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Impact of layout strategy and sector-coupling on the multi-criteria assessment of energy technology concepts for residential buildings

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ENERGY 2024年 313卷

作者： Schneeloch, Jana Eldakadosi, Mohamed Fraunhofer UMSICHT Fraunhofer Inst Environm Safety & Energy Technol Osterfelder Str 3 D-46047 Oberhausen Germany

Energy system transformation needs decentralisation, decarbonisation and sector-coupling. Local energy systems are often customarily designed using heuristics for the technology layout. However, optimisation of the technology layout and operation is considered a powerful tool. Therefore, we investigate the influence of an optimised layout and the coupling of the electricity and heat sector on economic, ecological and technological criteria. The criteria comprise the annual costs, CO2 emissions, and the energy performance in terms of self-sufficiency and self-consumption. We consider typical energy technology concepts with fixed technology combinations for the heat and electricity supply of residential buildings. The optimisation is performed using a mixed-integer linear programming under the objective of minimal costs. The assessment is done using the Analytic Hierarchy Process. An optimised layout is found to reduce costs at the expense of emissions and energy performance. In comparison to the heuristic layout, especially the storage sizes differ among the layout strategies. Sector-coupling leads to CO2 emission savings and a higher self-consumption of the PV energy produced within the system. The multi-criteria assessment reveals higher performance scores for heuristically laid out concepts under a local sensitivity analysis of the criteria weights.

关键词： Local energy systems mixed-integer linear programming Multi-criteria assessment Residential energy supply Sector-coupling

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Low-carbon integrated energy system scheduling considering electric vehicle demand response

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JOURNAL OF CLEANER PRODUCTION 2024年 480卷

作者： Wang, Lunjie Luo, Lin Yu, Miao Pei, Xiaodeng Liaoning Petrochem Univ Sch Informat & Control Engn Fushun Liaoning Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou Zhejiang Peoples R China Ningguo Power Supply Co State Grid Anhui Elect Power Co Ltd Huainan Anhui Peoples R China

Under the dual-carbon context, integrating electric vehicles (EVs) into the power grid faces numerous challenges. This paper proposes an electric vehicle demand response (EVDR) mechanism and applies it to a nonlinear integrated energy system (IES) to address carbon emissions and energy efficiency issues. To further reduce carbon emissions, the model incorporates carbon capture technology and carbon penalty mechanisms. The model is transformed into a mixed-integer linear programming (MILP) problem through piecewise linearization and solved using CPLEX. Experimental results show that the integrated energy scheduling model achieves load balancing through flexible EV charging scheduling, increases carbon emission costs by 1.5%, reduces total costs by 2.55%, and significantly enhances system flexibility. Additionally, the model effectively utilizes renewable energy, with zero curtailment costs for wind and photovoltaic power, reducing reliance on traditional energy sources and minimizing waste of renewable energy. The carbon capture system significantly lowers both carbon emission costs and total costs;increasing the capture rate limit by 20% can reduce total costs by 24.29%, enhancing the system's environmental friendliness. The introduction of carbon penalty mechanisms encourages system operators to adopt more environmentally friendly production methods, promoting the transition of energy systems towards low-carbon development. The proposed scheduling model has significant theoretical and practical implications for the intelligent management of future energy systems and the progress towards carbon neutrality.

关键词： Integrated energy system Demand response Electric vehicle Carbon capture systems mixed-integer linear programming

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User-Induced Heuristics for Security-Constrained Unit Commitment: Variable Influence Diving and Variable Significance Neighborhood Search

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IEEE Transactions on Power Systems 2025年第4期40卷 3334-3346页

作者： Li, Peijie Liao, Changtao Qi, Junjian Bai, Xiaoqing Wei, Hua Guangxi University School of Electrical Engineering Nanning530004 China South Dakota State University McComish Department of Electrical Engineering and Computer Science BrookingsSD57007 United States

