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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

Optimizing battery deployment: Aging trajectory prediction enabling homogenous performance grouping

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Journal of Energy Chemistry 2025年第1期100卷 565-577页

作者： Shuquan Wang Feng Gao Zhan Ma Hao Tian Yusen Zhang school of control science and engineering Shandong University Key Lab of Power System Intelligent Dispatch and control of Ministry of Education Shandong University

As battery deployments in electric vehicles and energy storage systems grow, ensuring homogeneous performance across units is crucial. We propose a multi-derivative imaging fusion(MDIF) model, employing advanced imaging and machine learning to predict battery aging trajectories from minimal initial data, thus facilitating effective performance grouping before deployment. Utilizing a derivative strategy and Gramian Angular Difference Field for dimensional enhancement, the MDIF model uncovers subtle predictive features from discharge curve data after only ten cycles. The architecture includes a parallel convolutional neural network with lateral connections to enhance feature integration and *** on a self-developed dataset, the model achieves an average root-mean-square error of 0.047 Ah and an average mean absolute percentage error of 1.60%, demonstrating high precision and *** robustness is further validated through transfer learning on two publicly available datasets, adapting with minimal retraining. This approach significantly reduces the testing cycles required, lowering both time and costs associated with battery testing. By enabling precise battery behavior predictions with limited data, the MDIF model optimizes battery utilization and deployment strategies, enhancing system efficiency and sustainability.

关键词： Lithium-ion battery Battery prognostics Fusion model Convolutional neural network Transfer learning

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

Embedding prescribed-time adaptive control protocol unveiling distributed consensus in multirobot systems via directed topology

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science China(Information sciences) 2025年

作者： Yonghao XIE Xinru MA Hengyu LI Shaorong XIE school of Computer engineering and science Shanghai University school of Mechatronic engineering and automation Shanghai University

Recently, multirobot systems （MRSs） have found extensive applications across various domains, including industrial manufacturing, collaborative formation of unmanned equipment, emergency disaster relief, and war scenarios[1]. These advancements are largely supported by the development of consistency control theory. However, traditional dynamicsfree models may cause instability in complex robotic systems. Lagrangian dynamics offers a better approach for modeling these systems, as it facilitates controller design and optimization analysis. Despite this, challenges persist with unknown parameters and nonlinear friction within the systems.

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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

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

Modeling and Capacity Configuration Optimization of CRH5 EMU On-Board Energy Storage System

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Energy engineering 2025年第1期122卷 307-329页

作者： Mingxing Tian Weiyuan Zhang Zhaoxu Su school of automation and Electrical engineering Lanzhou Jiaotong UniversityLanzhou730070China

In the context of the“dual carbon”goals,to address issues such as high energy consumption,high costs,and low power quality in the rapid development of electrified railways,this study focused on the China Railways High-Speed 5 Electric Multiple Unit and proposed a mathematical model and capacity optimization method for an onboard energy storage system using lithium batteries and supercapacitors as storage ***,considering the electrical characteristics,weight,and volume of the storage media,a mathematical model of the energy storage system was ***,to tackle problems related to energy consumption and power quality,an energy management strategy was proposed that comprehensively considers peak shaving and valley filling and power quality by controlling the charge/discharge thresholds of the storage *** optimization adopted a bilevel programming model,with the series/parallel number of storage modules as variables,considering constraints imposed by the Direct Current to Direct Current converter,train load,and *** improved Particle Swarm Optimization algorithm and linear programming solver were used to solve specific *** results show that the proposed onboard energy storage system can effectively achieve energy savings,reduce consumption,and improve power qualitywhile meeting the load and space limitations of the train.

关键词： Electrified railway regenerative braking bi-level programming on-board energy storage power quality capacity configuration

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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Electrocatalytic nitrite reduction to ammonia on isolated bismuth alloyed ruthenium

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Journal of Energy Chemistry 2025年第1期100卷 369-376页

作者： Shiyao Shang Fuzhou Wang Zeyi Sun Chaofan Qiang Ke Chu school of Materials science and engineering Lanzhou Jiaotong University

Electrochemical reduction of NO2-to NH3（NO2-RR） is recognized as an appealing approach for achieving renewable NH3synthesis and waste ***,we report isolated Bi alloyed Ru （Bi1Ru） as an efficient NO2-RR *** calculations and in situ electrochemical measurements reveal the creation of Bi1-Ru dual sites which can remarkably promote NO2-activation and suppress proton adsorption,while accelerating the NO2-RR protonation energetics to render a high NO2--to-NH3conversion ***,Bi1Ru assembled in a flow cell delivers an NH3yield rate of 1901.4μmol h-1cm-2and an NH3-Faradaic efficiency of 94.3%at an industrial-level current density of 324.3 mA *** study offers new perspectives for designing and constructing p-block single-atom alloys as robust and high-current-density NO2-RR catalysts toward the ammonia electrosynthesis.

关键词： Ammonia