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On the explicit formulation of reliability assessment of distribution systems with unknown network topology: Incorporation of DG, switching interruptions, and customer-interruption quantification

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APPLIED ENERGY 2022年 324卷

作者： Jooshaki, Mohammad Lehtonen, Matti Fotuhi-Firuzabad, Mahmud Munoz-Delgado, Gregorio Contreras, Javier Arroyo, Jose M. Geologian Tutkimuskeskus GTK Circular Econ Solut Unit Espoo Finland Aalto Univ Dept Elect Engn & Automat Espoo Finland Sharif Univ Technol Elect Engn Dept Tehran Iran Univ Castilla La Mancha Escuela Tecn Super Ingn Ind Ciudad Real Spain

This paper presents an original approach for the evaluation of reliability of active distribution networks with unknown topology. Built upon novel reformulations of conventional definitions for distribution reliability indices, the dependence of system-oriented reliability metrics on network topology is explicitly formulated using a set of mixed-integer linear expressions. Unlike previously reported works also modeling mathemat-ically the relationship between reliability assessment and network topology, the proposed approach allows considering the impact of distributed generation (DG) while accounting for switching interruptions. Moreover, for the first time in the emerging closely related literature, the nonlinearity and nonconvexity of the customer average interruption duration index are precisely characterized. The proposed mixed-integer linear model is suitable for various distribution optimization problems in which the operational topology of the network is not specified a priori. Aiming to exemplify its potential applicability, the proposed formulation is incorporated into a distribution reconfiguration optimization problem. The effectiveness and practicality of the proposed approach are numerically illustrated using various test networks.

关键词： Active distribution networks Customer-interruption quantification Distributed generation mixed-integer linear programming Reliability assessment Switching interruptions Unknown network topology

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An efficient blood supply chain network model with multiple echelons for managing outdated products

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Healthcare Analytics 2025年 7卷 100377-100377页

作者： Mansur, Agus Wangsa, Ivan Darma Rizky, Novrianty Vanany, Iwan Department of Industrial Engineering Faculty of Industrial Technology Universitas Islam Indonesia Yogyakarta 55584 Indonesia Department of Industrial and Systems Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

This study examines the lack of coordination between blood production and inventories in the blood supply chain networks. Prior studies neglect to optimize operational costs through blood production, inventory, and waste. We propose a mixed-integer linear programming approach addressing multiple echelons, types of blood, and blood bag shelf lifetime. The model is developed by determining the facility locations, assigning regional blood banks, and allocating the right products. Indonesia's blood supply chain is used as a case study to evaluate the applicability of the proposed model using optimization software. A sensitivity analysis is performed on production rate and patient demand to assess how these factors affect the overall cost of expired products. The results show that the proposed method's total cost and expired products are 4.69%–5.60% and 4.71%–5.75%, respectively. © 2024

关键词： Blood shelf life Blood supply chain mixed-integer linear programming Multi-product Multiple echelons Operational efficiency

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Resilience-Driven Post-disruption Restoration of Interdependent Critical Infrastructure Systems under Uncertainty: Modeling, Risk-Averse Optimization, and Solution Approaches

Resilience-Driven Post-disruption Restoration of Interdepend...

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作者： Alkhaleel, Basem A. University of Arkansas

学位级别：Ph.D., Doctor of Philosophy

Critical infrastructure networks (CINs) are the backbone of modern societies, which depend on their continuous and proper functioning. Such infrastructure networks are subjected to different types of inevitable disruptive events which could affect their performance unpredictably and have direct socioeconomic consequences. Therefore, planning for disruptions to CINs has recently shifted from emphasizing pre-disruption phases of prevention and protection to post-disruption studies investigating the ability of critical infrastructures (CIs) to withstand disruptions and recover timely from them. However, post-disruption restoration planning often faces uncertainties associated with the required repair tasks and the accessibility of the underlying transportation network. Such challenges are often overlooked in the CIs resilience literature. Furthermore, CIs are not isolated from each other, but instead, most of them rely on one another for their proper functioning. Hence, the occurrence of a disruption in one CIN could affect other dependent CINs, leading to a more significant adverse impact on communities. Therefore, interdependencies among CINs increase the complexity associated with recovery planning after a disruptive event, making it a more challenging task for decision makers. Recognizing the inevitability of large-scale disruptions to CIs and their impacts on societies, the research objective of this work is to study the recovery of CINs following a disruptive event. Accordingly, the main contributions of the following two research components are to develop: (i) resilience-based post-disruption stochastic restoration optimization models that respect the spatial nature of CIs, (ii) a general framework for scenario-based stochastic models covering scenario generation, selection, and reduction for resilience applications, (iii) stochastic risk-related cost-based restoration modeling approaches to minimize restoration costs of a system of interdependent critical infra

