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Coordinated Voltage Control of Microgrids With Integrated Electric Vehicles in Real-World Experiment and Application: Industrial Design and Implementation on EUREF-Campus in Berlin

IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN INDUSTRIAL E...

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IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN INDUSTRIAL ELECTRONICS 2024年第2期5卷 359-368页

作者： Chen, Jiawen Popova, Raisa Raab, Andreas F. Graute, Hannah Rojas, Mauricio Strunz, Kai Tech Univ Berlin Chair Sustainable Elect Networks & Sources Energy Dept Elect Engn & Comp Sci D-10587 Berlin Germany Deutsch Bahn Energie D-10318 Berlin Germany Schneider Elect D-10829 Berlin Germany

A coordinated voltage control algorithm is developed to facilitate the integration of electric vehicles into microgrids. This proposed algorithm reduces the voltage fluctuations via the optimal adjustment of the tap positions of on-load tap changers and of the reactive power injections of inverter-based distributed energy resources. Based on the proposed voltage control algorithm, an industrial implementation framework is established to enable the interactive communications among the on-load tap changers, the inverter-based distributed energy resources, etc. Compared to previous studies on voltage regulation in microgrids, this work is distinguished by the fact that the proposed voltage control algorithm is implemented and tested on a real-world experimental test site, known as the micro smart grid on the EUREF-Campus in Berlin. Constraints, such as actual process latencies are readily observed. The micro smart grid comprises a power transformer with on-load tap changer, rooftop photovoltaic systems, battery energy storage systems, and electric vehicle charging stations. The real-world test results confirm that the voltage deviations are significantly reduced by the coordinated voltage control algorithm, as opposed to the uncontrolled state. Thanks to the analysis of the trigger cycle of the proposed algorithm, the lifetime of the on-load tap changer can be extended while ensuring the effectiveness of voltage control.

关键词： Voltage control Reactive power Sensitivity On load tap changers Industrial electronics Electric vehicles Smart grids Active network management distributed energy resource electric vehicle microgrid mixed-integer linear programming on-load tap changer optimization photovoltaic systems renewable energy voltage control

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Optimal planning of hybrid energy storage systems in traction power supply system with railway power conditioner

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2023年第4期36卷

作者： Wang, Meng Liang, Zongyou Fu, Haochun Deng, Hao Chongqing Univ Sch Civil Engn Chongqing Peoples R China Jiang Tiao Construction Co Chongqing Railway Grp Co Ltd Chongqing Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu Peoples R China Southwest Jiaotong Univ Sch Elect Engn 999 Xian Rd Chengdu 611756 Sichuan Peoples R China

The railway power conditioner (RPC) is a promising technology to improve the regenerative braking energy (RBE) utilization and power quality of the traction power supply system (TPSS). The hybrid energy storage systems (HESS) play a key role in the economic operation of TPSS due to the high cost of the system. The capacity and power of HESS are the critical issues affecting the system performance and cost. Therefore, a hierarchical energy management model is proposed to optimize the HESS sizing and the scheduling of TPSS with RPC. At the master level, a nonlinear mathematical model is built to minimize the HESS cost, which includes capital cost, replacement cost, and recovery cost. At the slave level, considering voltage unbalance as a constraint, a mixed-integer linear programming model is established to minimize the electricity cost of railway operation. In order to solve the hierarchical nonlinear programming problem, an improved particle Swarm optimization (PSO) algorithm with a GUROBI solver embedded is proposed. Finally, the results show that the 14.08% cost reduction is achieved, and the standard limit value of voltage unbalance is satisfied.