The heuristics in mixed- integer linear programming (MILP) solvers are mostly general-purpose, which can only recognize some common structures. It sometimes may be challenging for them to obtain a high-quality feasible solution, thus reducing the overall efficiency of the MILP solver for solving large-scale security-constrained unit commitment (SCUC) problems. Based on the knowledge of the SCUC problem structure, this paper proposes two user-induced heuristics: variable influence diving (VID) and variable significance neighborhood search (VSNS). VID can iteratively select and fix fractional binary variables based on system parameters and the characteristics of SCUC, leading to the generation of a high-quality initial solution. In VSNS, a novel neighborhood-defining method is proposed, which identifies and fixes some principal binary variables by calculating a significance indicator. The proposed two heuristics can be built into any MILP solver and used for any node in branching, exploiting the input information after the presolve phase. The two heuristics are implemented in the CBC solver and tested on the IEEE 118- bus system, the Polish 2383- bus system, and the French system with 1888 buses, 1951 buses, and 2848 buses. The results demonstrate that the proposed approach can greatly enhance the computational efficiency for solving the SCUC problem. © 1969-2012 IEEE.

关键词： mixed-integer linear programming

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A mathematical programming model and dispatching rules for the repatriation program scheduling problem

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PUBLIC TRANSPORT 2025年第1期17卷 101-120页

作者： Al-Shihabi, Sameh Al-Abdallat, Yousef Saidan, Motasem N. Univ Sharjah Ind Engn & Engn Management Dept POB 27272 Sharjah U Arab Emirates Univ Jordan Sch Engn Ind Engn Dept Amman 11942 Jordan Univ Sharjah Modeling & Optimizat Sustainable Operat & Supply C Sharjah U Arab Emirates Univ Jordan Sch Engn Chem Engn Dept Amman 11942 Jordan

Commercial flights were temporarily suspended due to the Corona Virus Disease (COVID-19) pandemic in March 2020. Subsequently, several countries had to repatriate their citizens from hosting countries. Typically, flights are planned ahead of time in commercial aviation, after which passengers buy their seats;however, in repatriation, the opposite happens. Citizens express their wish to return to their country first, and flights are scheduled accordingly. Due to the suspension of commercial aviation, decision makers had to rely on their flag-carrying airlines to schedule repatriation flights. These flights were constrained by limitations like the airline's fleet capacity, the availability of quarantine locations, and pilots' availability, while the decision makers' objective was to return the most vulnerable citizens first. The present paper examines this problem, which we call the repatriation program scheduling problem (RPSP). The RPSP objective is to minimize the social impacts of keeping a country's citizens abroad by assigning flights to different countries to repatriate stranded citizens. Therefore, we develop a mixed-integer linear program to model the RPSP and solve the RPSP using a commercial solver. We also suggest and test several dispatching rules that decision makers can use to schedule repatriation flights. We then model a case resembling an actual case and study the heuristics' performances. Heuristics are found to lead to reasonable solutions for large quarantine capacities and limited pilot-airplane assignment options;otherwise, decision makers need to rely on exact solutions.

关键词： Scheduling Repatriation mixed-integer linear programming Dispatching rules Flights Quarantine

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Enhancing pharmaceutical cold supply chain: integrating medication synchronization and diverse delivery modes

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2024年

作者： Potters, Elise Mosalla Nezhad, Behzad Bernard, Victor Johan Hans, Erwin Asadi, Amin Univ Twente Dept Ind Engn & Business Informat Syst NL-7500 AE Enschede Netherlands Tecnol Monterrey Sch Engn & Sci Monterrey 64700 Mexico Sint Maartenskliniek Maartensapotheek NL-6574 NA Nijmegen Netherlands Univ Twente Ctr Healthcare Operat Improvement & Res NL-7500 AE Enschede Netherlands

The significance of last-mile logistics in the healthcare supply chain is growing steadily, especially in pharmacies where the growing prevalence of medication delivery to patients' homes is remarkable. This paper proposes a novel mathematical model for the last-mile logistics of the pharmaceutical supply chain and optimizes a pharmacy's logistical financial outcome while considering medication synchronization, different delivery modes, and temperature requirements of medicines. We propose a mathematical formulation of the problem using mixed-integer linear programming evolved from the actual problem of an outpatient pharmacy of a Dutch hospital. We create a case study by gathering, preparing, processing, and analyzing the associated data. We find the optimal solution, using Python MIP package and the Gurobi solver, which indicates the number of order batches, the composition of these batches, and the number of staff related to the preparation of the order batches. Our results show that our optimal solution increases the pharmacy's logistical financial outcome by 34%. Moreover, we propose other model variations and perform extensive scenario analysis to provide managerial insights applicable to other pharmacies and distributors in the last step of cold supply chains. Based on our scenario analysis, we conclude that improving medication synchronization can significantly enhance the pharmacy's logistical financial outcome.

