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Bi-Level Optimal Design of Integrated Energy system With Synergy of Renewables, Conversion, Storage, and Demand

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第2期61卷 2170-2181页

作者： Zhang, Lizhi Zhang, Hui Li, Fan Sun, Bo Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Shandong Police Coll Jinan 250200 Peoples R China

Integrated energy systems (IESs) that combine biogas, solar, and wind energy sources demonstrate considerable potential for effective utilization of renewable energy, which is instrumental for achieving carbon neutrality. The enhancement in their energetic and economic performances relies on optimal design methods that need to consider the combined optimization of capacity and operation and synergy between biogas production, energy conversion, storage, and demand. Therefore, this study proposes a bi-level optimal design method for a biogas-solar-wind IES. First, an exergy hub model is established to accurately describe the variations in the energy quantity and quality resulting from energy conversion processes. Then, the combined capacity and operation optimization problem of the IES is formulated as a bi-level iterative model, and a full-time-series clustering method based on multi-attribute weighting is employed to obtain typical source-load scenarios. The first level is designed to maximize the cost and exergy savings and determine the rated capacities of renewables, energy conversion and storage components;the second level synergistically optimizes the operation schemes of energy conversion, storage, and demand components by incorporating a thermodynamic model of biogas production along with an electrical demand response program. And the iterative optimization mechanisms between these two levels are established. Moreover, a hybrid algorithm combining a genetic algorithm and sequential quadratic programming method is developed to solve the bi-level model. Finally, the feasibility and effectiveness of the proposed method are verified through case studies.

关键词： Biogas Electricity Costs Optimization Renewable energy sources biological system modeling Resistance heating Production Heating systems Energy conversion Bi-level optimal design demand response exergy hub model hybrid algorithm integrated energy system

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Comprehensive Environmental Monitoring system for Industrial and Mining Enterprises Using Multimodal Deep Learning and CLIP Model

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IEEE ACCESS 2025年 13卷 19964-19978页

作者： Wang, Shuqin Cheng, Na Hu, Yan Anhui Wenda Univ Informat Engn Sch Comp Engn Hefei 231200 Peoples R China

Addressing the challenges of limited accuracy in anomaly detection within comprehensive environmental monitoring of industrial and mining enterprises, and the constraints posed by singular data modalities, this study proposes an integration of a multimodal Long Short-Term Memory (LSTM) model with the Contrastive Language-Image Pretraining (CLIP) model. The initial phase employs ResNet within the CLIP model for extracting image features, and a Transformer for encoding text features. Subsequently, feature vectors obtained from monitoring images and text are fused using a rudimentary concatenation method to generate a joint embedding representation. Principal Component Analysis (PCA) is then applied to diminish the dimensionality of the amalgamated features derived from environmental monitoring images, descriptive texts, and sensor data collected by industrial and mining enterprises. Finally, a multimodal LSTM model is leveraged to detect anomalies in the monitoring data by capturing long-term dependencies within time series information. The model was trained and evaluated using real-time data from a coal mining enterprise's environmental monitoring system spanning March to September 2023. Results reveal that the multimodal LSTM-CLIP model achieved an anomaly detection accuracy of 0.98 in environmental monitoring, marking a 0.10 improvement over the unimodal LSTM model, with a response time of merely 110.25 milliseconds. These findings underscore the efficacy of the multimodal LSTM-CLIP model in integrating multimodal information, thereby significantly enhancing the accuracy of anomaly detection and the speed of environmental anomaly warnings, ultimately ensuring the safety of industrial and mining enterprises.

关键词： Feature extraction Environmental monitoring Data models Accuracy Vectors Long short term memory Predictive models biological system modeling Visualization Transformers Industrial and mining enterprises integrated environmental monitoring system multimodal LSTM model CLIP model anomaly detection accuracy

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A Novel Explainable Impedance Identification Method Based on Deep Learning for the Vehicle-Grid system of High-Speed Railways

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2025年第1期11卷 2299-2310页

作者： Hu, Guiyang Meng, Xiangyu Wang, Xiaokang Liu, Zhigang Southwest Jiaotong Univ Sch Elect Engn Chengdu 610032 Peoples R China Tongji Univ Sch Elect Engn Shanghai 200092 Peoples R China

As many electric multiple units (EMUs) integrate into the traction network, low-frequency oscillation (LFO) issues may occur in railway systems. Impedance-based frequency-domain stability analysis is a common method used to analyze the stability of vehicle-grid systems. However, the vehicle-grid system is a complex single-phase system with numerous nonlinear components, making it difficult to establish accurate and online-analyzable impedance models. To address these challenges, this article proposes the residual feedforward neural network (ResFNN) suitable for EMU impedance identification. The ResFNN integrates residual connections into the deep feedforward neural network (FNN), which can improve the model prediction accuracy and get rid of the complex model derivation process. To explain the contribution of the input parameter to the model, the SHapley Additive exPlanation (SHAP) method is adopted in this article. Furthermore, according to the above contributions, this article proposes an unequal step size data collection method to optimize the operating point (OP) parameter step size and obtain a small but high-quality dataset. Then, combined with the vehicle-side and grid-side impedance models, this article can realize online vehicle-grid system stability analysis. Finally, actual case studies are conducted with multiple vehicles connected to the vehicle-grid system to validate the feasibility and accuracy of online stability analysis.

