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AIAA SciTech Forum

作者： Kim, Sung Jun Kim, Chae-Hyeon Choi, Han-Lim Korea Adv Inst Sci & Technol Dept Aerosp Engn Daejeon 34141 South Korea Korea Adv Inst Sci & Technol KI Robot Daejeon 34141 South Korea

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

This paper introduces a consensus-based task pruning method for the MILP formulation of the satellite observation task scheduling problem. The method aims to enhance computational efficiency in assigning observation tasks to multiple satellites within a given time horizon. By establishing upper bounds for the number of tasks per satellite, the approach facilitates a bidding process where satellites reach a consensus to ensure each task is singularly assigned. This method effectively constrains the search space for the MILP solver, focusing only on allocated tasks, which leads to a significant reduction in computation time. Experimental results indicate that our method can reduce computation time by up to 95.62% compared to the exact MILP approach, without compromising the optimality of the solution.

关键词： Satellite Constellations Earth Observation Satellite Constellations MATLAB mixed integer linear programming Algorithm Performance Multi Agent System Climate Monitoring Satellite Services Genetic Algorithm

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The Impact of Optimizing Hybrid Renewable Energy System on Wine Industry Sustainability 22

The Impact of Optimizing Hybrid Renewable Energy System on W...

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IEEE 22nd Mediterranean Electrotechnical Conference (MELECON)

作者： Jesus, Beatriz Cerveira, Adelaide Santos, Emanuel Baptista, Jose Univ Tras Os Montes & Alto Douro Dept Engn Vila Real Portugal Univ Tras Os Montes & Alto Douro Dept Math LIAAD INESCTEC UTAD Pole Vila Real Portugal EDS Energy Drawing Syst Av Cidade Leon 506 P-5300358 Braganca Portugal Univ Tras Os Montes & Alto Douro Dept Engn CPES INESCTEC UTAD Pole Vila Real Portugal

ISBN: (纸本)9798350387032;9798350387025

Motivated by the imperative of sustainable practices, the wine industry is increasingly adopting renewable energy technologies to address environmental concerns and ensure its long-term viability amidst rising fossil fuel costs and greenhouse gas emissions. Hybrid renewable energy systems (HRES) have emerged as a solution to improve energy efficiency and mitigate the variability of renewable resources, allowing for better system load factors, greater stability of power supply, and optimized use of infrastructure. Therefore, this study aims to design a HRES that integrates solar and wind energy to sustainably fed an irrigation system in a favorable technical-economic context. This research presents a mixed integer linear programming (MILP) model that optimizes the profit generated by a grid-connected HRES over 20 years and obtains the optimal system sizing. The study focuses on the farm Quinta do Vallado, Portugal, and examines two distinct Cases. Over 20 years, the implementation of the hybrid system has resulted in savings of approximately 61%.

关键词： Hybrid Renewable Energy System Irrigation System mixed integer linear programming Vertical-axis wind turbine Optimisation

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Data-driven model predictive control for vehicle path-tracking using a recurrent neural network 7

Data-driven model predictive control for vehicle path-tracki...

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7th IFAC Conference on Engine and Powertrain Control, Simulation and Modeling (E-COSM)

作者： Yi, Jian Wu, Xialai Lin, Ling Qin, Jiabin Chen, Junghui Huzhou Univ Huzhou Key Lab Intelligent Sensing & Optimal Cont Huzhou 313000 Peoples R China Chung Yuan Christian Univ Dept Chem Engn Taoyuan 32023 Taiwan

Path tracking control is crucial for autonomous vehicle driving. Traditional nonlinear model predictive control (NMPC) for path tracking demands extensive computation, making real-time implementation challenging. This paper introduces a Fast Model Predictive Control (FMPC) method for vehicle path tracking, utilizing a recurrent neural network (RNN) with symmetric saturating linear transfer functions (SSL-RNN) in the hidden layer. The proposed approach leverages the SSL-RNN model to accurately capture vehicle dynamics Consequently, the optimal control problem in MPC is reformulated as a mixed integer linear programming problem, facilitating swift solutions. Simulation experiments validate the proposed method's efficacy. Compared to traditional vehicle mechanism model-based MPC and RNN-based NMPC, our FMPC demonstrates superior accuracy in path tracking and significantly enhances controller solution efficiency. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： model predictive control mixed integer linear programming symmetric saturating linear transfer function recurrent neural network vehicle path tracking.

