Sustainable Energy Grids & Networks最新文献

Coordinated curtailment of uncontrollable distributed energy resources in isolated power systems 孤立电力系统中不可控分布式能源的协同弃电

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2026-01-27 DOI: 10.1016/j.segan.2026.102124

Phivos Therapontos , Savvas Panagi , Charalambos A Charalambous , Petros Aristidou

The escalating integration of renewable energy sources (RES) into isolated, low-inertia power systems presents considerable challenges to maintaining frequency stability. To preserve operational security, system operators often impose stringent requirements that can necessitate RES curtailment, particularly during periods of low demand. While such measures predominantly affect large-scale distributed energy resources (DERs), prolonged curtailment scenarios may also compel output reductions from numerous small-scale, often uncontrollable, DERs (UDERs). Prevailing control strategies for UDERs typically rely on the deployment of dedicated control and communication hardware at each UDER site, incurring significant capital expenditure and implementation complexity. This paper introduces a novel methodology for the coordinated curtailment of UDERs, which circumvents the need for such supplementary equipment. The proposed approach utilizes the system frequency as an implicit communication conduit, leveraging the inherent active power-frequency (P-f) response capabilities of UDER inverters. A data-driven framework is employed to optimize a global active power-frequency reduction characteristic, tailored from historical operational data. This characteristic is subsequently implemented in a decentralized manner by individual UDERs, thereby effectively mitigating investment costs and cybersecurity vulnerabilities associated with conventional control architectures. The performance and efficacy of the proposed methodology are demonstrated through dynamic simulations on a model of the isolated, low-inertia power system of Cyprus.

将可再生能源（RES）逐步整合到孤立的低惯性电力系统中，对保持频率稳定性提出了相当大的挑战。为了保证运行安全，系统运营商通常会施加严格的要求，这可能导致RES的缩减，特别是在低需求时期。虽然这些措施主要影响大规模分布式能源（DERs），但长期弃风情景也可能迫使许多小规模、通常不可控的分布式能源（DERs）减少产量。UDER的主流控制策略通常依赖于在每个UDER站点部署专用控制和通信硬件，这导致了大量的资本支出和实施复杂性。本文介绍了一种新的方法来协调削减uder，从而避免了对这些补充设备的需要。该方法利用系统频率作为隐式通信通道，利用UDER逆变器固有的有源工频（P-f）响应能力。基于历史运行数据，采用数据驱动框架优化全局有源工频降低特性。该特性随后由各个uder以分散的方式实现，从而有效降低与传统控制架构相关的投资成本和网络安全漏洞。通过对塞浦路斯孤立的低惯性电力系统模型的动态仿真，证明了所提出方法的性能和有效性。

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引用次数: 0

The allocation of system costs: Future-proofed methodologies for decarbonising European power sectors 系统成本的分配：面向未来的欧洲电力部门脱碳方法

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2026-02-02 DOI: 10.1016/j.segan.2026.102143

José Pablo Chaves Ávila , Paolo Mastropietro , Matteo Troncia , Pedro González , Tomás Gómez San Román

The increased use of renewable energy sources, among other factors, is causing system costs to grow quickly in European power sectors, especially those related to frequency control and congestion management. Currently, most European countries allocate these costs to consumers using simplistic methodologies, either via network tariffs or specific volumetric charges. These methodologies require urgent reform. This article reviews the economic theory and European experiences regarding the allocation of system costs and puts forward a comprehensive high-level proposal to improve the design of these charges. Balancing capacity costs should be partially embedded in the imbalance price, with price caps limiting the possibility of very high prices during periods of low imbalance volumes. Congestion management costs, like network expansion costs, are driven by transmission capacity scarcity and should be recovered through network tariffs. Any system costs that cannot be allocated according to cost causality should be recovered through stabilised residual charges that do not distort the efficient signals sent by cost-reflective charges and prices. Discounts and exemptions for certain categories of end users should only apply to these residual charges. The impact of this proposal has been tested in a case study based on the Spanish power system.

