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Distributionally robust electricity-carbon collaborative scheduling of integrated energy systems based on refined joint model of heating networks and buildings

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Renewable Energy 2025年 251卷

作者： Chen, Haipeng Song, Jianzhao Li, Zhiwei Shui, Siyuan Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China Department of Electrical Engineering Northeast Electric Power University Jilin 132012 China School of Automation and Engineering Northeast Electric Power University Jilin 132012 China Northeast Electric Power University Jilin 132012 China

The rapid increase in the grid integration demand of intermittent renewable energy (RE) has raised higher requirements for the flexible regulation and energy storage capacity of integrated energy systems (IES) with combined heat and power (CHP) units. To solve the issues of efficient accommodation of high proportion RE and low-carbon economic operation of the system, this study proposes an optimal scheduling method for IES based on a refined joint model of heating networks and buildings. Firstly, a refined thermal energy transmission model integrating heating networks and buildings is established to accurately depict the dynamic thermal energy transmission process within the system. Then, this paper proposes an electricity - carbon demand response considering the dynamic carbon emission factor, guiding users to use electricity in a low-carbon way. A distributionally robust optimization (DRO) scheduling model is established to quantify the impact of RE output uncertainty, which is efficiently solved using the column and constraint generation algorithm. The simulation results show that the proposed DRO model ensures the robustness of system operation while improving low-carbon economic benefits. Notably, the RE consumption rate is increased by 9.52 % compared with traditional scheduling methods, which provides an essential reference for promoting the sustainable development of IES. © 2025

关键词： Thermal energy

Observer-based finite time H ∞ control of nonlinear discrete time-varying systems with an adaptive event-triggered Mechanism

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Journal of the Franklin Institute 2020年第16期357卷 11668-11689页

作者： Menghua Chen Hamid Reza Karimi Jian Sun School of Automation Beijing Institute of Technology Beijing 100081 China Department of Mechanical Engineering Politecnico di Milano Milan 20156 Italy Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing 100081 China

The problem of finite time stability and synthesis is discussed for nonlinear discrete time-varying systems by using the T-S fuzzy model. To reduce the communication network resources, two adaptive event-triggered mechanisms (AETMs) are taken into consideration in sensor-to-observer (S/O) and observer-to-controller (O/C) channels. The thresholds of the AETMs are adjusted according to the estimation error rather than some fixed ones. Sufficient conditions for developing an observer-based time-varying control are obtained, which ensure the time-varying error system to be finite time stable and satisfy the H ∞ performance simultaneously. Moreover, using the singular value decomposition technique, recursive linear matrix inequalities conditions are obtained for computing the gains of the controller and observer. Finally, the superiority and applicability of the proposed method are demonstrated by examples.

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Energy-Efficient Multi-UAV-Enabled MEC systems with Space-Air-Ground Integrated Networks

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arXiv 2024年

作者： Liu, Wenchao Zhang, Xuhui Ren, Jinke Shen, Yanyan Wang, Shuqiang Yang, Bo Guan, Xinping Cui, Shuguang Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong518055 China Southern University of Science and Technology Guangdong518055 China The Shenzhen Future Network of Intelligence Institute The School of Science and Engineering The Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong518055 China Shenzhen University of Advanced Technology Guangdong518055 China The Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China The School of Science and Engineering The Shenzhen Future Network of Intelligence Institute The Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Guangdong Shenzhen518172 China

With the development of artificial intelligence integrated next-generation communication networks, mobile users (MUs) are increasingly demanding the efficient processing of computation-intensive and latency-sensitive tasks. However, existing mobile computing networks struggle to support the rapidly growing computational needs of the MUs. Fortunately, space-air-ground integrated network (SAGIN) supported mobile edge computing (MEC) is regarded as an effective solution, offering the MUs multi-tier and efficient computing services. In this paper, we consider an SAGIN supported MEC system, where a low Earth orbit satellite and multiple unmanned aerial vehicles (UAVs) are dispatched to provide computing services for MUs. An energy efficiency maximization problem is formulated, with the joint optimization of the MU-UAV association, the UAV trajectory, the task offloading decision, the computing frequency, and the transmission power control. Since the problem is non-convex, we decompose it into four subproblems, and propose an alternating optimization based algorithm to solve it. Simulation results confirm that the proposed algorithm outperforms the benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

