IET Energy Systems Integration最新文献_第2页

A Multiscale Lightweight Deep Learning Approach for Short-Term Multi-Energy Load Forecasting in Integrated Energy Systems 综合能源系统短期多能量负荷预测的多尺度轻量级深度学习方法

IF 1.7

IET Energy Systems Integration Pub Date : 2026-06-09 DOI: 10.1049/esi2.70048

Jingshu Wang, Liyuan Zhao, Junhua Gu, Ying Lei

{"title":"A Multiscale Lightweight Deep Learning Approach for Short-Term Multi-Energy Load Forecasting in Integrated Energy Systems","authors":"Jingshu Wang, Liyuan Zhao, Junhua Gu, Ying Lei","doi":"10.1049/esi2.70048","DOIUrl":"https://doi.org/10.1049/esi2.70048","url":null,"abstract":"Accurate multi-energy load forecasting plays a vital role in the optimal scheduling of integrated energy systems (IES). However, existing forecasting methods face two major challenges: insufficient extraction of complex features within load data, which limits prediction accuracy, and ever-increasing model complexity, which leads to a sharp rise in computational resource demands. To tackle the above-mentioned challenges, this research introduces a load forecasting scheme derived from multiscale lightweight deep learning. First, the dynamic coupling characteristics of load data are analysed using correlation coefficients to determine model-specific input configurations. Then, an improved variant channel multiscale bottleneck residual network (VC-MBResNet) is proposed to obtain high-dimensional load feature data. Finally, by leveraging shared underlying parameters and an enhanced adaptive loss function for each subtask-using both soft and hard weight-sharing strategies—the extracted features are integrated into a multi-task learning (MTL) framework. A bidirectional long short-term memory (BiLSTM) network is employed as the shared layer, with an attention mechanism embedded in its hidden layers to enhance the focus on critical temporal features. Experimental results demonstrate that the proposed approach surpasses existing models in terms of both prediction accuracy and computational efficiency.","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/esi2.70048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Temperature Estimation Model of Planar Solid Oxide Fuel Cell Stack 平面固体氧化物燃料电池堆温度估算模型研究

IF 1.7

IET Energy Systems Integration Pub Date : 2026-05-30 DOI: 10.1049/esi2.70045

Anan Zhang, Qin Xiao, Chuan Liu, Huang Huang, Qian Li, Wei Yang

{"title":"Research on Temperature Estimation Model of Planar Solid Oxide Fuel Cell Stack","authors":"Anan Zhang, Qin Xiao, Chuan Liu, Huang Huang, Qian Li, Wei Yang","doi":"10.1049/esi2.70045","DOIUrl":"https://doi.org/10.1049/esi2.70045","url":null,"abstract":"The solid oxide fuel cell (SOFC) is an innovation power generation device in the form of an electric stack. The temperature distribution is crucial for the stable and safe operation of the system. Considering the difficulty of accurately measuring the internal temperature of the device, this paper proposes a temperature estimation method based on the Luenberger-sliding mode observer (L+S) and the Kalman filter (KF) observer. First, a discrete-time state-space model of the SOFC stack is established using a one-dimensional (1D) theoretical model that considers system noise. Secondly, the input variables of the observer are filtered and combined with the condition number of the measurement matrix to produce the optimal combination. The estimation performance of the model is enhanced through decoupling of error systems and vibration suppression. The Kalman filter observer model is constructed by combining the stack model with the Kalman filter (KF) algorithm. Then, the Luenberger-sliding mode observer model is constructed based on the theories of the Luenberger observer (L) and the sliding mode observer (S). The estimation performance of this model is enhanced through the decoupling of the error system and vibration suppression. The Kalman filter observer model is constructed by integrating the electric stack model with the Kalman filter (KF) algorithm. This method solves the time difference between the observed state and the actual system state by filtering noise to improve the estimation accuracy of the model. Finally, a multidimensional performance evaluation index system was established, and the estimation performance of various observer models was compared through simulation experiments and data analysis to verify the effectiveness of the temperature estimation model proposed in this article.","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/esi2.70045","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physics-Guided Transformer for Distributed Photovoltaic Power Forecasting Based on Copula Asymmetric Dependence Analysis 基于Copula非对称依赖分析的分布式光伏功率预测物理导向变压器

