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EV Scheduling Framework for Peak Demand Management in LV Residential Networks

IEEE SYSTEMS JOURNAL 2022年第1期16卷 1520-1528页

作者： Rafique, Sohaib Nizami, Mohammad Sohrab Hasan Irshad, Usama Bin Hossain, Mohammad Jahangir Mukhopadhyay, Subhas Chandra Macquarie Univ Sch Engn Sydney NSW 2109 Australia Univ Technol Sydney Sch Elect & Data Engn Sydney NSW 2007 Australia

Increased electrification in the residential and transport sectors is changing the energy demand profiles significantly, which results in reshaped peak demand. These changes in demand profiles can cause grid overloading and jeopardize network reliability especially when the excessive use of electricity within a network is uncoordinated. In this article, an aggregated coordination mechanism is proposed for electric vehicle (EV) charge-discharge scheduling to manage the peak demand in the low-voltage (LV) residential networks. The proposed model uses mixed-integer-programming-based optimization approach to minimize the cost of energy while managing the peak demand and complying with grid constraints. A stochastic model is presented to account for the uncertainties associated with forecast inaccuracies of the day-ahead scheduling. The proposed strategy is assessed by means of simulation studies considering an LV residential neighborhood in Sydney, Australia. The results indicate the effectiveness of the proposed strategy to minimize the cost of electricity for the EV owners while managing the peak demand for the grid operators. Comparison with the state-of-the-art EV scheduling strategies indicates that the proposed strategy can improve the load factor of the local network up to 36%, the peak-to-average ratio up to 27%, and cost reductions up to 56%.

关键词： Uncertainty Load modeling Predictive models Batteries Stochastic processes Degradation Tariffs Electric vehicle (EV) energy management mixed-integer programming peak demand management stochastic modeling vehicle-to-grid (V2G)

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Novel approach to energy-efficient flexible job-shop scheduling problems

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ENERGY 2022年第PartB期238卷 121773-121773页

作者： Rakovitis, Nikolaos Li, Dan Zhang, Nan Li, Jie Zhang, Liping Xiao, Xin Univ Manchester Ctr Proc Integrat Dept Chem Engn & Analyt Sci Manchester M13 9PL Lancs England Wuhan Univ Sci & Technol Sch Machinery & Automat Dept Ind Engn Wuhan 430081 Hubei Peoples R China Chinese Acad Sci Inst Proc Engn Beijing 100190 Peoples R China

In this work, we develop a novel mathematical formulation for the energy-efficient flexible job-shop scheduling problem using the improved unit-specific event-based time representation. The flexible job-shop is represented using the state-task network. It is shown that the proposed model is superior to the existing models with the same or better solutions by up to 13.5 % energy savings in less computational time. Furthermore, it can generate feasible solutions for large-scale instances that the existing models fail to solve. To efficiently solve large-scale problems, a grouping-based decomposition approach is proposed to divide the entire problem into smaller subproblems. It is demonstrated that the proposed decomposition approach can generate good feasible solutions with reduced energy consumption for large-scale examples in significantly less computational time (within 10 min). It can achieve up to 43.1 % less energy consumption in comparison to the existing gene-expression programming-based algorithm. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Scheduling mixed-integer programming Flexible job-shops Energy-efficient Unit-specific event-based

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A Fuzzy-Based Approach for Cholera Risk Assessment and Vaccine Allocation

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2022年第8期24卷 3366-3383页

作者： Qasem, Ahmed Gailan Lam, Sarah S. Aqlan, Faisal SUNY Binghamton Dept Syst Sci & Ind Engn Binghamton NY 13902 USA Univ Louisville Dept Ind Engn Louisville KY 40292 USA

Common interventions to control the spread of cholera include improving sanitation, hygiene, and access to safe drinking water and providing epidemic regions with sufficient treatment kits and oral vaccines. Due to resources limitation, these interventions should be guided by a risk assessment of cholera-affected regions, thereby targeting regions based on their risk level. Cholera risk assessment is very challenging because of the lack of precise and reliable data. This study proposes an approach for cholera risk assessment and vaccine allocation, which consists of two phases: (i) cholera risk assessment, where a fuzzy inference system (FIS) is proposed to evaluate the risk level of cholera-affected regions based on five cholera risk indicators: (1) attack rate, (2) case fatality rate, (3) the number of internally displaced persons, (4) accessibility of water, sanitation and hygiene, and (5) accessibility of cholera treatment;(ii) cholera vaccine allocation, where a mixed-integer programming model is used to optimize the allocation of limited vaccine doses among multiple regions over multiple periods while considering the risk level, population of regions, and vaccine efficacy. The model answers the questions of where, what amounts, and when to send vaccines during a 2-year horizon. Implementation of the proposed approach is illustrated using a case study from Yemen, which is currently experiencing the world's worst cholera outbreak according to the World Health Organization. The results reveal the usefulness of the proposed approach in mapping the cholera risk, which in turn is used as effective guidance for the allocation of cholera vaccine.

