2032年，将成本最小化模型应用于印度电力部门，以评估在替代煤炭退役和改造方案下系统成本、二氧化碳排放和灵活性之间的权衡。研究结果表明，忽视这些制约因素会导致高估TPPs供应峰值需求的潜力，而低估储能的作用。极端的策略，如完全淘汰煤炭TPPs或不完全淘汰旧的TPPs，可能在经济上或环境上都不有利。为目前的印度燃煤电厂实现更高的灵活性，将抵消对储能的需求，减少可再生能源的弃电，但会略微增加二氧化碳排放。报告进一步强调，要实现印度现有的国家自主贡献，每年将需要约1270亿美元的支出。拟议的统一建模框架将提供一个可复制的工具，将调度现实主义纳入长期能源系统规划，使其他类似化石能源主导地区的系统规划者能够研究电力部门未来的成本效益和减排途径。

{"title":"The flexibility dilemma―managing cost and emissions in a fossil fuel-dominated power sector under increasing penetration of variable renewable energy sources","authors":"Subhadip Bhattacharya , Rangan Banerjee , Venkatasailanathan Ramadesigan , Ariel Liebman , Roger Dargaville","doi":"10.1016/j.apenergy.2025.127199","DOIUrl":"10.1016/j.apenergy.2025.127199","url":null,"abstract":"<div><div>The limited flexibility of thermal power plants (TPP) is crucial when assessing the transition to higher penetration of variable renewable energy resources (VRES). This study develops an enhanced TIMES-based power-sector optimisation framework that integrates unit-level technical and operational constraints into a multi-stage capacity expansion model with hourly dispatch resolution. This approach endogenises TPP-specific dynamics—including minimum load, ramp rates, start-up/shut-down behaviour, minimum on/off duration, and part-load efficiency variation—features that are typically simplified or omitted in conventional long-term energy system models. The cost-minimising model is applied to India's power sector, beginning from 2020 until 2032, to assess the trade-off between system cost, CO₂ emissions, and flexibility under alternative coal retirement and retrofitting scenarios.</div><div>The results show that disregarding these constraints leads to overestimating the potential of TPPs to supply peak demand and underestimating the role of energy storage. Extreme strategies, such as a complete phase-down of coal TPPs or not retiring old TPPs altogether, might not be either economically or environmentally beneficial. Achieving higher flexibility for the present Indian coal TPP fleet would offset the requirement for energy storage and reduce renewable energy curtailment, but would marginally increase CO<sub>2</sub> emissions. It further highlights that meeting India's existing NDC will require around USD 127 billion in annual spending. The proposed unified modelling framework will provide a replicable tool for integrating dispatch realism into long-term energy system planning, enabling system planners in other similar fossil-dominated regions to investigate future cost-effective and emission mitigation pathways for the power sector.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"405 ","pages":"Article 127199"},"PeriodicalIF":11.0,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On-demand electro-/thermo-chromic smart windows with self-adaptive sensible heat storage for multimode synergistic building energy conservation 多模式协同建筑节能的自适应感应蓄热随需电/热致变色智能窗

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-11 DOI: 10.1016/j.apenergy.2025.127222

Hongchao Peng , Runfang Fu , Haibo Wang , Ziming Liu , Yingchun Gu , Qin Yang , Sheng Chen , Bin Yan

Conferring smart windows with sensible heat storage capacity could enable powerful energy and optical modulation capacity in buildings toward the desired net-zero carbon goal. However, fabricating transparent chromic devices using thermal energy storage materials to achieve heat storage and on-demand optical modulation remains a challenge. Herein, an energy-efficient smart window that enables heat storage capacity and multimode optical modulation is demonstrated by integrating a thermochromic (TC) hydrogel with thermal energy storage ability and an electrochromic (EC) material. Specifically, the developed chromic device exhibits an outstanding specific heat capacity of ∼4.45 J·g⁻¹·K⁻¹ for self-adaptively (SA) storing/releasing the heat and can achieve high optical modulation (∆T_Sol = 69.90 % with a wavelength range of 250–2500 nm) through optional heat/electricity combinations. These intriguing properties endow the smart window with multiple management modes toward heat and transmittance including the SA mode, EC mode, and TC mode, which can realize an indoor temperature drop of 21.8 °C, 24.1 °C, and 28.0 °C under a sun irradiation of 1 kW·m⁻². Energy simulation results further demonstrate the substantial building energy conservation (43.32 MJ·m⁻²) of this smart window while providing remarkable indoor comfort. This work offers a viable yet simple strategy to realize more energy-efficient buildings with a minimum carbon footprint for global carbon neutrality.

