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Overview and Prospect of Distributed Energy P2P Trading

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Energy Engineering 2025年第1期122卷 379-404页

作者： Jiajia Liu Mingxing Tian Xusheng Mao school of automation and Electrical Engineering Lanzhou Jiaotong UniversityLanzhou730070China

After a century of relative stability in the electricity sector,the widespread adoption of distributed energy resources,along with recent advancements in computing and communication technologies,has fundamentally altered how energy is consumed,traded,and *** change signifies a crucial shift as the power system evolves from its traditional hierarchical organization to a more decentralized *** the heart of this transformation are innovative energy distribution models,like peer-to-peer(P2P)sharing,which enable communities to collaboratively manage their energy *** effectiveness of P2P sharing not only improves the economic prospects for prosumers,who generate and consume energy,but also enhances energy resilience and *** allows communities to better leverage local resources while fostering a sense of collective responsibility and collaboration in energy ***,there is still no extensive implementation of such sharing models in today’s *** on distributed energy P2P trading is still in the exploratory stage,and it is particularly important to comprehensively understand and analyze the existing distributed energy P2P trading *** paper contributes with an overview of the P2P markets that starts with the network framework,market structure,technical approach for trading mechanism,and blockchain technology,moving to the outlook in this field.

关键词： Distributed energy P2P market mechanisms classification and comparison

Bilevel Optimal Scheduling of Island Integrated Energy System Considering Multifactor Pricing

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Energy Engineering 2025年第1期122卷 349-378页

作者： Xin Zhang Mingming Yao Daiwen He Jihong Zhang Peihong Yang Xiaoming Zhang school of automation and Electrical Engineering Inner Mongolia University of Science and TechnologyBaotou014010China

In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy system(IIES).The upper level represents the integrated energy operator,and the lower level is the electricity-heatgas load *** to the benefit conflict between the upper and lower levels of the IIES,a dynamic pricing mechanism for coordinating the interests of the upper and lower levels is proposed,combined with factors such as the carbon emissions of the IIES,as well as the lower load interruption *** price of selling energy can be dynamically adjusted to the lower LA in the mechanism,according to the information on carbon emissions and load interruption *** benefits and win-win situations are achieved between the upper and lower ***,CPLEX is used to iteratively solve the bilevel optimization *** optimal solution is selected according to the joint optimal discrimination *** results indicate that the sourceload coordinate operation can reduce the upper and lower operation *** the proposed pricingmechanism,the carbon emissions and load interruption power of IEO-LA are reduced by 9.78%and 70.19%,respectively,and the capture power of the carbon capture equipment is improved by 36.24%.The validity of the proposed model and method is verified.

关键词： Bilevel optimal scheduling load aggregator integrated energy operator carbon emission dynamic pricing mechanism

Endoscopy-assisted lightweight diagnosis system based on transformers for colon polyp detection

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Optoelectronics Letters 2025年第1期21卷 57-64页

作者： FAN Weiming YU Jiahui JU Zhaojie school of automation and Electrical Engineering Shenyang Ligong University Department of Biomedical Engineering Zhejiang University school of Computing University of Portsmouth

The integration of endoscopy has significantly propelled the diagnosis and treatment of gastrointestinal diseases, with colonoscopy establishing itself as the primary method for early diagnosis and preventive care in colorectal cancer(CRC). Although deep learning holds promise in mitigating missed polyp rates, modern endoscopy examinations pose additional challenges, such as image blurring and atomizing. This study explores lightweight yet powerful attention mechanisms, introducing the spatial-channel transformer(SCT), an innovative approach that leverages spatial channel relationships for attention weight calculation. The method utilizes rotation operations for inter-dimensional dependencies, followed by residual transformation, encoding inter-channel and spatial information with minimal computational overhead. Extensive experiments on the CVC-Clinic DB polyp detection dataset, addressing endoscopy pitfalls, underscore the superiority of our SCT over other state-of-the-art methods. The proposed model maintains high performance, even in challenging scenarios.

关键词： Endoscopy

Luminescence: a new perspective for investigating perovskite precursors

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Science China Materials 2025年第01期 312-313页

作者： Haofeng Zheng Shaocong Hou school of Electrical Engineering and automation Wuhan University Wuhan University Shenzhen Research Institute

In the past two decades, perovskite materials have made significant progress in optoelectronic semiconductors, emerging as formidable candidates for the next generation of commercial solar cells, light-emitting diodes (LEDs), and *** the fundamental solution chemistry and photophysics in solution-processed perovskite devices is crucial for comprehending to achieve highly efficient optoelectronic devices.