关键词： Interdependent infrastructure networks mixed-integer linear programming Post-disruption restoration Risk measures Stochastic optimization

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Global and Preference-based Optimization with mixed Variables using Piecewise Affine Surrogates

arXiv

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arXiv 2023年

作者： Zhu, Mengjia Bemporad, Alberto IMT School for Advanced Studies Lucca Piazza San Francesco 19 Lucca55100 Italy

Optimization problems involving mixed variables (i.e., variables of numerical and categorical nature) can be challenging to solve, especially in the presence of mixed-variable constraints. Moreover, when the objective function is the result of a complicated simulation or experiment, it may be expensive-to-evaluate. This paper proposes a novel surrogate-based global optimization algorithm to solve linearly constrained mixed-variable problems up to medium size (around 100 variables after encoding). The proposed approach is based on constructing a piecewise affine surrogate of the objective function over feasible samples. We assume the objective function is black-box and expensive-to-evaluate, while the linear constraints are quantifiable, unrelaxable, a priori known, and are cheap to evaluate. We introduce two types of exploration functions to efficiently search the feasible domain via mixed-integer linear programming solvers. We also provide a preference-based version of the algorithm designed for situations where only pairwise comparisons between samples can be acquired, while the underlying objective function to minimize remains unquantified. The two algorithms are evaluated on several unconstrained and constrained mixed-variable benchmark problems. The results show that, within a small number of required experiments/simulations, the proposed algorithms can often achieve better or comparable results than other existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： mixed-integer linear programming

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Design and Optimization of Heterogeneous Coded Distributed Computing with Nonuniform File Popularity

arXiv

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arXiv 2023年

作者： Deng, Yong Dong, Min Department of Software Engineering Lakehead University Thunder BayONP7B 5E1 Canada Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 3G4 Canada Department of Electrical Computer and Software Engineering Ontario Tech University OshawaONL1G 0C5 Canada

This paper studies MapReduce-based heterogeneous coded distributed computing (CDC) where, besides different computing capabilities at workers, input files to be accessed by computing jobs have nonuniform popularity. We propose a file placement strategy that can handle an arbitrary number of input files. Furthermore, we design a nested coded shuffling strategy that can efficiently manage the nonuniformity of file popularity to maximize the coded multicasting opportunity. We then formulate the joint optimization of the proposed file placement and nested shuffling design variables to optimize the proposed CDC scheme. To reduce the high computational complexity in solving the resulting mixed-integer linear programming (MILP) problem, we propose a simple two-file-group-based file placement approach to obtain an approximate solution. Numerical results show that the optimized CDC scheme outperforms other alternatives. Also, the proposed two-file-group-based approach achieves nearly the same performance as the conventional branch-and-cut method in solving the MILP problem but with substantially lower computational complexity that is scalable over the number of files and workers. For computing jobs with aggregate target functions that commonly appear in machine learning applications, we propose a heterogeneous compressed CDC (C-CDC) scheme to further improve the shuffling efficiency. The C-CDC scheme uses a local data aggregation technique to compress the data to be shuffled for the shuffling load reduction. We again optimize the proposed C-CDC scheme and explore the two-file-group-based low-complexity approach for an approximate solution. Numerical results show the proposed C-CDC scheme provides a considerable shuffling load reduction over the CDC scheme, and also, the two-file-group-based file placement approach maintains good performance. Copyright © 2023, The Authors. All rights reserved.

关键词： mixed-integer linear programming

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Decision Support for Bundle Shipment Choices: Orthogonal Two-dimensional Bin Packing with Practical Constraints 10th

Decision Support for Bundle Shipment Choices: Orthogonal Two...

