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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Xu, Yunwen Wu, Na Li, Dewei Xi, Yugeng Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

This paper proposes a distributed control framework to optimize the offset for a path in a traffic network with arbitrary topology. Each intersection along the target path applies the model predictive control to optimize their own phase sequence and green splits with the objective of minimizing the sum of queue lengths. The first intersection on this path is regarded as the main intersection and responsible for optimizing the start green time and duration of the first phase on this path with a weighted objective according to the real-time traffic information, while the other intersections take the constraints of offset imposed by intersections ahead into consideration. The signal cycles of these intersections are fixed but allowed to be different. For computation efficiency, the nonlinear optimization problem is approximately reformulated as a mixed-integer linear programming problem. Numerical experiments on a calibrated network of Caohejing District in Shanghai indicate that our proposed method can effectively decrease delay time and waiting time especially at medium and high traffic loads. Copyright (C) 2020 The Authors.

关键词： Traffic signal control Online offset Model predictive control Distributed control framework mixed-integer linear programming

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A Genetic Algorithm Approach for a Truck and Trailer Routing Problem in a Loading/Unloading Bays Application 19th

A Genetic Algorithm Approach for a Truck and Trailer Routing...

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19th Mexican International Conference on Artificial Intelligence (MICAI)

作者： Bricia Galindo-Muro, Ana Mora-Vargas, Jaime Gaston Cedillo-Campos, Miguel Regis-Hernandez, Fabiola Tecnol Monterrey Monterrey Estado De Mexic Mexico Inst Mexicano Transporte Queretaro Mexico

ISBN: (纸本)9783030608842;9783030608835

Nowadays urban mobilities represent a necessity more than a challenge. Urban centers have a large vehicle congregation in the streets, causing difficulties in the last mile operations for urban freight. Further to the traffic problems, authorities imposed strict regulations in the cities for freight vehicles. As a consequence of both of these limitations, the freight vehicles can't optimally execute their activities. In this research, a Genetic Algorithm is developed for the resolution of the Truck and Trailer Routing Problem (TTRP). Urban freight dynamics for loading/unloading bays are represented through mixed-integer linear programming (MILP) model. The obtained results for instances up to 100 customers shows that the approach presented provides competitive solutions with the best known in the area.

关键词： Loading bays Truck and Trailer mixed-integer linear programming Genetic algorithm

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Joint Radar-Communications Exploiting Optimized OFDM Waveforms

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REMOTE SENSING 2021年第21期13卷

作者： Ahmed, Ammar Zhang, Yimin D. Hassanien, Aboulnasr Temple Univ Dept Elect & Comp Engn Philadelphia PA 19122 USA Wright State Univ Dept Elect Engn Dayton OH 45435 USA

We propose novel Joint Radar-communication spectrum sharing strategies exploiting orthogonal frequency-division multiplexing (OFDM) waveforms that concurrently achieve the objectives of both radar and communication systems. An OFDM transmitter is considered that transmits dual-purpose OFDM subcarriers such that all the subcarriers are exploited for the primary radar function and further exclusively allocated to the secondary communication function serving multiple users. The waveform optimization is performed by employing mutual information (MI) as the optimization criterion for both radar and communication operations. For the purpose of radar performance optimization, we consider the MI between the frequency-dependent target response and the transmit OFDM waveforms. On the other hand, communication system performance is evaluated in terms of the MI between the frequency-dependent communication channels of communication users with the transmit OFDM subcarriers. These optimization objectives not only enable the transmit power allocation of the OFDM subcarriers, but also govern the subcarrier distribution among the communication users. Two resource optimization scenarios are considered, resulting in radar-centric and cooperative resource allocation strategies that exploit convex and mixed-integer linear programming optimization problems for power allocation and subcarrier distribution, respectively. We further present a chunk subcarrier allocation approach that applies to both optimization strategies to reduce the computational complexity with a trivial performance loss. Simulation results are presented to illustrate the effectiveness of the proposed strategies.

