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Understanding cross-market strategic behaviors of prosumers: An equilibrium-driven evolutionary game approach

Journal of Cleaner Production 2025年 513卷

作者： Kang, Kai Su, Yifan Yang, Peng Wang, Zhaojian Zhang, Yunfan Qi, Ning Liu, Feng State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing100084 China School of Electrical Engineering Chongqing University Chongqing400044 China School of Electronics and Information Xi'an Polytechnic University Xi'an710048 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China China Electric Power Planning & Engineering Institute Beijing100088 China Department of Earth and Environmental Engineering Columbia University New YorkNY10027 United States

The rise of prosumers and the success of the sharing economy have catalyzed the development of energy sharing market (ESM), which is set to coexist with the conventional distribution energy market (DEM). However, the behaviors of prosumers within this evolving landscape remain underexplored due to the complex interactions among prosumers, ESM, and DEM. This paper presents a novel equilibrium-driven evolutionary game (EDEG) model that allows prosumers with bounded rationality to trade energy and choose participation in DEM, ESM, or both. Unlike previous studies, prosumers’ profits in this model are determined by the market-clearing equilibria, which in turn influence their market selection. The prosumers’ bidding strategies make the market clearing a hard-solving generalized Nash game (GNG). To overcome this, we convert the GNG into an equivalent, more tractable optimization problem, and then incorporate the generalized Nash equilibrium (GNE) into the EDEG model. Building on this foundation, we derive a solution algorithm and establish sufficient convergence conditions for the model. Additionally, by analyzing the equilibria, the EDEG model sheds light on the prosumers’ market selection preferences, cross-market strategic behaviors, sharp price jumping in ESM, and the conditions necessary for the stable coexistence of ESM and DEM. Furthermore, an improved pricing scheme for DEM is proposed to mitigate cross-market arbitrage behaviors and alleviate market price volatility. Finally, the simulations of six prosumer groups over 10,000 days (approximately 27.4 years) validate the theoretical findings and show the effectiveness of the improved scheme in suppressing cross-market arbitrage and stabilizing the electricity price for ESM. © 2025

关键词： Cross-market strategic behavior Distribution energy market Energy sharing market Evolutionary game Generalized Nash equilibrium

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A Stock Border Compensation Technique for Gaseous Energy Scheduling in Steel Enterprises Under Uncertainty

SSRN

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SSRN 2024年

作者： Zhang, Liu Zheng, Zhong Chai, Yi Wang, Yongzhou Zhang, Kai Huang, Shipeng Chen, Sujun College of Materials Science and Engineering Chongqing University Chongqing400000 China College of Automation Chongqing University Chongqing400000 China Key Laboratory of Complex System Safety and Control Ministry of Education Chongqing400000 China Ministry of Energy and Environment Shougang Jingtang United Iron and Steel Co. Ltd. Tangshan063200 China

This study proposes a stock boundary compensation method to address the uncertainty of gaseous energy in integrated steel plants. The uncertainty disrupts the balance of temporary storage, resulting in energy waste, environmental pollution, and disturbances in steel production. The method accurately compensates for the impact of gaseous energy uncertainty on storage. Firstly, the gas energy system and its scheduling model are described using the example of oxygen and by-product gas systems. The storage deviation caused by gaseous energy uncertainty is quantitatively analyzed using formulas. Subsequently, an optimization problem is formulated to determine the optimal margin for compensating the storage boundary using a data-driven approach. Comparative experiments demonstrate that the proposed method not only significantly enhances the safety of gaseous energy storage subject to uncertainty, but also outperforms traditional robust optimization and stock fluctuation optimization methods in terms of system robustness against uncertainty and operating cost optimality. © 2024, The Authors. All rights reserved.

