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Optimal Sizing of Renewable Microgrid for Flow Shop Systems under Island Operations

Procedia Manufacturing 2020年 51卷 1779-1784页

作者： Tongdan Jin Vinod Kumar Subramanyam Krystel K. Castillo-Villar Fei Sun Ingram School of Engineering Texas State University San Marcos TX 78666 USA Mechanical Engineering Department/ The Texas Sustainable Energy Research Institute The University of Texas at San Antonio San Antonio TX 78249 USA

This paper addresses a critical question pertaining to manufacturing sustainability: is it economically viable to implement an island microgrid to power a flow shop system under power demand and supply uncertainty? Though many studies on microgrid sizing are available, the majority assume the microgrid is interconnected with main grid. This paper aims to size wind turbine, photovoltaic and battery storage to energize a multi-stage flow shop system in island mode. A mixed-integer, non-linear programming model is formulated to optimize the renewable portfolio and capacity with the goal of minimizing the levelized cost of energy. The island microgrid is tested in three locations with diverse climate profiles. The results show that net zero energy flow shop production is economically feasible in the areas where the average wind speed exceed 8 m/s at 80-meter tower height, or the battery cost drops below $100,000/MWh. Sensitivity analyses are further carried out with respect to installation cost, demand response program, production scalability, and weather seasonality.

关键词： Levelized cost of energy island microgrid zero energy building flow shop scheduling mixed-integer programming

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Co-optimization of thermal power plant flowchart, thermodynamic cycle parameters, and design parameters of components

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ENERGY 2020年 193卷 483-496页

作者： Kler, Aleksandr M. Potanina, Yulia M. Marinchenko, Andrey Y. Russian Acad Sci ESI SB RAS Siberian Branch Melentiev Energy Syst Inst 130 Lermontov Str Irkutsk Russia

The improvement in the efficiency of an energy plant depends on a rational development of its flowchart and choice of parameters along with the load schedule, equipment reliability, operating mode, etc. It is advisable to study such complex technical systems with the methods of mathematical modeling and optimization. The paper presents an approach to the development of optimal flowcharts and selection of parameters of energy plants. The approach is based on the combination of a method for optimization of the most complex flowchart and a method for solving discrete-continuous problems of nonlinear mathematical programming. A case study of the co-optimization of design parameters, operating parameters and equipment mix for the integrated gasification combined-cycle plant is demonstrated. The optimization calculations were carried out by the criterion of the minimum price of electricity for a given internal rate of return on investment and the maximum energy efficiency of the plant. Several optimal solutions meeting the different criteria are obtained. The proposed approach can be used for optimization of flowcharts and parameters of other complicated energy plants (high-efficiency combined-cycle plants, ultra-supercritical steam cycles, integrated power plants for electricity and synthetic liquid fuel co-production from coal, etc.). (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Mathematical modeling Thermal power plants Methods of optimization mixed-integer programming Reversible processes Branch-and-bound method

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Optimal design and operation of River Basin Storage

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2020年 95卷

作者： Burrow, Andy Newman, Alexandra Colorado Sch Mines Dept Mech Engn 1500 Illinois St Golden CO 80401 USA

The State of Colorado's Stream Simulation Model (StateMod) provides comparative analysis for historical and future water resource decisions and policies along the Lower South Platte River. In order to identify locations for increased water storage, our research uses simulated data produced by StateMod as input to a mixed integer-linear optimization model. This model minimizes the cost of meeting unmet demands by assigning network flow of, and designing additional storage for, excess water while adhering to constraints that force the physical and topographical structures of the river. Using historical, measured flow data from 1962 through 2012, we extend the capability of StateMod by considering solutions with the following characteristics: (1) a single-reservoir solution, (2) a solution in which we only expand existing reservoirs, and (3) a solution without the constraints in (1) or (2). We conclude that, for the time horizon considered, the optimal method to mitigate shortages is with the construction of a combination of smaller surface and sub-surface reservoirs, and a corresponding prescribed flow. The total increased storage volume is 25,378 acre-feet (AF). Our work can be used as a strategic analysis tool by planners and engineers to quickly identify the most effective reservoir locations and the order in which to build them, rather than examining every potential storage site and the time at which it should be built, if at all. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Water resource planning mixed-integer programming Underlying network structure Reservoir design River basin management

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mixed-integer second-order cone programming for global optimization of compliance of frame structure with discrete design variables

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2016年第2期54卷 301-316页

作者： Kanno, Yoshihiro Tokyo Inst Technol Mat & Struct Lab Yokohama Kanagawa 2268503 Japan Univ Tokyo Dept Math Informat Tokyo 1138656 Japan

