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IEEE/AIAA Transportation Electrification Conference / Electric Aircraft Technologies Symposium (ITEC+EATS)

作者： Mohammadi, Masoud Heydari, Shoeib Fajri, Poria Harirchi, Farshad Yi, Zonggen Univ Nevada Elect & Biomed Engn Dept Reno NV 89557 USA Amazon Web Serv Detroit MI USA Idaho Natl Lab Energy Storage & Elect Transportat Dept Idaho Falls ID USA

ISBN: (纸本)9781665405607

This paper focuses on finding an optimal energy-aware speed trajectory of an Autonomous Electric Vehicle (AEV) considering regenerative braking capability and its limitations. A position-based Electric Vehicle (EV) energy consumption model is used to emulate vehicle-road operating conditions. It is assumed that the EV is driven in an urban area where the route is only constrained by maximum speed limits and traffic signs. The eco-driving problem is formulated as a mixed integer linear programming (MILP) problem and is solved for two different case studies to demonstrate the importance of considering regenerative braking in identifying optimal speed trajectory of AEVs. The MILP problem is coded in Python and CPLEX is used as a solver for the optimization problem. The results show a variation in the optimal speed trajectories and confirm that when regenerative braking limitations are considered in the calculations leading to an energy-aware speed trajectory, energy consumption can be reduced. This study sets forth a framework for optimizing the braking profile of an AEV by realistically taking into account the vehicle's regenerative braking limitations which ultimately yields an optimal speed trajectory.

关键词： Energy consumption Urban areas Decision making Electric vehicles Trajectory mixed integer linear programming Optimization

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Research on Equipment Configuration and Operation Optimization of Microgrid System 4

Research on Equipment Configuration and Operation Optimizati...

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4th Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Meng, Zhengji Meng, Liang Wang, Lei Zhou, Hao Hu, Xuekai State Grid Hebei Elect Power Res Inst Grid Res Ctr Shijiazhuang Hebei Peoples R China

ISBN: (纸本)9781665479141

The microgrid system is an effective means to improve the energy supply and consumption mode of the system. Therefore, this paper studies a microgrid system equipment configuration and operation optimization method. Firstly, each equipment in the microgrid system is modeled;secondly, with the goal of optimizing the overall economy of investment and operation, a single-layer solution model for equipment capacity planning and operation optimization is constructed. Finally, the fast solution of the model is realized by the mixed integer linear programming algorithm. The results of an example show that the proposed method can realize the optimization of equipment configuration and operation of the microgrid system.

关键词： microgrid mixed integer linear programming configuration planning operational planning

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Toward Low-Overhead Inter-Switch Coordination in Network-Wide Data Plane Program Deployment 42

Toward Low-Overhead Inter-Switch Coordination in Network-Wid...

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42nd IEEE International Conference on Distributed Computing Systems (ICDCS)

作者： Chen, Xiang Liu, Hongyan Xiao, Qingjiang Guo, Kaiwei Sun, Tingxin Ling, Xiang Liu, Xuan Huang, Qun Zhang, Dong Zhou, Haifeng Zhang, Fan Wu, Chunming Zhejiang Univ Hangzhou Peoples R China Peking Univ Beijing Peoples R China Fuzhou Univ Fuzhou Peoples R China Chinese Acad Sci Inst Software Beijing Peoples R China Yangzhou Univ Yangzhou Jiangsu Peoples R China Southeast Univ Nanjing Peoples R China

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

In modern networks, administrators realize their desired functions such as network measurement in several data plane programs. They often employ the network-wide program deployment paradigm that decomposes input programs into matchaction tables (MATs) while deploying each MAT on a specific programmable switch. Since MATs may be deployed on different switches, existing solutions propose the inter-switch coordination that uses the per-packet header space to deliver crucial packet processing information among switches. However, such coordination introduces non-trivial per-packet byte overhead, leading to significant end-to-end network performance degradation. In this paper, we propose Hermes, a program deployment framework that aims to minimize the per-packet byte overhead. The key idea of Hermes is to formulate the network-wide program deployment as a mixed-integer linear programming (MILP) problem with the objective of minimizing the per-packet byte overhead. In view of the NP hardness of the MILP problem, Hermes further offers a greedy-based heuristic that solves the problem in a near-optimal and timely manner. We have implemented Hermes on Tofino-based switches. Our experiments show that compared to existing frameworks, Hermes decreases the per-packet byte overhead by 156 bytes while preserving end-to-end performance in terms of flow completion time and goodput.

关键词： Degradation Impedance matching Computational modeling Switches mixed integer linear programming Distributed computing

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Heterogeneous Coded Distributed Computing with Nonuniform Input File Popularity

Heterogeneous Coded Distributed Computing with Nonuniform In...