除其他因素外，可再生能源使用的增加导致欧洲电力部门的系统成本迅速增长，特别是与频率控制和拥塞管理有关的系统成本。目前，大多数欧洲国家使用简单的方法将这些成本分配给消费者，要么通过网络关税，要么通过特定的容量收费。这些方法急需改革。本文回顾了有关制度成本分配的经济学理论和欧洲经验，并提出了完善制度成本设计的综合建议。平衡产能成本应该部分嵌入到不平衡价格中，价格上限限制了在不平衡量较低时期出现非常高价格的可能性。拥塞管理成本和网络扩展成本一样，是由传输容量的稀缺性驱动的，应该通过网络资费来弥补。任何不能根据成本因果关系分配的系统成本，都应该通过稳定的剩余费用来弥补，这种剩余费用不会扭曲反映成本的费用和价格发出的有效信号。对某些类别的最终用户的折扣和豁免应仅适用于这些剩余费用。该建议的影响已在基于西班牙电力系统的案例研究中得到验证。

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引用次数: 0

Changing the paradigm of distribution networks planning and operation: A systematic review of the distributed energy resources impact 改变配电网规划和运行模式：分布式能源影响的系统回顾

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2025-12-02 DOI: 10.1016/j.segan.2025.102070

Lovro Lukač, Tomislav Antić, Tomislav Capuder

The connection of new distributed energy resources (DER) is recently being delayed by the system operators primarily due to the approach where each connection request is separately assessed. The lack of coordination between connection requests is making the process time-consuming and also creating virtual congestion in the network, hindering further investments. With the development of advanced analytical tools and increased observability, Distribution System Operators (DSOs) are starting to adopt new planning and operational approaches. Calculating a network’s hosting capacity (HC) is one of the most investigated planning concepts in modern power systems. However, HC is a conservative approach that considers worst-case scenarios, thereby limiting new connections to the network. This has created the need to develop the dynamic operating envelopes (DOE) concept. DOEs are envisioned as the concept bridging the gap between planning and operational phases, as well as an approach to test the relaxation of conservative fixed connection rules defined in national grid codes. A step further is the near real-time upgrade of DOE defined as P-Q flexibility regions, improving the previous concepts by estimating system-level service provision capabilities. The concept is based on controlling active and reactive power and, consequently, increasing system’s flexibility. The paper contributes in the form of an extensive review of modeling techniques and algorithms, defining the necessary dataset for each of the concepts and models. Furthermore, it discusses the importance of including various technical constraints. Additionally, the paper identifies necessary improvements in data collection to properly assess the value and constraints of DER providing services to the distribution system.

新分布式能源（DER）的连接最近被系统运营商延迟，主要原因是每个连接请求都被单独评估的方法。连接请求之间缺乏协调使得这一过程非常耗时，同时也在网络中造成了虚拟拥塞，阻碍了进一步的投资。随着先进分析工具的发展和可观察性的提高，配电系统运营商（dso）开始采用新的规划和操作方法。网络承载能力的计算是现代电力系统规划中研究最多的概念之一。然而，HC是一种考虑最坏情况的保守方法，因此限制了网络的新连接。这就产生了开发动态操作包络（DOE）概念的需求。did被设想为弥合规划和运营阶段之间差距的概念，以及测试国家电网规范中定义的保守固定连接规则放松的方法。更进一步的是将DOE定义为P-Q灵活性区域的近实时升级，通过估计系统级服务提供能力来改进先前的概念。这个概念是基于控制有功和无功功率，从而增加系统的灵活性。本文对建模技术和算法进行了广泛的回顾，为每个概念和模型定义了必要的数据集。此外，还讨论了包括各种技术限制的重要性。此外，本文还确定了数据收集方面的必要改进，以正确评估为配电系统提供服务的DER的价值和限制。

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引用次数: 0

Utilization of machine learning approaches for enhancing sustainability of electric vehicles with optimization of lithium-ion battery health status 利用机器学习方法优化锂离子电池健康状态，提高电动汽车的可持续性

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2026-02-02 DOI: 10.1016/j.segan.2026.102144

