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23rd International Conference on Computational Science, ICCS 2023

作者： He Sun, Jia Choudhury, Salimur Queen’s University KingstonON Canada

ISBN: (纸本)9783031360237

mixed integer programming (MIP) is an extremely versatile subclass of mathematical optimization problems. Applications of MIP are ubiquitous in our world today, ranging from scheduling to network design to production planning. The standard approach in state-of-the-art commercial solvers is called branch-and-cut. The selection of these cuts is an integral part of the branch-and-cut process as high-quality cuts can greatly increase solving efficiency. Currently, cut selection is decided by heuristics that both require expert knowledge and lack generalizability. In this paper, we propose an efficient and highly generalizable cut selection scheme based on semi-supervised learning. First, we design a cut evaluation metric that labels cuts based on whether they are efficient or not. Then, we train a deep learning classification model with unsupervised pre-training as a ranking function for cuts. In our evaluation, the proposed model outperforms standard heuristics and is comparable to existing machine learning approaches. Furthermore, the model is shown to be generalizable over both problem size and problem class. ©The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： mixed-integer linear programming

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mixed-Precision integer-Arithmetic-Only Neural Network-Based Equalizers for DML-Based Short-Reach IM/DD Systems 49

Mixed-Precision Integer-Arithmetic-Only Neural Network-Based...

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49th European Conference on Optical Communications, ECOC 2023

作者： Xu, Zhaopeng Ji, Honglin Yang, Yu Qiao, Gang Wu, Qi Li, Jia Lu, Weiqi Liu, Lulu Wang, Shangcheng Wei, Jinlong He, Zhixue Hu, Weisheng Shieh, William Peng Cheng Laboratory Shenzhen518055 China State Key Lab of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Shanghai200240 China School of Engineering Westlake University Hangzhou310024 China

ISBN: (纸本)9781839539954

We propose mixed-precision integer-Arithmetic-only NNs for low-complexity equalization in a 50-Gb/s 25-km DML-based PAM4 IM/DD link based on input neuron partitioning. The proposed scheme avoids floating-point weight operations and saves 26.3% memory at a minimum NN size compared with fixed-precision quantization counterpart without degrading BER. © The Institution of Engineering & Technology 2023.

关键词： mixed-integer linear programming

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A location-allocation model for influenza pandemic outbreaks: A case study in India

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OPERATIONS MANAGEMENT RESEARCH 2022年第1-2期15卷 487-502页

作者： Devi, Yashoda Patra, Sabyasachi Singh, Surya Prakash Indian Inst Management Kashipur Operat Management Sr Decis Sci Kashipur 244713 Uttarakhand India Indian Inst Technol Delhi Dept Management Studies Delhi India

Previous pandemics in 1918, 1957, 1968, and the ongoing COVID-19 pandemic have provided sufficient evidence of health concerns caused by influenza pandemics. The existing health care system is overwhelmed by the surging demand of susceptible and infected individuals due to the COVID-19 outbreak. It is crucial to identify and isolate infected individuals to prevent pandemic spread. Thus, a mixed-integer linear programming model is proposed in this study for the location-allocation of health care facility networks (i.e., temporary testing laboratories). The objective of this study is to ensure that test samples from various geographical locations reach testing laboratories as soon as possible and at minimum cost to ensure timely testing. Hence, the proposed model has two objectives: (i) minimization of the total cost and (ii) minimization of the maximum travel time from a patient node to a testing facility. Furthermore, to prevent capacity underutilization, the capacity of temporary testing laboratories is tailored in the model. A case study in Maharashtra, India, is used to demonstrate the real-life applicability of the proposed model. The study results has interesting implications for decision- and policy-makers.