关键词： Digital storage

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Learning Pre-Grasp Manipulation of Flat Objects in Cluttered Environments using Sliding Primitives

Learning Pre-Grasp Manipulation of Flat Objects in Cluttered...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Jiaxi Wu Haoran Wu Shanlin Zhong Quqin Sun Yinlin Li State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China Department of Automation University of Science and Technology of China Heifei China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Science Beijing China Science and Technology on Thermal Energy and Power Laboratory Wuhan China

Flat objects with negligible thicknesses like books and disks are challenging to be grasped by the robot because of the width limit of the robot's gripper, especially when they are in cluttered environments. Pre-grasp manipulation is conducive to rearranging objects on the table and moving the flat objects to the table edge, making them graspable. In this paper, we formulate this task as Parameterized Action Markov Decision Process, and a novel method based on deep reinforcement learning is proposed to address this problem by introducing sliding primitives as actions. A weight-sharing policy network is utilized to predict the sliding primitive's parameters for each object, and a Q-network is adopted to select the acted object among all the candidates on the table. Meanwhile, via integrating a curriculum learning scheme, our method can be scaled to cluttered environments with more objects. In both simulation and real-world experiments, our method surpasses the existing methods and achieves pre-grasp manipulation with higher task success rates and fewer action steps. Without fine-tuning, it can be generalized to novel shapes and household objects with more than 85% success rates in the real world. Videos and supplementary materials are available at https://***/view/pre-grasp-sliding.

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Point Cloud based Motion State Estimation Method for Autonomous Driving

Point Cloud based Motion State Estimation Method for Autonom...

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2023 International Conference on Advanced Mechatronic systems, ICAMechS 2023

作者： Jiang, Tao Zhu, Enzhi Wu, Sidong Zhang, Linshuai Gu, Shuoxin Xu, Lin Wang, Qian Tang, Xianggui Chengdu Univ. of Traditional Chinese Med. Unmanned Syst. Intelligent Percept. Contr. Technol. Eng. Laboratory of Sichuan Province Chengdu University of Information Technology School of Intelligent Medicine Chengdu China Chengdu University of Information Technology School of Automation Chengdu China Chengdu Univ. of Info. Technol. Unmanned Syst. Intelligent Perception Control Technology Engineering Laboratory of Sichuan Province Chengdu University of Information Technology School of Automation Chengdu China Chengdu University of Traditional Chinese Medicine School of Automation Chengdu Univ. of Info. Technol. Intl. Jt. Res. Ctr. of Robotics and Intell. Syst. of Sichuan Prov. School of Intelligent Medicine Chengdu China Chengdu University of Information Technology International Joint Research Center of Robotics and Intelligence System of Sichuan Province School of Automation Chengdu China

ISBN: (纸本)9798350307474

Generally, motion vector information primarily stems from moving objects, while static objects contribute minimally to the estimation task. Traditional approaches for motion vector estimation typically rely on scene flow methods that depend on deep models to extract features from individual points at a high cost. These methods then acquire flow information through complex matching mechanisms or feature decoding. Such approaches are computationally expensive and exhibit substantial latency. Moreover, they neglect the importance of motion objects in motion vector estimation and the interference from static objects. Therefore, this paper introduces a novel method that first performs point cloud motion segmentation and subsequently estimates motion vectors. This approach focuses on leveraging point cloud information annotated with motion objects to estimate three-dimensional scene flow more effectively. By employing motion segmentation, we can obtain annotations for moving objects, enabling greater emphasis on the estimation of motion vectors for more challenging cases. In our experiments conducted on the KITTI dataset, the proposed method demonstrates superior performance compared to existing scene flow estimation methods. Specifically, without considering motion segmentation errors, the error in the motion direction is only 0.0363 m/s, showcasing better performance. Additionally, our method achieves an error of 0.076 m for three-dimensional endpoint error (EPE3D), showcasing distinct advantages over current scene flow networks. © 2023 IEEE.

关键词： Errors

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Research on NLOS environmental ranging method based on gradient boosting tree

Research on NLOS environmental ranging method based on gradi...