Yijun Xu , Xuan Zhang , Andong Wang

Lithium-ion batteries play a central role in electric vehicles (EVs), renewable energy storage, and modern power networks due to their high energy density and efficiency. However, accurately estimating their State of Health (SOH) remains a major challenge, as battery degradation is governed by complex electrochemical and thermal processes influenced by dynamic operating conditions. Although numerous machine learning (ML) approaches have been proposed, many existing methods rely on narrowly scoped datasets, struggle with nonlinear degradation behavior, or lack robustness under real-world variability. These limitations hinder their applicability in large-scale sustainable energy systems. To address these gaps, this study introduces an Ensemble Stacking Regressor designed to provide accurate, generalizable, and noise-resilient SOH estimation. The framework integrates Extreme Gradient Boosting Random Forest (XGBRF), Histogram-based Gradient Boosting Regressor (HGBR), and Extra Trees Regressor (ETR), combined through a Support Vector Regression (SVR) meta-model. Extensive feature extraction from voltage, current, temperature, and internal resistance profiles enables the model to capture multi-dimensional degradation patterns essential for reliable SOH assessment. Experimental results on four MIT battery datasets reveal consistently high accuracy, with R² values above 0.990 and low RMSE, MAE, and RSE metrics. Additional validation on NASA and Oxford datasets confirms strong generalization, while noise-perturbation tests demonstrate high resilience under uncertain measurement conditions. These findings indicate that the proposed framework could enhance battery reliability, support smarter energy management strategies, and strengthen the integration of EVs and storage systems into sustainable energy networks. The model can offer a robust and transferable solution for improving SOH monitoring across diverse applications.

锂离子电池因其高能量密度和效率，在电动汽车、可再生能源存储和现代电网中发挥着核心作用。然而，准确估计电池的健康状态（SOH）仍然是一个重大挑战，因为电池的退化受到动态操作条件影响的复杂电化学和热过程的控制。尽管已经提出了许多机器学习（ML）方法，但许多现有方法依赖于狭窄范围的数据集，与非线性退化行为作斗争，或者在现实世界的可变性下缺乏鲁棒性。这些限制阻碍了它们在大规模可持续能源系统中的适用性。为了解决这些差距，本研究引入了一个集成叠加回归器，旨在提供准确、可推广和抗噪声的SOH估计。该框架集成了极端梯度增强随机森林（XGBRF）、基于直方图的梯度增强回归器（HGBR）和额外树回归器（ETR），并通过支持向量回归（SVR）元模型相结合。从电压、电流、温度和内阻曲线中广泛提取特征，使该模型能够捕获多维退化模式，这对于可靠的SOH评估至关重要。在4个MIT电池数据集上的实验结果显示，准确率始终较高，R²值均在0.990以上，RMSE、MAE和RSE指标均较低。对NASA和Oxford数据集的进一步验证证实了强泛化，而噪声摄动测试显示了在不确定测量条件下的高弹性。这些发现表明，所提出的框架可以提高电池可靠性，支持更智能的能源管理策略，并加强电动汽车和存储系统与可持续能源网络的整合。该模型可以提供一个健壮的、可转移的解决方案，用于改进跨不同应用程序的SOH监测。

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引用次数: 0

Moderately extreme weather contributions to power supply inadequacy: Identification using rapid loss-of-load estimation 中等极端天气对电力供应不足的贡献：使用快速负荷损失估计的识别

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2026-01-28 DOI: 10.1016/j.segan.2026.102135

Ruijie Chen , Benjamin F. Hobbs , Zongxiang Lu , Ying Qiao

Power systems with high shares of variable renewable energy (VRE) are increasingly vulnerable to extreme weather events. While existing studies typically identify extremes based on meteorological thresholds, such as sustained low wind, low solar radiation, or extreme temperatures, these individual-variable extremes do not always cause the most severe power shortages. In contrast, moderately extreme events, namely compound weather conditions that are not individually severe in any single meteorological variable but jointly create unfavorable electricity supply-demand imbalances, can pose greater risks. To address this gap, this study aims to develop a consequence-based framework that directly identifies weather events causing the most severe power inadequacy risks, rather than relying solely on meteorological definitions of extremes. First, multi-decadal time series of wind, solar, and electricity demand are generated under various future capacity mixes with high VRE penetration. Then, a computationally efficient loss-of-load estimation method is proposed based on algebraic computations rather than mathematical optimization to identify weather events most likely to cause severe power shortfalls. Finally, power shortage risks are evaluated using power system economic dispatch simulations and compared across different types of extreme weather. Simulation results show that the proposed method can estimate loss-of-load with high accuracy and at a speed hundreds of times faster than dispatch optimization models. The case study reveals that identified events often involve moderately low VRE output and moderately high demand occurring simultaneously, resulting in severe shortages. At equal occurrence frequencies, these identified events pose several times the risk to power supply adequacy compared to individual-variable extremes and should be prioritized in power system planning.