关键词： Cholera Risk assessment Vaccine allocation Fuzzy inference system mixed-integer programming

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Modeling design and control problems involving neural network surrogates

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2022年第3期83卷 759-800页

作者： Yang, Dominic Balaprakash, Prasanna Leyffer, Sven Univ Calif Los Angeles Los Angeles CA 90095 USA Argonne Natl Lab Lemont IL USA

We consider nonlinear optimization problems that involve surrogate models represented by neural networks. We demonstrate first how to directly embed neural network evaluation into optimization models, highlight a difficulty with this approach that can prevent convergence, and then characterize stationarity of such models. We then present two alternative formulations of these problems in the specific case of feedforward neural networks with ReLU activation: as a mixed-integer optimization problem and as a mathematical program with complementarity constraints. For the latter formulation we prove that stationarity at a point for this problem corresponds to stationarity of the embedded formulation. Each of these formulations may be solved with state-of-the-art optimization methods, and we show how to obtain good initial feasible solutions for these methods. We compare our formulations on three practical applications arising in the design and control of combustion engines, in the generation of adversarial attacks on classifier networks, and in the determination of optimal flows in an oil well network.

关键词： mixed-integer programming Nonlinear programming Complementarity constraints Machine learning Neural networks

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Production planning with parallel lines and limited batch splitting: Mathematical model and a case study in the white goods sector

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2022年第10期73卷 2216-2227页

作者： Bergamini, Fernanda Paula Ribeiro, Carolina Martins Munari, Pedro Ferreira, Deisemara Univ Fed Sao Carlos Sao Carlos Brazil Univ Fed Sao Carlos Sorocaba Brazil

This paper addresses the optimization of production planning in the white goods sector, motivated by the real case of a home appliances manufacturer. The main characteristics of this productive process are multiple parallel assembly lines with distinct unrelated speeds that vary depending on product type;a tight limit on the maximum number of split batches;multiple product types that share different types of resources;and constraints on production capacity. For the company, it is important to consider all these characteristics in an integrated manner, which results in a complex decision-making process. To provide effective support to decision-making in this context, we propose an optimization model that seeks to meet the demand in the planning horizon considering all the mentioned characteristics, while minimizing the processing times and the number of split batches. The results of computational experiments with instances based on real data provided by the company showed that the proposed approach is effective and presents good quality solutions for the case study, suggesting significant productivity gains if put into practice. They indicate improvements in production times by up to 9.43% and reductions in batch splitting by approximately 55% with respect to the current operation of the company.

关键词： Production planning lot sizing parallel machines batch splitting mixed-integer programming

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A scenario-based optimization model for determining the capacity of a residential off-grid PV-battery system

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SOLAR ENERGY 2022年 233卷 478-488页

作者： Cho, Dongjin Valenzuela, Jorge Auburn Univ Dept Ind & Syst Engn 1301 Shelby Ctr Auburn AL 36849 USA

Renewable energy sources offer a promising method of environmentally friendly and sustainable electricity generation in remote areas. Among various renewable energy sources, solar power is usable everywhere as long as there is solar radiation. It is particularly useful for isolated areas where there is no access to any other source of electricity. However, the inherent uncertainty of solar radiation and climate changes may result in a high initial investment cost that could make this type of energy system economically infeasible. To obtain a PV-battery system at a reasonable investment cost, a home-backup diesel generator is assumed to be available. We develop a scenario-based optimization model to determine the capacity of residential off-grid PV-battery systems that consider solar radiation uncertainty and hourly energy consumption patterns. The optimization model determines the PV-battery capacities to supply the energy demanded at each period by minimizing the annual equivalent investment cost and the fuel cost of using the diesel generator. The impact of scenarios on the model results is evaluated by changing the number of scenarios.

关键词： PV-battery capacity Solar irradiance uncertainty Energy consumption scheduling mixed-integer programming Stochastic optimization

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Design of biobased supply chains on a life cycle basis: A bi-objective optimization model and a case study of biobased polyethylene terephthalate (PET)

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SUSTAINABLE PRODUCTION AND CONSUMPTION 2022年 30卷 706-719页

作者： Garcia-Velasquez, Carlos Leduc, Sylvain van der Meer, Yvonne Maastricht Univ Aachen Maastricht Inst Biobased Mat AMIBM Maastricht Netherlands Int Inst Appl Syst Anal IIASA Laxenburg Austria

Bioplastics are considered a sustainable alternative to (partly) substitute fossil-based plastics. Nevertheless, it is still uncertain if the use of biomass for the production of bioplastics can mitigate the environmental impact of fossil-based plastics and simultaneously provide economic benefits. An optimization model is proposed to design biobased supply chain networks that account for economic (total costs) and environmental (greenhouse gas emissions) criteria. Life cycle costing and life cycle assessment were used to evaluate the economic and environmental costs of the biobased polyethylene terephthalate (PET) production using sugar beet and wheat as feedstock. The 100% biobased PET production evidenced higher economic and environmental costs than the 30% biobased PET production. The feedstock selection played a key role, whereas the use of wheat for both 30% and 100% biobased PET had the highest costs and greenhouse gas emissions. It is highlighted that the economic performance of the biobased terephthalic acid (PTA) production, the feedstock selection (sugar beet), and the carbon tax scenario ( > 100(sic)/t CO2) are key parameters for designing a sustainable biobased PET supply chain. (c) 2022 The Author(s). Published by Elsevier Ltd on behalf of Institution of Chemical Engineers.