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Minimizing Carbon Emission in Hybrid Flow Shop Scheduling: A Comparative Analysis of Flow -based and Set Partitioning Formulations 5

Minimizing Carbon Emission in Hybrid Flow Shop Scheduling: A...

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5th International Conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Ferreira, Guilherme de Souza Mateus, Geraldo Robson Ravetti, Martin Gomez Univ Fed Minas Gerais Dept Engn Prod Ave Antonio Carlos 6627 BR-13270901 Belo Horizonte MG Brazil Univ Fed Minas Gerais Dept Ciencias Comp Belo Horizonte MG Brazil

This article addresses the Hybrid Flow Shop Green Scheduling Problem with Sequence and Machine Dependent Setup Times (HFSGSP-SMDST) in the context of Industry 4.0. The objective is to minimize carbon emission while optimizing scheduling. Two mathematical formulations, a flow -based positional formulation (FB) and a set partitioning positional formulation (HCG), are proposed to provide tighter lower bounds. The FB formulation is solved using a commercial solver, while the HCG algorithm incorporates an Iterated Local Search (ILS) to generate initial solutions and an upper bound for the problem. Computational experiments demonstrate that HCG achieves better lower bounds, although it requires more time compared to FB. The proposed algorithms offer valuable solutions for carbon emission optimization and scheduling in Industry 4.0, with a trade-off between lower bounds and convergence speed.

关键词： green scheduling hybrid flow shop setup times unrelated parallel machines mixed integer linear programming hybrid column generation

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A Comparative Study of PSO and MILP Optimization Algorithms for Economic Dispatch in Grid-Tied Microgrids 4th

A Comparative Study of PSO and MILP Optimization Algorithms ...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： El Qasery, Mouna Barkouki, Bouthaina E. L. Laamim, Mohamed Rochd, Abdelilah Mahir, Oumaima Abbou, Ahmed Mohammed V Univ Mohamadia Sch Engineers Dept Elect Engn Rabat Morocco Univ Sidi Mohamed Ben Abdellah Lab Signals Syst & Components Fes Morocco Fac Sci & Tech Mohammedia Lab Sci Ingenieur & Biosci LSIB Mohammadia Morocco Hassan II Univ ENSET Elect Engn & Intelligent Syst Lab Green Energy Pk Mohammedia Mohammadia Morocco Green Energy Pk Benguerir Morocco

ISBN: (纸本)9783031686740;9783031686757

The study contributes to addressing the challenge of economic load dispatching inmicrogrids, offering insights into efficient energy management techniques. In an energy management system, the economic dispatch (ED) strategy aims to minimize operating energy costs while ensuring a reliable power supply. The implementation of the ED strategy relies on the use of various optimization methods. This paper presents a comparative study of two optimization algorithms to investigate their performance in solving economic dispatch problems. The two algorithms, which are mixed integer linear programming (MILP) and particle swarm optimization (PSO), were implemented in the Matlab environment and compared in terms of the computational capability and optimization results. By comparing PSO and MILP algorithms, the study aims to identify the most effective approach for optimizing power dispatch in grid-connected microgrids. To validate the proposed approach, a case study of the Solar Decathlon Africa Village project in Ben Guerir, Morocco, is conducted. This real-world application serves to evaluate and verify the efficacy of the methodology in practical settings.