可变可再生能源（VRE）比例高的电力系统越来越容易受到极端天气事件的影响。虽然现有的研究通常根据气象阈值来确定极端情况，例如持续的低风、低太阳辐射或极端温度，但这些个体变量的极端情况并不总是导致最严重的电力短缺。相比之下，中度极端事件，即复合天气条件，在任何单一气象变量中都不是单独严重的，但共同造成不利的电力供需失衡，可能带来更大的风险。为了解决这一差距，本研究旨在开发一个基于后果的框架，直接识别导致最严重的电力不足风险的天气事件，而不是仅仅依赖于极端天气的气象定义。首先，在具有高VRE渗透率的各种未来容量组合下，产生了数十年的风能、太阳能和电力需求时间序列。在此基础上，提出了一种基于代数计算而非数学优化的高效的失载估计方法，以识别最可能导致严重缺电的天气事件。最后，利用电力系统经济调度模拟对电力短缺风险进行了评估，并对不同类型的极端天气进行了比较。仿真结果表明，该方法能以比调度优化模型快数百倍的速度估计出高准确度的空载。案例研究表明，已确定的事件通常涉及中度低VRE输出和中度高需求同时发生，导致严重短缺。在相同的发生频率下，这些已确定的事件对电力供应充分性的风险是个别变量极端事件的几倍，应在电力系统规划中优先考虑。

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引用次数: 0

Enhancing the stability of DC microgrids with novel negative current injection control to achieve fault ride-through capability 采用新型负注入电流控制提高直流微电网的稳定性，实现故障穿越能力

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2026-01-27 DOI: 10.1016/j.segan.2026.102139

Rohit Kumar Rastogi, Manoj Tripathy

This paper presents a novel Negative Current Injection (NCI)-based control strategy for Distributed Generators (DGs) in DC Microgrids (DC MGs), enhancing system stability and Fault Ride-Through (FRT) capability. Unlike conventional methods such as adaptive droop control or physical Fault Current Limiters (FCLs), the proposed approach directly regulates fault currents without requiring additional hardware, ensuring improved protection and transient performance. It effectively mitigates high fault currents arising from increasing Electric Vehicle (EV) penetration, load fluctuations, and high-impedance faults (HIFs). By integrating seamlessly with existing protection schemes, this method prevents converter overloading and stabilizes bus voltage during both transient and steady-state conditions. Simulation results on a 380 V low-voltage DC (LVDC) microgrid demonstrate that the proposed control limits fault currents to 1.1 pu with a current interruption of no more than 10% of the rated value, while extending FRT time to 100

μ

s before saturation. Moreover, it maintains bus voltage drops within 0.7–0.95 pu across varying fault resistances and locations, showcasing superior performance over existing techniques.

提出了一种基于负注入电流（NCI）的新型直流微电网分布式发电机（dg）控制策略，提高了系统稳定性和故障穿越能力。与自适应下垂控制或物理故障电流限制器（FCLs）等传统方法不同，该方法直接调节故障电流，而无需额外的硬件，确保了更好的保护和瞬态性能。它有效地减轻了由于电动汽车（EV）渗透增加、负载波动和高阻抗故障（hif）而产生的高故障电流。通过与现有保护方案无缝集成，该方法可以防止转换器过载，并在瞬态和稳态条件下稳定母线电压。在380 V低压直流（LVDC）微电网上的仿真结果表明，该控制方法将故障电流限制在1.1 pu以内，电流中断不超过额定值的10%，同时将FRT时间延长至100 μs。此外，在不同的故障电阻和位置上，它将母线电压降保持在0.7-0.95 pu之间，表现出比现有技术更优越的性能。

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引用次数: 0

Privacy-preserving energy optimization via multi-stage federated learning for micro-moment recommendations 基于多阶段联合学习的微时刻推荐隐私保护能量优化

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2025-12-09 DOI: 10.1016/j.segan.2025.102100

Md Mosarrof Hossen , Aya Nabil Sayed , Faycal Bensaali , Armstrong Nhlabatsi , Muhammad E.H. Chowdhury