Frame structures are extensively used in mechanical, civil, and aerospace engineering. Besides generating reasonable designs of frame structures themselves, frame topology optimization may serve as a tool providing us with conceptual designs of diverse engineering structures. Due to its nonconvexity, however, most of existing approaches to frame topology optimization are local optimization methods based on nonlinear programming with continuous design variables or (meta)heuristics allowing some discrete design variables. Presented in this paper is a new global optimization approach to the frame topology optimization with discrete design variables. It is shown that the compliance minimization problem with predetermined candidate cross-sections can be formulated as a mixed-integer second-order cone programming problem. The global optimal solution is then computed with an existing solver based on a branch-and-cut algorithm. Numerical experiments are performed to examine computational efficiency of the proposed approach.

关键词： Frame topology optimization Global optimization Discrete optimization mixed-integer programming Second-order cone programming

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A heuristic approach for scheduling activities with 'OR'-precedence constraints at an underground mine

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INTERNATIONAL JOURNAL OF MINING RECLAMATION AND ENVIRONMENT 2020年第10期34卷 748-762页

作者： Nancel-Penard, Pierre Morales, Nelson Rojas, Valentina Gonzalez, Tomas Univ Chile Fac Phys & Math Sci Dept Min Engn Delphos Mine Planning Lab Santiago Chile Univ Chile Adv Min Technol Ctr Santiago Chile

Medium-term development planning of underground mines requires scheduling multiple activities to comply with long-term milestones, and to obtain a time span as short as possible. However, the planning must also respect the availability of construction resources and precedence constraints, which in our case can be disjunctive, that is with more than one alternative predecessor. In this paper, we present an optimisation model to find the schedule of minimum length, satisfying all the constraints mentioned. We develop a heuristic approach to solve it and show that it can be used to produce feasible development plans in a real mine.

关键词： Underground mine planning mixed-integer programming heuristic 'OR'-precedence constraints mine development

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A two-step mathematical programming framework for undercut horizon optimization in block caving mines

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RESOURCES POLICY 2020年 65卷

作者： Noriega, Roberto Pourrahimian, Yashar Ben-Awuah, Eugene Univ Alberta Min & Rock Sci Dev & Innovat Lab MRDIL Sch Min & Petr Engn Dept Civil & Environm Engn 6-243 Donadeo Innovat Ctr Engn9211 116th St Edmonton AB T6G 1H9 Canada Laurentian Univ Bharti Sch Engn F220 935 Ramsey Lake Rd Sudbury ON P3E 2C6 Canada

The definition of caving economic limits is one of the initial steps in the planning and design of caving projects. This paper proposes a binary optimization framework to integrate the caving envelope and production schedule that maximizes the net present value of the project under technical constraints that model the caving operation mechanics. The constraints considered in the framework are mining capacities, draw rates, maximum and minimum column heights, horizontal and vertical precedences, undercut development rates and the maximum relative adjacent height of draw between columns. An early-start algorithm is used to reduce the number of decision variables and a sliding-time heuristic is applied to significantly reduce the computing time. The framework is implemented in a MATLAB environment with CPLEX as the optimization engine. A case study is presented for the section of a copper deposit, where different horizons were evaluated to select the optimal undercut level and define the caving envelope and initial production schedule. Results were obtained in under 20 min, which allows the method to be efficiently used to evaluate multiple scenarios.

关键词： Block caving mixed-integer programming Undercut horizon Economic envelope Production scheduling

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Mathematical Models for a Flexible Job Shop Scheduling Problem with Machine Operator Constraints 9

Mathematical Models for a Flexible Job Shop Scheduling Probl...

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9th IFAC/IFIP/IFORS/IISE/INFORMS Conference on Manufacturing Modelling, Management and Control (IFAC MIM)

作者： Kress, Dominik Mueller, David Univ Siegen Management Informat Sci Kohlbettstr 15 D-57068 Siegen Germany

We consider a flexible job shop scheduling problem that incorporates machine operators and aims at makespan minimization. In a detailed overview of the related literature, we reveal the fact that the research in this field is mainly concerned with (meta-)heuristic approaches. Only few papers consider exact approaches. In order to promote the use of exact approaches and in order to facilitate the evaluation of the performance of heuristic approaches, we present two mathematical models, a mixed-integer programming model and a constraint programming model, that are analyzed and compared with a state-of-the-art heuristic in computational tests with a standard solver. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Scheduling Flexible job shop Workforce constraints mixed-integer programming Constraint programming