关键词： Impedance Power system stability Circuit stability Accuracy Stability criteria biological system modeling Analytical models Electric multiple units (EMUs) impedance model online stability analysis residual feedforward neural network (ResFNN) vehicle-grid system

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Engineering Scheme Decision system Integrating Case Reasoning and BIM Technology

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IEEE ACCESS 2025年 13卷 20040-20053页

作者： Zhang, Qi Hubei Polytech Univ Sch Civil Engn & Architecture Huangshi 435003 Hubei Peoples R China

To solve the problem of lack of science and poor reusability of experience in traditional engineering scheme decision-making, which leads to the increase of time and cost of pre-decision-making, the study first uses case-based reasoning and ontology to construct a solution library for engineering solution decision-making system, and standardizes the cases using methods such as eigenfrequency. In addition, a retrieval mechanism based on residual similarity is designed to achieve effective retrieval of similar cases. The experiment outcomes denoted that the resource utilization rate of the traditional scheme was 75% before implementation, but decreased to 72% after implementation, a decrease of 3%. The resource utilization rate of the decision-making system scheme was 75% before implementation, and increased to 80% after implementation, an increase of 5%. The results indicated that the decision system scheme designed by the research performed better in terms of resource utilization, could more efficiently utilize resources, and reduce waste. The average decision accuracy of integrating CBR and BIM systems was 92%, significantly higher than the 84% of traditional decision systems. The CBR technology improved the scientificity and reliability of decision-making through continuous updating and optimization of the case library.

关键词： Decision making biological system modeling Libraries Buildings Data models Costs Cognition Accuracy Solid modeling Optimization BIM technology engineering plan decision-making system case-based reasoning search mechanism design

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Meijer-G Function With Continued Product and Integer Exponent: Performance of Multi-Aperture UOWC system Over EGG Turbulence

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2025年第2期17卷

作者： Kumar, Arvind Choudhury, Nikumani Bandyopadhyay, Jayendra N. Zafaruddin, S. M. Birla Inst Technol & Sci Dept Elect & Elect Engn Pilani Campus Pilani 333031 Rajasthan India Birla Inst Technol & Sci Dept Comp Sci & Informat Syst Hyderabad Campus Pilani 500078 India Birla Inst Technol & Sci Dept Phys Pilani Campus Pilani 333031 Rajasthan India

Signal transmission over underwater optical wireless communication (UOWC) experiences the combined effect of oceanic turbulence and pointing errors statistically modeled using the sum of two Meijer-G functions. There is a research gap in the exact statistical analysis of multi-aperture UOWC systems that use selection combining diversity techniques to enhance performance compared to single-aperture systems. In this paper, we develop a general framework for the continued product and positive integer exponent for the sum of Meijer-G functions to analyze the exact statistical performance of the UOWC system in terms of multivariate Fox-H function for both independent and non-identically distributed (***.d.) and independent and identically distributed (i.i.d.) channels. We also approximate the performance of a multi-aperture UOWC system with i.i.d. channels using the single-variate Fox-H function. Using the generalized approach, we present analytical expressions for average bit-error rate (BER) and ergodic capacity for the considered system operating over exponential-generalized gamma (EGG) oceanic turbulence combined with zero-boresight pointing errors. We also develop asymptotic expressions for the average BER at a high signal-to-noise (SNR) to capture insights into the system's performance. Our simulation findings confirm the accuracy of our derived expressions and illustrate the impact of turbulence parameters for ***.d. and i.i.d. models for the average BER and ergodic capacity, which may provide a better estimate for the efficient deployment of UOWC.

