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Comparative Economic Optimization for an Overseas Hydrogen Supply Chain Using mixed-integer linear programming

ACS SUSTAINABLE CHEMISTRY & ENGINEERING 2021年第42期9卷 14249-14262页

作者： Kim, Ayeon Kim, Heehyang Lee, Hyunjun Lee, Boreum Lim, Hankwon Ulsan Natl Inst Sci & Technol Sch Energy & Chem Engn Ulsan 44919 South Korea Yale Univ Dept Chem & Environm Engn New Haven CT 06520 USA Ulsan Natl Inst Sci & Technol Dept Energy Engn Ulsan 44919 South Korea

As environmental problems become serious, many countries have been striving to change fossil-based energy to renewable and sustainable hydrogen energy. However, there are large capacity differences for each country's hydrogen production, making hydrogen trading necessary. Although extensive research has investigated hydrogen technologies and economics, to the best of our knowledge, no study has examined the optimization of the overall hydrogen supply chain, from overseas supply to domestic consumption, considering various feasibility scenarios. This is a case study on the hydrogen supply chain for South Korea, which is expected to be one of the major hydrogen-importing countries, considering the decarbonized hydrogen requirements of the importing country and the production capacities of exporting countries over two decades. This study's optimized results for a hydrogen supply chain via mixed-integer linear programming reveal that it is most feasible for South Korea to import blue hydrogen from Qatar and Russia and green hydrogen from UAE and India, using liquefied hydrogen in the near term. This is because of the significantly lesser resource prices compared to other countries. The share of blue hydrogen supply dominates in the near term, while the green hydrogen supply is expected to gradually prevail over blue hydrogen due to an exponential drop in the renewable electricity price. With the price drop of green hydrogen, green hydrogen purchases from other countries in tandem with the UAE are predicted, rather than the blue hydrogen supply, considering that long-term demand will exceed the UAE's predicted capacity.

关键词： Overseas H-2 supply chain Economic analysis Case study mixed-integer linear programming

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Solution for short-term hydrothermal scheduling with a logarithmic size mixed-integer linear programming formulation

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ENERGY 2019年 171卷 770-784页

作者： Jian, Jinbao Pan, Shanshan Yang, Linfeng Guangxi Univ Coll Elect Engn Nanning 530004 Peoples R China Guangxi Univ Nationalities Coll Sci Nanning 530006 Peoples R China Guangxi Univ Coll Comp Elect & Informat Nanning 530004 Peoples R China

Short-term hydrothermal scheduling issue is usually hard to tackle on account of its highly non-convex and non-differentiable characteristics. A popular strategy for handling these difficulties is to reformulate the issue by various linearization techniques. However, in this process, a fairly large number of continuous/binary variables and constraints will be introduced, which may result in a heavy computational burden. In this study, a logarithmic size mixed-integer linear programming formulation is presented for this issue, that is, only a logarithmic size of binary variables and constraints will be required to piecewise linearize the nonlinear functions. Based on such a formulation, a global optimum is therefore can be solved efficiently. To remove the linearization errors and cope with the network loss, a derivable nonlinear programming formulation is derived. By optimizing this formulation via the powerful interior point method, where the previous global solution of mixed-integer linear programming formulation is used as the starting point, a promising feasible solution is consequently attained. Numerical results show that the presented logarithmic size mixed-integer linear programming formulation is more efficient than the generalized one and when it is incorporated into the solution procedure, the proposed methodology is competitive with currently state-of-the-art approaches. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Short-term hydrothermal scheduling Piecewise linearize Logarithmic size mixed-integer linear programming Non-linear programming

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Optimal structural design of residential cogeneration systems with battery based on improved solution method for mixed-integer linear programming

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ENERGY 2015年 84卷 106-120页

作者： Wakui, Tetsuya Yokoyama, Ryohei Osaka Prefecture Univ Dept Mech Engn Naka Ku Sakai Osaka 5998531 Japan

An optimal structural design model of residential cogeneration systems with a battery is developed using an MILP (mixed-integer linear programming) approach. A battery is introduced as a device candidate to increase operational flexibility of cogeneration units without electric power export. In this model, the selection from device candidates and multi-period operation of selected devices, in which various operational restrictions are considered, are simultaneously optimized so as to minimize annual primary energy consumption. For a battery, not only charging and discharging losses and an upper limit of charging and discharging electric power but also charging-discharging status and electric power consumption in a built-in bidirectional inverter are uniquely incorporated into the model. In addition, the solution method for this MILP problem is improved using a simple decomposition approach. The developed model is then applied to the structural design of a residential cogeneration system with a battery for simulated energy demands in Japan. The results reveal the effectiveness of the simple decomposition approach and the increase in the energy-saving effect of the residential cogeneration system by the introduction of the battery, as a consequence of the increase in the electric capacity factor of the cogeneration unit by the charge of surplus electric power. Moreover, it is shown that this increase strongly depends on the battery performances. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Cogeneration Battery Structural design Operational planning Optimization mixed-integer linear programming