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Multi-model ensemble learning for battery state-of-health estimation:Recent advances and perspectives

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Journal of Energy Chemistry 2025年第1期100卷 739-759页

作者： Chuanping Lin Jun Xu Delong Jiang Jiayang Hou Ying Liang Zhongyue Zou Xuesong Mei State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Shaanxi Key Laboratory of Intelligent Robots School of Mechanical Engineering Xi'an Jiaotong University Department of Electrical Engineering and automation Luoyang Institute of Science and Technology school of Mechanical Engineering Hangzhou Dianzi University

The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ens

关键词： Contrastive Learning

Stability and Stabilization of Sampled-Data Based LFC for Power Systems:A Data-Driven Method

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IEEE/CAA Journal of Automatica Sinica 2025年第1期 291-293页

作者： Yu-Long Fan Chuan-Ke Zhang Yong He the school of automation China University of Geosciences the Hubei Key Laboratory of Advanced Control and Intelligent automation for Complex Systems the Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education IEEE

Dear Editor,This letter is concerned with stability analysis and stabilization design for sampled-data based load frequency control(LFC) systems via a data-driven method. By describing the dynamic behavior of LFC systems based on a data-based representation, a stability criterion is derived to obtain the admissible maximum sampling interval(MSI) for a given controller and a design condition of the PI-type controller is further developed to meet the required MSI. Finally, the effectiveness of the proposed methods is verified by a case study.

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An autonomous dynamic recovery technology for autonomous underwater vehicles based on unmanned surface vehicles

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Science China Technological Sciences 2025年

作者： Yueying WANG Yiqi SHI Guanghui WEN Huaicheng YAN school of Mechatronic Engineering and automation Shanghai University Department of Systems Science School of Mathematics Southeast University school of Information Science and Engineering East China University of Science and Technology

Autonomous underwater vehicles (AUVs) have attracted considerable attention due to their vast potential applications, such as offshore oil exploration, underwater rescue, military reconnaissance, and marine scientific...

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Optimal Scheduling of an Independent Electro-Hydrogen System with Hybrid Energy Storage Using a Multi-Objective Standardization Fusion Method

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Energy Engineering 2025年第1期122卷 63-84页

作者： SuliangMa ZeqingMeng Mingxuan Chen Yuan Jiang school of Electrical and Control Engineering North China University of TechnologyBeijing100144China ChinaThree Gorges Technology Co. Ltd.Beijing100032China school of automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China

In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is ***,it presents a challenge due to the significant deviations in values ofmultiple optimization objective functions caused by their physical *** deviations seriously affect the scheduling process.A novel standardization fusion method has been established to address this issue by analyzing the variation process of each objective function’s *** optimal scheduling results of IEHS with HESS indicate that the economy and overall energy loss can be improved 2–3 times under different optimization *** proposed method better balances all optimization objective functions and reduces the impact of their *** the cost of BESS decreases by approximately 30%,its participation deepens by about 1 ***,if the price of the electrolyzer is less than 15￥/kWh or if the cost of the fuel cell drops below 4￥/kWh,their participation will increase *** study aims to provide a more reasonable approach to solving multi-objective optimization problems.

关键词： Electro-hydrogen system multi-objective optimization standardization method hybrid energy storage system

Translation of steel surface defect detection algorithm with fusion of multiple attention detection heads

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Optoelectronics Letters 2025年

作者： SHI Tao CUI Jie LI Song Tianjin University of Technology， school of Electrical Engineering and automation North China University of Science and Technology， school of Electrical Engineering

This paper proposes an end-to-end defect detection algorithm model called YOLOv5-CJ to address the issues of missed detections and low accuracy in visual steel surface defect detection. By incorporating the C3＿MSBlock module， which is designed based on Res2Net and contains multi-scale information， into the backbone network， the model can represent multi-scale features at a finer granularity level and increase the network's receptive field. In the detection head， the DyHead attention mechanism is introduced， which integrates scale awareness， spatial awareness， and task awareness， to help the model better cope with complex industrial scenes with large background interference， significant defect scale variations， and easily confused defect categories. Soft NMS is employed to enhance the recognition capability in overlapping regions. Experimental results demonstrate that the proposed YOLOv5-CJ model outperforms YOLOv5s，achieving an average precision (mAP0.5 and mAP0.5:0.95) improvement of 1.9% and 7.2% on the NEU-DET dataset， and 5.3% and 4.3% on the GC10-DET dataset， respectively， validating the feasibility and effectiveness of the proposed model.

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