IF 1.7

IET Energy Systems Integration Pub Date : 2026-05-29 DOI: 10.1049/esi2.70046

Lili Zheng, Hengrui Ma, Shidong Wu, Aotian Yuan, Changhua Yang, Qing La, Pin Li

{"title":"Physics-Guided Transformer for Distributed Photovoltaic Power Forecasting Based on Copula Asymmetric Dependence Analysis","authors":"Lili Zheng, Hengrui Ma, Shidong Wu, Aotian Yuan, Changhua Yang, Qing La, Pin Li","doi":"10.1049/esi2.70046","DOIUrl":"https://doi.org/10.1049/esi2.70046","url":null,"abstract":"Distributed photovoltaic (DPV) power forecasting is essential for grid stability but remains challenging due to strong intermittency and meteorological uncertainty. Existing data-driven models often lack physical interpretability and struggle to capture asymmetric dependence structures, leading to unreliable predictions during extreme weather. This paper proposes a copula-guided transformer (CGT) framework that integrates statistical dependence mining with physics-informed deep learning. Specifically, Gaussian copula is used for global feature screening, whereas Clayton and Gumbel copulas quantify asymmetric tail dependencies—revealing the conditional lower-tail inhibitory effect of rainfall and the upper-tail driving effect of irradiance on power output. These copula-derived parameters are embedded as physical priors into a guided encoder, where a temporal convolutional network (TCN) dynamically regulates attention weights to enhance physical consistency. Validated on real-world DPV data from China, the CGT model offers significant performance advantages. The results demonstrate superior robustness across clear-sky, rainy and high-volatility scenarios by effectively mitigating spurious overestimation and response lag.","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/esi2.70046","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148174694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 1.7

IET Energy Systems Integration Pub Date : 2026-05-24

引用次数: 0

IF 1.7

IET Energy Systems Integration Pub Date : 2026-05-08

引用次数: 0

A Data-Driven Framework for Grid Frequency Regulation Leveraging Aggregated Flexibility of Heterogeneous Resources Considering Network Delays 考虑网络延迟、利用异构资源聚合灵活性的电网频率调节数据驱动框架

IF 1.7

IET Energy Systems Integration Pub Date : 2026-03-30 DOI: 10.1049/esi2.70039

Kingshuk Roy, Sanjoy Debbarma

{"title":"A Data-Driven Framework for Grid Frequency Regulation Leveraging Aggregated Flexibility of Heterogeneous Resources Considering Network Delays","authors":"Kingshuk Roy, Sanjoy Debbarma","doi":"10.1049/esi2.70039","DOIUrl":"https://doi.org/10.1049/esi2.70039","url":null,"abstract":"The rise of large-scale renewables has exacerbated frequency instability, revealing the limits of conventional frequency regulation frameworks in controlling deviations and incentivising fast-acting units (FAUs). Although the mileage-based payment framework promotes FAUs’ participation in automatic generation control (AGC) services, efficient instruction dispatch (ID) across an increasing number of heterogeneous AGC units remains challenging. The existing mileage-based payment framework lacks validation under intermittent generation or generation outages scenarios. This work proposes a data-driven ID framework with a modified payment scheme, adding a penalty term for refining mileage calculation to handle intermittent generation or generation outages during AGC operation. The framework uses a multihead attention-based encoder–decoder model, where the encoder extracts latent features and the decoder predicts unit-specific instructions. Attention mechanism improves accuracy by prioritising critical features, whereas L2 normalisation, dropout and k-fold cross-validation enhance models' robustness under unforeseen scenarios. The model aggregates the flexibility of multiple FAUs into a single entity, termed the FAU aggregator. Trained on a synthetic dataset generated from the evolutionary optimisation-based ID framework and validation on an interconnected system accounting for disturbance due to intermittent renewable energy sources' output, FAU variability and stochastic communication effects. The results demonstrate a reduction in both frequency deviation and area control error in comparison with other ID frameworks.","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/esi2.70039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147585121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 1.7