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Optimal Multi-Scenario, Multi-Objective Allocation of Fault Indicators in Electrical Distribution Systems Using a mixed-integer linear programming Model

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IEEE TRANSACTIONS ON SMART GRID 2019年第4期10卷 4508-4519页

作者： Acosta, Jhair S. Lopez, Juan Camilo Rider, Marcos J. Univ Estadual Campinas Dept Syst & Energy Sch Elect & Comp Engn BR-13083852 Campinas SP Brazil

In this paper, a mixed-integer nonlinear programming (MINLP) model for the optimal multiscenario allocation of fault indicators (FIs) in electrical distribution systems (EDS) is presented. The original MINLP model is linearized to obtain an equivalent mixed-integer linear programming (MILP) model. The proposed MILP formulation is a precise, flexible, and scalable optimization model whose optimal solution is guaranteed by commercial solvers. In order to improve the practicality and scope of the proposed method, different demand levels, topologies, and N - 1 contingencies are included as scenarios within the proposed model. The flexibility of the model is also emphasized by adding a custom noncontinuous interruption cost function. The objective function minimizes the average cost of energy not supplied and the present value of the overall investments made over a discrete planning horizon. Since the proposed model is convex, other conflicting objectives can be considered using a simple step-by-step approach to construct the optimal Pareto front. In order to demonstrate the efficiency and scalability of the proposed method, two different EDS are tested: a 69-node RBTS4 benchmark and a real Brazilian distribution system. Results show the efficiency of the proposed method to improve the overall reliability of the system even when few FIs are installed.

关键词： Allocation of fault indicators electrical distribution systems energy not supplied mixed-integer linear programming multi-scenario multi-objective optimization

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A linearized formulation of AC multi-year transmission expansion planning: A mixed-integer linear programming approach

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ELECTRIC POWER SYSTEMS RESEARCH 2014年 114卷 93-100页

作者： Akbari, Tohid Bina, Mohammad Tavakoli KN Toosi Univ Technol Fac Elect Engn Tehran Iran

This paper presents a method in expansion planning of transmission systems using the AC optimal power flow (AC-OPF). The AC-OPF provides a more accurate picture of power flow in the network compared to the DC optimal power flow (DC-OPF) that is usually considered in the literature for transmission expansion planning (TEP). While the AC-OPF-based TEP is a mixed-integer nonlinear programming problem, this paper transforms it into a mixed-integer linear programming environment. This transformation guarantees achievement of a global optimal solution by the existing algorithms and software. The proposed model has been successfully applied to a simple 3-bus power system, Carver's 6-bus test system, 24-bus IEEE reliability test system (RTS) as well as a realistic power system. Detailed case studies are presented and thoroughly analyzed. Simulations show the effectiveness of the proposed method on the TEP. (C) 2014 Elsevier B.V. All rights reserved.

关键词： AC-OPF linearized power flow mixed-integer linear programming Transmission expansion planning

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A new mixed-integer linear programming model for rescue path planning in uncertain adversarial environment

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COMPUTERS & OPERATIONS RESEARCH 2012年第12期39卷 3420-3430页

作者： Berger, Jean Boukhtouta, Abdeslem Benmoussa, Abdelhamid Kettani, Ossama Def R&D Canada Valcartier Quebec City PQ Canada Def R&D Canada CORA Ottawa ON Canada Modellium Inc Quebec City PQ Canada

Efficient vehicle path planning in hostile environment to carry out rescue or tactical logistic missions remains very challenging. Most approaches reported so far rely on key assumptions and heuristic procedures to reduce problem complexity. In this paper, a new model is proposed to solve the discrete rescue path planning problem for a single agent navigating in uncertain adversarial environment. It relies on a novel and simplified mathematical mixed-integer linear programming formulation aimed at minimizing traveled distance and threat exposure. Exploiting a user-defined survivability function approximation and survivability threshold, the approximate model allows constructing a solution providing an adjustable optimality gap interval on the optimal solution. Experimental results show the value of the proposed approach in computing near optimal solutions reasonably fast for various problem instances. Crown Copyright (c) 2012 Published by Elsevier Ltd. All rights reserved.