Leader-Tracking in a Shape-Preserving Formation With Bearing-Only Measurements

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IFAC-PapersOnLine 2020年第2期53卷 5958-5963页

作者： Ziwen Yang Shanying Zhu Cailian Chen Gang Feng Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China. Department of Biomedical Engineering City University of Hong Kong Kowloon Hong Kong SAR.

This paper studies the problem of shape-preserving formation control for multi-agent systems where only bearing measurements are available. Most existing methods to solve the problems assume that accurate global or local position measurements of agents are available, or use estimators to get these information without analysis of estimators’ effects on the closed loop system stability. In this work, we propose a framework integrating an estimator to get relative positions using bearing-only measurements and an estimator-based controller to achieve tracking a pair of leaders in a formation with a preserved shape. The estimator is designed by exploiting orthogonality to cope with the high non-linearity of bearing measurements, based on which the controller is designed with the estimated relative positions. With rigorous theoretical analysis of the closed-loop system, we characterize the leaders which can be tracked by followers in a shape-preserving formation using bearing-only measurements, and the asymptotic stability of the closed-loop system can be guaranteed. Simulations testify the effectiveness of the proposed framework.

关键词： Shape-preserving control integrated estimation control leader-follower formation bearing-only measurements

An Efficient Evolutionary Algorithm for High-Speed Train Rescheduling Under a Partial Station Blockage

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SSRN

SSRN 2023年

作者： Wang, Rongsheng Zhang, Qi Dai, Xuewu Yuan, Zhiming Zhang, Tao Ding, Shuxin Jin, Yaochu China Academy of Railway Sciences Corporation Limited Beijing100081 China Signal and Communication Research Institute China Academy of Railway Sciences Corporation Limited Beijing100081 China Train Operation Control Laboratory for High-Speed Railway National Engineering Research Center of System Technology for High-Speed Railway and Urban Rail Transit Beijing100081 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Faculty of Technology Bielefeld University BielefeldD-33615 Germany Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom

This paper investigates the high-speed train rescheduling (HSTR) problem under a partial station blockage and proposes an efficient problem-specific strengthen elitist genetic algorithm (PS-SEGA) for HSTR. An HSTR model that is subject to train operation constraints is established to minimize the total train delay. The PS-SEGA framework consists of the following main steps. Firstly, a permutation-based encoding method is developed to define an efficient search space based on a train departure sequence. A decoding method is employed to output the rescheduled timetable and objective value based on the departure sequence. Moreover, a hybrid initialization method involving an efficient heuristic strategy (EHS) is put forward to accelerate the convergence speed of Evolutionary algorithms (EAs). Using problem-specific knowledge, EHS generates an efficient and feasible solution for the initial population. Finally, a restart strategy is presented to maintain genetic diversity. Compared with other advanced EAs and their improved variants also using the PS-SEGA framework, experimental results demonstrate the effectiveness of the proposed PS-SEGA for addressing HSTR scenarios under the partial station blockage. As for the scenarios that CPLEX can not obtain optimal solutions within 10 minutes, PS-SEGA can all provide quasi-optimal solutions in real-time. Furthermore, compared with the other two strategies (i.e., First-Scheduled-First-Served and EHS), PS-SEGA can find the best departure sequence that can output the timetable with a quasi-optimal solution. © 2023, The Authors. All rights reserved.

