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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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Human Following Robot Based on Object Detection for Aerospace Application

Human Following Robot Based on Object Detection for Aerospac...

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Aerospace Electrical engineering and Informatics (ICAAEEI), International Conference of Adisutjipto on

作者： Arwin Datumava Wahvudi Sumari Ahnaf Bagus Aryandika Ndaru Atmi Purnami Denny Dermawan Ika Noer Syamsiana Department of Electrical Engineering Cognitive Artificial Intelligence Research Group State Polytechnic of Malang Malang Indonesia Electrical Engineering Study Program Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia Aerospace Navigation Instrumentation Control Automation and Robotics System Research Group Electrical Engineering Study Program Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia Aerospace Digital System Microprocessor Microcontroller and Programmable Logic Devices Research Group Electrical Engineering Study Program Adisutjipto Institute of Aerospace Technology Yogyakarta Indonesia

ISBN: (数字)9798331507930

ISBN: (纸本)9798331507947

In this research, the Human Following Robot (HFR) prototype has been designed and implemented using YOLO v3-Tiny and Tensor Flow Lite on Raspberry Pi hardware named Rewang. The HFR Rewang is designed to assist aircraft technicians by bringing maintenance tool kits to aircraft shelters. Rewang's movement is based on a camera as a vision sensor following the movement of human entities. Image input is processed by YOLO v3-Tiny through the determination of the bounding box, the calculation of Euclidean Distance, and the determination of the pixel value of the camera as the basis of Rewang's movement which is regulated by an L298N driver motor that controls two Direct Current (DC) motors for the left and right wheels. The test results show that Rewang can move at an average speed of 0.18 m/s at 2 m with a travel time of 11 seconds with a light intensity of more than 250 lux. The minimum and maximum distance of object detection is as far as 120 cm and 400 cm, with an object detection accuracy of 95.8%. Rewang allows the HFR to have a larger carrying capacity to bring more complete aircraft maintenance tools and is prospective for other aerospace and general applications.

关键词： YOLO Tensors Accuracy Prototypes Wheels Cameras Hardware Maintenance Aircraft Aerospace industry

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Improvement of the Electrical Properties of Silicone Rubber Doped with Nano-zirconia Grafted by Voltage Stabilizer

Improvement of the Electrical Properties of Silicone Rubber ...

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International Conference on Electrical Materials and Power Equipment (ICEMPE)

作者： Xiaoqian Zhu Yuanxiang Zhou Yunxiao Zhang Ming Chen Xuanhao Liu State Key Lab of Control and Simulation of Power System and Generation Equipment & Department of Electrical Engineering Tsinghua University Beijing China College of Electrical Engineering and Automation Fuzhou University Fuzhou China Xiamen Power Supply Company State Grid Fujian Electric Power Company Xiamen China The Wind Solar Storage Division of State Key Laboratory of Control and Simulation of Power System and Generation Equipment School of Electrical Engineering Xinjiang University Urumqi China

Enhancing the electrical performance of silicone rubber (SiR) for cable accessories is noteworthy as they are almost the weakest parts of the cable system. Two types of voltage stabilizers were grafted onto the surface of nano-zirconia (ZrO 2 ) particles, and the grafted nanoparticles were added to the SiR. Measurements such as breakdown strength, and conductivity were carried out to evaluate the electrical performance of SiR nanocomposites. The electrical performance measurement results indicated that the breakdown strength of the nanocomposite SiR improved while the conductance current was reduced. The trap distribution showed that the improvement of the electrical performance of the nanocomposite SiR is mainly due to the decrease of the shallow trap density or the increase of the deep trap density.

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Opportunistic Wireless control Over State-Dependent Fading Channels*

Opportunistic Wireless Control Over State-Dependent Fading C...

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IEEE Conference on Decision and control

作者： Shuling Wang Peizhe Li Shanying Zhu Cailian Chen Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

The heterogeneous system consisting of the wireless control system (WCS) and mobile agent system (MAS) is ubiquitous in Industrial Internet of Things (IIoT) systems. Within this system, the positions of mobile agents may lead to shadow fading on the wireless channel that the WCS is controlled over and can significantly compromise the WCS’s performance. This paper focuses on the controller design for the MAS to ensure the performance of WCS in the presence of WCS and MAS coupling. Firstly, the constrained finite field network (FFN) with profile-dependent switching topology is adopted to proceed the operational control for the MAS. By virtue of the algebraic state space representation (ASSR) method, an equivalent form is obtained for the WCS and MAS coupling. A necessary and sufficient condition in terms of constrained set stabilization is then established to ensure the Lyapunov-like performance with expected decay rate. Finally, a graphical method together with the breath-first searching is provided to design state feedback controllers for the MAS. With this method, it is easy to check the constrained set stabilization of MAS and to ensure the performance requirements of WCS in the presence of WCS and MAS coupling. The study of an illustrative example shows the effectiveness of the proposed method.