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Enhancing low-rank solutions in semidefinite relaxations of Boolean quadratic problems

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IFAC-PapersOnLine 2020年第2期53卷 1894-1899页

作者： Vito Cerone Sophie M. Fosson Diego Regruto Department of Control and Computer Engineering Politecnico di Torino Italy

Boolean quadratic optimization problems occur in a number of applications. Their mixed integer-continuous nature is challenging, since it is inherently NP-hard. For this motivation, semidefinite programming relaxations (SDR’s) are proposed in the literature to approximate the solution, which recasts the problem into convex optimization. Nevertheless, SDR’s do not guarantee the extraction of the correct binary minimizer. In this paper, we present a novel approach to enhance the binary solution recovery. The key of the proposed method is the exploitation of known information on the eigenvalues of the desired solution. As the proposed approach yields a non-convex program, we develop and analyze an iterative descent strategy, whose practical effectiveness is shown via numerical results.

关键词： mixed-integer programming binary optimization semidefinite programming relaxations low-rank semidefinite programming

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GPU-accelerated Principal-Agent Game for Scalable Citizen Science 19

GPU-accelerated Principal-Agent Game for Scalable Citizen Sc...

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2nd ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS)

作者： Kabra, Anmol Xue, Yexiang Gomes, Carla P. Cornell Univ Dept Comp Sci Ithaca NY 14853 USA Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA

ISBN: (纸本)9781450367141

Citizen science programs have been instrumental in boosting sustainability projects, large-scale scientific discovery, and crowd-sourced experimentation. Nevertheless, these programs witness challenges in submissions' quality, such as sampling bias resulting from citizens' preferences to complete some tasks over others. The sampling bias frequently manifests itself in the program's dataset as spatially clustered submissions, which reduce the efficacy of the dataset for subsequent scientific studies. To address the spatial clustering problem, programs use reward schemes obtained from game-theoretical models to incentivize citizens to perform tasks that are more meaningful from a scientific point of view. Herein we propose a GPU-accelerated approach for the Avicaching game, which was recently introduced by the eBird citizen science program to incentivize birdwatchers to collect bird data from under-sampled locations. Avicaching is a Principal-Agent game, in which the principal corresponds to the citizen science program (eBird) and the agents to the birdwatchers or citizen scientists. Previous approaches for solving the Avicaching game used approximations based on mixed-integer programming and knapsack algorithms combined with learning algorithms, using standard CPU hardware. Following the recent advances in scalable deep learning and parallel computation on Graphical Processing Units (GPUs), we propose a novel approach to solve the Avicaching game, which takes advantage of neural networks and parallelism for large-scale games. We demonstrate that our approach better captures agents' behavior, which allows better learning and more effective incentive distribution in a real-world bird observation dataset. Our approach also allows for massive speedups using GPUs. As Avicaching is representative of games that are aimed at reducing spatial clustering in citizen science programs, our scalable reformulation for Avicaching enables citizen science programs to tackle sampling b

关键词： Citizen Science Computational Sustainability Machine Learning mixed-integer programming Parallel Computation

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Towards Constant Calculation in Disjunctive Inequalities Using Wound Treatment Optimization 18th

Towards Constant Calculation in Disjunctive Inequalities Usi...

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18th Mexican International Conference on Artificial Intelligence (MICAI)

作者： Ponce, Hiram Antonio Marmolejo-Saucedo, Jose Martinez-Villasenor, Lourdes Univ Panamer Fac Ingn Augusto Rodin 498 Mexico City 03920 DF Mexico

ISBN: (纸本)9783030337490;9783030337483

When using the mixed-integer programming to model situations where the limit of the variables follows a box constraint, we find nonlinear problems. To solve this, linearization techniques of these disjunctive inequality constraints are typically used, including constants associated to the variable bounds called M-constants or big-M. Calculation of these constants is an open problem since their values affect the reliability of the optimal solution and convergence of the optimization algorithm. To solve this problem, this work proposes a new population-based metaheuristic optimization method, namely wound treatment optimization (WTO) for calculating the M-constant in a typical domain known as the fixed-charge transportation problem. WTO is inspired on the social wound treatment present in ants after raids. This method allows population diversity that allows to find near-optimal solutions. Experiments of the WTO method on the fixed-charge transportation problem validated its performance and efficiency to find tighten solutions of the M-constant that minimizes the objective function of the problem.

关键词： mixed-integer programming Big-M Metaheuristics Optimization Fixed-charge transportation

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