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Frequency Constrained proactive scheduling for Secure Microgrid Formation in Wind Power Penetrated Distribution Systems

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IEEE TRANSACTIONS ON SMART GRID 2025年第2期16卷 989-1002页

作者： Cai, Sheng Xie, Yunyun Zhang, Yuping Bao, Weiyu Wu, Qiuwei Chen, Chen Guo, Jian Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China Shandong Univ Sch Elect Engn Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT8 5BN North Ireland Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Peoples R China

Microgrid formation (MF) is a core solution for increasing the resilience of distribution systems in extreme situations. However, a significant power imbalance at the MF onset will result in the violation of dynamic frequency constraints, especially in low-inertia wind power penetrated distribution systems (WPP-DSs). To ensure the secure MF after emergencies, this paper proposes a frequency constrained proactive scheduling method for WPP-DSs. The primary frequency response (PFR) model is proposed to describe the microgrid frequency dynamics after islanding, where wind turbines are deloaded to provide a primary reserve. Then the PFR model is incorporated into the scheduling model to ensure frequency security during MF process. The proposed method proactively dispatches controllable units to mitigate the power imbalance and to reserve power for frequency regulation. After emergencies, the primary reserve is released, and adaptive microgrids are securely formed to sustain critical services. The PFR model is formulated as algebraic differential equations (ADEs), which makes it difficult to solve the model directly. Thus, the PFR model is discretized into difference equations and further linearized to facilitate solution. Simulation results validate the merits of the proposed method in reducing the conservatism of proactive scheduling strategies and improving the microgrid security during MF process.

关键词： Wind power penetrated distribution system proactive scheduling microgrid formation power imbalance dynamic frequency security Wind power penetrated distribution system proactive scheduling microgrid formation power imbalance dynamic frequency security

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LL-PGS: A Lightweight and Low-Latency proactive Grant scheduling Algorithm for Industrial IoT

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第2期14卷 295-299页

作者： Lai, Shilin Li, Jin Zhang, Dongxu Zhang, Min Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China

To efficiently realize the low-latency scheduling of ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) services in Industrial Internet of Things (IIoT), we propose a lightweight and, low-latency proactive grant scheduling (LL-PGS) algorithm. In the proposed LL-PGS, a lightweight channel-queue prediction algorithm, and an adaptive two-tier scheduling priority adjustment module are organically integrated. Through the cooperation between above modules, the proposed LL-PGS facilitates the low-latency and on-demand physical resource blocks (PRBs) scheduling without extra state report and grant feedback. Extensive simulation results demonstrate that the proposed LL-PGS can significantly reduce the signaling overhead and uplink latency without sacrificing throughput. Compared with conventional scheduling algorithms including round robin (RR), proportional fair (PF), exponential proportional fair (EXP/PF), and modification of largest weighted delay first (M-LWDF), the proposed LL-PGS can increase the average uplink throughput for eMBB services by 12.42%, and reduce latency for URLLC services to 1ms.

关键词： Job shop scheduling Simulation Ultra reliable low latency communication Predictive models Throughput Prediction algorithms scheduling Delays Uplink Industrial Internet of Things proactive scheduling lightweight machine learning

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proactive scheduling of steelmaking-continuous casting with uncertain processing times under carbon emission reduction

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2024年 212卷 421-433页

作者： Zhou, Yaluo Xiang, Hengju Zhou, Wenzhe Liu, Wenguang Zhang, Ruicheng North China Univ Sci & Technol Coll Elect Engn Tangshan 063210 Peoples R China Shougang Jingtang Linited Iron & Steel Co Ltd Tangshan 063200 Peoples R China