关键词： hybrid energy storage systems mixed-integer linear programming railway power conditioner traction power supply system

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UniAP: Unifying Inter- and Intra-Layer Automatic Parallelism by mixed integer Quadratic programming

arXiv

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arXiv 2023年

作者： Lin, Hao Wu, Ke Li, Jie Li, Jun Li, Wu-Jun National Key Laboratory for Novel Software Technology School of Computer Science Nanjing University Nanjing210023 China

Distributed learning is commonly used for training deep learning models, especially large models. In distributed learning, manual parallelism (MP) methods demand considerable human effort and have limited flexibility. Hence, automatic parallelism (AP) methods have recently been proposed for automating the parallel strategy optimization process. Existing AP methods suffer from sub-optimal solutions because they do not jointly optimize the two categories of parallel strategies (i.e., inter-layer parallelism and intra-layer parallelism). In this paper, we propose a novel AP method called UniAP, which unifies inter- and intra-layer automatic parallelism by mixed integer quadratic programming. To the best of our knowledge, UniAP is the first parallel method that can jointly optimize the two categories of parallel strategies to find an optimal solution. Experimental results show that UniAP outperforms state-of-the-art methods by up to 3.80× in throughput and reduces strategy optimization time by up to 107× across five Transformer-based models. Copyright © 2023, The Authors. All rights reserved.

关键词： mixed-integer linear programming

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Identifying potential routes and airports in Brazil: An integration of the route selection and fleet assignment problems

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CASE STUDIES ON TRANSPORT POLICY 2025年 20卷

作者： Nagano, Marcelo Seido de Jesus, Thiago Dias Rossi, Fernando Luis Univ Sao Paulo Sao Carlos Sch Engn Ave Trabalhador Saocarlense400 BR-13566590 Sao Carlos SP Brazil Fed Inst Educ Sci & Technol Sao Paulo Barretos SP Brazil Univ Sao Paulo Inst Ciencias Matemat & Computacao Trabalhador Sao Carlense Ave400 Centre BR-13566 590 Sao Carlos SP Brazil

The strategic planning of airline networks is critical for maximizing profitability and operational efficiency. Among the key challenges faced by airlines is the Route Selection Problem (RSP)-determining which routes to operate based on demand, economic conditions, and infrastructure-and the Fleet Assignment Problem (FAP)- optimally assigning aircraft to routes to minimize costs. While these problems are typically addressed separately, their integration can yield more robust and profitable solutions. This study presents a novel mathematical model that integrates RSP and FAP using mixed integer linear programming (MILP) to optimize network profitability and fleet utilization. The model was validated through a case study of a fictional regional airline in Southeast Brazil, analyzing 41 potential locations. The results identified 28 profitable routes, including 11 destinations currently without regular flights and one without an airport. By optimizing fleet allocation and route selection, the model provides a data-driven framework for airlines, policymakers, and investors to enhance network efficiency and identify underserved markets. This study demonstrates that an integrated approach to route selection and fleet assignment can significantly improve decision-making in the airline industry, offering a scalable methodology for network expansion and strategic investment.

关键词： Route Selection Problem Fleet Assignment Problem mixed-integer linear programming Airline

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Optimization of the speed curve for heavy-haul trains considering cyclic air braking: an MILP approach

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ENGINEERING OPTIMIZATION 2023年第5期55卷 876-890页

作者： Su, Shuai Huang, Yucheng Liu, Wentao Tang, Tao Cao, Yuan Liu, Hongjie Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China

The most difficult problem for the control of heavy-haul trains is the cyclic air braking on long steep downward slopes. Unsuitable positions of the application of air braking will cause large longitudinal impulses, overspeeding and even accidents. This article proposes a mixed-integer linear programming (MILP) approach to the optimization of the speed curve of heavyhaul trains. First, with the objectives of maximizing the operation distance and minimizing the air braking time within a given period, an optimization model of a heavy-haul train is formulated considering multiple practical constraints, such as the speed limit, release time and releasing speed. Second, the optimization problem is transformed into an MILP problem by linearizing the nonlinear conditions, and is solved with existing CPLEX (R) solvers. Finally, some case studies are conducted with actual data from the Shuohuang railway in China to illustrate the effectiveness of the proposed method.