关键词： Healthcare facility location mixed-integer linear programming Pandemic outbreak Healthcare in developing countries

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A linear AC-OPF Formulation for Unbalanced Distribution Networks

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2021年第5期57卷 4462-4472页

作者： Giraldo, Juan S. Vergara, Pedro P. Lopez, Juan Camilo Nguyen, Phuong Hong Pateralds, Nikolaos G. Univ Twente EEMCS Fac Math Operat Res Grp NL-7522 NB Enschede Netherlands Delft Univ Technol Intelligent Elect Power Grids Grp NL-2628 CD Delft Netherlands Univ Estadual Campinas Sch Elect & Comp Engn BR-13083970 Campinas Brazil Eindhoven Univ Technol Energy Syst Grp NL-5612 AP Eindhoven Netherlands

linear optimal power flow (OPF) formulations are powerful tools applied to a large number of problems in power systems, e.g., economic dispatch, expansion planning, state estimation, congestion management, electricity markets, among others. This article proposes a novel mixed-integer linear programming formulation for the ac-OPF of three-phase unbalanced distribution networks. The model aims to minimize the total energy production cost while guaranteeing the network's voltage and current magnitude operational limits. New approximations of the Euclidean norm, which is present in the calculation of nodal voltage and branch current magnitudes, are introduced by applying a linear transformation of weighted norms and a set of intersecting planes. The accuracy, optimality, feasibility, and scalability of the proposed linearizations are compared with common linear approximations in the literature using two unbalanced distribution test systems. The obtained results show that the proposed formulation is computationally more efficient (almost twice) while being as accurate and more conservative than the benchmarked approaches with maximum errors lower than 0.1%. Thus, its potential application in a variety of distribution systems operation and planning optimization problems is endorsed.

关键词： Substations programming Computational modeling Scalability Reactive power Distribution networks Admittance Ac optimal power flow Euclidean norm approximation mixed-integer linear programming unbalanced distribution networks

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Capacitated Vehicle Routing Problem with Time Windows

Capacitated Vehicle Routing Problem with Time Windows

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21st International Symposium on Production Research (ISPR) - Digitizing Production System

作者： Tanel, Aleyna Kinay, Begum Karakul, Deniz Ozyoruk, Efecan Iskifoglu, Elif Ozogul, Ezgi Ustaoglu, Meryem Yuksel, Damla Ornek, Mustafa Arslan Yasar Univ Dept Ind Engn Izmir Turkey

ISBN: (纸本)9783030904210;9783030904203

Since distribution activities have great importance for firms, supply management is a widely studied concept in many sectors. This study demonstrates an application of a Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). The problem is in the form of a fixed destination multi depots visited by multi-travelling salesmen and the distance-travel time matrix is assumed to be asymmetric. The objective of the problem is to minimize the longest route time of each vehicle. This is achieved by developing a mixed-integer linear programming model (MILP) for the problem. Additionally, since the problem is NP-hard, a general heuristic method is developed to solve the problem for larger instances in negligible computational times. Results show that the balance between the individual route times of the vehicles is provided and the time window limit is ensured. The paper also discusses the results and presents concluding remarks.

关键词： Multi depot Multi-travelling salesman problem MDMTSP Min-Max mTSP mixed-integer linear programming Capacitated vehicle routing problem Time windows Heuristics 2opt algorithm

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Physics-informed deep learning and linear programming for efficient optimization of combined cycle power plants

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 232卷

作者： Hosseini, Mohammad Mehdi Meguerdijian, Saro Golmohammadi, Azarang Beyond Limits Glendale CA 91203 USA