关键词： Joint Radar-communications mutual information mixed-integer linear programming spectrum sharing orthogonal frequency-division multiplexing (OFDM)

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The nth-plant scenario for blended feedstock conversion and preprocessing nationwide: Biorefineries and depots

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APPLIED ENERGY 2021年 294卷

作者： Hossain, Tasmin Jones, Daniela Hartley, Damon Griffel, L. Michael Lin, Yingqian Burli, Pralhad Thompson, David N. Langholtz, Matthew Davis, Maggie Brandt, Craig North Carolina State Univ Biol & Agr Engn Raleigh NC 27695 USA Idaho Natl Lab Bioenergy Anal Idaho Falls ID 83415 USA Oak Ridge Natl Lab Environm Sci Div Oak Ridge TN USA

The sustainability of the biofuel industry depends on the development of a mature conversion technology on a national level that can take advantage of the economies of scale: the nth-plant. Defining the future location and supply logistics of conversion plants is imperative to ultimately transform the nation's renewable biomass resources into cost-competitive, high-performance feedstock for production of biofuels and bioproducts. Since the US has put restrictions on production levels of conventional biofuels from edible resources, the nation needs to plan for the widespread accessibility and development of the cellulosic biofuel scenario. Conventional feedstock supply systems will be unable to handle cellulosic biomass nationwide, making it essential to expand the industry with an advanced feedstock supply system incorporating a distributed network of preprocessing depots and conversion plants, or biorefineries. Current studies are mostly limited to designing supply systems for specific regions of the country. We developed a national database with potential locations for depots and biorefineries to meet the nation's target demand of cellulosic biofuel. Blended feedstock with switchgrass and corn stover (harvested by either a two-or three-pass method) are considered in a mixed integer linear programming model to deliver on-spec biomass that considers both, a desired quantity and quality at the biorefinery. The model solves for a network of varying size depots that supply to biorefineries of 725,000 dry tons/year. A total delivered feedstock cost that is less than $79.07/dry tons (2016$) is evaluated for years 2022, 2030, and 2040. In 2022, 124 depots and 59 biorefineries could be supplied with 42.8 million dt of corn stover and switchgrass. In 2030 and 2040, the total accessible biomass could increase to 215% and 393% respectively when compared to 2022. However, an $8/dry tons reduction in targeted delivery cost could reduce total accessible biomass by 67%. Kansas, N

关键词： Corn stover Switchgrass Biofuel Feedstock quality Biomass supply chain mixed-integer linear programming

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Extending an integer Formulation for the Guillotine 2D Bin Packing Problem

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Procedia Computer Science 2021年 195卷 499-507页

作者： Henrique Becker Olinto Araujo Luciana S. Buriol Department of Theoretical Computer Science Federal University of the Rio Grande do Sul (UFRGS). Porto Alegre Brasil Industrial Technical College Federal University of Santa Maria. Santa Maria Brasil

We employ a state-of-the-art mixed-integer linear programming (MILP) formulation of the literature, and our enhanced version of it, to solve a classical instance dataset for the Guillotine 2D variants of both the Knapsack Problem (G2KP) and the Bin Packing Problem (G2BPP). The results with the G2KP allow us to establish our reimplementation as fair to the original implementation (not available). As far as we know, before this work the considered instances have never been optimally solved for the considered variant of the G2BPP, i.e., the unlimited stages variant, only for the simpler two-staged variant. We also believe this work is the first to gather empirical results of a pure MILP formulation for the G2BPP, even considering the possibility of adaptation was previously known. As we focus on pure and adaptable formulations, we do not employ pricing frameworks or problem-specific heuristics in this short paper. We examine the differences in the running times caused by the change of problems, formulations, number of threads, and, for a subset of the runs, the solver random seed. Some of our findings follow: except for a few G2BPP instances, our enhanced formulation has better timings; 8 of the 30 considered instances have better solutions for unlimited stages G2BPP than for the two-staged G2BPP; the speed-up with 12 threads is smaller than expected and, for the G2BPP, the solver random seed may have a larger effect than the number of threads.