赋予具有显热存储能力的智能窗户可以使建筑物具有强大的能量和光调制能力，从而实现所需的净零碳目标。然而，利用热能储存材料制造透明的彩色器件来实现热量储存和按需光调制仍然是一个挑战。本文通过集成具有热储能能力的热致变色（TC）水凝胶和电致变色（EC）材料，展示了一种具有储热能力和多模光学调制能力的节能智能窗口。具体来说，所开发的铬器件具有出色的比热容（~ 4.45 J·g−1·K−1），用于自适应（SA）存储/释放热量，并且可以通过可选的热/电组合实现高光学调制（∆TSol = 69.90%，波长范围为250-2500 nm）。这些有趣的特性赋予了智能窗多种散热和透光管理模式，包括SA模式、EC模式和TC模式，在1 kW·m−2的太阳照射下，可实现室内温度下降21.8℃、24.1℃和28.0℃。能源模拟结果进一步证明，该智能窗在提供卓越的室内舒适度的同时，具有可观的建筑节能效果（43.32 MJ·m−2）。这项工作提供了一个可行而简单的策略，以实现更节能的建筑，以最小的碳足迹实现全球碳中和。

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引用次数: 0

Joint optimization of charging infrastructure and fleet mix for CO₂-constrained feeder services 为限制二氧化碳排放的支线服务联合优化充电基础设施和车队组合

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-10 DOI: 10.1016/j.apenergy.2025.127216

Haruko Nakao , Tai-Yu Ma , Richard D. Connors , Francesco Viti

This study addresses the electrification of demand-responsive feeder services, a form of public transport designed to connect rural and low-demand areas to mass transit hubs. Electrifying demand-responsive transport requires planning the charging infrastructure carefully, considering the trade-offs of charging efficiency and charging infrastructure costs. This study addresses the joint planning of fleet size and charging infrastructure for a demand-responsive feeder service under stochastic demand, given a user-defined CO₂ emissions reduction policy. We propose a bi-level optimization model where the upper-level determines charging station configuration given stochastic demand, and the lower-level solves a mix fleet feeder (first and last mile) service routing problem under the CO₂ emission and capacitated charging station constraints. An efficient deterministic annealing metaheuristic is proposed to solve the CO₂-constrained mixed fleet routing problem. The metaheuristic solves up to 500 requests within 3 min, demonstrating the practical applicability of the proposed solution. We applied the model to a real-world case study in Bettembourg, Luxembourg, with two types of electric minibuses and gasoline ones, under different CO₂ reduction targets considering rapid (125 kW) and super-fast (220 kW) chargers, given 200 requests per day. The results show that using 24-seat minibuses leads to significant cost savings (−49 % on average) compared to that of 10-seat minibuses. Due to their larger battery capacity, charger availability has a smaller impact on the operational costs of 24-seat minibuses. The proposed method provides a flexible tool for joint charging infrastructure and fleet size planning.

这项研究解决了需求响应支线服务的电气化问题，这是一种旨在将农村和低需求地区与公共交通枢纽连接起来的公共交通形式。实现需求响应型交通的电气化需要仔细规划充电基础设施，考虑充电效率和充电基础设施成本的权衡。本文研究了在随机需求下，给定用户自定义的二氧化碳减排政策，对需求响应型支线服务的车队规模和充电基础设施进行联合规划。本文提出了一种双层优化模型，其中上层决定随机需求下的充电站配置，下层解决在CO2排放和充电站容量约束下的混合馈线（首最后一英里）服务路径问题。提出了一种有效的确定性退火元启发式算法来解决co2约束下的混合车队路由问题。元启发式在3分钟内解决了多达500个请求，证明了所提出的解决方案的实际适用性。我们将该模型应用于卢森堡贝腾堡的现实案例研究，在每天200个请求的情况下，考虑到快速（125千瓦）和超高速（220千瓦）充电器，在不同的CO₂减少目标下，有两种类型的电动小巴和汽油小巴。结果表明，与使用10座小巴相比，使用24座小巴可显著节省成本（平均为- 49%）。由于电池容量更大，充电器的可用性对24座小巴的运营成本影响较小。该方法为联合充电基础设施和车队规模规划提供了一种灵活的工具。