关键词： supply chain management mixed-integer linear programming healthcare delivery medication synchronization

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Multi-objective route planning of an unmanned air vehicle in continuous terrain: An exact and an approximation algorithm

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European Journal of Operational Research 2025年第3期322卷 960-977页

作者： Dasdemir, Erdi Köksalan, Murat Tezcaner Öztürk, Diclehan Department of Industrial Engineering Hacettepe University Ankara 06800 Turkey Technology and Operations Ross School of Business University of Michigan Ann ArborMI48109 United States Department of Industrial Engineering Middle East Technical University Ankara 06800 Turkey

Unmanned Aerial Vehicles (UAVs) are widely used for military and civilian purposes. Effective route planning is an important component of their successful missions. In this study, we address the route planning problem of a UAV tasked with collecting information from various target locations in a protected terrain. We consider multiple targets, three objectives, and time-dependent information availability. Modeling the movement of UAVs in a continuous terrain in the presence of multiple objectives is complex. Conflicting objectives typically lead to a continuum of efficient trajectory options between two targets. We formulate the routing problem as a mixed-integer programming (MIP) model that captures the movement in the continuous terrain. We demonstrate the superiority of the continuous terrain formulation over the simplified discretized terrain formulation. We also develop an approximation algorithm that reduces the computational requirements of the MIP model substantially while ensuring a desired level of precision. © 2024 Elsevier B.V.

关键词： mixed-integer linear programming

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Model and heuristics for the multi-manned assembly line worker integration and balancing problem

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第24期62卷 8719-8744页

作者： Michels, Adalberto Sato Costa, Alysson M. Univ Melbourne Sch Math & Stat Peter Hall Bldg813 Swanston St Melbourne Vic 3052 Australia Univ Melbourne ARC Training Ctr Optimisat Technol Integrated Meth Melbourne Australia

This paper examines the balancing of assembly lines with multi-manned stations and a heterogeneous workforce. Both topics received considerable attention in the literature, but not in an integrated fashion. Combining these two characteristics gives rise to a highly combinatorial Multi-manned Assembly Line Worker Integration and Balancing Problem. When considering multi-manned stations, the already coupled decisions on assigning tasks to heterogeneous workers and workers to stations must be further linked with task scheduling assessments. We propose a mixed-integer linear programming model and develop two heuristic solution procedures, which tackle the problem with a hierarchical decomposition approach. Computational tests on a large dataset indicate that the proposed method can obtain good primal bounds in short computational times. We demonstrate that these results can be applied to the monolithic model either as a warm start or in a proximity search procedure to obtain synergistic gains with statistically significant differences. From a managerial perspective, we show that multi-manned stations can reduce the assembly line's length even in the presence of a heterogeneous workforce, which is crucial for many industries manufacturing large-size products.

关键词： Assembly line balancing multi-manned station heterogeneous workforce integration mixed-integer linear programming decomposition heuristic proximity search

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A graph-based approach for integrating massive data in container terminals with application to scheduling problem

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第16期62卷 5945-5965页

作者： Liu, Suri Wang, Wenyuan Zhong, Shaopeng Peng, Yun Tian, Qi Li, Ruoqi Sun, Xubo Yang, Yi Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Liaoning Peoples R China Dalian Univ Technol Sch Transportat & Logist Dalian Liaoning Peoples R China Ctr Urban Governance Zhejiang Int Urbanol Res Ctr Hangzhou Peoples R China Dalian Univ Technol Sch Informat & Commun Engn Dalian Liaoning Peoples R China Minist Transport Transport Planning & Res Inst Beijing Peoples R China

The deployment of the Industrial Internet of Things (IIoT) in smart container terminals provides a foundation for sensing and recording all operational processes. However, little effort has been devoted to integrating the massive data regarding interoperability challenges, thus limiting the value of data in advancing the intelligent evolution of ports. In this research, we propose a graph-based approach to organise operational records semantically, thereby facilitating data-driven decision-making in container terminals. We first construct a knowledge graph for operational processes in container terminals, employing a tailored procedure for the automatic conversion of operational records into triples. By utilising the graph information, we propose a novel method that integrates reinforcement learning (RL) with a mathematical solver for optimising scheduling problems. The quay crane scheduling problem (QCSP) is illustrated as an example to elaborate on the technical details. Based on a dataset from a real-world container terminal, numerical studies demonstrate the superiority of the proposed framework in terms of information retrieval efficiency and solution quality compared with the traditional data organisation approach.