关键词： mixed-integer programming Life cycle assessment Carbon pricing policies Life cycle costing Sugar beet Wheat

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Encoding Frequency Constraints in Preventive Unit Commitment Using Deep Learning With Region-of-Interest Active Sampling

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IEEE TRANSACTIONS ON POWER SYSTEMS 2022年第3期37卷 1942-1955页

作者： Zhang, Yichen Cui, Hantao Liu, Jianzhe Qiu, Feng Hong, Tianqi Yao, Rui Li, Fangxing Argonne Natl Lab Div Energy Syst Lemont IL 60439 USA Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA Univ Tennessee Dept EECS Knoxville TN 37909 USA

With the increasing penetration of renewable energy, frequency response and its security are of significant concerns for reliable power system operations. Frequency-constrained unit commitment (FCUC) is proposed to address this challenge. Despite existing efforts in modeling frequency characteristics in unit commitment (UC), current strategies can only handle oversimplified low-order frequency response models and do not consider wide-range operating conditions. This paper presents a generic data-driven framework for FCUC under high renewable penetration. Deep neural networks (DNNs) are trained to predict the frequency response using real data or high-fidelity simulation data. Next, the DNN is reformulated as a set of mixed-integer linear constraints to be incorporated into the ordinary UC formulation. In the data generation phase, all possible power injections are considered, and a region-of-interest active sampling is proposed to include power injection samples with frequency nadirs closer to the UFLC threshold, which enhances the accuracy of frequency constraints in FCUC. The proposed FCUC is investigated on the IEEE 39-bus system. Then, a full-order dynamic model simulation using PSS/E verifies the effectiveness of FCUC in frequency-secure generator commitments.

关键词： Generators Stability criteria Frequency response Power system stability Trajectory Renewable energy sources Predictive models Unit commitment renewable integration frequency response mixed-integer programming deep learning active learning

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An iterated greedy matheuristic for scheduling in steelmaking-continuous casting process

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2022年第2期60卷 623-643页

作者： Hong, Juntaek Moon, Kyungduk Lee, Kangbok Lee, Kwansoo Pinedo, Michael L. Pohang Univ Sci & Technol Dept Ind & Management Engn Pohang 37673 South Korea NYU Dept Technol Operat & Stat New York NY USA

Steelmaking-Continuous Casting (SCC) is a bottleneck in the steel production process and its scheduling has become more challenging over time. In this paper, we provide an extensive literature review that highlights challenges in the SCC scheduling and compares existing solution methods. From the literature review, we collect the essential features of an SCC process, such as unrelated parallel machine environments, stage skipping, and maximum waiting time limits in between successive stages. We consider an SCC scheduling problem with as objective the minimisation of the weighted sum of cast break penalties, total waiting time, total earliness, and total tardiness. We formulate the problem as a mixed-integer linear programming model and develop an iterated greedy matheuristic that solves its subproblems to find a near-optimal solution. Through numerical experiments, we show that our algorithm outperforms two types of genetic algorithms when applied to test instances.

关键词： Scheduling steelmaking-continuous casting process hybrid flow shop mixed-integer programming matheuristic

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Model-based solution approach for a short-term flight rescheduling problem in aerial passenger transportation to maritime units

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2022年第6期29卷 3400-3434页

作者： De la Vega, Jonathan Santana, Mateus Pureza, Vitoria Morabito, Reinaldo Bastos, Yan Ribas, Paulo Cesar Univ Fed Sao Carlos Dept Prod Engn BR-13565905 Sao Carlos SP Brazil Petrobras SA Res & Dev Ctr Av Horacio de Macedo 950 Rio De Janeiro RJ Brazil

This paper addresses the problem faced by a Brazilian oil and gas company of recovering flights for passenger transportation (mainly teams of employees) to maritime units. Due to unexpected events such as bad weather or aircraft mechanical failures, the original timetable very often cannot be fully met, resulting in flight delays on the same day or even postponements to the following days. As a result, the operation of the maritime units and the scheduling of employee shifts are affected to some extent. Based on a case study conducted at the company, we present a detailed continuous-time mixed-integer programming model that aims to include pending flights in the daily scheduling of an aerodrome with a minimum overall delay and usage of aircraft (helicopters), subject to flights with different rescheduling priorities, aerodrome and aircraft time windows, single runways at the aerodrome and single landing spots at each maritime unit, postponement and shift regulations, heterogeneous fleet of helicopters, mandatory stops for the crew to rest and have lunch, among others. We also present a discrete-time simplification of the former model and some simple solution approaches based on these models in order to cope with larger problem instances. The approach performance is assessed using real-life problem instances whose data were collected in the case study, using a general-purpose optimization software. The results show the potential of these approaches in dealing with this short-term flight rescheduling problem.

关键词： aircraft recovery problem short-term flight rescheduling aerial passenger transportation mixed-integer programming discrete-time approximations oil and gas industry

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