关键词： Bin Packing Problem Two-dimensional Formulation mixed-integer linear programming Pseudo-Polynomial

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Advancing the Thermal Network Representation for the Optimal Design of Distributed Multi-Energy Systems

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FRONTIERS IN ENERGY RESEARCH 2021年 9卷

作者： Wang, Danhong Li, Xiang Marquant, Julien Carmeliet, Jan Orehounig, Kristina Swiss Fed Labs Mat Sci & Technol Urban Energy Syst Lab Dubendorf Switzerland Swiss Fed Inst Technol Chair Architecture & Bldg Syst Zurich Switzerland Swiss Fed Inst Technol Chair Bldg Phys Zurich Switzerland

This paper investigates modeling methods with thermal network representation under the scope of the optimal design and operation of Distributed Multi-Energy System (D-MES). Two modeling approaches are compared: A mixed-integer linear programming (MILP) optimization model and a district heating network (DHN) simulation model. The MILP model was developed for the simultaneous design of the network layout, the sizing, and locations of energy generation and storage technologies to minimize both costs and carbon emissions. The thermal network is represented with a simplified linear approximation. The DHN simulation model is a thermal-hydraulic model to address the non-linear operational performance regarding hourly heat losses, pumping energy, and temperature distribution along with the network. The discrepancies in the network's costs and operational performances from the two models are identified. The MILP model is further improved by adding new constraints. Results from both MILP models are compared and demonstrated with a case study. It reveals that the state-of-art MILPmodel with simplified network representation suffices for optimal selection and sizing for most of the technologies. Although more computationally intensive, the refined model can address the operational issues with distributed design solutions.

关键词： mixed-integer linear programming district heating networks distributed multi-energy systems optimization model simulation model

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AutoMoG 3D: Automated Data-Driven Model Generation of Multi-Energy Systems Using Hinging Hyperplanes

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FRONTIERS IN ENERGY RESEARCH 2021年 9卷

作者： Kaemper, Andreas Holtwerth, Alexander Leenders, Ludger Bardow, Andre Rhein Westfal TH Aachen Inst Tech Thermodynam Aachen Germany Swiss Fed Inst Technol Energy & Proc Syst Engn Dept Mech & Proc Engn Zurich Switzerland Forschungszentrum Julich Inst Energy & Climate Res Energy Syst Engn IEK 10 Julich Germany

The optimal operation of multi-energy systems requires optimization models that are accurate and computationally efficient. In practice, models are mostly generated manually. However, manual model generation is time-consuming, and model quality depends on the expertise of the modeler. Thus, reliable and automated model generation is highly desirable. Automated data-driven model generation seems promising due to the increasing availability of measurement data from cheap sensors and data storage. Here, we propose the method AutoMoG 3D (Automated Model Generation) to decrease the effort for data-driven generation of computationally efficient models while retaining high model quality. AutoMoG 3D automatically yields mixed-integer linear programming models of multi-energy systems enabling efficient operational optimization to global optimality using established solvers. For each component, AutoMoG 3D performs a piecewise-affine regression using hinging-hyperplane trees. Thereby, components can be modeled with an arbitrary number of independent variables. AutoMoG 3D iteratively increases the number of affine regions. Thereby, AutoMoG 3D balances the errors caused by each component in the overall model of the multi-energy system. AutoMoG 3D is applied to model a real-world pump system. Here, AutoMoG 3D drastically decreases the effort for data-driven model generation and provides an accurate and computationally efficient optimization model.

关键词： data-driven modeling regression analysis piecewise affine mixed-integer linear programming hinging hyperplanes

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An optimal stochastic energy management system for resilient microgrids

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APPLIED ENERGY 2021年 300卷

作者： Silva, Jessica Alice A. Lopez, Juan Camilo Arias, Nataly Banol Rider, Marcos J. Silva, Luiz C. P. da Univ Campinas UNICAMP Dept Syst & Energy DSE Ave Albert Einstein BR-13083852 Campinas SP Brazil