Human behavior significantly impacts domestic energy consumption, making it essential to monitor and improve these consumption patterns. Traditional methods often rely on centralized servers to gather and analyze consumption data, which can lead to significant privacy risks as personalized information becomes accessible online. To address this challenge, this study aims to optimize household energy consumption while preserving data privacy by proposing an innovative two-stage Federated Learning (FL) framework that delivers real-time micro-moment-based recommendations. Leveraging FL enables efficient model training across diverse end-user applications while preserving data privacy. The proposed framework employs a two-stage FL training methodology, utilizing the DRED and QUD datasets, and achieves substantial performance improvements. A comparative evaluation of three FL algorithms (FedAvg, FedProx, Mime-lite) identifies the most suitable aggregation strategy. The model achieves robust performance, with approximately 98 % accuracy and F1-score in the second training stage. These findings demonstrate the effectiveness of FL in enabling personalized, privacy-preserving energy recommendations. The novelty of this work lies in combining micro-moment prediction with a multi-stage FL architecture tailored for smart home energy optimization. This study highlights the potential of FL to enhance energy efficiency and sustainability while safeguarding user privacy, paving the way for future research in energy optimization and sustainable living.

人类行为对国内能源消费有重大影响，因此必须监测和改善这些消费模式。传统的方法通常依赖于集中式服务器来收集和分析消费数据，这可能会导致重大的隐私风险，因为个性化信息可以在网上访问。为了应对这一挑战，本研究旨在通过提出一种创新的两阶段联邦学习（FL）框架来优化家庭能源消耗，同时保护数据隐私，该框架可提供基于实时微时刻的建议。利用FL可以在保护数据隐私的同时，跨不同的最终用户应用程序进行有效的模型训练。提出的框架采用两阶段FL训练方法，利用DRED和QUD数据集，并实现了实质性的性能改进。通过对三种FL算法（fedag, FedProx, Mime-lite）的比较评估，确定了最合适的聚合策略。该模型达到了鲁棒性，在第二阶段的训练中准确率约为98%，得分为f1。这些发现证明了FL在实现个性化、保护隐私的能源建议方面的有效性。这项工作的新颖之处在于将微矩预测与为智能家居能源优化量身定制的多级FL架构相结合。这项研究强调了FL在保护用户隐私的同时提高能源效率和可持续性的潜力，为未来能源优化和可持续生活的研究铺平了道路。

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引用次数: 0

Optimal reactive current compensation for smart grids using linear programming: A novel algorithm with theoretical and real-world data validation 基于线性规划的智能电网最优无功电流补偿：一种具有理论和实际数据验证的新算法

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2025-12-11 DOI: 10.1016/j.segan.2025.102081

Francisco G. Montoya , Jorge Ventura , Xabier Prado , Jorge Mira

This paper presents an innovative optimization approach for reactive current compensation in modern distribution networks, based on a novel algorithmic solution using linear programming techniques. The proposed method determines optimal shunt compensator parameters by effectively linearizing nonlinear systems in high-harmonic environments without requiring negative reactive elements. Unlike traditional methods, this approach ensures reliable compensator values across diverse operational scenarios, making it particularly valuable for smart grid applications where power quality and energy efficiency are crucial. The theoretical framework is validated through comprehensive mathematical analysis and simulations, complemented by a real-world case study using data from an actual installation. Results demonstrate the method’s effectiveness in handling non-sinusoidal conditions through both theoretical cases and actual power system measurements. Furthermore, a parametric analysis of the real-world data reveals a key practical insight: a reduced-order compensator, targeting only the most dominant harmonics, can achieve nearly all of the source current reduction provided by a full compensator, thus offering an optimal trade-off between cost and performance. This research contributes to power systems theory by providing a computationally efficient and flexible approach for power quality enhancement in modern distribution systems.

本文提出了一种新颖的基于线性规划算法的现代配电网无功电流补偿优化方法。该方法在不需要负无功元件的情况下，通过对高谐波环境下的非线性系统进行有效线性化来确定最优并联补偿器参数。与传统方法不同，该方法可确保在各种操作场景中可靠的补偿器值，这对于电能质量和能源效率至关重要的智能电网应用特别有价值。理论框架通过全面的数学分析和模拟得到验证，并辅以使用实际安装数据的实际案例研究。理论算例和实际电力系统测量结果均表明了该方法在处理非正弦工况时的有效性。此外，对真实世界数据的参数分析揭示了一个关键的实用见解：仅针对最主要谐波的降阶补偿器可以实现全补偿器提供的几乎所有源电流减小，从而在成本和性能之间提供最佳权衡。该研究为现代配电系统的电能质量提高提供了一种计算效率高且灵活的方法，为电力系统理论的发展做出了贡献。

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引用次数: 0

A risk-optimized pre-disaster defense strategy for island integrated energy systems based on energy storage configuration 基于储能配置的海岛综合能源系统风险优化灾前防御策略