Efficient operation of combined cycle power plants (CCPP) involves solving mixed -integer nonlinear optimization problems, constrained by thermodynamic differential equations, thermal constraints, and temporal dependencies between variables. The optimization problem aims to maximize profit for day -ahead operation by minimizing short-term and long-term costs, and accounting for volatility in ambient conditions and energy price. In operational terms, the challenge lies in developing an efficient and robust optimization algorithm capable of determining optimal values for thousands of temporal variables. It must also have the capacity to evaluate a multitude of scenarios encompassing various weather and market conditions. To address this challenge, this paper proposes a hybrid optimizer that utilizes deep learning models trained on historical operational data of a real CCPP, along with a surrogate physics -driven piecewise-linear model of CCPP, to determine the optimal values of thousands of control setpoints, with strong temporal co -dependencies, within a two -minute runtime required by industry constraints. A multi -resolution architecture is proposed to efficiently implement this optimizer. The algorithm sequentially solves the problem in coarse and fine resolutions to accelerate convergence. Final results are checked for feasibility using pre -trained deep learning models, ensuring accuracy and validity. The study shows that the proposed model provides comparable results to the exact solution while reducing computational costs, up to 95%. This research has practical implications for power plant system operators, as well as traders and dispatchers, enabling them to make optimal decisions for day -ahead contracting purposes and CCPP operation in a reasonable time, ultimately leading to more efficient and cost-effective power plant management. Furthermore, the methodology introduced in this work could potentially be extended to other industrial applications that requ

关键词： Combined cycle power plants (CCPP) Day-ahead optimization mixed-integer linear programming Deep learning models

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Short-term peak shaving model of cascade hybrid pumped storage hydropower station retrofitted from conventional hydropower

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 162卷

作者： Luo, Bin Liu, Xinyu Chen, Yongcan Zhou, Can Long, Xin Tsinghua Univ State Key Lab Hydrosci & Engn Beijing 100084 Peoples R China Tianfu Yongxing Lab Chengdu 610213 Sichuan Peoples R China Southwest Univ Sci & Technol Sch Civil Engn & Architecture Mianyang 621010 Sichuan Peoples R China Tsinghua Univ Sichuan Energy Internet Res Inst Chengdu 610042 Sichuan Peoples R China

The integration of pumped storage units with conventional cascade hydropower to form a cascade hybrid pumped storage hydropower station (CHPHPS) is considered one of the effective approaches to expedite the development of pumped storage. However, more complex hydraulic and electrical coupling relationships pose challenges to the operation of these stations. In this study, the typical peak shaving mode of CHPSHS is initially analyzed, and a corresponding peak shaving model is proposed. The objective function of the model is to minimize the peak-valley difference in the remaining load on the grid. In terms of modelling, the refined modelling method using the unit as the scheduling unit is used to successfully reflect the differentiated operating characteristics of different types of units. In terms of model solving, state variables are introduced to facilitate the decoupling and switching of operational states. Furthermore, linearization techniques such as piecewise linear approximation, integration of 0-1 integer variables, and discretization of generation head are utilized to handle nonlinear constraints. The model is converted into a mixed-integer linear programming (MILP) problem and solved using the CPLEX tool with Java programming. A case study conducted in southwest China shows that CHPSHS can enhance the peak shaving ability and reduce the peak-valley difference in the remaining load on the grid, providing valuable reference for retrofitting conventional cascade hydropower stations.

关键词： Cascade hydropower stations Hybrid pumped storage Short-term peak shaving mixed-integer linear programming

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Physics-informed machine learning with optimization-based guarantees: Applications to AC power flow

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 157卷

作者： Jalving, Jordan Eydenberg, Michael Blakely, Logan Castillo, Anya Kilwein, Zachary Skolfield, J. Kyle Boukouvala, Fani Laird, Carl Sandia Natl Labs 1515 Eubank SE Albuquerque NM 87185 USA Georgia Inst Technol Dept Chem & Biomol Engn 311 Ferst Dr Atlanta GA 30332 USA Carnegie Mellon Univ Dept Chem Engn 5000 Forbes Ave Pittsburgh PA 15213 USA Pasteur Labs Brooklyn NY 11205 USA NextEra Analyt St Paul MN 55107 USA

This manuscript presents a complete framework for the development and verification of physics -informed neural networks with application to the alternating -current power flow (ACPF) equations. Physics -informed neural networks (PINN)s have received considerable interest within power systems communities for their ability to harness underlying physical equations to produce simple neural network architectures that achieve high accuracy using limited training data. The methodology developed in this work builds on existing methods and explores new important aspects around the implementation of PINNs including: (i) obtaining operationally relevant training data, (ii) efficiently training PINNs and using pruning techniques to reduce their complexity, and (iii) globally verifying the worst -case predictions given known physical constraints. The methodology is applied to the IEEE -14 and 118 bus systems where PINNs show substantially improved accuracy in a data -limited setting and attain better guarantees with respect to worst -case predictions.