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IEEE International Conference on Communications (ICC)

作者： Deng, Yong Dong, Min Ontario Tech Univ Dept Elect Comp & Software Engn Oshawa ON Canada

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

This paper studies the heterogeneous coded distributed computing (CDC) where input files required for job access have nonuniform popularity. We propose a file placement strategy that can handle an arbitrary number of input files and a nested coded shuffling strategy to effectively explore coded multicasting opportunities. We then formulate the joint optimization of the proposed file placement strategy and shuffling design variables into a mixed-integer linear programming (MILP) problem. To reduce the computational complexity, we propose a simple two-file-group-based approach to obtain an approximate solution. Numerical results show that the proposed two-file-group-based approach achieves nearly the same performance as solving the MILP problem using the conventional branch-and-cut method but with substantially lower computational complexity.

关键词： Conferences Multicast communication mixed integer linear programming Distributed computing Computational complexity Optimization

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AUTOMATING ANTENNA SCHEDULING PROBLEMS USING QUANTUM COMPUTING AND DEEP REINFORCEMENT LEARNING

AUTOMATING ANTENNA SCHEDULING PROBLEMS USING QUANTUM COMPUTI...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Wilson, Brian Goh, Edwin Guillaume, Alex Alimo, Ryan Claudet, Thomas Venkataram, Hamsa CALTECH Jet Prop Lab 4800 Oak Grove Dr Pasadena CA 91109 USA

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

A challenging, week-long antenna scheduling problem (12 antennas, 30 missions to track) was solved using three techniques: formulated as a QUBO problem and solved on the D-Wave hybrid (quantum-classical) solver, formulated as a MILP problem and conventionally solved, and formulated as a DeepRL problem in which an agent learns a policy to deconflict the schedule. All three techniques were able to solve overscheduled problems for multiple weeks with competitive results for the satisfied time fraction for each mission (actual / requested). The quantum hybrid approach showed promise for scaling to larger problems with shorter running times.

关键词： antenna scheduling optimization quantum computing deep reinforcement learning mixed integer linear programming

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Alternative mixed integer linear programming models for identifying the most efficient decision making unit in data envelopment analysis

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COMPUTERS & INDUSTRIAL ENGINEERING 2012年第2期62卷 546-553页

作者： Wang, Ying-Ming Jiang, Peng Fuzhou Univ Sch Publ Adm Decis Sci Inst Fuzhou 350108 Peoples R China Hefei Univ Technol Sch Management Hefei 230009 Peoples R China Chongqing Technol & Business Univ Environm & Biol Engn Inst Chongqing 400067 Peoples R China

A mixed integer linear model for selecting the best decision making unit (DMU) in data envelopment analysis (DEA) has recently been proposed by Foroughi [Foroughi, A. A. (2011a). A new mixed integer linear model for selecting the best decision making units in data envelopment analysis. Computers and Industrial Engineering, 60(4), 550-554], which involves many unnecessary constraints and requires specifying an assurance region (AR) for input weights and output weights, respectively. Its selection of the best DMU is easy to be affected by outliers and may sometimes be incorrect. To avoid these drawbacks, this paper proposes three alternative mixed integer linear programming (MILP) models for identifying the most efficient DMU under different returns to scales, which contain only essential constraints and decision variables and are much simpler and more succinct than Foroughi's. The proposed alternative MILP models can make full use of input and output information without the need of specifying any assurance regions for input and output weights to avoid zero weights, can make correct selections without being affected by outliers, and are of significant importance to the decision makers whose concerns are not DMU ranking, but the correct selection of the most efficient DMU. The potential applications of the proposed alternative MILP models and their effectiveness are illustrated with four numerical examples. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Data envelopment analysis Most efficient DMU mixed integer linear programming Multiple criteria decision analysis

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Balancing the great British electricity system - Bulk dispatch optimisation

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 234卷

作者： Bukhsh, Waqquas Bejan, Andrei Univ Strathclyde Dept Elect & Elect Engn Glasgow Scotland Natl Grid ESO Balancing Transformat Programme Wokingham England

The Balancing Mechanism, managed by the British electricity system operator, National Grid ESO, is designed to maintain a balance between electricity generation and demand. It achieves this by purchasing extra generation (or demand reduction) through accepted offers and extra demand (or generation reduction) through accepted bids from Balancing Mechanism Units (BMUs) in real -time. The current manual approach for instructing BMUs becomes increasingly challenging as access to the market widens and the number of BMUs grows. This paper introduces a proof-of-concept optimisation model to assist Control Room engineers in making optimal decisions for a large number of BMUs. We outline the requirements for instructing BMUs, provide their mathematical formulation, and illustrate this using simple examples. Computational performance results based on test cases with up to 500 units are presented.