关键词： Genetic algorithms

Unsupervised learning of depth, optical flow and pose with occlusion from 3D geometry

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arXiv 2020年

作者： Wang, Guangming Zhang, Chi Wang, Hesheng Wang, Jingchuan Wang, Yong Wang, Xinlei Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Beijing Institute of Control Engineering Beijing100080 China DeepBlue Academy of Sciences Shanghai200240 China

—In autonomous driving, monocular sequences contain lots of information. Monocular depth estimation, camera ego-motion estimation and optical flow estimation in consecutive frames are high-profile concerns recently. By analyzing tasks above, pixels in the first frame are modeled into three parts: the rigid region, the non-rigid region, and the occluded region. In joint unsupervised training of depth and pose, we can segment the occluded region explicitly. The occlusion information is used in unsupervised learning of depth, pose and optical flow, as the image reconstructed by depth, pose and flow will be invalid in occluded regions. A less-than-mean mask is designed to further exclude the mismatched pixels which are interfered with motion or illumination change in the training of depth and pose networks. This method is also used to exclude some trivial mismatched pixels in the training of the flow net. Maximum normalization is proposed for smoothness term of depth-pose networks to restrain degradation in textureless regions. In the occluded region, as depth and camera motion can provide more reliable motion estimation, they can be used to instruct unsupervised learning of flow. Our experiments in KITTI dataset demonstrate that the model based on three regions, full and explicit segmentation of occlusion, rigid region and non-rigid region with corresponding unsupervised losses can improve performance on three tasks significantly. Copyright © 2020, The Authors. All rights reserved.

关键词： Unsupervised learning

Topology inference for multi-agent cooperation under unmeasurable latent input

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arXiv 2020年

作者： Jiao, Qing Li, Yushan He, Jianping Shi, Ling Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

Topology inference is a crucial problem for cooperative control in multi-agent systems. Different from most prior works, this paper is dedicated to inferring the directed network topology from the observations that consist of a single, noisy and finite time-series system trajectory, where the cooperation dynamics is stimulated with the initial network state and the unmeasurable latent input. The unmeasurable latent input refers to intrinsic system signal and extrinsic environment interference. Considering the time-invariant/varying nature of the input, we propose two-layer optimization-based and iterative estimation-based topology inference algorithms (TO-TIA and IE-TIA), respectively. TO-TIA allows us to capture the separability of global agent state and eliminates the unknown influence of time-invariant input on system dynamics. IE-TIA further exploits the identifiability and estimability of more general time-varying input and provides an asymptotic solution with desired convergence properties, with higher computation cost compared with TO-TIA. Our novel algorithms relax the dependence of observation scale and leverage the empirical risk reformulation to improve the inference accuracy in terms of the topology structure and edge weight. Comprehensive theoretical analysis and simulations for various topologies are provided to illustrate the inference feasibility and the performance of the proposed algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Multi agent systems

Combined Nonlinear control of Non-Affine MIMO system with Input and State Delays

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Journal of Physics: Conference Series 2021年第1期1901卷

作者： E L Eremin L V Nikiforova E A Shelenok Information and Control Systems Department Amur State University Blagoveshchensk Russia Automation and System Engineering Department Pacific National University Khabarovsk Russia

The article studies algorithms synthesis of a multiply connected system for controlling non-affine plants, the state variables of which are not available for direct measurement. It is shown that the decentralized control law constructed on the basis of the hyperstability criterion eliminates the influence of non-linearities and compensates external and parametric perturbations in the tracking multiply connected system with the required accuracy.

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A framework of hierarchical attacks to network controllability

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arXiv 2020年

作者： Lou, Yang Wang, Lin Chen, Guanrong The Department of Electrical Engineering City University of Hong Kong Hong Kong The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China

Network controllability robustness reflects how well a networked dynamical system can maintain its controllability against destructive attacks. This paper investigates the network controllability robustness from the perspective of a malicious attack. A framework of hierarchical attack is proposed, by means of edge- or node-removal attacks. Edges (or nodes) in a target network are classified hierarchically into categories, with different priorities to attack. The category of critical edges (or nodes) has the highest priority to be selected for attack. Extensive experiments on nine synthetic networks and nine real-world networks show the effectiveness of the proposed hierarchical attack strategies for destructing the network controllability. From the protection point of view, this study suggests that the critical edges and nodes should be hidden from the attackers. This finding helps better understand the network controllability and better design robust networks. © 2020, CC BY.

关键词： Complex networks