To solve the proactive scheduling problem of steelmaking-continuous casting with uncertain processing time, a fuzzy scheduling model is established to minimize the total completion time, maximum elapsed time, and carbon emission, considering machine energy consumption. An improved multi-objective beluga whale optimization algorithm (MOBWO-VNS) is proposed to solve it. In MOBWO-VNS, the process-machine matching rule is used to enhance the quality of initial solutions, an adaptive layered evolution strategy is designed to improve the convergence speed, and a variable neighborhood search strategy is introduced to strengthen the algorithm's local search capability. The diversity and convergence of the algorithm are verified by the benchmark function. A comparison experiment between deterministic scheduling and fuzzy scheduling is carried out using the actual production data of a steel plant, and the feasibility and robustness of fuzzy scheduling are verified. Large-scale scheduling evaluation experiments are designed using large-scale data, and the results show that the proposed model and algorithm reduce the total completion time, maximum elapsed time, and carbon emissions by 7.6 %, 19.2 %, and 2.2 %, respectively. Results show that this study can optimize the time and energy consumption targets, improve the production efficiency of enterprises, and provide strong support for the green transformation of steel enterprises.

关键词： Steelmaking-continuous casting Uncertain processing time proactive scheduling Energy consumption Improved multi-objective beluga whale optimization

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proactive scheduling for steel plants with unrelated parallel machines and time uncertainty

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 188卷

作者： Lian, Xiaoyuan Zheng, Zhong Zhu, Mingmei Gao, Xiaoqiang Chongqing Univ Sch Mat Sci & Engn Chongqing 400044 Peoples R China Henan Polytech Univ Energy Econ Res Ctr Sch Business Adm Sch Business Adm Jiaozuo 454000 Peoples R China Chongqing Univ Sch Econ & Business Adm Chongqing 400044 Peoples R China

This paper deals with the steel plant proactive scheduling problem with unrelated parallel machines and time uncertainty to enhance the rationality of machine assignment and processing path, improve the schedule robustness and guarantee capability of energy. First, a scheduling model is formulated with the objectives of average process time, matching degree and oxygen consumption fluctuation. Then, an improved preferencebased NSGA-II is designed to solve the problem. The algorithm introduces the preference information into the optimization process and designs an individual sorting method based on the technique for order preference by similarity to ideal solution (TOPSIS) to guide the evolution direction. Besides, to improve the local search ability, variable neighborhood search is incorporated to generate new solutions with high quality. Computational experiments prove that the algorithm can effectively address the proactive scheduling problem. The sensitivity analysis and results of different scheduling models verify the robustness and superiority in machine matching and energy synergy of the model.

关键词： Steel plant proactive scheduling Unrelated parallel machines Time uncertainty

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proactive scheduling for mmWave Wireless LANs

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COMPUTER COMMUNICATIONS 2024年 228卷

作者： Deng, Ang Blough, Douglas M. Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

To cope with growing wireless bandwidth demand, millimeter wave (mmWave) communication has been identified as a promising technology to deliver Gbps throughput. However, due to the susceptibility of mmWave signals to blockage, applications can experience significant performance variability as users move around due to rapid and significant variation in channel conditions. In this context, proactive schedulers that make use of future data rate prediction have potential to bring a significant performance improvement as compared to traditional schedulers. In this work, we explore the possibility of proactive scheduling that uses mobility prediction and some knowledge of the environment to predict future channel conditions. We present both an optimal proactive scheduler, which is based on an integer linear programming formulation and provides an upper bound on proactive scheduling performance, and a greedy heuristic proactive scheduler that is suitable for practical implementation. Extensive simulation results show that proactive scheduling has the potential to increase average user data rate by up to 35% over the classic proportional fair scheduler without any loss of fairness and incurring only a small increase in jitter. The results also show that the efficient proactive heuristic scheduler achieves from 60% to 75% of the performance gains of the optimal proactive scheduler. Finally, the results show that proactive scheduling performance is sensitive to the quality of mobility prediction and, thus, use of state-of-the-art mobility prediction techniques will be necessary to realize its full potential.