This paper presents a stochastic mixed-integer nonlinear programming model for the optimal energy management system of unbalanced three-phase of alternating current microgrids. The proposed model considers the following random variables: nodal demands, nodal renewable generation and voltage reference at the point of common coupling. Furthermore, the proposed model is aimed at providing resilient energy management system solutions via contingency constraints. The proposed mixed-integer nonlinear programming model is transformed into a mixed-integer linear programming model through a set of linearizations that can be solved via off-the-shelf convex programming solvers. The analyzed microgrid comprises photovoltaic generation, energy storage systems, electric vehicle chargers, direct load control, and non-renewable generation, which operates when the microgrid is in islanded mode. The stochastic nature of the problem is considered through a scenario-based approach. The solution to the model determines the day-ahead operation of the microgrid resources that minimizes the average operational cost. An unexpected islanded operation at any given time is considered via contingency constraints. Tests are performed using data of the real microgrid at the Laboratory of Intelligent Electrical Networks (LabREI), at University of Campinas. Results show that the proposed model produces resilient day-ahead energy management system solutions while minimizing the average operational costs and maximizing the use of local renewable energy sources.

关键词： Energy management system Microgrids mixed-integer linear programming Contingency constraints

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Energy Curtailment Scheduling MILP Formulation for an Islanded Microgrid with High Penetration of Renewable Energy

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ENERGIES 2021年第19期14卷

作者： Park, Woan-Ho Abunima, Hamza Glick, Mark B. Kim, Yun-Su Gwangju Inst Sci & Technol Grad Sch Energy Convergence Gwangju 61005 South Korea Hawaii Nat Energy Inst Honolulu HI 96822 USA

The efficiency of photovoltaic (PV) cells has improved significantly in the last decade, making PV generation a common feature of the sustainable microgrid. As the PV-powered microgrid reaches high penetrations of intermittent PV power, optimum scheduling of over-production is necessary to minimize energy curtailment. Failure to include an accurate assessment of curtailed energy costs in the scheduling process increases wasted energy. Moreover, applying an objective function without considering the cost coefficients results in an inefficient concentration of curtailed power in a specific time interval. In this study, we provide an optimization method for scheduling the microgrid assets to evenly distribute curtailment over the entire daily period of PV generation. Each of the curtailment intervals established in our optimization model features the application of different cost coefficients. In the final step, curtailment costs are added to the objective function. The proposed cost minimization algorithm preferentially selects intervals with low curtailment costs to prevent the curtailment from being concentrated at a specific time. By inducing even distribution of curtailment, this novel optimization methodology has the potential to improve the cost-effectiveness of the PV-powered microgrid

关键词： curtailment cost gradation optimal scheduling mixed-integer linear programming islanded microgrid

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Preprocessing and cutting planes with conflict graphs

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COMPUTERS & OPERATIONS RESEARCH 2021年 128卷

作者： Brito, Samuel Souza Santos, Haroldo Gambini Univ Fed Ouro Preto Dept Comp Ouro Preto Brazil Univ Fed Ouro Preto Dept Comp & Sistemas Ouro Preto Brazil

This paper addresses the development of conflict graph-based algorithms and data structures into the COIN-OR Branch-and-Cut (CBC) solver, including: (i) an efficient infrastructure for the construction and manipulation of conflict graphs;(ii) a preprocessing routine based on a clique strengthening scheme that can both reduce the number of constraints and produce stronger formulations;(iii) a clique cut separator capable of obtaining dual bounds at the root node LP relaxation that are 19.65% stronger than those provided by the equivalent cut generator of a state-of-the-art commercial solver, 3.62 times better than those attained by the clique cut separator of the GLPK solver and 4.22 times stronger than the dual bounds obtained by the clique separation routine of the COIN-OR Cut Generation Library;and (iv) an odd-cycle cut separator with a new lifting module to produce valid odd-wheel inequalities. The average gap closed by this new version of CBC was up to four times better than its previous version. Moreover, the number of mixed-integer programs solved by CBC in a time limit of three hours was increased by 23.53%. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： mixed-integer linear programming Conflict graphs Preprocessing Cutting planes Clique inequalities Odd-cycle inequalities

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