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Distributed Optimization Control of Frequency and Voltage During Initial Black Start in Virtual Power Plant Mode

Smart Power 2025年第2期53卷 49-57页

作者： Sai, Xiangyu Liu, Yan Zhi, Nan Zhang, Dan Ren, Yufei Yunnan Electric Power Dispatching and Control Center Kunming650032 China Department of Electrical Engineering North China Electric Power University Baoding071003 China

Targeting the problem of power grid stability caused by the increasing penetration rate of renewable energy，the paper proposes a frequency and voltage optimization control method using virtual power plant technology to unite dispersed distributed renewable energy units participating in black start. Firstly，the initial black start control model of distributed renewable energy unit cluster is established aimed at minimizing the transient frequency and voltage deviation. Secondly，the multi-agent consensus algorithm and hierarchical control method are used to realize the overall optimization and power scheduling of the initial black-start voltage and frequency control of distributed generation cluster. Finally，the effectiveness of the distributed optimal control method is verified by simulation，and the simulation results show that the proposed method has better control stability in the frequency and voltage than the centralized control method. © 2025 Zhihui Dianli Bianjibu. All rights reserved.

关键词： scheduling algorithms

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scheduling Green Energy Wireless Charging of IoT Devices 26

Scheduling Green Energy Wireless Charging of IoT Devices

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26th IEEE Asia-Pacific Conference on Communications {APCC)

作者： Jia, Yongxing Liu, Xilong Ansari, Nirwan Sha, Qiuyu Yunnan Univ Sch Informat Sci & Engn Kunming 650504 Yunnan Peoples R China New Jersey Inst Technol Adv Networking Lab Newark NJ 07102 USA

ISBN: (纸本)9781728172545

Using green energy to charge Internet of Things (IoT) devices is becoming a technically viable option. Green far-field Wireless Power Transmission can help powering IoT terminal devices. Keeping the maximum number of IoT devices (IoTDs) in their normal working modes is to furthest prolong the lifetime of the IoT network. In this work, for a given charging area, we first determine association between IoTDs and the green energy base station (GEBS), and then efficiently power IoTDs wirelessly. In green far-field wireless charging, we propose a dual-threshold model for IoTDs to facilitate wireless charging. In the dual-threshold model, we define two thresholds, i.e., Alarm Threshold and Working Threshold. Based on the dual-threshold model, we then propose the Dual-Threshold Orderly Charging (DTOC) algorithm to efficiently charge their surrounding IoTDs in a specific order to improve the charging efficiency. Finally, we validate the performance of the proposed algorithm through extensive simulations.

关键词： Performance evaluation Energy loss Base stations scheduling algorithms Conferences Inductive charging Wireless power transmission

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QL-TSCH-plus: A Q-learning Distributed scheduling Algorithm for TSCH Networks

QL-TSCH-plus: A Q-learning Distributed Scheduling Algorithm ...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Mehdi Kherbache Moufida Maimour Eric Rondeau CRAN Laboratory CNRS UMR 7039 Campus Sciences BP 70239 Université de Lorraine Nancy France

ISBN: (数字)9798350311617

ISBN: (纸本)9798350311624

Addressing the requirements of Industrial Internet of Things (IIoT) in Industry 4.0, the Time Slotted Channel Hopping (TSCH) protocol of the IEEE 802.15.4e amendment has been proposed. However, the lack of a defined scheduling procedure in the standard remains an open research area. Existing reinforcement learning-based scheduling proposals demonstrate great potential for this technique due to the ongoing observations within the network environment. Beneficial for real-world scenarios where network conditions are volatile and unpredictable. This work presents QL- TSCH-plus, an enhancement of the existing QL- TSCH scheduler that reduces energy consumption by adapting the Action Peeking mechanism to a distributed scheme. Instead of continuously listening to neighboring nodes communication, QL- TSCH-plus allows nodes to broadcast the learned transmission slots for updating the Action Peeking Tables and allocating reception slots, reducing energy use by up to 47% compared to QL-TSCH. This novel approach also maintains reliability and timeliness, demonstrating significant potential for efficient scheduling in TSCH networks, making it suitable for the IIoT.

关键词： Energy consumption Job shop scheduling Q-learning scheduling algorithms Routing Fourth Industrial Revolution Resource management

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Access Craft scheduling of Stereo Garage Based on Improved Hybrid Simulated Annealing Algorithm 4

Access Craft Scheduling of Stereo Garage Based on Improved H...