关键词： Apertures Analytical models Adaptive optics Diversity reception Optical receivers NOMA biological system modeling Signal to noise ratio Optical variables control Optical transmitters Average BER diversity techniques EGG model ergodic capacity Fox-H Meijer-G function multi-aperture oceanic turbulence performance analysis pointing errors selection combining UOWC

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An Intercellular Communication system for Intra-Body Communication Networks

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IEEE TRANSACTIONS ON COMMUNICATIONS 2025年第2期73卷 817-831页

作者： Rudsari, Hamid Khoshfekr Damrath, Martin Zoofaghari, Mohammad Balasingham, Ilangko Veletic, Mladen Oslo Univ Hosp Dept Rheumatol Dermatol & Infect Dis N-0372 Oslo Norway Norwegian Univ Sci & Technol Dept Elect Syst N-7491 Trondheim Norway Yazd Univ Dept Elect Engn Yazd 89195741 Iran Oslo Univ Hosp Intervent Ctr N-0372 Oslo Norway

Intercellular communication is crucial for organ function, with extracellular vesicles (EVs) acting as common messengers for almost all cells. This study proposes a novel EV-mediated intercellular communication system that uses a modulation technique regulated via altered intracellular-cytosolic calcium dynamics regulation. As a case study, intercellular communication within a cardiac muscle is considered with cardiomyocyte cells serving as transceivers. Through molecular communication theory, we propose a comprehensive model addressing EV release kinetics, propagation, degradation, and uptake. A linear time-invariant Poisson channel model is developed and closed-form expressions, verified through particle-based simulations, are derived for EV detection probabilities at the receiver. A closed-form bit error probability is derived and validated through Monte Carlo simulations. By selecting an optimal receiver threshold: 1) bit error rate (BER) in EV-mediated intercellular communication is robust against adverse effects from cardiac disorders, such as myocardial infarction;2) transmitter design can be optimized by minimizing actuation signal amplitude and pulse width;3) BER stays stable across heart rates for different distances, demonstrating the robustness of EV-mediated communication. This study enhances our ability to engineer precise and reliable intra-body cardiac communications, which may offer valuable applications for cardiovascular disease treatment, for example, the realization of biological lead-less multi-nodal pacemakers.

关键词： Transmitters Calcium Receivers Extracellular Vesicles Receptor (biochemistry) biological system modeling Extracellular vesicles molecular communication Poisson process cardiovascular disease intercellular communication

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Linear Programming-Based Decision Support system of Solar Power Reserve Management in Pulsation Free Current Circulation (PFCC) for Agricultural systems

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IEEE ACCESS 2025年 13卷 44510-44525页

作者： Saivineeth, M. Venkateswarlu, B. Vellore Inst Technol Sch Adv Sci Dept Math Vellore 632014 India

Sustainably powering agricultural systems including greenhouses, irrigation, and farm equipment relies on the Pulsation Free Current Circulation system's (PFCC) efficient use of solar energy reserves. Improving the distribution of solar power reserves in light of the fact that solar energy output varies over time is the primary goal of this research. Energy reserve expenses are reduced and storage battery operating life is extended using an interval linear programming technique. Power distribution that is both dependable and efficient is achieved by use of the system's models, which take into consideration factors like allocation costs, power reserve and network losses. Through the use of a two-tiered structural approach, the optimization procedure guarantees balanced dispatching and rapid responsiveness to fluctuating energy needs by managing distribution and reserve capacity in tandem. Enhancing the sustainability of solar-powered agricultural systems, the suggested technology minimizes battery deterioration and ensures energy availability during important agricultural activities. Multi-power reserve systems are also used to reduce distributed energy swings, which makes the system even more reliable and stable. According to the findings of the tests, the technology has the power to greatly improve the efficiency and longevity of solar energy in farming, with an output efficiency of 0.99(in decimal). This strategy tackles the problem of energy management and sets the stage for renewable energy solutions that can be easily scaled and adjusted for use in agriculture. This will guarantee the sustainability and effectiveness of these solutions in the long run.

关键词： biological system modeling Water resources Optimization Wind farms Resource management Costs Load modeling Crops Wind power generation Wind forecasting Pulsation free current circulation solar power reserve linear programming farming-system solar power allocation

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Operational Economics of Renewable Energy-Hydrogen system With Hydrogen-Powered Transportation

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IEEE TRANSACTIONS ON SMART GRID 2025年第1期16卷 718-727页

作者： Lai, Xinyi Yang, Jiajia Wen, Fushuan Dong, Zhao Yang CSG Elect Power Res Inst Guangzhou 510663 Guangdong Peoples R China James Cook Univ Coll Sci & Engn Townsville Qld 4810 Australia Zhejiang Univ Sch Elect Engn Hangzhou 310027 Zhejiang Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

