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distributed optimization for Autonomous Restoration in DER-Rich Distribution System

IEEE TRANSACTIONS ON POWER DELIVERY 2023年第5期38卷 3205-3217页

作者： Konar, Srayashi Srivastava, Anurag K. Dubey, Anamika Washington State Univ WSU Sch Elect Engn & Comp Sci Pullman WA 99163 USA West Virginia Univ Lane Dept Comp Sci & Elect Engn Morgantown WV 26506 USA

Autonomous service restoration (ASR) of active distribution network (ADN) reduces service restoration time with the help of measurement devices, smart switches and distributed energy resources (DERs) considering the system's operational and radiality constraints. Restoration scheme can be deployed in both centralized and distributed manner. However, with increasing data points, the requirement for measurement availability at the control center for the decision support makes centralized optimization challenging. The decomposition and coordination scheme of distributed algorithms enhances its ability to solve large scale optimization problems. Moreover, distributed optimization enables robustness, scalability and resiliency compared to centralized optimization. In this work, a) the distributed ASR problem is solved using a novel penalty-driven distributed alternating direction method of multipliers algorithm (PD-ADMM), b) a switch level decomposition and coordination technique is proposed, c) load restoration, DER utilization are considered directly and radiality of ADN is enforced using commodity flow model. The applicability of the algorithm is validated using modified IEEE 33-bus, IEEE 123-bus and 1069-bus test systems for contingency cases, communication failure, scalability, accuracy and sensitivity to number of partitions, starting points, change in DER/solar generation.

关键词： Active distribution network autonomous restoration distributed optimization mixed integer problem penalty function

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distributed optimization for consensus performance of delayed fractional-order double-integrator multi-agent systems

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NEUROCOMPUTING 2023年第1期522卷 105-115页

作者： Liu, Jun Zhou, Nan Qin, Kaiyu Chen, Badong Wu, Yonghong Choi, Kup-Sze Chengdu Univ Informat Technol Sch Automat Chengdu Peoples R China Chengdu Univ Sch Elect Informat & Elect Engn Chengdu Peoples R China Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China Wuhan Univ Technol Sch Sci Wuhan Peoples R China Hong Kong Polytech Univ Ctr Smart Hlth Hong Kong Peoples R China

This paper addresses distributed optimization problems concerning consensus in delayed fractional -order double-integrator multi-agent systems (FDMSs). To start with, an optimized distributed protocol with state-fractional-order-derivative feedback (SF) is presented for delayed FDMSs. Then, the consensus problems are studied for the two kinds of delayed FDMSs with SF in the presence of symmetric time -delays over undirected network topology and asymmetric time-delays over directed network topology. Next, by the means of graph theory, matrix theory and frequency-domain analysis method, the sufficient conditions to guarantee consensus of delayed FDMSs with SF are derived. Compared to the traditional distributed protocol without SF, the proposed distributed optimization protocol with SF are taken into account to enable better consensus performance in delayed FDMSs with SF. Finally, numerical experi-ments are carried out to verify the feasibility of our theoretical results.(c) 2022 Elsevier B.V. All rights reserved.

关键词： distributed optimization Consensus Fractional-order Double-integrator Multi-agent systems Network topology State-fractional-order-derivative feedback Time-delays

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distributed optimization of Uncertain Multiagent Systems With Disturbances and Actuator Faults via Exosystem Observer-Based Output Regulation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2023年第2期70卷 897-909页

作者： Kang, Jian Guo, Ge Yang, Guanghong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

This paper investigates a distributed optimization problem for heterogeneous linear multi-agent systems with uncertain parameters and external disturbances. For each agent, the optimal solution of the global objective functions can be regarded as an unknown constant reference signal. From this angle, the problem is transformed into an output regulation problem for an exosystem whose states and outputs are to be estimated. To ensure the trackability of the estimated signal, a fundamental design condition for the exosystem observer to achieve robust output regulation is given in the context of internal model control. Exact global optimization is then realized by embedding an appropriate distributed optimization algorithm (i.e., the exosystem observer) in each agent to estimate the global optimal solution. The result for exosystem observer design is extended to yield an adaptive fault-tolerant control algorithm to deal with a class of practical actuator fault uncertainties. The exosystem observer-based output regulation method provides a general distributed robust optimization framework for uncertain systems with disturbances and actuator faults, which can directly apply to various consensus problems. Simulations are provided to illustrate the effectiveness of the method.