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2024 8th International Conference on Electrical, Mechanical and Computer engineering (ICEMCE)

作者： Wenyu Li Shun Cao Xianda Liu Minghao Wang Shuan Sheng School of Information Engineering Shenyang University of Chemical Technology Shenyang Liaoning China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang Liaoning China Key Laboratory of Networked Control System Chinese Academy of Sciences Shenyang Liaoning China Shenyang National Research Institute of Intelligent Robots Co. Ltd. Liaoning China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

Indoor distance measurement plays a critical role in positioning services. The complexity of indoor environments, coupled with a limited and small sample size, results in suboptimal distance accuracy. This paper proposes a method for non-line-of-sight distance measurement based on a gradient boosting tree model, effectively addressing the issue of sample size dependency. The random hyperparameter settings of the gradient boosting tree model lead to prolonged fitting times. Additionally, the model utilizes a fixed learning rate, which limits the analysis of features in small samples. To tackle these concerns, we develop a grid optimization algorithm that identifies the best hyperparameter combinations through a hyperparameter grid search. We also implement a weighted value approach and a cosine annealing algorithm to precisely update the learning rate according to training conditions, thus maximizing the learning of small sample features. Our proposed model significantly enhances distance accuracy in non-line-of-sight environments under constraints of training sample size and variety. Comparative experiments demonstrate that our model improves distance accuracy by 63% relative to other machine learning models, with a mean square error of only 0.168m.

关键词： Training Analytical models Accuracy Stability criteria Fitting Power system stability Boosting Distance measurement Data models Optimization

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Research on Geomembrane Leakage Detection Method Based on Quadruped Robot

Research on Geomembrane Leakage Detection Method Based on Qu...

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Chinese automation Congress (CAC)

作者： Pengcheng Zhang Ningning Hu Xiao Wang Qingbiao Xi Haohao Zhao Xingchen Wu Linfeng Qin Dongli Yuan Investigation & Testing Research Institute Shanghai Investigation Design & Research Institute Co. Ltd. Shanghai China System Department 2 Xi'an ASN Technology Group Co. Ltd. Xi'an China School of Electronic and Control Engineering Chang'an University Xi'an China School of Automation Northwestern Polytechnical University Xi'an China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Aiming at the problem of leakage detection of seepage control geomembranes in landfills, a fully autonomous dipole method detection system based on a quadruped robot equipped with a dipole sensor is designed. The feasibility and superiority of using a quadruped robot are analyzed on the basis of the detection principle of the dipole method. Due to the problems existing in the process of upgrading the artificial static detection to autonomous dynamic detection, a dynamic detection method based on a quadruped robot is designed while the structure construction of the dipole method on the quadruped robot is completed. The experimental results show that the efficiency of geomembrane leakage detection can be improved with detection system method employed in this paper.

关键词： Legged locomotion Costs automation Landfills Dogs Robot sensing systems Software Quadrupedal robots Intelligent control

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Self-Organized Criticality in C. Elegans Neural Network

Self-Organized Criticality in C. Elegans Neural Network

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Chinese control Conference (CCC)

作者： Xiao Gu Jian Liu Ye Yuan Peng Zhao Kuankuan Xin Tao Fang Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Shanghai Engineering Research Centre of Intelligent Control and Management Shanghai China Institute of Machine Intelligence University of Shanghai for Science and Technology Shanghai China Queensland Brain Institute The University of Queensland Brisbane QLD Australia

The self-organized criticality is a fundamental property for organisms to perform normal functional activities. Extensive large-scale biological neural network researches have found the existence of self-organized criticality through avalanche phenomenon, which is typical characteristic of self-organized criticality. However, such research method is not suitable for small-scale biological neural network, because number of neurons in small-scale biological neural network is not large enough to support the emergence of avalanche phenomenon. It has been demonstrated that branching parameter can also be used to describe the propagation of activity in the neural network. Here, in this paper, the C. elegans neural network composed of 302 neurons is selected to investigate the existence of self-organized criticality using branching parameter. Based on the obtained connectome of C. elegans, a directed graph model is constructed to simulate the gradient potential of neurons and the activity of C. elegans, then, the existence of self-organized criticality in the neural network of C. elegans is proved by using branching parameter method. Meanwhile, the result is further confirmed by analyzing the calcium activity data of C. elegans obtained from biological experiments.