关键词： grid-connected microgrid energy management system economic dispatch mixed integer linear programming particle swarm optimization

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Multipurpose Charging Schedule Optimization Method for Electric Buses: Evaluation Using Real City Data

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IEEE ACCESS 2022年 10卷 56067-56080页

作者： Tomizawa, Yuki Ihara, Yuto Kodama, Yasuhiro Iino, Yutaka Hayashi, Yasuhiro Ikeda, Ohsei Yoshinaga, Jun Waseda Univ Dept Elect Engn & Biosci Tokyo Tokyo 1698555 Japan Waseda Univ Adv Collaborat Res Org Smart Soc Tokyo 1620041 Japan TEPCO Power Grid Inc Tokyo TOKYO 10085 Japan

The use of electric vehicles (EVs) and photovoltaics (PV) is increasing worldwide. Transportation networks require the effective use of renewable energy (RE) for EVs, whereas power networks require local consumption of PV energy, mainly at the initiative of local governments. Although many previous studies have addressed these requirements using private EVs as mobile storages, their uncertainty and uncontrollability remain highly problematic. Therefore, our previous studies focused on electric buses because of their high controllability and certainty. These studies involved the development and evaluation of two independent minimization problems-kilowatts (KW) and kilowatt-hours (KWH) of surplus RE-as a charging schedule optimization method using mixed integer linear programming. However, the feasibility of simultaneously minimizing KW and KWH still presented technical problems. This study aims to extend and generalize the method to the simultaneous minimization of KW and KWH of the PV-derived reverse power flow (RPF). With this multiobjective optimization, the KW peak-cut of the RPF improves the hosting capacity and increases the availability of connectable RE resources, whereas the minimization of KWH promotes the local consumption of RE and decarbonization of public transportation. Two simulations using detailed data of actual bus operations and actual power flow data for a real city confirmed the feasibility of simultaneously optimizing KW and KWH and the relationship between these indicators and number of EV chargers. The optimized charging of the 17 electric buses achieved a maximum of 211.4 kW peak-cut and 1318.4 kWh RE-RPF absorption, reducing CO2 emissions by 495.7 kg/day.

关键词： Transportation Schedules Power systems Minimization Spatiotemporal phenomena Smart cities Load flow Charging control electric bus mixed integer linear programming multiobjective optimization photovoltaic reverse power flow sector coupling smart city

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Makespan Minimisation in Hybrid Flexible Flowshops with Buffers and Machine-Dependent Transportation Times 43rd

Makespan Minimisation in Hybrid Flexible Flowshops with Buff...

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43rd IFIP WG 5.7 International Conference on Advances in Production Management Systems (APMS)

作者： Vatikiotis, Stavros Mpourdakas, Ilias Papathanasiou, Dimitrios Mouratidis, Ioannis Athens Univ Econ & Business Dept Management Sci & Technol ELTRUN Res Lab Athens Greece

ISBN: (纸本)9783031716478;9783031716454;9783031716447

Hybrid Flexible Flowshop Scheduling (HFFS) is the problem where a set of jobs must be processed in a given sequence of stages and each stage has a set of (typically identical) parallel machines. The flexibility of HFFS allows a job to skip some stages. Modern production environments, e.g., assembly lines, exhibit additional structure, namely limited-capacity buffers and transportation times between subsequent stages, while the layout also imposes that such times are machine-to-machine dependent. We propose two formal models, namely a mixed-integer linear Program (MILP) that incorporates transportation times but not buffers and a Constraint Program (CP) that handles both, given a sequence of all jobs per machine. This sequence is provided by the MILP or constructive heuristics or a Genetic Algorithm (GA). The scalability and performance of all methods is evaluated computationally on large-scale real-life instances of about 500 jobs on 15 stages with up to 5 machines per stage and 30 machines in total.

关键词： Hybrid Flexible Flowshop Transportation Times Buffers mixed integer linear programming Constraint programming

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Heterogeneous Satellite Task Scheduling with Revisit Time Minimization using MILP Formulation

Heterogeneous Satellite Task Scheduling with Revisit Time Mi...