关键词： Neural networks mixed-integer linear programming Verification Physics-informed machine learning AC power flow

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Design and investment strategy optimization of district cooling system during the ramp-up phase

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ENERGY AND BUILDINGS 2024年 321卷

作者： Su, Lingqi Yang, Zheng Jain, Rishee K. ENGIE Siradel Energy Informat Lab St Gregoire France Stanford Univ Urban Informat Lab Stanford CA USA

District Cooling (DC) systems, known for their efficiency and integration of renewable energy sources, encounter substantial design and investment complexities during the initial ramp-up phase. This critical phase is characterized by increasing cooling demand, necessitating a scientific approach to equipment selection and sizing that spans multiple temporal scales. A key yet frequently underestimated element in DC systems is the inclusion of backup chiller. While traditionally reserved to emergency use, its proactive interactions with regular system operations can significantly diminish operational expenses, despite the inherent complexities. Our study presents an innovative mixed-integer linear programming (MILP) optimization framework. This framework is designed to manage and optimize the intricate DC system design and investment strategy during the ramp-up phase, with a particular focus on the strategic inclusion of backup chillers. To demonstrate this framework, our research undertakes a comprehensive analysis of a real-world DC project in Kuala Lumpur. The findings show that the MILP optimization delivers substantial reductions in Life Cycle Costs (LCC) up to 3.33% across various system configurations. This is of significant value for energy efficiency and sustainable urban energy systems.

关键词： District cooling system Load ramp-up Optimization mixed-integer linear programming System design Investment strategy

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Optimization of battery energy storage system (BESS) sizing in different electricity market types considering BESS utilization mechanisms and ownerships

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JOURNAL OF CLEANER PRODUCTION 2024年 470卷

作者： Zhang, Ruixiaoxiao Lee, Minhyun Huang, Lijie Ni, Meng Hong Kong Polytech Univ Dept Bldg & Real Estate Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Smart Energy RISE Dept Bldg & Real Estate Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Sustainable Urban Dev RISUD Dept Bldg & Real Estate Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Res Inst Smart Energy RISE Kowloon Hong Kong Peoples R China

To elucidate the optimal techno-economic role of battery energy storage system (BESS), this study proposes optimal sizing of BESS in various scenarios based on BESS installation in existing photovoltaic systems. The proposed scenarios include different electricity market types (i.e., peer-to-grid, peer-to-peer, and energy storage sharing) considering utilization mechanism (i.e., individual-design or shared-design) and ownership (i.e., user- owned or developer-owned) of the BESS. Towards this end, this study develops comprehensive and systematic mathematical models for BESS sizing using mixed-integer linear programming algorithm, and the BESS sizing is optimized to maximize the total net present value. The developed models are validated in the Hong Kong context considering four peers, and the empirical data of electric load profile as well as the photovoltaic installed capacity are collected. As a result, it is indicated that the optimal BESS capacity in energy storage sharing scenario is the least. In terms of electricity bill saving, user-owned BESS is regarded as the model yielding the highest electricity bill savings. The breakdown of net present value exposes that under the same ownership, the optimal BESS capacity is determined by different utilization mechanisms. Regarding the total net present value, it is found that developer-owned BESS could achieve the maximum economic merits. Nonetheless, the distribution loss through energy storage sharing is also the highest. The policy implications of this study primarily emphasize incentivizing user-owned BESS, promoting energy storage sharing, supporting shared BESS infrastructure, and encouraging a diversity of electricity trading mechanisms to enhance the integration and efficiency of renewable energy systems.

关键词： Battery energy storage system Sizing optimization mixed-integer linear programming Utilization mechanism and ownership Peer trading Energy storage sharing

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