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10th International Conference on Operations Research and Enterprise Systems (ICORES)

作者： Zhou, Yizi Liu, Jiyin Mandania, Rupal Li, Shuyang Chen, Kai Xu, Rongjun Huawei Technol Co Ltd Bantian Huawei Base Shenzhen Peoples R China Loughborough Univ Sch Business & Econ Loughborough Leics England

ISBN: (纸本)9789897584855

In this paper, we study a real life application of an orthogonal two dimensional bin packing problem (2DBPP) with a small assortment of different shapes of bins and items. A telecommunication equipment company ships its products to customers several times daily by trucks and it currently uses the less-than-truckload (LTL) shipping option which is priced as unit price per volume times the total volume of products. However, depending on scale of products to be shipped, full-truckload (FTL) shipping which is priced as a single price per truck may be cheaper. In this paper we aim to help the company to decide on the delivery choice as well as on how to pack the truck optimally when FTL shipping is selected. We model the problem as a variant of an orthogonal 2D-BPP using mixed-integer linear programming (MIP) with the objective to minimise the total cost of delivery. Practical conditions influencing the feasibility of packing patterns are also considered. Practical guidance such as those on some modelling techniques and the application of the model, are applied to enhance the solving efficiency. The problems are solved by commercial solver CPLEX to optimality up to 34 pallets within an acceptable time. For lager problems, we adopt an approach to combine same items into larger rectangles and then pack them. This increases the solvability to 154 pallets. On average, our method help the company save 26% shipping cost.

关键词： Orthogonal 2D-BPP LTL Shipping FTL Shipping mixed-integer linear programming

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A two-stage multi-objective stochastic optimization strategy to minimize cost for electric bus depot operators

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JOURNAL OF CLEANER PRODUCTION 2022年第0期332卷 129856-129856页

作者： Rafique, S. Nizami, M. S. H. Irshad, U. B. Hossain, M. J. Mukhopadhyay, S. C. Macquarie Univ Sch Engn N Ryde NSW 2109 Australia Univ Technol Sydney Sch Elect & Data Engn Sydney NSW 2007 Australia

The electrification of large-scale bus depots will bring challenges for bus depot operators (BDOs) aiming to minimize operational costs. A potential solution for BDOs managing electric buses (EBs) with large batteries that are capable of vehicle-to-grid (V2G) operational functionalities, along with on-site generation facilities, is to actively participate in the energy market. This paper proposed a novel two-stage multi-objective stochastic optimization-based energy management system (EMS) for BDOs to trade energy in a day-ahead (DA) energy market while considering the expected cost of energy imbalances in the real-time (RT) market. The optimization model for charge-discharge scheduling is based on a mixed-integer linear programming approach. The uncertainties associated with the DA forecast are complemented by a stochastic model. Minimizing the cost of electricity and the penalty costs for battery capacity degradation are the objectives that need to be met simultaneously. The proposed EMS is evaluated using simulation studies considering a hypothetical bus depot in Australia. The effectiveness of the proposed EMS for overall financial savings is validated by comparing its performance with state-of-the-art scheduling strategies. Results show that the net energy cost for the proposed EMS is 38% lower compared to a deterministic scheduling strategy that does not allow V2G flexibilities, 33% lower compared to a deterministic scheduling strategy that allows V2G flexibilities and 17% lower compared to a stochastic scheduling strategy that minimizes overall costs.

关键词： Bus depot operator Energy management system Electric bus mixed-integer linear programming Multi-objective optimization Stochastic

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Optimal Decomposition for the Monthly Contracted Electricity of Cascade Hydropower Plants Considering the Bidding Space in the Day-Ahead Spot Market

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WATER 2022年第15期14卷 2347-2347页

作者： Wu, Yang Su, Chengguo Liu, Shuangquan Guo, Hangtian Sun, Yingyi Jiang, Yan Shao, Qizhuan Yunnan Power Grid Co Ltd Yunnan Power Dispatching Control Ctr Kunming 650011 Yunnan Peoples R China Zhengzhou Univ Sch Hydraul Sci & Engn Zhengzhou 450001 Peoples R China

With the gradual opening of China's electricity market, it is effective for cascade hydropower plants to simultaneously participate in both the monthly contract market and the day-ahead spot market to obtain higher power generation benefits. Hence, this paper studies the optimal decomposition model for the monthly contracted electricity of cascade hydropower plants considering the bidding space in the day-ahead spot market. The close hydraulic and electric connection between cascade hydropower plants, the implementation requirements of contracted electricity, and the uncertainty of the day-ahead market clearing price are all well considered. Several linearization techniques are proposed to address the nonlinear factors, including the objective function and the power generation function. A successive approximation (SA) approach, along with a mixed-integer linear programming (MILP) approach, is then developed to solve the proposed model. The presented model is verified by taking the decomposition of the monthly contracted electricity of cascade hydropower plants in China as an example. The results indicate that the developed model has high computational efficiency and can increase the power generation benefits compared with the conventional deterministic model. The effect of the penalty coefficient for imbalanced monthly contracted electricity is also evaluated, which provides a practical reference for market managers.

