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2nd International Conference on Cyber-Energy Systems and Intelligent Energy, ICCSIE 2024

作者： Jun, Dong Bin, Guo Shang, Xuebin Wu, Xiaoqiang Yun, Zhao Lu, Yuxin Guangdong Power Grid Co. Ltd. Guangzhou Power Supply Bureau Guangdong China Csg Electric Power Research Institute Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid Guangdong China

ISBN: (纸本)9798350373318

Bad data identification is an essential prerequisite for state estimation in distribution networks. When multiple leverage measurements with bad data occur in the system, traditional residual-based methods are ineffective in identifying bad data, leading to potential misjudgments and missed detections. Additionally, traditional mixed-integer nonlinear programming methods for solving this issue suffer from low efficiency due to the large number of integer variables and nonlinear constraints in the model. Therefore, a bad data identification method based on mixed-integer linear programming is proposed. By employing the data-physical fusion-driven linearization method for linearization, a mixed- integer linear programming-based bad data identification model is constructed. Case studies demonstrate that the bad data identification method based on mixed-integer linear programming can significantly improve solution efficiency while ensuring accuracy in identification. © 2024 IEEE.

关键词： mixed-integer linear programming

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MILP Model for Energy- Efficient Scheduling in Cloud Data Center

MILP Model for Energy- Efficient Scheduling in Cloud Data Ce...

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2024 International Conference on Advanced Technologies for Communications, ATC 2024

作者： Phuong, Tuan Tran Quoc, Huy Nguyen Duy, Khanh Ly Doan Nhu, Tai Do Ho Chi Minh City University of Foreign Languages - Information Technology Faculty of Information Technology Ho Chi Minh Viet Nam Saigon University Information Technology Department Ho Chi Minh Viet Nam University of Information Technology Vnuhcm Faculty of Information Systems Ho Chi Minh Viet Nam

ISBN: (纸本)9798350353983

In the Era of Industry 4.0, the shift from physical infrastructure to cloud data centers has become a growing trend among companies and enterprises of various scales. However, the growth of modern cloud data centers has raised concerns about the energy consumption of these systems. Several existing studies on energy-efficient scheduling have proposed linear programming models before introducing heuristic algorithms to solve the problem. These models are typically straightforward and sometimes do not fully represent the mathematical aspects of the problem's constraints. Our research aims to provide a deeper mathematical insight into the energy-efficient scheduling problem. This paper focuses on heterogeneous cloud data centers employing virtualization technology and predefined job requests, including release time, deadline, and execution time. We propose a mixed integer linear programming (MILP) model to optimize the scheduling solution. The model includes an objective function built to compute the optimal solution, along with constraints represented by equations and inequalities to ensure the solution meets the requirements of problem. Additionally, Dynamic Power Management (DPM) is integrated into the model to further reduce the energy consumption. Experimental results demonstrate that integrating the proposed MILP model with the DPM technique significantly reduces energy consumption compared to the model that do not apply DPM. © 2024 IEEE.

关键词： mixed-integer linear programming

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A Study of a State-of-the-Art Algorithm for mixed-integer linear Bilevel Optimization 22nd

A Study of a State-of-the-Art Algorithm for Mixed-Integer Li...

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22nd Regular meeting of the Portuguese-Association-of-Operational-Research (APDIO)

作者： Alves, Maria Joao Antunes, Carlos Henggeler Soares, Ines Univ Coimbra Fac Econ CeBER INESC Coimbra Coimbra Portugal Univ Coimbra Dept Elect & Comp Engn INESC Coimbra Coimbra Portugal INESC Coimbra Coimbra Portugal

ISBN: (纸本)9783031464393;9783031464386

In this paper, we address mixed-integer linear bilevel optimization problems. In bilevel optimization, a (lower-level) optimization problem is included in the constraints of another (upper-level) optimization problem. Thus, this framework is especially adequate to model hierarchical decision processes. We analyze a state-of-the-art algorithm developed for this type of problems, which is based on an optimal-value-function reformulation and consists of an iterative deterministic bounding procedure. Computational experiments are made with data instances with different characteristics. The performance of the algorithm in the different groups of problems is discussed.