关键词： Heavy-haul trains cyclic air braking long steep downward slopes mixed-integer linear programming

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Investigation of Electric Vehicles Contributions in an Optimized Peer-to-Peer Energy Trading System

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IEEE ACCESS 2023年 11卷 12489-12503页

作者： Al-Sorour, Ameena Fazeli, Meghdad Monfared, Mohammad Fahmy, Ashraf A. A. Swansea Univ Fac Sci & Engn Dept Elect & Elect Engn Swansea SA1 8EN Wales Swansea Univ ASTUTE Swansea SA1 8EN Wales Helwan Univ Dept Elect Power & Machines Helwan 11795 Egypt

The rapid increase in integration of Electric Vehicles (EVs) and Renewable Energy Sources (RESs) at the consumption level poses many challenges for network operators. Recently, Peer-to-Peer (P2P) energy trading has been considered as an effective approach for managing RESs, EVs, and providing market solutions. This paper investigates the effect of EVs and shiftable loads on P2P energy trading with enhanced Vehicle to Home (V2H) mode, and proposes an optimized Energy Management Systems aimed to reduce the net energy exchange with the grid. mixed-integer linear programming (MILP) is used to find optimal energy scheduling for smart houses in a community. Results show that the V2H mode reduces the overall energy costs of each prosumer by up to 23% compared to operating without V2H mode (i.e., EVs act as a load only). It also reduces the overall energy costs of the community by 15% compared to the houses operating without the V2H mode. Moreover, it reduces the absolute net energy exchanged between the community and the grid by 3%, which enhances the energy independence of the community.

关键词： Batteries Costs State of charge Peer-to-peer computing Vehicle-to-grid Tariffs Electric vehicles mixed integer linear programming Electric vehicle energy management system local consumption mixed-integer linear programming peer-to-peer energy trading

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Fair Least Core: Efficient, Stable and Unique Game-Theoretic Reward Allocation in Energy Communities by Row-Generation

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第2期3卷 170-181页

作者： Fioriti, Davide Bigi, Giancarlo Frangioni, Antonio Passacantando, Mauro Poli, Davide University of Pisa Department of Energy Systems Territory and Construction Engineering PI Pisa56122 Italy University of Pisa Dipartimento di Informatica PI Pisa56127 Italy University of Milano-Bicocca Department of Business and Law MI Milan20126 Italy

Energy Communities are increasingly proposed as a tool to boost renewable penetration and maximize citizen participation in energy matters. These policies enable the formation of legal entities that bring together power system members, enabling collective investment and operation of energy assets. However, designing appropriate reward schemes is crucial to fairly foster individuals to join, as well to ensure collaborative and stable aggregation, maximizing community benefits. Cooperative Game Theory, emphasizing coordination among members, has been extensively proposed for ECs and microgrids;however, it is still perceived as obscure and difficult to compute due to its exponential computational requirements. This study proposes a novel framework for stable fair benefit allocation, named Fair Least Core, that provides uniqueness, reproducibility, stability and fairness. A novel row-generation algorithm is also proposed that allows to efficiently compute the imputations for coalitions of practical size. A case study of ECs with up to 100 members shows the stability, reproducibility, fairness and efficiency properties of proposed model. The results also highlight how the market power of individual users changes as the community grows larger, which can steer the development of practical reliable, robust and fair reward allocations for energy system applications © 2023 IEEE.

关键词： mixed-integer linear programming

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A proposed framework for supplier selection and order allocation using machine learning clustering and optimization techniques

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Journal of Data, Information and Management 2024年第3期6卷 235-254页

作者： Husna, Asma ul Ghasempoor, Ahmad Amin, Saman Hassanzadeh Department of Mechanical Industrial and Mechatronics Engineering Toronto Metropolitan University 350 Victoria Street Toronto M5B 2K3 ON Canada