关键词： distributed optimization linear systems robust output regulation fault-tolerant control multi-agent systems

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distributed optimization Method for Spacecraft Attitude Control and Vibration Suppression

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2023年第4期46卷 752-760页

作者： Hu, Yabo Bernelli-Zazzera, Franco Geng, Yunhai Harbin Inst Technol Res Ctr Satellite Technol Harbin 150001 Peoples R China Polytech Univ Milan Dept Aerosp Sci & Technol I-20156 Milan Italy

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distributed optimization Over Time-Varying Graphs With Imperfect Sharing of Information

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第7期68卷 4420-4427页

作者： Reisizadeh, Hadi Touri, Behrouz Mohajer, Soheil Univ Minnesota Minneapolis MN 55455 USA Univ Calif San Diego La Jolla 92093 CA USA

We study strongly convex distributed optimization problems where a set of agents are interested in solving a separable optimization problem collaboratively. In this article, we propose and study a two-time-scale decentralized gradient descent algorithm for a broad class of lossy sharing of information over time-varying graphs. One time-scale fades out the (lossy) incoming information from neighboring agents, and one time-scale regulates the local loss functions' gradients. We show that assuming a proper choice of step-size sequences, certain connectivity conditions, and bounded gradients along the trajectory of the dynamics, the agents' estimates converge to the optimal solution with the rate of O(T-1/2). We also provide novel tools to study distributed optimization with diminishing averaging weights over time-varying graphs.

关键词： Convex optimization distributed multiagent system distributed optimization gradient descent algorithms time-varying graphs

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distributed optimization Subject to Inseparable Coupled Constraints: A Case Study on Plant-Wide Ethylene Process

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第4期19卷 5412-5421页

作者： Liu, Weihan Wang, Ting Li, Zhongmei Ye, Zhencheng Peng, Xin Du, Wenli East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China

Plant-wide optimization plays a vital role in improving the overall performance of large-scale industrial processes. Considering the modeling complexity and convergence difficulty of centralized plant-wide optimization, in this article, we propose a distributed framework by decomposing the global optimization problem into a set of subproblems, where multiple local units interact with each other between nodes. According to the proposed framework, plant-wide optimization problem can be effectively solved by distributed optimization. To eliminate the limitations of existing distributed algorithms, we introduce constraint node to describe the inseparable coupled constraints between nodes. By combining Lagrange duality and parameter projection, the proposed algorithm can solve optimization problems with multiple constraints. Taking ethylene production process as an example, the global energy consumption optimization is guaranteed without the whole-process mechanism model. Numerical simulation and industrial experimental results demonstrate that the proposed algorithm can reduce the energy consumption of the entire ethylene process with fewer computation time.

关键词： optimization Energy consumption Production Raw materials Informatics Feeds Data models Constraint node distributed optimization energy saving parameter projection plant-wide optimization

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distributed optimization of Nonlinear Multiagent Systems via Event-Triggered Communication

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第6期70卷 2092-2096页

作者： Liu, Dan Shen, Mouquan Jing, Yanhui Wang, Qing-Guo Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing 211816 Peoples R China Xinyang Normal Univ Sch Math & Stat Xinyang 464000 Peoples R China Beijing Normal Univ Inst Artificial Intelligence & Future Networks Zhuhai 519087 Peoples R China Beijing Normal Univ & Hong Kong Baptist Univ Unite Guangdong Prov Key Lab Interdisciplinary Res & App Zhuhai 519087 Peoples R China

This brief is concerned with a distributed optimization problem over an undirected multi-agent network, where each agent is assigned a private cost function which is strongly convex with a Lipschitz gradient. First, an event-triggered communication strategy is adopted to reduce network communication, and an event-based auxiliary system is constructed to estimate the optimal solution. The estimation is used to generate a reference signal and its high-order derivatives, which is an approximation of the optimal solution. Then, a backstepping control algorithm is developed to drive all agents' output to the reference signal. In terms with the Lyapunov stability theory and the algebraic graph theory, it can be proved that the distributed optimization problem is solved by the developed control algorithm. Different from the existing algorithms, eigenvalues of the Laplacian matrix are not used in our proposed control design. Finally, a simulation example is presented to validate the theoretical result.