关键词： Bilevel optimization mixed-integer linear programming Optimal value-function reformulation

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Optimal Dynamic Reconfiguration of Distribution Networks

Optimal Dynamic Reconfiguration of Distribution Networks

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North American Power Symposium (NAPS)

作者： Fatima, Rida Butt, Hassan Zahid Li, Xingpeng Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA

ISBN: (纸本)9798350315097

The aim of distribution networks is to meet their local area power demand with maximum reliability. As the electricity consumption tends to increase every year, limited line thermal capacity can lead to network congestion. Continuous development and upgradation of the distribution network is thus required to meet the energy demand, which poses a significant increase in cost. The objective of this research is to analyze distribution network topologies and introduce a topology reconfiguration scheme based on the cost and demand of electricity. Traditional electrical distribution networks are static and inefficient. To make the network active, an optimal dynamic network topology reconfiguration (DNTR) is proposed to control line switching and reconnect some loads to different substations such that the cost of electricity can be minimized. The proposed DNTR strategy was tested on a synthetic radial distribution network with three substations each connecting to an IEEE 13-bus system. Simulation results demonstrated significant cost saving in daily operations of this distribution system.

关键词： Distribution network topology Dynamic distribution system mixed-integer linear programming Network reconfiguration Power system flexibility Topology control

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Power System Recovery Coordinated with (Non-)Black-Start Generators

Power System Recovery Coordinated with (Non-)Black-Start Gen...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Zhao, Meng Maloney, Patrick R. Ke, Xinda Ceballos, Juan Carlos Bedoya Fan, Xiaoyuan Elizondo, Marcelo A. Pacific Northwest Natl Lab Richland WA 99354 USA

ISBN: (纸本)9781665464413

Power restoration is an urgent task after a blackout, and recovery efficiency is critical when quantifying system resilience. Multiple elements should be considered to restore the power system quickly and safely. This paper proposes a recovery model to solve a direct-current optimal power flow (MOPE) based on mixed-integer linear programming (MILP). Since most of the generators cannot start independently, the interaction between black-start (BS) and non-black-start (NBS) generators must be modeled appropriately. The energization status of the NBS is coordinated with the recovery status of transmission lines, and both of them are modeled as binary variables. Also, only after an NBS unit receives cranking power through connected transmission lines, will it be allowed to participate in the following system dispatch. The amount of cranking power is estimated as a fixed proportion of the maximum generation capacity. The proposed model is validated on several test systems, as well as a 1393-bus representation system of the Puerto Rican electric power grid. Test results demonstrate how the recovery of NBS units and damaged transmission lines can he optimized, resulting in an efficient and well-coordinated recovery procedure.

关键词： black-start generators direct-current optimal power flow power system recovery mixed-integer linear programming non-black-start generators

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Battery electric buses charging schedule optimization considering time-of-use electricity price

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Journal of Intelligent and Connected Vehicles 2022年第2期5卷 138-145页

作者： Jia He Na Yan Jian Zhang Yang Yu Tao Wang Beijing Key Laboratory of Traffic Engineering Beijing University of TechnologyBeijingChina Transport for New South Wales SydneyAustralia School of Vehicle and Mobility Tsinghua UniversityBeijingChina

Purpose–This paper aims to optimize the charging schedule for battery electric buses(BEBs)to minimize the charging cost considering the time-ofuse electricity ***/methodology/approach–The BEBs charging schedule optimization problem is formulated as a mixed-integer linear programming *** objective is to minimize the total charging cost of the BEB *** charge decision of each BEB at the end of each trip is to be *** types of constraints are adopted to ensure that the charging schedule meets the operational requirements of the BEB fleet and that the number of charging piles can meet the demand of the charging ***–This paper conducts numerical cases to validate the effect of the proposed model based on the actual timetable and charging data of a bus *** results show that the total charge cost with the optimized charging schedule is 15.56%lower than the actual total charge cost under given *** results also suggest that increasing the number of charging piles can reduce the charging cost to some extent,which can provide a reference for planning the number of charging ***/value–Considering time-of-use electricity price in the BEBs charging schedule will not only reduce the operation cost of electric transit but also make the best use of electricity resources.

关键词： Battery electric bus Charging schedule mixed-integer linear programming Time-of-use electricity price

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A Data-Driven Optimization Method for Strongly Non-Separable mixed-integer Problems 13

A Data-Driven Optimization Method for Strongly Non-Separable...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Sato, Takahiro Graduate School of Engineering Muroran Institute of Technology Muroran Japan

ISBN: (纸本)9798350308365

This paper presents a data-driven optimization method based on tree search-based reinforcement learning to solve strongly non-separable mixed-integer problems. With this method, some variables that mainly affect performance and non-separability are stochastically determined through a modified tree search. Then, other variables are stochastically estimated and optimized by evolutionary algorithms. These probabilities are adapted with a reinforcement learning approach. As a result, the present method makes it possible to easily solve non-separable mixed-integer problems. Moreover, in the reinforcement learning, previous optimization results can be utilized in the next optimizations. The present method is applied to a design problem of an induction motor, which is one of the practical engineering and non-separable mixed-integer optimization problems. It is shown that the present method can solve the design problem under various conditions. © 2024 IEEE.