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4th International Conference on Robotics, Control and Automation Engineering (RCAE) / 4th International Conference on Advanced Mechanical and Electrical Engineering (AMEE)

作者： Yi, Guohong Zhou, Wan Li, Shengpu Chen, Yangjun Wuhan Inst Technol Sch Comp Sci & Engn Wuhan Peoples R China

ISBN: (纸本)9781665427302

In order to improve the stereo garage access efficiency, the Improved Hybrid Simulated Annealing Algorithm is proposed, which is applied to the stereo garage, to determine which robot, transport and elevator to use, to find the best path and get the optimal scheduling results. In order to solve the problem that the genetic algorithm is easy to converge prematurely to obtain a local optimal solution, the algorithm combines the simulated annealing algorithm in the genetic algorithm to further search the results obtained by the genetic algorithm to avoid falling into the local optimal solution. Then, improve the steps of the traditional algorithm to get better results. According to simulation results, compared with other scheduling algorithms, the Improved Hybrid Simulated Annealing Algorithm can significantly improve the efficiency of the stereo garage, and get the more excellent results.

关键词： Stereo garage Genetic algorithm scheduling algorithms Simulated annealing algorithm

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QoS-Aware scheduling in New Radio Using Deep Reinforcement Learning 32

QoS-Aware Scheduling in New Radio Using Deep Reinforcement L...

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32nd IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC)

作者： Stigenberg, Jakob Saxena, Vidit Tayamon, Soma Ghadimi, Euhanna Ericsson AB Stockholm Sweden

ISBN: (纸本)9781728175867

Fifth-generation (5G) New Radio (NR) cellular networks support a wide range of new services, many of which require an application-specific quality of service (QoS), e.g. in terms of a guaranteed minimum bit-rate or a maximum tolerable delay. Therefore, scheduling multiple parallel data flows, each serving a unique application instance, is bound to become an even more challenging task compared to the previous generations. Leveraging recent advances in deep reinforcement learning, in this paper, we propose a QoS-Aware Deep Reinforcement learning Agent (QADRA) scheduler for NR networks. In contrast to state-of-the-art scheduling heuristics, the QADRA scheduler explicitly optimizes for the QoS satisfaction rate while simultaneously maximizing the network performance. Moreover, we train our algorithm end-to-end on these objectives. We evaluate QADRA in a full scale, near-product, system level NR simulator and demonstrate a significant boost in network performance. In our particular evaluation scenario, the QADRA scheduler improves network throughput by 30% while simultaneously maintaining the QoS satisfaction rate of VoIP users served by the network, compared to state-of-the-art baselines.

关键词： Cellular networks scheduling algorithms Heuristic algorithms Quality of service Reinforcement learning Throughput New Radio

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Optimal scheduling and non-cooperative distributed model predictive control for multiple robotic manipulators

Optimal scheduling and non-cooperative distributed model pre...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Gafur, Nigora Yfantis, Vassilios Ruskowski, Martin Tech Univ Kaiserslautern Dept Mech & Proc Engn Chair Machine Tools & Control Syst D-67663 Kaiserslautern Germany German Res Ctr Artificial Intelligence DFKI D-67663 Kaiserslautern Germany

ISBN: (纸本)9781665417143

Application of multiple robotic manipulators in a shared workspace is still restricted to repetitive tasks limiting their flexible deployment for production systems. Still, existing motion control algorithms cannot be performed online for arbitrary environments in case of multiple manipulators cooperating with each other. In this work we propose a scalable and real-time capable motion control algorithm based on non-cooperative distributed model predictive control. Furthermore, we propose an optimal scheduling algorithm, which provides optimal setpoints to each robot's motion controller that prevents possible deadlocks beforehand. We validate our approach on a simulative setup of four robotic manipulators for multiple pick and place scenarios.

关键词： scheduling algorithms Optimal scheduling System recovery Manipulators Prediction algorithms Trajectory Task analysis

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AoI Minimization Charging at Wireless-Powered Network Edge 42

AoI Minimization Charging at Wireless-Powered Network Edge

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42nd IEEE International Conference on Distributed Computing Systems (ICDCS)

作者： Chen, Quan Guo, Song Xu, Wenchao Cai, Zhipeng Cheng, Lianglun Gao, Hong Guangdong Univ Technol Sch Comp Guangzhou Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Hong Kong Polytech Univ Shenzhen Res Inst Dept Comp Shenzhen Peoples R China Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Zhejiang Normal Univ Coll Math & Comp Sci Jinhua Zhejiang Peoples R China

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

Age of Information (AoI) has emerged as a new metric to measure data freshness from the destination's perspective. The problem of optimizing AoI has been attracting extensive interests recently. However, existing works mainly focused on scheduling data transmission for AoI optimization. While at wireless-powered network edge, the charging plan of source nodes also requires to be computed in advance, which means the system AoI is determined by not only the data transmission decision but also the charging plan. Thus, in this paper, we investigate the first work to optimize the weighted peak AoI from the point of charging at wireless-powered network edge with a directional charger. Firstly, to minimize the weighted sum of average peak AoI, the AoI minimization problem is transformed to a charging time optimization problem with respect to the overlapped charging areas and average peak AoI, and an approximate algorithm is proposed to obtain the required charging time for each source node. Then, an age-based scheduling algorithm is proposed to compute the charging and data transmission decisions for each source node simultaneously, which can not only optimize the weighted sum of average peak AoI but also guarantee the maximum peak AoI for each source node. The proposed algorithm is proved to have an approximation ratio of up to (1+phi), where phi is a much smaller value related to the weight of each source node. Finally, the simulation results verify the high performance of proposed algorithms in terms of average and maximum peak AoI.