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2026-02-14 DOI: 10.1016/j.segan.2026.102155

Dongyue Zhou, Xueping Pan, Jinpeng Guo, Xiaorong Sun, Yongkai Wei

The unique natural environment of islands and the increasing frequency of extreme disasters, exacerbated by climate change, will inevitably lead to high failure probabilities of Island Integrated Energy Systems (IIES), and deteriorate its inspection and recovery process, thus posing substantial challenges to the operational safety of IIES. To reduce the risk of IIES under typhoon and its secondary disasters, a novel risk-optimized pre-disaster defense strategy is proposed by configuring energy storages (ESs). Firstly, the failure probabilities of IIES are calculated by considering combined effects of typhoon and its secondary disaster, and the cascading failure model is used to evaluate overall failure probability of IIES. Secondly, the risk and resilience of IIES is compared and discussed. Followed by this, a new ES optimal configuration strategy is proposed to minimize the risk of IIES under the combined effects of typhoon and its secondary disasters, which is validated by a test IIES system. Results show that the risk is reduced by 26.4 % after ES deployment, and the overall cost is 15 % and 27 % less than the results of resilience-based optimization strategy. This study contributes to improving the operational safety and risk resistance capability of IIES with reasonable investment.

岛屿独特的自然环境和日益频繁的极端灾害，加之气候变化的加剧，必然导致岛屿综合能源系统（IIES）的高故障概率，并使其检查和恢复过程恶化，从而对IIES的运行安全提出了重大挑战。为了降低台风及其次生灾害下电力系统的风险，提出了一种新的风险优化的灾前防御策略，即配置储能系统。首先，考虑台风及其次生灾害的综合影响，计算IIES的失效概率，并采用级联失效模型对IIES的整体失效概率进行评估；其次，对工业园区的风险和弹性进行了比较和讨论。在此基础上，提出了一种新的ES优化配置策略，以最大限度地降低台风及其次生灾害联合影响下IIES的风险，并通过IIES试验系统进行了验证。结果表明，与基于弹性的优化策略相比，ES部署后风险降低了26.4 %，总成本分别降低了15 %和27 %。本研究有助于在合理投资的前提下提高IIES的运行安全性和抗风险能力。

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引用次数: 0

Real-time analytical equivalent factor ECMS for multi-mode hybrid electric vehicles 多模混合动力汽车实时分析等效因子ECMS

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2026-03-01 Epub Date: 2026-02-06 DOI: 10.1016/j.segan.2026.102145

Wei Wang , Zhenjiang Cai , Yi Tian , Jian Wang

This study develops a real-time optimization framework for the Equivalent Factor (EF) in multi-mode HEV energy management systems. Leveraging Pontryagin’s Minimum Principle (PMP), a convex optimization problem for the EF s(t) is formulated. Closed-form solution of Karush-Kuhn-Tucker (KKT) conditions yields near-optimal analytical EF solutions with precise time-varying boundaries. The key advantage of this work lies in obtaining an analytical solution through a standard convex optimization and KKT framework without requiring any adaptive mechanism. The proposed Online Analytical EF-based ECMS (OEF-ECMS) replaces heuristic adaptive tuning mechanisms (e.g., PI controllers) with deterministic analytics, eliminating parametric dependencies while meeting real-time control requirements. Simulations demonstrate OEF-ECMS’s superiority over conventional Adaptive ECMS (A-ECMS) in analytical efficiency through online generation of near-optimal EF solutions and significant fuel economy improvements under dynamic operating conditions.

本研究开发了多模式HEV能量管理系统中等效因子（EF）的实时优化框架。利用庞特里亚金最小值原理（PMP），给出了一个EF (t)的凸优化问题。Karush-Kuhn-Tucker （KKT）条件的闭型解产生具有精确时变边界的近最优解析EF解。这项工作的关键优势在于通过标准凸优化和KKT框架获得解析解，而不需要任何自适应机制。提出的基于在线分析ef的ECMS （OEF-ECMS）用确定性分析取代启发式自适应调谐机制（例如PI控制器），在满足实时控制要求的同时消除了参数依赖性。模拟结果表明，OEF-ECMS在分析效率方面优于传统的自适应ECMS (A-ECMS)，在线生成接近最优的EF解决方案，并在动态运行条件下显著提高燃油经济性。

引用次数: 0