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引用次数: 0

QSTAformer: A quantum-enhanced Transformer for robust short-term voltage stability assessment against adversarial attacks QSTAformer：一种量子增强变压器，用于抗敌对攻击的鲁棒短期电压稳定性评估

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-10 DOI: 10.1016/j.apenergy.2025.127196

Yang Li , Chong Ma , Yuanzheng Li , Sen Li , Yanbo Chen , Zhaoyang Dong

Short-term voltage stability assessment (STVSA) is critical for secure power system operation. While classical machine learning-based methods have demonstrated strong performance, they still face challenges in robustness under adversarial conditions. This paper proposes QSTAformer—a tailored quantum-enhanced Transformer architecture that embeds parameterized quantum circuits (PQCs) into attention mechanisms—for robust and efficient STVSA. A dedicated adversarial training strategy is developed to defend against both white-box and gray-box attacks. Furthermore, diverse PQC architectures are benchmarked to explore trade-offs between expressiveness, convergence, and efficiency. To the best of our knowledge, this is the first work to systematically investigate the adversarial vulnerability of quantum machine learning-based STVSA. Case studies on the IEEE 39-bus system demonstrate that QSTAformer achieves competitive accuracy, reduced complexity, and stronger robustness, underscoring its potential for secure and scalable STVSA under adversarial conditions.

短期电压稳定评估（STVSA）是电力系统安全运行的关键。虽然经典的基于机器学习的方法已经表现出强大的性能，但它们在对抗条件下的鲁棒性仍然面临挑战。本文提出了一种定制的量子增强Transformer架构，该架构将参数化量子电路（pqc）嵌入到注意力机制中，以实现鲁棒和高效的STVSA。一种专门的对抗训练策略被开发来防御白盒和灰盒攻击。此外，还对不同的PQC体系结构进行基准测试，以探索表达性、收敛性和效率之间的权衡。据我们所知，这是第一次系统地研究基于量子机器学习的STVSA的对抗性漏洞。对IEEE 39总线系统的案例研究表明，QSTAformer实现了具有竞争力的准确性，降低了复杂性，并且具有更强的鲁棒性，强调了其在对抗条件下安全且可扩展的STVSA的潜力。

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引用次数: 0

Diagnosing inconsistencies in battery energy storage systems: A framework integrating electrical, thermal, and aging perspectives 诊断电池储能系统的不一致性：一个集成电学、热学和老化观点的框架

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-09 DOI: 10.1016/j.apenergy.2025.127203

Jingbo Qu , Jiale Shen , Weihan Li , Tianyu Wang , Yijie Wang , Ruixiang Zheng , Mian Li , Zhaoguang Wang

Battery Energy Storage Systems (BESSs) are vital for grid stability and renewable energy integration. However, the inconsistencies across cells and packs can impair the performance and safety. Existing diagnostic approaches primarily address the voltage imbalance, offering only a partial view of the BESS reliability. This paper proposes a unified inconsistency diagnosis framework that simultaneously evaluates electrical, thermal, and aging inconsistencies. A low-rank subspace projection method enables reliable voltage inconsistency detection under low-resolution data and varying operational profiles. To capture thermal imbalance, the Thermal Consistency Coefficient (TCC) is introduced as a physics-based metric that quantifies pack-level thermal inconsistency using sparse sensor data. For aging assessment, an enhanced Least Squares (LS) method is developed to robustly estimate the health status of the battery pack under fluctuating loads. These perspectives are integrated through an entropy-weighted fusion scheme, yielding an objective and unified inconsistency score. Validation on a battery cluster within a 1.5MWh in-service BESS demonstrates the framework’s ability to identify inconsistent cells and quantify pack-level inconsistencies across voltage, thermal, and aging perspectives.