关键词： cascade hydropower plants decomposition of the monthly contracted electricity day-ahead spot market uncertainty of the clearing price successive approximation mixed-integer linear programming

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Quantifying energy flexibility of commuter plug-in electric vehicles within a residence-office coupling virtual microgrid. Part I: System configuration, quantification framework, and optimization model

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ENERGY AND BUILDINGS 2022年 254卷 111551-111551页

作者： Yu, Zhenyu Lu, Fei Zou, Yu Yang, Xudong China Acad Bldg Res Inst Bldg Environm & Energy Beijing 100013 Peoples R China Tsinghua Univ Dept Bldg Sci Beijing 100084 Peoples R China

China's decarbonization goals have accelerated its transition from a fossil-fuel-based power system to one with a high penetration level of renewable energy, thereby stimulating a massive demand for flexible grid services. Plug-in electric vehicles (PEVs) could offer a huge potential for energy flexibility in future power systems, especially in terms of mitigating high power peaks and power ramps. This work focuses on the flexibility assessment and optimal dispatch of commuter PEVs within a microgrid system. First, a virtual microgrid system including residential buildings, an office, distributed generation, and commuter PEVs was established. Second, a generic framework was proposed to quantify the flexibility potential of PEVs for reshaping the net load profile, along with several indicators to identify the flexibility characteristics in multiple dimensions. Third, a mixed-integer linear programming model was developed to schedule the charging and discharging processes of PEVs and calculate the flexibility indicators. This paper aims to explain the system configuration of the microgrid and the quantification framework and formulation of the optimization model. As such, it serves as the basis of a case study involving scenario and sensitivity analyses of PEV flexibility, presented in the companion paper (Part II). (c) 2021 Elsevier B.V. All rights reserved.

关键词： Flexibility Electric vehicle Virtual microgrid Vehicle-to-building Load flattening mixed-integer linear programming

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Shaving electric bills with renewables? A multi-period pinch-based methodology for energy planning

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ENERGY 2022年第PartD期239卷 122320-122320页

作者： Kong, Karen Gah Hie How, Bing Shen Lim, Juin Yau Leong, Wei Dong Teng, Sin Yong Ng, Wendy Pei Qin Moser, Irene Sunarso, Jaka Swinburne Univ Technol Fac Engn Comp & Sci Res Ctr Sustainable Technol Jalan Simpang Tiga Sarawak 93350 Malaysia Kyung Hee Univ Coll Engn Dept Environm Sci & Engn Integrated Engn 1732 Deogyeong Daero Yongin 17104 Gyeonggi Do South Korea Monash Univ Malaysia Sch Engn Chem Engn Discipline Jalan Lagoon Selatan Bandar Sunway 47500 Selangor Darul Malaysia Radboud Univ Nijmegen Inst Mol & Mat POB 9010 NL-6500 GL Nijmegen Netherlands Univ Teknol Brunei Petr & Chem Engn Programme Area Jalan Tungku Link BE-1410 Gadong Brunei Swinburne Univ Technol Fac Sci Engn & Technol Sch Software & Elect Engn Melbourne Vic 3122 Australia

As the energy demand keeps growing with the world's increasing population, concerns have arisen about the carbon emissions that contribute to critical environmental issues. These environmental concerns drive many nations to commit themselves in pursuing sustainable energy resources. Different process integration techniques have been developed to achieve a viable energy planning sector. In reference to the literature review, the use of time-sliced based models in optimization problems is still sparse. Hence, this paper aims to develop a time-sliced based optimization model that can be integrated into energy integration models. With that, a multi-period pinch-based methodology (targeting, scheduling, and optimization) for electric power systems with the hybridization of renewables and fossil-based energy is proposed. The prospective framework is designed to meet the emissions limit, fulfil energy demand, and minimize the electricity bill. An illustrative case study in Malaysia incorporated with an actual billing system is used to demonstrate the effectiveness of the proposed framework. The results show that it is capable of reducing the total electricity bill by 35% and achieving a 71% reduction of emissions by using solar PV as the renewables. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Optimization Renewable energy Carbon-constrained energy planning mixed-integer linear programming Automated targeting model Time-sliced based optimization model

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