关键词： Millimeter-wave proactive scheduling Wireless LAN

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proactive scheduling of repetitive construction processes to reduce crews idle times and delays

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ARCHIVES OF CIVIL ENGINEERING 2021年第4期67卷 287-302页

作者： Jaskowski, Piotr Biruk, Slawomir Krzeminski, Michal Lublin Univ Technol Fac Civil Engn & Architecture Nadbystrzycka Str 40 PL-20618 Lublin Poland Warsaw Univ Technol Fac Civil Engn Armii Ludowej Str 16 PL-00637 Warsaw Poland

Duration of construction projects can be reduced by harmonizing construction processes: adjusting productivity rates of specialized crews and enabling the crews to work in parallel as in a production line. This is achievable in the case of projects whose scope can be divided into units where a similar type of work needs to be conducted in the same sequence. A number of repetitive project scheduling methods have been developed to assist the planner in minimizing the execution time and smoothing resource profiles. However, the workflow, especially in construction, is subject to disturbance, and the actual process durations are likely to vary from the as-scheduled ones. The inherent variability of process durations results not only in delays of a particular process in a particular unit but also in the propagation of disruptions throughout the initially well-harmonized schedule. To counteract the negative effects of process duration variability, a number of proactive scheduling methods have been developed. They consist in some form of predicting the conditions to occur in the course of the project and implementing a strategy to mitigate disturbance propagation. This paper puts forward a method of scheduling repetitive heterogeneous processes. The method aims to reduce idle time of crews. It is based on allocating time buffers in the form of breaks between processes conducted within units. The merits of the method are illustrated by an example and assessed in the course of a simulation experiment.

关键词： project scheduling repetitive construction processes proactive scheduling risk management in construction simulation method

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proactive scheduling of Hydrogen Systems for Resilience Enhancement of Distribution Networks 2

Proactive Scheduling of Hydrogen Systems for Resilience Enha...

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2nd Annual IEEE Kansas Power and Energy Conference (KPEC)

作者： Haggi, Hamed Sun, Wei Fenton, James M. Brooker, Paul Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA Univ Cent Florida Florida Solar Energy Ctr Cocoa FL USA Orlando Util Commiss Orlando FL USA

ISBN: (纸本)9781665441193

Recent advances in smart grid technologies bring opportunities to better control the modern and complex power grids with renewable integration. The operation of power systems, especially distribution network (DN), is facing with preeminent challenges from cyber-physical-human (CPH) threats and natural disasters. In order to provide better response against threats and improve the resilience of power grid, proactive plans and operational schemes are required by system operators to minimize the damages caused by CPII threats. To that end, this paper proposes a proactive plan for DN operation by using hydrogen (H2) systems to enhance the resilience through cost-effective long-term energy storage. Unlike batteries, H2 energy can be stored in the storage tanks days before the extreme event, and transformed into power by stationary fuel cell units to reduce load curtailment caused by CPH threats. The proposed framework is validated by testing on 33-node test feeder. Simulation results demonstrate that 112 systems can improve the resilience of DN during N - m outages with more than 10 hours.

关键词： Distributed energy resources (DERs) Distribution Network Hydrogen Systems proactive scheduling Resilience Improvement

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A multi-objective binary-encoding differential evolution algorithm for proactive scheduling of agile earth observation satellites

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ADVANCES IN SPACE RESEARCH 2019年第10期63卷 3258-3269页

作者： Li, Zhiliang Li, Xiaojiang Space Engn Univ Sch Space Informat Beijing 101416 Peoples R China Space Engn Univ Grad Sch Beijing Peoples R China

A robust model and a Multi-objective Binary-encoding Differential Evolution (MBDE) algorithm are proposed in this paper for agile earth observation satellite proactive scheduling considering satellite resource failure and emergency task insertion. Firstly, considering that the reserved slack time in schedule can absorb uncertainties, two indicators are proposed: schedule profits and slack time, based on which, a robust model of proactive scheduling is established. Secondly, to solve the multi-objective model, solutions are represented via binary-encoding, mutation, crossover, and selection operators are re-designed, besides, an external archive update strategy is adopted to store elitist solutions in the evolution process, moreover, non-dominated Pareto set evaluation metrics are improved. Finally, computational results have demonstrated that, compared with NSGA-II and SPEA-2, the MBDE algorithm is able to obtain well-distributed solutions with good convergence more efficiently. The study can provide the method support for proactive scheduling of agile earth observation satellites. (C) 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.