关键词： Smart container terminal knowledge graph operational data mixed-integer linear programming reinforcement learning

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Generation Maintenance Scheduling for Power Systems Considering the Risk Quantification of Hybrid Uncertainty

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IEEE Transactions on Power Systems 2025年第4期40卷 3499-3512页

作者： Yang, Xiao Zhao, Yong Li, Yuanzheng Huang, Cheng Ding, Qiang Key Laboratory of Image Information Processing and Intelligent Control of Ministry of Education of China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Power Science Research Institute of State Grid Jiangsu Electric Power Company China China Electric Power Research Institute Company China

The accurate quantification of risk caused by uncertainty forms a crucial foundation for formulating the generation maintenance scheduling (GMS) of power systems. However, the probability distribution functions (PDFs) of uncertain variables such as wind power and load are challenging to model accurately or are unknown, which makes it difficult to measure their economic risk and formulate appropriate GMS for power systems. To address this issue, we consider hybrid uncertainty from wind power and load, and propose a novel interval-probabilistic worst conditional value-at-risk (IP-WCVaR)-based generation maintenance scheduling method. Firstly, a novel IP-WCVaR method is proposed to measure the risk of the interval and probabilistic hybrid uncertainty, and the analytical mathematical model of the IP-WCVaR is derived through typical scenarios of probability correction. On this basis, the positive and negative spinning reserve models are established using the IP-WCVaR and then integrated into the GMS model, which enhances the resilience of the power system. Finally, the new risk-averse GMS model is formulated as the lower and upper boundary optimal models, which are transformed into tractable mixed integer linear programming problems based on the interval extreme value theory. The effectiveness and superiority of the proposed IP-WCVaR method are verified on the modified IEEE 24-bus and IEEE 118-bus power systems. © 1969-2012 IEEE.

关键词： mixed-integer linear programming

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Hydrogen supply chain for future hydrogen-fuelled railway in the UK: Transport sector focused

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年 86卷 1-13页

作者： Xu, Zhan Zhao, Ning Yan, Yan Zhang, Yuqi Zhang, Tongtong Wu, Dawei Hillmansen, Stuart Gao, Shigen Univ Birmingham Birmingham Ctr Rail Res & Educ Birmingham B15 2TT England Southeast Univ Sch Mech Engn Nanjing 210000 Peoples R China Univ Birmingham Mech Engn Birmingham B15 2TT England Univ Birmingham Birmingham Ctr Energy Storage Birmingham B15 2TT England Univ Birmingham Sch Chem Engn Birmingham B15 2TT England Beijing Jiaotong Univ Sch Automat & Intelligence Beijing 100044 Peoples R China

Though being attractive on railway decarbonisation for regional lines, excessive cost caused by immature hydrogen supply chain is one of the significant hurdles for promoting hydrogen traction to rolling stocks. Therefore, we conduct bespoke research on the UK's hydrogen supply chain for railway, concentrating on hydrogen transportation. Firstly, a map for the planned hydrogen production plants and potential hydrogen lines is developed with the location, capacity, and usage. A spatially explicit model for the hydrogen supply chain is then introduced, which optimises the existing grid-based methodology on accuracy and applicability. Compressed hydrogen at three pressures, and liquid hydrogen are considered as the mediums, incorporating by road and rail transport. Furthermore, three scenarios for hydrogen rail penetration are simulated respectively to discuss the levelised cost and the most suitable national transport network. The results show that the developed model with mix-integer linear programming (MILP) can well design the UK's hydrogen distribution for railway traction. Moreover, the hydrogen transport medium and vehicle should adjust to suit for different era where the penetration of hydrogen traction varies. The levelised cost of hydrogen (LCOH) decreases from 6.13 pound/kg to 5.13 pound/kg on average from the conservative scenario to the radical scenario. Applying different transport combinations according to the specific situation can satisfy the demand while reducing cost for multi-supplier and multitargeting hydrogen transport.

关键词： Hydrogen supply chain Railway Hydrogen transport Levelised cost of hydrogen mixed-integer linear programming

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