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Design of a Novel Autonomous Docking system With a Supporting Robotic Fish

Design of a Novel Autonomous Docking System With a Supportin...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Huijie Dong Chunyang Zhao Tiezhu Qiao Jian Wang Di Chen Yan Meng Junzhi Yu Ministry of Education Key Laboratory of Advanced Transducers and Intelligent Control System Taiyuan University of Technology Taiyuan China Institute of Automation Laboratory of Cognitive and Decision Intelligence for Complex System Chinese Academy of Sciences Beijing China Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China College of Electronics and Information Engineering Guangdong Ocean University Zhanjiang China

Underwater supporting robots serving as a relay of energy supplements and communication for other underwater equipment are promising for ocean exploration, development, and protection. This paper proposes a novel autonomous docking system centered on a designed supporting robotic fish named ‘CourierFish’. Specifically, CourierFish is capable of docking with a surface dock station for supplying itself and docking with a seafloor platform for supporting equipment in the platform. A visual navigation scheme integrating LED and ArUco markers is presented for accurate localization. The control approach for docking motion is also illustrated. Simulations and aquatic experiments are performed to verify the feasibility of the proposed docking system. The obtained results lay a solid foundation for the construction of various underwater equipment and robot networks.

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Pose Estimation of Moving Vehicles Based on Heuristic Rules for Autonomous Driving

Pose Estimation of Moving Vehicles Based on Heuristic Rules ...

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IEEE International Conference on Robotics and Biomimetics

作者： Yuesheng He Wei Zhang Ming Yang Department of Automation Shanghai Jiao Tong University Shanghai Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Shenzhen Deeproute.ai Co. Ltd

ISBN: (纸本)9781665481106

Estimating the pose information on moving vehi-cles is one of the most fundamental functions of autonomous driving for detecting and tracking moving objects. The current methods are often based on the prior information of the point cloud distribution, however, the distribution in the actual scene changes dynamically, and is easy not to meet a priori. This paper proposes a 3D pose estimation method based on heuristic rules. In a single frame point cloud, the edge length and edge visibility are used to heuristically screen the candidate orientations established by the convex hull sampling method. On consecutive frame point clouds, tracking heuristics are employed to obtain smoother orientation estimation results. The experimental results on the KITTI dataset and the data from the actual scene of vehicles show that the proposed method reduces the average object-orientation error and improves the accuracy of estimation effectively.

关键词： Point cloud compression Three-dimensional displays Target tracking Biomimetics Pose estimation Object detection Sampling methods

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An OpenPLC-based Active Real-time Anomaly Detection Framework for Industrial control systems

An OpenPLC-based Active Real-time Anomaly Detection Framewor...

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Chinese automation Congress (CAC)

作者： Chengxu Zheng Xiaopeng Wang Xiaoyu Luo Chongrong Fang Jianping He The Dept. of Automation Shanghai Jiao Tong University the Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China NSFOCUS Technologies Group Co. Ltd Beijing China

In recent years, the design of anomaly detectors has attracted a tremendous surge of interest due to security issues in industrial control systems (ICS). Restricted by hardware resources, most anomaly detectors can only be deployed at the remote monitoring ends, far away from the control sites, which brings potential threats to anomaly detection. In this paper, we propose an active real-time anomaly detection framework deployed in the controller of OpenPLC, which is a standardized open-source PLC and has high scalability. Specifically, we add adaptive active noises to control signals, and then identify a linear dynamic system model of the plant offline and implement it in the controller. Finally, we design two filters to process the estimated residuals based on the obtained model and use χ 2 detector for anomaly detection. Extensive experiments are conducted on an industrial control virtual platform to show the effectiveness of the proposed detection framework.

关键词： Adaptation models Industrial control Detectors Real-time systems Software Security Task analysis

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