关键词： mixed-integer linear programming

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An optimization model for effective reserve configuration considering source and load uncertainty 4

An optimization model for effective reserve configuration co...

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4th International Conference on New Energy and Power Engineering, ICNEPE 2024

作者： Bao, Bo Xie, Xiangzhong Yang, Yun Yang, Yinguo Zheng, Hao Guo, Ruipeng Guangdong Power Grid Corporation Power Dispatching and Control Center Guangzhou China Zhejiang University Polytechnic Institute Hangzhou China Zhejiang University College of Electrical Engineering Hangzhou China

ISBN: (纸本)9798331516086

With the rapid exploration of the wind and solar energies, the uncertainty of sources and loads bring risks to the security of power grid. The traditional reserve configuration method did not consider the impact of line congestion, resulting in a large number of ineffective reserve capacity. In this paper, the effective reserve configuration problem is transformed into the worst impact that the power grid can withstand. The bad situation mainly considers the new energy output is less than the predicted output, the load oversupply and the reduction of direct current (DC) power input. The security constraints in both predicted state and adverse state, as well as the relationship constraints between the two, are the constraints of reserve configuration problem. This proposed model is a mixed integer linear programming problem, which can be solved by optimization software, such as CPLEX. The analysis results of the Southern Power Grid case show that the combination of units obtained by this method can reduce the ineffective standby capacity due to line congestion, which is of practical value to the power grid scheduling. © 2024 IEEE.

关键词： mixed-integer linear programming

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Unit Commitment in a System Integrated with Adiabatic Compressed Air Energy Storage 6

Unit Commitment in a System Integrated with Adiabatic Compre...

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6th International Conference on Energy, Power and Grid, ICEPG 2024

作者： Zhou, Yu Zhang, Xuelin Li, Guangkuo Ma, Linrui Wang, Yazhou Zhang, Tong Xue, Xiaodai Tsinghua University Department of Electrical Engineering Beijing China Qinghai University School of Energy and Electrical Engineering Xining China

ISBN: (纸本)9798350377798

Adiabatic compressed air energy storage (A-CAES) has grown rapidly in China recently. The paper investigates the unit commitment (UC) problem for a hybrid system containing thermal plants and an A-CAES plant. The time-varying charging characteristics of A-CAES are analyzed and then modeled with a pressure-based state of charge (SOC). The hybrid system's UC model is constructed and finally reformulated in a mixed-integer linear programming (MILP) way. A modified IEEE-30 bus system is used to verify the model's effectiveness. Numerical results demonstrate that integrating A-CAES could reduce the number of unit startups and shutdowns and lower the system operating costs. Finally, the effect of the initial state of A-CAES on the UC results is studied. © 2024 IEEE.

关键词： mixed-integer linear programming

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Design and optimization of a sustainable and resilient mask supply chain during the COVID-19 pandemic: a multi-objective approach

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ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2022年 1-46页

作者： Alizadeh-Meghrazi, Milad Tosarkani, Babak Mohamadpour Amin, Saman Hassanzadeh Popovic, Milos R. Ahi, Payman Univ Toronto Inst Biomed Engn Toronto ON Canada Univ British Columbia Sch Engn Okanagan Campus Kelowna BC Canada Toronto Metropolitan Univ Dept Mech & Ind Engn Toronto ON Canada Myant Inc Etobicoke ON Canada

Wearing a mask or a face covering became mandatory in indoor public spaces to reduce the spread of coronavirus disease 2019 (COVID-19). The Ontario government (i.e., a province of Canada) encouraged medical supply producers to switch their operations to produce personal protective equipment (e.g., masks) during the COVID-19 pandemic. In this regard, there are several uncertain parameters (e.g., operational costs, market demand, and capacity levels of facilities) affecting the performance of producers in a medical supplies market. In this study, we propose a flexible optimization model to configure a robust mask supply chain network under uncertainty. Furthermore, companies are supposed to undertake their operations based on sustainable manners, in compliance with provincial policy, in Ontario. Therefore, the proposed flexible optimization model is extended to a robust multi-objective model to investigate sustainable strategies in a mask supply chain network design problem. The applicability of this model is demonstrated for the Greater Toronto Area, Canada.

关键词： Supply chain network Multi-objective model mixed-integer linear programming Robust optimization COVID-19 outbreak

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