关键词： Measurement scheduling algorithms Simulation Data integrity Approximation algorithms Minimization Information age

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Learning to Identify Top Elo Ratings: A Dueling Bandits Approach 36

Learning to Identify Top Elo Ratings: A Dueling Bandits Appr...

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36th AAAI Conference on Artificial Intelligence / 34th Conference on Innovative Applications of Artificial Intelligence / 12th Symposium on Educational Advances in Artificial Intelligence

作者： Yan, Xue Du, Yali Ru, Binxin Wang, Jun Zhang, Haifeng Chen, Xu Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Kings Coll London Dept Informat London England Univ Oxford Machine Learning Res Grp Oxford England UCL Dept Comp Sci London England Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9781577358763

The Elo rating system is widely adopted to evaluate the skills of (chess) game and sports players. Recently it has been also integrated into machine learning algorithms in evaluating the performance of computerised AI agents. However, an accurate estimation of the Elo rating (for the top players) often requires many rounds of competitions, which can be expensive to carry out. In this paper, to improve the sample efficiency of the Elo evaluation (for top players), we propose an efficient online match scheduling algorithm. Specifically, we identify and match the top players through a dueling bandits framework and tailor the bandit algorithm to the gradient-based update of Elo. We show that it reduces the per-step memory and time complexity to constant, compared to the traditional likelihood maximization approaches requiring O(t) time. Our algorithm has a regret guarantee of (O) over tilde(root T), sublinear in the number of competition rounds and has been extended to the multidimensional Elo ratings for handling intransitive games. We empirically demonstrate that our method achieves superior convergence speed and time efficiency on a variety of gaming tasks.

关键词： scheduling algorithms

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Synthesis of scheduling Heuristics by Composition and Recombination 4th

Synthesis of Scheduling Heuristics by Composition and Recomb...

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4th International Conference on Optimization and Learning (OLA)

作者： Maeckel, Dominik Winkels, Jan Schumacher, Christin TU Dortmund Univ Dept Comp Sci 14 Software Engn Dortmund Germany TU Dortmund Univ Dept Comp Sci 4 Modeling & Simulat Dortmund Germany

ISBN: (纸本)9783030856717;9783030856724

In many machine scheduling studies, individual algorithms for each problem have been developed to cope with the specifics of the problem. On the other hand, the same underlying fundamentals (e.g. Shortest Processing Time, Local Search) are often used in the algorithms and only slightly modified for the different problems. This paper deals with the synthesis of machine scheduling algorithms from components of a repository. Especially flow shop and job shop problems with makespan objective are considered to solve with Shortes/Longest Processing Time, NEH, Giffler & Thompson algorithms. For these components, the paper includes an exemplary implementation of an agile scheduling system that uses the Combinatory Logic Synthesizer to recombine components of scheduling algorithms to solve a given scheduling problem. Special attention is given to the composition heuristics and the process of recombination to executable programs. The advantages of this componentization are discussed and illustrated with examples. It will be shown that algorithms can be generalized to deal with scheduling problems of different machine environments and production constraints.

关键词： scheduling algorithms

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Leveraging Reinforcement Learning for online scheduling of real-time tasks in the Edge/Fog-to-Cloud computing continuum 20

Leveraging Reinforcement Learning for online scheduling of r...

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IEEE 20th International Symposium on Network Computing and Applications (NCA)

作者： Mattia, Gabriele Proietti Beraldi, Roberto Sapienza Univ Rome Dept Comp Control & Management Engn Antonio Ruber Rome Italy

ISBN: (纸本)9781665495509

The computing continuum model is a widely accepted and used approach that make possible the existence of applications that are very demanding in terms of low latency and high computing power. In this three-layered model, the Fog or Edge layer can be considered as the weak link in the chain, indeed the computing nodes whose compose it are generally heterogeneous and their uptime cannot be compared with the one offered by the Cloud. Taking into account these inexorable characteristics of the continuum, in this paper, we propose a Reinforcement Learning based scheduling algorithm that makes per-job request decisions (online scheduling) and that is able to maintain an acceptable performance specifically targeting real-time applications. Through a series of simulations and comparisons with other fixed scheduling strategies, we demonstrate how the algorithm is capable of deriving the best possible scheduling policy when Fog or Edge nodes have different speeds and can unpredictably fail.

关键词： scheduling algorithms

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