关键词： Laplace equations Eigenvalues and eigenfunctions Cost function Backstepping Communication networks Behavioral sciences Multi-agent systems Event-triggered control distributed optimization multiagent systems

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distributed optimization for intelligent IoT under unstable communication conditions

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COMPUTER COMMUNICATIONS 2023年第1期199卷 42-49页

作者： Yuan, Yuan Yu, Jiguo Zhang, Liangxu Cai, Zhipeng Qilu Univ Technol Shandong Acad Sci Sch Comp Sci & Technol Jinan 250353 Shandong Peoples R China Qilu Univ Technol Big Data Inst Shandong Acad Sci Jinan 250353 Shandong Peoples R China Qilu Univ Technol Shandong Acad Sci Shandong Fundamental Res Ctr Comp Sci Jinan 250353 Shandong Peoples R China Shandong Univ Sch Comp Sci & Technol Jinan 266237 Peoples R China Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA

The design of distributed optimization algorithms for intelligent Internet of Thing (IIoT) environments has attracted extensive attention. However, the dynamic network environment, the delay of information transmission between devices and the risk of privacy disclosure pose great challenges to algorithm designs. To address this problem, we propose a distributed gradient descent algorithm, which can converge under very mild assumptions that there will be an unbounded (stochastic) delay from sender to receiver. To the best of our knowledge, our proposed algorithm is the first work to take unbounded stochastic communication delays into consideration, in addition to time-varying networks and privacy disclosure. Rigorous analysis shows that our algorithm can converge under such mild assumptions. Extensive experiments exhibit that the proposed algorithm performs well in complex IIoT environments.

关键词： distributed optimization Intelligent IoT Unstable communications

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distributed optimization for Second-Order Discrete-Time Multiagent Systems With Set Constraints

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第9期34卷 5629-5639页

作者： Zou, Yao Xia, Kewei Huang, Bomin Meng, Ziyang Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Inst Artificial Intelligence Beijing 100083 Peoples R China Beijing Inst Technol Adv Res Inst Multidisciplinary Sci Beijing 100081 Peoples R China Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Hebei Peoples R China Tsinghua Univ Dept Precis Instrument Beijing 100084 Peoples R China

The optimization problem of second-order discrete-time multiagent systems with set constraints is studied in this article. In particular, the involved agents cooperatively search an optimal solution of a global objective function summed by multiple local ones within the intersection of multiple constrained sets. We also consider that each pair of local objective function and constrained set is exclusively accessible to the respective agent, and each agent just interacts with its local neighbors. By borrowing from the consensus idea, a projection-based distributed optimization algorithm resorting to an auxiliary dynamics is first proposed without interacting the gradient information of local objective functions. Next, by considering the local objective functions being strongly convex, selection criteria of step size and algorithm parameter are built such that the unique solution to the concerned optimization problem is obtained. Moreover, by fixing a unit step size, it is also shown that the optimization result can be relaxed to the case with just convex local objective functions given a properly chosen algorithm parameter. Finally, practical and numerical examples are taken to verify the proposed optimization results.

关键词： optimization Heuristic algorithms Linear programming Multi-agent systems Convergence Topology distributed algorithms Consensus convex functions discrete-time systems distributed optimization multiagent systems

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distributed optimization of Disturbed Nonlinear Multi-Agent Systems via Adaptive Fault-Tolerant Output Regulation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第12期70卷 4479-4483页

作者： Kang, Jian Guo, Ge Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Peoples R China

This brief considers a distributed fault-tolerant optimization problem of disturbed nonlinear multi-agent systems, which is converted to an output regulation issue of tracking an exosystem with unknown states. To solve this problem, a new adaptive fault-tolerant controller including exponential terms is yielded, with giving two fundamental design conditions of the exosystem state observer to achieve asymptotic or exponential convergence and compensate for unbounded external disturbances. The proposed exosystem observer-based adaptive fault-tolerant output regulation approach supplies a common distributed optimization or cooperative control framework for faulty nonlinear multi-agent systems. A simulation example is presented to verify the effectiveness.

关键词： distributed optimization nonlinear systems adaptive fault-tolerant control exponential convergence

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