关键词： Couplings Wireless communication Fading channels State feedback Mobile agents Optical wavelength conversion Switches

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Multi-type and Multi-level Feature Fusion Network for RGBD Indoor Semantic Segmentation

Multi-type and Multi-level Feature Fusion Network for RGBD I...

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Chinese control and Decision Conference, CCDC

作者： Yuwen Xia Chaochen Gu Kaijie Wu Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9781665478977

RGBD images, which refer to the three-channel color images augmented with depth channel, have been greatly exploited to improve the performance of scene semantic segmentation. RGBD semantic segmentation task is of great significance to help the development of robot navigation and grasping. However, most existing methods addressing RGBD semantic segmentation lack effective feature fusion modules. To address the aforementioned problem, we propose a novel network with two novel modules for feature fusion, namely Channel-Attention based Complementary Feature Fusion Module (CAC-FFM) and Cross-Layer Feature Fusion Module (CL-FFM). Specifically, CAC-FFM, which is essentially based on the channel-attention mechanism, effectively utilizes the complementary information of RGB and depth to generate fusion features, and CL-FFM captures patch-wise features from low-level feature maps to assist the training of high-level features so as to further optimize the segmentation results. Experimental results on the publicly available NYUDv2 dataset validate the effectiveness and superiority of our proposed method.

关键词： Training Cross layer design Correlation Navigation Semantic segmentation Grasping Excavation

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Building Temperature and Humidity Adaptive control for a Multi-Zone HVAC system Using Hybrid Modeling Method

Building Temperature and Humidity Adaptive Control for a Mul...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Yuliang Jiang Shanying Zhu Qimin Xu Bo Yang Xinping Guan Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9781728119496

In this paper, the heating, ventilation and air conditioning (HVAC) system as the contributor of building climate management is studied on multi-zone climate dynamics modelling and control for occupancy comfort. The first-principles model together with data-driven model as the hybrid model is proposed for comprehensive multi-zone hygro-thermal dynamics. The unmodeled climate dynamics such as unknown nonlinearity and uncertainty is modeled in a linear form based on the compact form dynamic linearization (CFDL) method. Then model free adaptive control (MFAC) scheme including controller design and parameter estimation is proposed to realize multi-zone climate control and occupancy comfort. The dynamic response performance of the hybrid model-based adaptive control and the first-principles model-based adaptive control has been tested in a multi-zone building with four different zones. The results show that the proposed hybrid model-based adaptive control scheme can achieve obvious performance improvement in minimizing steady-state error.

关键词： Adaptation models HVAC Parameter estimation Uncertainty Atmospheric modeling Buildings Ventilation

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controllability of Networked Sampled-data systems

arXiv

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arXiv 2022年

作者： Yang, Zixuan Wang, Xiaofan Wang, Lin The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China

The controllability of networked sampled-data systems with zero-order holders on the control and transmission channels is explored, where single- and multi-rate sampling patterns are considered, respectively. The effects of sampling on the controllability of networked systems are analyzed, with some necessary and/or sufficient controllability conditions derived. Different from the sampling control of single systems, the pathological sampling of node systems could be eliminated by an appropriate design of network structure and inner couplings. While for singular topology matrices, the pathological sampling of single nodes will cause the entire system to lose controllability. Moreover, any periodic sampling will not affect the controllability of networked systems with specific node dynamics. All the results indicate that whether a networked system is under pathological sampling or not is jointly determined by mutually coupled factors. Copyright © 2022, The Authors. All rights reserved.

关键词： controllability

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Adaptive Multi-Core Real- Time Scheduling Based on Reinforcement Learning

Adaptive Multi-Core Real- Time Scheduling Based on Reinforce...