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AIAA SciTech Forum

作者： Kim, Chae-Hyeon Kim, Sung Jun Choi, Han-Lim Korea Adv Inst Sci & Technol Dept Aerosp Engn Daejeon 34141 South Korea Korea Adv Inst Sci & Technol KI Robot Daejeon 34141 South Korea

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

This paper presents a mixed-integer linear programming formulation for the task scheduling of heterogeneous satellites and demonstrates its performance through numerical experiments with a standard mixed-integer linear programming solver. The main objective of the heterogeneous satellite task scheduling problem is to allocate tasks to targets, maximizing the number of allocations while encouraging collaborations by heterogeneous satellites for each target. Given this objective with multiple candidate solutions, the optimal solution is chosen by minimizing the maximum revisit time. To further refine the feasible solution space, additional constraints are proposed with brief proof of their optimality and lead to more optimal scheduling solutions characterized by higher objective values with reduced computation times.

关键词： Satellites mixed integer linear programming Optimization Method Orbital Elements Periapsis Numerical Integration Right Ascension Computer Systems Python

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Optimal Energy and IT Service Emergency Schedule for Internet Data Center 56

Optimal Energy and IT Service Emergency Schedule for Interne...

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56th North American Power Symposium

作者： Zhao, Zhongqi Fan, Lei Han, Zhu Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA Univ Houston Dept Engn Technol Houston TX 77004 USA

ISBN: (纸本)9798331521042;9798331521035

In the era of Artificial intelligence (AI), internet data centers (IDCs) play a crucial role in supporting the global information infrastructure. However, large language model (LLM) training consumes a lot of energy, which poses unique challenges to IDC, especially in power emergencies. In order to prevent power outages from causing huge economic losses to IDC, a good energy management system is essential. In this study, we propose a new mixed-integer linear programming (MILP) model of an IDC during an electricity emergency to maximize the profit while minimize operation cost. On power side, we considered renewable energy, traditional thermal power generators, uninterruptible power supply (UPS), heating, ventilation, and air conditioning (HVAC) system. On IT job scheduling side, we allow jobs to be done, held, discarded and transferred to other IDC. Finally, we analyze and visualize the computational results and verify the correctness of the proposed model.

关键词： Data Center mixed integer linear programming Optimization Energy Management Emergency Response

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Superstructure Optimization of International Hydrogen Supply Chain with Technological Options 12

Superstructure Optimization of International Hydrogen Supply...

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12th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM)

作者： Jung, Juyeong Lee, Jay H. Chung, Wonsuk Heo, Seongmin Korea Adv Inst Sci & Technol KAIST Dept Chem & Biomol Engn Daejeon South Korea Univ South Carolina Dept Chem & Mat Sci & Aerosp & Mech Engn Columbia SC 90007 USA Korea Inst Sci & Technol KIST Clean Energy Res Ctr Seoul 02792 South Korea

In the pursuit of reducing carbon emissions, the integration of renewable energy into society's energy portfolio plays a crucial role despite challenges tied to their inherent unpredictability and seasonal variations. Hydrogen, as a mean of storing and transporting renewable energy becomes crucial in addressing these challenges. Notwithstanding its potential, the misalignment of cost-competitive hydrogen production hubs with demand centers calls for a thorough examination of global hydrogen distribution. The versatility of hydrogen transportation (e.g., pipelines, ships, trucks) and various forms like compressed hydrogen, liquid hydrogen, ammonia, and liquid organic hydrogen carriers (LOHC) adds complexity to designing an economically efficient hydrogen supply chain (HSC). Addressing previous scholarly limitations focused on existing technologies, this study advocates for a superstructure-based modeling approach for HSC that includes promising technologies. Through a case study involving a production site in the Middle East and a demand site in Texas, a dedicated HSC superstructure network is formulated, facilitating the identification of cost-effective hydrogen supply pathways using mixed integer linear programming Ultimately, this effort aims to establish a comprehensive framework to discern the most sustainable and financially viable technologies, identifying the optimal configuration within the HSC. Copyright (c) 2024 The Authors.

关键词： Hydrogen supply chain Process modeling Superstructure optimization mixed integer linear programming

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