关键词： Path planning Constrained shortest path mixed-integer linear programming

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Accelerating operation optimization of complex chemical processes: A novel framework integrating artificial neural network and mixed-integer linear programming

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CHEMICAL ENGINEERING JOURNAL 2024年 481卷

作者： Zhou, Jianzhao Shi, Tao Ren, Jingzheng He, Chang Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Hong Kong Polytech Univ Res Ctr Resources Engn Carbon Neutral Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Adv Mfg Dept Ind & Syst Engn Hung HomKowloon Hong Kong Peoples R China Sun Yat Sen Univ Guangdong Engn Ctr Petrochem Energy Conservat Sch Mat Sci & Engn Guangzhou 510275 Peoples R China

We present an automated framework that integrates rectified linear unit activated artificial neural network (ReLU-ANN) and mixed-integer linear programming (MILP) to enable efficient operational-level optimization of complex chemical processes. Initially, data is generated through rigorous simulations to pre-train surrogate models based on ReLU-ANN (classification and regression), and subsequently, MILP is employed for optimization by linearly formulating these models. This novel framework efficiently handles complex convergence constraints through a classification neural network which will be used for high-throughput screening data for regression, while simultaneously implementing an 'optimizing while learning' strategy. By iteratively updating the neural network based on optimization feedback, our approach streamlines the optimization process and ensure the feasibility of optimum solution. To demonstrate the versatility and robustness of our proposed framework, we examine three representative chemical processes: extractive distillation, organic Rankine cycle, and methanol synthesis. Our results reveal the framework's potential in enhancing optimization effect while concurrently reducing computational time, surpassing the capabilities of typical optimization algorithms. As for the three processes, optimization effectiveness improved by 10.11%, 28.69%, and 5.45%, respectively, while execution time were reduced by 71.71%, 54.49%, and 59.38%. This notable enhancement in optimization efficiency stems from a substantial reduction in costly while ineffective objective function evaluations. By seamless integration of ReLU-ANN and MILP, our proposed framework holds promise for improving the optimization of complex chemical processes, yielding superior results within significantly reduced timeframes compared to traditional approaches.

关键词： Distillation Organic Rankine cycle Methanol synthesis Operational level optimization Artificial neural network mixed-integer linear programming

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Testing copositivity via mixed-integer linear programming

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linear ALGEBRA AND ITS APPLICATIONS 2021年 609卷 218-230页

作者： Anstreicher, Kurt M. Univ Iowa Dept Business Analyt Iowa City IA 52242 USA

We describe a simple method to test if a given matrix is copositive by solving a single mixed-integer linear programming (MILP) problem. This methodology requires no special coding to implement and takes advantage of the computational power of modern MILP solvers. Numerical experiments demonstrate that the method is robust and efficient. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Copositive matrix Copositive programming mixed-integer linear programming

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A mixed-integer linear programming approach to the optimization of event-bus schedules: a scheduling application in the tourism sector

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JOURNAL OF SCHEDULING 2014年第6期17卷 621-629页

作者： Brandinu, Gianluca Trautmann, Norbert Univ Bern Dept Business Adm CH-3012 Bern Switzerland

This paper deals with "The Enchanted Journey," which is a daily event tour booked by Bollywood-film fans. During the tour, the participants visit original sites of famous Bollywood films at various locations in Switzerland;moreover, the tour includes stops for lunch and shopping. Each day, up to five buses operate the tour. For operational reasons, however, two or more buses cannot stay at the same location simultaneously. Further operative constraints include time windows for all activities and precedence constraints between some activities. The planning problem is how to compute a feasible schedule for each bus. We implement a two-step hierarchical approach. In the first step, we minimize the total waiting time;in the second step, we minimize the total travel time of all buses. We present a basic formulation of this problem as a mixed-integer linear program. We enhance this basic formulation by symmetry-breaking constraints, which reduces the search space without loss of generality. We report on computational results obtained with the Gurobi Solver. Our numerical results show that all relevant problem instances can be solved using the basic formulation within reasonable CPU time, and that the symmetry-breaking constraints reduce that CPU time considerably.

关键词： Applications Tourism industry mixed-integer linear programming

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Dig-limits optimization through mixed-integer linear programming in open-pit mines

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2018年第2期69卷 171-182页

作者： Sari, Yuksel Asli Kumral, Mustafa McGill Univ Dept Min & Mat Engn Montreal PQ Canada

As a type of general layout problems, dig-limits optimization focuses on generating the ore-waste boundaries of a bench sector in an open-pit mining operation. Typically, blast holes are dense;therefore, selective mining units (SMUs) are small, which is not compatible with loading equipment. Loader cannot select ore-waste boundaries of SMUs because the arm of the excavator is generally longer than SMU sizes. Therefore, clusters of SMUs being compatible with loader movements need to be formed. In this paper, the dig-limits optimization problem is shown to be NP-hard and formulated to maximize profit to be obtained from a mining sector such that ore and waste clusters corresponding to mine excavator movements are considered and solved by mixed-integer linear programming. To see the efficiency of the proposed approach, a case study is conducted on seven sectors of a bench in a gold mine. The results showed that the approach is practical and has potential to increase the value of operation. The resulting average economic value of seven sectors is $129,060. Additionally, optimal design of one bench solved by the model is compared to a manual design of a mining engineer and a deviation of 6.4% has been observed.

关键词： Dig-limits optimization short-term mine planning dilution/loss management mixed-integer linear programming

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