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International Conference on control and automation (ICCA)

作者： Yonghui Liang Hui Li Fei Shen Qimin Xu Shuna Hua Shanying Zhu Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Re-search Center of Intelligent Control and Management Shanghai China Key Laboratory of Wireless Sensor Network and Communications Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China North Information Control Research Academy Group Company Ltd. Nanjing China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

With the transformation towards industrial intel-ligence, multi-core processors are increasingly being applied in real-time networked control systems to ensure secure exe-cution of sensing, computing and actuating tasks under time constraints. However, existing scheduling methods result in either low CPU utilization or many missed task deadlines in dynamic systems. In this paper, we propose a two-layer scheduling architecture to address this issue by fully exploring the complex dependency between real-time tasks. To be specific, the local layer determines task execution priorities considering both dependency between tasks and deadline constraints by utilizing a reinforcement learning approach. Moreover, to better utilize the parallel capabilities of multi-core processors and reduce temporal collisions, this paper minimizes the requested core count for the task set based on a greedy strategy. The global layer designs a scheduling algorithm based on the pre-empt method and provides schedulability analysis of multiple task sets. Experimental results validate the correctness of the proposed scheduling approach, and efficiency is demonstrated through comparisons with baseline method.

关键词： Job shop scheduling Multicore processing Scheduling algorithms Networked control systems Reinforcement learning Real-time systems Sensors

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Streaming-Data-Driven Distributionally Robust Joint Operation of Multi-Microgrids and Off-Site Hydrogen Refueling Stations under Uncertainties

Streaming-Data-Driven Distributionally Robust Joint Operatio...

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IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)

作者： Longyan Li Chao Ning Haifeng Qiu Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China School of Electrical & Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350399677

This paper proposes a novel online-learning-enabled hierarchical distributionally robust energy management framework for multi-microgrids (MMGs) with off-site hydrogen refueling stations (HRSs). The proposed framework integrates day-ahead scheduling, intra-day rescheduling and real-time control. The day-ahead scheduling is implemented via a central energy management system for optimal joint scheduling of the MMG systems and off-site HRSs, then the intra-day rescheduling is carried out to optimize the real-time operation and to generate the reference for local controllers. To adequately characterize the statistical properties of renewable and load uncertainties from data streams, we leverage a Dirichlet process mixture model to construct an online-update ambiguity set, which encodes multimodality and local moment information. Based upon this ambiguity set, an online-learning distributionally robust model predictive control (OL-DRMPC) is developed to enhance the tracking performance of energy storage references and to reduce conservatism while guaranteeing probabilistic operational constraints. Its effectiveness is verified in a case study.

关键词： Renewable energy sources Uncertainty Hydrogen Asia Mixture models Probabilistic logic Real-time systems

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Enhanced Industrial Process Modeling with Transfer-Incremental-Learning: A Parallel Sae Approach and its Application to a Sulfur Recovery Unit

SSRN

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SSRN 2023年

作者： Mou, Tianhao Liu, Jinfeng Zou, Yuanyuan Li, Shaoyuan Xibilia, Maria Gabriella Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Department of Chemical and Materials Engineering University of Alberta EdmontonABT6G 1H9 Canada Department of Engineering University of Messina Messina98166 Italy

In industrial processes, quality variable prediction is important for process control and monitoring. The emergence of deep learning (DL) methods and their excellent prediction performance present a potential paradigm revolution in the modeling of quality variable prediction. However, in real-world production, the lack of offline labeled data and the presence of time-varying data distributions commonly exist, which seriously prohibits practical applications of DL-based predictive models. This paper introduces an enhanced quality variable prediction framework, Transfer-Incremental-Learning Parallel Stacked Autoencoders (TIL-PSAE), to address this issue. TIL-PSAE establishes an effective predictive model through an integration of three key components: a parallel model structure, a transfer- learning (TL)-based offline training strategy that accumulates knowledge from other similar processes, and an incremental-learning (IL)-based online adaptation strategy that preserves offline knowledge. The model structure contains two parallel SAEs, which separately extract process-invariant features and target-process-specific features. The offline training involves sequential training using data from different domains, facilitating the accumulation of knowledge from different domains into different parts of model. During online adaptation, the accumulated knowledge remains unchanged while a new combination is learned, thus improving online prediction accuracy and avoiding knowledge forgetting. The proposed model is applied to a sulfur recovery unit of four parallel sub-units. Experimental results demonstrate the effectiveness of the proposed model, in terms of both offline prediction performance and online prediction performance. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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