关键词： Agile earth observation satellite proactive scheduling Robust model Multi-objective optimization

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Analysis of robustness in proactive scheduling: A graphical approach

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COMPUTERS & INDUSTRIAL ENGINEERING 2010年第2期58卷 193-198页

作者： Ghezail, Feiza Pierreval, Henri Hajri-Gabouj, Sonia Ctr Urbain Nord INSAT URAII Tunis 1080 Tunisia IFMA CNRS UMR 6158 LIMOS F-63175 Aubiere France

scheduling environments are generally disturbed by different types of unforeseen events, such as machine breakdowns, unavailability of tools, and arrival of urgent orders. To maintain good performances when these perturbations occur, a schedule has to be robust. These last years, robustness has become an essential issue for scheduling problems. In the related literature, the robustness of schedules is generally evaluated in a quantitative manner, using different possible measures which usually quantify the capacity of a schedule to remain efficient in unexpected conditions. In this article, robustness is addressed from a more qualitative viewpoint. Flowshop scheduling problems are emphasized upon. Instead of evaluating scheduling using a measure, two graphical representations of scheduling solutions are proposed. They provide assistance to the decision maker in understanding the consequences of possible perturbations, so that a suited decision about which solution to choose can be made. The first type of plot shows the extent to which disruptions can be admitted without any consequences on the performance criterion. It also allows the identification of critical periods, where no disruption can be admitted. The second type of plot aims at representing the consequences of disruptions, of different possible length with different possible Occurrences, on the performance. An example, comparing two candidate solutions, of a flowshop problem, illustrates the benefits of this approach, which is complementary from a quantitative analysis, based on common robustness measures. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： proactive scheduling Graphical analysis Robustness Decision support

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Data-Driven Model Predictive Control-Based proactive scheduling for Commercial Microgrid Considering Anomaly Detection

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IEEE SYSTEMS JOURNAL 2023年第1期17卷 443-454页

作者： Thanh, Vo-Van Su, Wencong Univ Michigan Dearborn Dearborn MI 48128 USA

One of the challenging tasks for commercial microgrids with distributed generation is to ensure critical loads resilience during high-impact, low-frequency extreme events. This article proposes a data-driven model predictive control-based proactive scheduling strategy for microgrids subject to extreme weather events. The goal is to maximize the utility value of the building by realizing the difference between the comfort level of critical loads and the total operating cost of the commercial microgrid during the planning horizon. A two-layer framework is proposed to solve the considered problem. First, the aggregate demand load measured by smart meters is processed using a combined long short-term memory and fuzzy neural network algorithm to eliminate anomaly data. Then, the obtained real data are applied through the deep-Q network method to seek appropriate proactive scheduling solutions. However, the rewards obtained from the deep-Q network method are random because of the uncertainties in extreme events. Therefore, robust optimization namely, robust deep reinforcement learning, is incorporated to enhance the robustness of the solutions. The proposed framework ensures the optimality and feasibility of the commercial microgrid's proactive scheduling strategy within the uncertainties in extreme events and the different intensity levels of anomalous data. Extensive simulation results prove the effectiveness of the proposed optimization model and algorithm for enhancing commercial microgrid resilience in extreme events. Furthermore, it also shows good ability in detecting smart meters anomalous data, with a stable convergence value of the performance index, to secure commercial microgrid smart meter data.

关键词： Smart meters Cogeneration HVAC Microgrids Resilience Real-time systems Costs Commercial microgrid deep Q-network demand response long short-term memory neural network proactive scheduling resilience robust deep reinforcement learning robust optimization

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