关键词： Power systems analysis Electricity system modelling Power dispatch mixed integer linear programming

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An MILP model based on a processing strategy of complex multisource constraints for the short-term peak shaving operation of large-scale cascaded hydropower plants

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RENEWABLE ENERGY 2024年 231卷

作者： Fang, Zhou Liao, Shengli Zhao, Hongye Cheng, Chuntian Liu, Benxi Wang, Huan Li, Shushan Dalian Univ Technol Inst Hydropower & Hydroinformat Dalian 116024 Peoples R China Power Dispatching Control Ctr China Southern Power Guangzhou 510623 Peoples R China

Hydropower with flexible regulation plays an important role in short-term peak shaving operations. However, short-term peak shaving operation is a challenging problem due to the large scale, the nonconvex and nonlinear characteristics, and the complex multisource tasks. This study proposes a mixed integer linear programming (MILP) model, termed MILPoPSC, based on a processing strategy for complex multisource constraints, tailored for short-term peak shaving in large-scale cascaded hydropower plants. The MILP model, designed to incorporate multisource tasks by abstracting them into constraints, ensures that task requirements are met. A novel multisource constraint transformation method is introduced to derive constraint expressions related to power flow, facilitating the unification of constrained variables. Additionally, a classification and integration method based on restriction mode and set theory is proposed to improve solving efficiency by integrating constrains of the same type. The proposed method was applied to 7 hydropower cascade plants in the Wujiang River. The results showed that the linearization method and the processing strategy of complex multisource constraints can successfully reduce the complexity of the MILP model without affecting the solution quality. This indicates that MILPoPSC has good practical value for the short-term peak shaving operation of large-scale cascaded hydropower plants in China.

关键词： Cascade hydropower plants Short-term peak shaving operation mixed integer linear programming Multisource constraints

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Electric vehicle charging scheduling with mobile charging stations

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JOURNAL OF CLEANER PRODUCTION 2024年 434卷

作者： Li, Hua Son, Dongmin Jeong, Bongju Yonsei Univ Dept Ind Engn 50 Yonsei Ro Seoul 03722 South Korea

Mobile charging station (MCS) is a brand-new technology to electric vehicle charging, which is a supplementary service for addressing the shortcomings associated with fixed charging station (FCS), such as prolonged waiting time of charging, charging congestion of FCS, and user travel anxiety. While MCSs offer convenience to electric vehicle users, the challenge faced in the MCS operation is that MCS equipped with large batteries to provide charging services to users by visiting them one by one not only leads to high operating costs, but also prolongs the waiting time for users to utilize MCS services. To overcome the issues, a novel framework is proposed by optimizing the MCS operation in economic efficiency. In this study, a variant of mixed integer linear programming (MILP) model is developed to maximize the MCS operator's profits with considering the user's perspective, including clustering, and covering user demands, setting temporary charging location for users, dispatching MCSs for charging and discharging, and scheduling EV charging. As the scale of the problem increases, the solution time of using the proposed MILP model is inefficient. Whereas an improved genetic algorithm is developed for solving a large-scale of the proposed model. Solomon's benchmarking instance data is adopted to evaluate the performance and validity of the proposed algorithm, complemented by various sensitivity analyses aimed at providing managerial insights into MCS operations.

关键词： Mobile charging Dispatching Charging scheduling Genetic algorithms mixed integer linear programming Electric vehicle

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Augmenting electric vehicle fast charging stations with battery-flywheel energy storage

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JOURNAL OF ENERGY STORAGE 2024年 97卷

作者： Mouratidis, Panagiotis Batterien Montage Zentrum BMZ Germany GmbH Zeche Gustav 1 D-63791 Karlstein Germany

This work investigates the economic efficiency of electric vehicle fast charging stations that are augmented by battery-flywheel energy storage. Energy storage can aid fast charging stations to cover charging demand, while limiting power peaks on the grid side, hence reducing peak power demand cost. The investigated fast charging station is based on a common DC bus, to which all electrical equipment is connected. The arrival time to the charging station is described by a normal distribution for passenger cars and a uniform distribution for heavyduty vehicles, which result in a stochastic charging profile. The size of the fast charging station is optimized through mixed integer linear programming, so that its net present value is maximized. The results reveal that the battery-flywheel augmented fast charging station can achieve a net present value that is up to 12 % greater than that of a fast charging station without energy storage. Nevertheless, due to the additional investment cost for energy storage, fast charging stations without storage achieve a higher internal rate of return and a lower discounted payback period than fast charging stations with energy storage.

关键词： Electric vehicle fast charging Energy storage Lithium-ion battery storage Flywheel storage mixed integer linear programming

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