IET Energy Systems Integration Pub Date : 2026-03-30

引用次数: 0

IF 1.7

IET Energy Systems Integration Pub Date : 2026-03-24

引用次数: 0

Parametric Sensitivity Analysis of a Multi-Gigawatt Offshore Multi-Energy System Using Reduced-Order Models 基于降阶模型的多兆瓦海上多能系统参数敏感性分析

IF 1.7

IET Energy Systems Integration Pub Date : 2026-03-24 DOI: 10.1049/esi2.70041

Nakul Narayanan K, Chunjun Huang, Maria Camila Castrillon Franco, Jose Luis Rueda Torres

{"title":"Parametric Sensitivity Analysis of a Multi-Gigawatt Offshore Multi-Energy System Using Reduced-Order Models","authors":"Nakul Narayanan K, Chunjun Huang, Maria Camila Castrillon Franco, Jose Luis Rueda Torres","doi":"10.1049/esi2.70041","DOIUrl":"https://doi.org/10.1049/esi2.70041","url":null,"abstract":"When multiple grid-forming and grid-following converters operate within an offshore energy system (OES), dynamic interactions among them can lead to poorly damped oscillations and potential instability. This paper proposes a computationally efficient reduced-order state-space modelling framework for small-signal stability and parametric sensitivity analysis of large-scale OESs. The approach replaces complex full-order analytical modelling with a practical MATLAB/Simulink-based linearisation procedure, enabling tractable stability assessment of systems comprising multiple wind plants, electrolysers, HVDC links and network components. The reduced-order model preserves the dominant dynamics while significantly decreasing the number of states, thereby improving computational efficiency for eigenvalue and sensitivity analyses. Using the linearised model, the influence of key control parameters is systematically quantified to provide explicit guidance for controller tuning and damping improvement. The accuracy of the proposed model is validated through comparison with detailed electromagnetic transient (EMT) simulations in PSCAD/EMTDC, demonstrating close agreement in dynamic responses and stability characteristics.","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/esi2.70041","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148012221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Emergency Power Sharing by Using a Self-Traction System in a Mass Rapid Transit Railway 基于自牵引系统的快速轨道交通应急电力共享

IF 1.7

IET Energy Systems Integration Pub Date : 2026-03-20 DOI: 10.1049/esi2.70040

Banri Khemkladmuk, Chanchai Techawatcharapaikul, Thanatchai Kulworawanichpong

{"title":"Emergency Power Sharing by Using a Self-Traction System in a Mass Rapid Transit Railway","authors":"Banri Khemkladmuk, Chanchai Techawatcharapaikul, Thanatchai Kulworawanichpong","doi":"10.1049/esi2.70040","DOIUrl":"https://doi.org/10.1049/esi2.70040","url":null,"abstract":"Systemwide traction power outages in mass rapid transit systems (MRTSs) can cause trains to stop between stations, leading to service disruption and urgent passenger evacuation requirements. Onboard emergency self-traction systems (ESTSs) can mitigate this risk by enabling a stranded train to reach the nearest station at low speed. However, installing and maintaining ESTS on every train can increase implementation and lifecycle costs, motivating approaches that can ensure evacuation with partial ESTS deployment. This article introduces a power-sharing framework for ESTS in an MRTS. The primary aim is to reduce the number of ESTS units needed by facilitating power sharing between trains equipped with ESTS and those without it, while guaranteeing that all trains can safely and quickly evacuate passengers to the nearest station during a systemwide power outage. This study examines the Bangkok Mass Transit System (BTS) Skytrain Silom Line (dark green line) in Thailand, through computer simulation parameterised by real-world line and operational data, to evaluate the viability of this concept. It analyses variables such as the quantity of trains outfitted with ESTS, the distance from the train to the station where it halted, particular train specifications and additional pertinent aspects. The results reveal that ESTS can efficiently provide backup power to equipped trains and share surplus energy with other trains within the sharing radius with a success rate of 72.41%. This underscores the viability of ESTS operations and aids in the advancement of a more sustainable and efficient MRTS, despite the inherent constraints of ESTS.","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":"8 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/esi2.70040","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147567387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0