Green hydrogen, produced from renewable energy sources, is undergoing a global resurgence as a clean energy solution driving decarbonization and fueling sustainable mobility. However, the major challenge facing the hydrogen economy is its scalability, particularly in the transportation sector. In response, this research develops innovative business models that utilize the low-emission attributes of green hydrogen to alleviate its financial constraints. The decrease in costs, in turn, can stimulate the growth of green hydrogen-powered transportation and facilitate the transition toward a carbon-neutral future. Key components of the proposed business workflow are elaborated, mathematical formulations of market parameters are derived, and case studies are presented to demonstrate the feasibility and efficiency of the proposed business models. Notably, the results demonstrate that the substantial costs associated with current hydrogen-powered transportation can be effectively subsidized in the proposed business models. Under current carbon emission prices of 92 USD/ton, the levelized cost of driving can be halved. Furthermore, these models create new opportunities for stakeholders across the entire hydrogen-powered transportation value chain, contributing to the overall prosperity and growth of the hydrogen industry. This highlights the significance and promising potential of the proposed business models within the green hydrogen credit framework.

关键词： Hydrogen Transportation biological system modeling Production Costs Green hydrogen Fuels Fuel cell electric vehicle green hydrogen business model operational economics hydrogen credit

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Rigorous Indoor Wireless Communication system Simulations With Deep Learning-Based Radio Propagation Models

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IEEE JOURNAL ON MULTISCALE AND MULTIPHYSICS COMPUTATIONAL TECHNIQUES 2025年 10卷 58-68页

作者： Bakirtzis, Stefanos Qiu, Kehai Chen, Jiming Song, Hui Zhang, Jie Wassell, Ian Ranplan Wireless Network Design Ltd Res & Dev Dept Cambridge CB23 3UY England Univ Cambridge Dept Comp Sci & Technol Cambridge CB3 0FD England Ranplan Wireless Network Design Ltd Cambridge CB23 3UY England Univ Sheffield Dept Elect & Elect Engn Sheffield S10 2TN England

Recently, there has been a surge in the development of data-driven propagation models. These models aspire to distill knowledge from propagation solvers or measured data and eventually become capable of predicting characteristics related to radiowave propagation. In this paper, we present the functionality of a generalizable and robust data-driven propagation model that enables efficient and reliable simulations of indoor wireless communication systems (IWCSs). In particular, we modify our previously introduced model, EM DeepRay, to consider the impact of antenna directivity, and we present a training and inference strategy that allows the simulation of large-scale and complicated IWCSs. Our data-driven model is trained over a rich data set comprising diverse building geometries, frequency bands, and antenna radiation patterns. Benchmarking its performance with that of a ray-tracer in complicated IWCSs with real-world measured data yields similar results that have a distinct advantage in terms of computational time. Ultimately, our work paves the way for replacing legacy IWCSs simulators, with high-fidelity artificial intelligence-based models.

关键词： Antenna radiation patterns biological system modeling Computational modeling Wireless communication Tensors Solid modeling Geometry Data models Three-dimensional displays Generative adversarial networks Radio propagation artificial intelligence deep learning ray-tracing digital twin 5G

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Bidding Strategy for Hybrid PV-BESS Plants via Knowledge-data-complementary Learning

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Journal of Modern Power systems and Clean Energy 2025年第1期13卷 365-378页

作者： Xiaoge Huang Zhenhuan Ding Zhao Liu Tianqiao Zhao Pei Zhang Xiaojun Wang IEEE the Department of Electrical and Computer Engineering State University of New York at Binghamton the School of Artificial Intelligence Anhui University the School of Electrical Engineering Beijing Jiaotong University the Brookhaven National Lab the School of Electrical Automation and Information Engineering Tianjin University

The hybrid photovoltaic(PV)-battery energy storage system(BESS) plant(HPP) can gain revenue by performing energy arbitrage in low-carbon power systems. However, multiple operational uncertainties challenge the profitability and reliability of HPP in the day-ahead market. This paper proposes two coherent models to address these challenges. Firstly, a knowledge-driven penalty-based bidding(PBB) model for HPP is established, considering forecast errors of PV generation, market prices, and under-generation penalties. Secondly, a data-driven dynamic error quantification(DEQ) model is used to capture the variational pattern of the distribution of forecast errors. The role of the DEQ model is to guide the knowledgedriven bidding model. Notably, the DEQ model aims at the statistical optimum, but the knowledge-driven PBB model aims at the operational optimum. These two models have independent optimizations based on misaligned objectives. To address this, the knowledge-data-complementary learning(KDCL) framework is proposed to align data-driven performance with knowledge-driven objectives, thereby enhancing the overall performance of the bidding strategy. A tailored algorithm is proposed to solve the bidding strategy. The proposed bidding strategy is validated by using data from the National Renewable Energy Laboratory(NREL) and the New York Independent system Operator(NYISO).

关键词： Optimization Uncertainty Power systems Power system dynamics biological system modeling Data models Task analysis

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