The process of selecting the most suitable suppliers and allocating orders to them is critical in supply chain management. This research proposes a new framework to address the challenges of Supplier Selection and Order Allocation (SS&OA) by introducing a two-phase combined approach. In the first phase, three Machine Learning (ML) clustering techniques (i.e., K-means clustering, Gaussian Mixture Model, and Balance Iterative Reducing and Clustering using Hierarchies) are employed to identify a small group of suitable suppliers from a large pool of potential suppliers. The accuracy of the developed clustering models is assessed using Silhouette score technique. In the second phase, we focus on one of the clusters based on the results of Phase 1. In this phase, a new multi-objective model is developed for SS&OA that considers multi-source, multi-period, and multi-product scenarios. Compromise method is utilized to obtain efficient solutions. The framework is applied to extensive, real historical contract data from Canada's Public Works and Government Services Canada (PWGSC) on behalf of federal departments and agencies. The results indicate that K-means clustering model is the most accurate among the models examined for this dataset, and the choice of ML clustering techniques has a significant impact on SS&OA. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Machine learning clustering algorithms mixed-integer linear programming Multi-objective optimization Order allocation Supplier selection

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An Intelligent Electricity Scheduling Model for Cement Plants Based on STN 2

An Intelligent Electricity Scheduling Model for Cement Plant...

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IEEE 2nd International Conference on Power Science and Technology (ICPST)

作者： Huang, Yuanping Su, Rui Yang, Jin Li, Jiale Du, Zhaobin Huang, Zirui Yunnan Power Grid Co Ltd Dali Power Supply Bur Dali Peoples R China South China Univ Technol Sch Elect Power Guangzhou Peoples R China

ISBN: (纸本)9798350349047;9798350349030

Implementing demand-side management for industrial users is an essential measure to alleviate the inadequate regulation capability of the new power system. However, the demand response of industrial users mostly relies on manual decision-making, leading to issues of response unpredictability and irrational decision-making. To effectively utilize the regulatory role of industrial loads and enhance the intelligence of demand response among industrial users, this paper proposes an intelligent electricity scheduling model based on the State-Task Network (STN). Firstly, focusing on typical high-energy-consuming loads such as cement plants, a mathematical model describing the characteristics of cement production lines is established using the STN method, while considering the electricity consumption of production processes to model the electricity load of cement plants. Building upon this, comprehensively considering the influences of user-owned photovoltaic power generation and price-based demand response (time-of-use electricity price, TOU) in a general manner, an electricity intelligent scheduling model to minimize electricity costs is established. Finally, it is applied to the actual production of a cement plant in the Dali region, and the results indicate that the model has great application effects. It can effectively reduce users' energy costs and reasonably evaluate their potential for participating in demand response.

关键词： Industrial flexible loads intelligent electricity scheduling state-task network demand response mixed-integer linear programming

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Simultaneous lot sizing and scheduling in the animal feed premix industry

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OPERATIONAL RESEARCH 2023年第2期23卷 33页

作者： Milenkovic, Milos Val, Susana Bojovic, Nebojsa Univ Belgrade Fac Transport & Traff Engn Vojvode Stepe 305 Belgrade 11000 Serbia Zaragoza Logist Ctr Ave Ranillas5 Edificio A Bajo Zaragoza 50018 Spain

Feed represents a significant contributor to the total costs of animal production. Improved efficiency of animal food production may reduce feed cost and gain a positive impact on supply as well as the demand in animal food production. In this paper a discrete-time MILP-based scheduling model is proposed for optimization of animal food premix production process. The objective of the model is to minimize the total production and manpower costs while maximizing the utilization of parallel equipment units at the same time. The set of constraints captures all operational constraints from a specific production plant. Testing on a real case study proves the superiority of proposed approach in comparison to the current practice and paves the way for its industrial implementation. A framework for industrial application of the proposed model is described. In terms of capabilities, the proposed tailor-made solution represents a superior alternative to the general purpose software currently available on the market.

关键词： Animal food production Scheduling Lot-sizing Operator assignment mixed-integer linear programming

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