电池储能系统（BESSs）对于电网稳定和可再生能源的整合至关重要。然而，电池和电池组之间的不一致性可能会损害性能和安全性。现有的诊断方法主要针对电压不平衡，仅提供BESS可靠性的部分视图。本文提出了一个统一的不一致诊断框架，可以同时评估电、热和老化不一致。低秩子空间投影方法能够在低分辨率数据和不同的操作剖面下可靠地检测电压不一致。为了捕获热不平衡，引入了热一致性系数（TCC）作为基于物理的度量，使用稀疏传感器数据量化包级热不一致性。在老化评估方面，提出了一种改进的最小二乘（LS）方法来稳健地估计波动负载下电池组的健康状态。这些观点通过一个熵加权融合方案进行整合，产生一个客观和统一的不一致性评分。在1.5MWh的BESS中对电池组进行了验证，证明了该框架能够识别不一致的电池，并从电压、热量和老化的角度量化电池组级别的不一致。

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引用次数: 0

Physics-informed baseline load estimation for high-frequency demand response 基于物理的高频需求响应基线负荷估计

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-09 DOI: 10.1016/j.apenergy.2025.127155

Yujie Wang, Xiangyu Zhang, Mengmeng Cai, Qinran Hu

With the accelerating integration of renewable energy into power systems, demand response (DR) has proven effective for coordinating demand-side flexibility and facilitating renewable integration. To address the growing intermittency and uncertainty resulting from increasing renewable penetration, grid operation is likely to shift from occasional DR events to more frequent and granular activations, referred to as high-frequency demand response (HFDR). For compensation fairness and implementation effectiveness, accurate customer baseline load (CBL) estimation is essential for DR programs. However, traditional CBL estimation methods, which heavily rely on finding similar reference days, may struggle in HFDR scenarios due to sample scarcity. To address this, we introduce a CBL estimation method that is suitable for HFDR. First, a mathematical model is established to map observable system states to unobservable baseline loads, focusing on thermostatically controlled loads represented by electric hot water cylinders. Then, we implement a semi-supervised learning framework that incorporates mutual information, based on variational autoencoders, to infer latent representations of water usage patterns. Finally, mutual information maximization is incorporated into the learning process to retain dependencies between input features and latent representations, supporting the subsequent baseline load estimation. In practical applications, our method first infers unobservable water usage patterns before using them to estimate baseline loads. Comprehensive validation using NREL ResStock simulation data demonstrates the effectiveness and robustness of our method across varying DR intensities and frequencies, maintaining consistently low estimation errors and strong correlation between inferred and actual usage patterns.

随着可再生能源并网速度的加快，需求响应（DR）已被证明在协调需求侧灵活性和促进可再生能源并网方面是有效的。为了解决可再生能源渗透率增加所带来的日益增长的间歇性和不确定性，电网运行可能会从偶尔的DR事件转变为更频繁和颗粒状的激活，即高频需求响应（HFDR）。为了保证补偿的公平性和实施的有效性，准确的客户基线负荷（CBL）估计对DR计划至关重要。然而，传统的CBL估计方法严重依赖于寻找相似的参考日，由于样本稀缺，在HFDR场景中可能会遇到困难。为了解决这个问题，我们引入了一种适用于HFDR的CBL估计方法。首先，建立了一个数学模型，将可观测的系统状态映射到不可观测的基线负荷，重点关注以电热热水瓶为代表的恒温控制负荷。然后，我们实现了一个半监督学习框架，该框架结合了基于变分自编码器的互信息，以推断用水模式的潜在表示。最后，将互信息最大化纳入学习过程，以保留输入特征和潜在表征之间的依赖关系，支持随后的基线负载估计。在实际应用中，我们的方法首先推断不可观测的用水模式，然后使用它们来估计基线负荷。使用NREL ResStock模拟数据的综合验证表明，我们的方法在不同DR强度和频率下的有效性和鲁棒性，保持了持续的低估计误差以及推断和实际使用模式之间的强相关性。

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