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Optimizing the Hyperparameters of a mixed integer linear programming Solver to Speed up Electric Vehicle Charging Control 1

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23rd European International Conference on the Applications of Evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

作者： Ishihara, Takahiro Limmer, Steffen Tokyo Inst Technol Grad Sch Engn Dept Syst & Control Tokyo 1528552 Japan Honda Res Inst Europe GmbH D-63073 Offenbach Germany

ISBN: (数字)9783030437220

ISBN: (纸本)9783030437213;9783030437220

Optimization of charging profiles for controlled charging of electric vehicles is commonly done via mixed integer linear programming. The runtime of the optimization can represent an issue for the practical use. However, by tuning the parameter setting of the employed solver, it is possible to speed up the optimization process. The present work evaluates two popular hyperparameter tuning tools - irace (iterated racing) and SMAC (sequential model-based algorithm configuration) - for the optimization of parameters of the SCIP (Solving Constraint integer Programs) solver with the objective to speed up the solving process for four common variants of the electric vehicle charging scheduling problem. Based on the results, the most important solver parameters are identified. It is shown that by tuning a very limited number of parameters, speed-ups of 60% and more can be achieved.

关键词： Hyperparameter optimization SMAC irace mixed integer linear programming SCIP Electric vehicles

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Optimal Load Control and Scheduling through Distributed mixed-integer linear programming 30

Optimal Load Control and Scheduling through Distributed Mixe...

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30th Mediterranean Conference on Control and Automation (MED)

作者： Yfantis, Vassilios Motsch, William Bach, Nico Wagner, Achim Ruskowski, Martin Tech Univ Kaiserslautern Chair Machine Tools & Control Syst Dept Mech & Proc Engn D-67663 Kaiserslautern Germany Technol Initiat SmartFactory KL eV D-67663 Kaiserslautern Germany German Res Ctr Artificial Intelligence DFKI D-67663 Kaiserslautern Germany

ISBN: (纸本)9781665406734

This paper presents a mixed-integer linear programming-based optimization model for simultaneous optimal load control and scheduling of distributed systems coupled through their energy consumptions. The subsystems are able to adjust their energy consumption during the execution of a task and aim at minimizing their completion time and energy cost. The overall problem is solved in a distributed fashion, where each subsystem optimizes its individual operation without sharing sensitive information. To this end, dual decomposition is employed and a new algorithm to update the dual variables is presented. It relies on a transformation of the gradient of the quadratically approximated dual function and the subsequent solution of a regression problem. The proposed algorithm makes efficient use of information collected in previous iterations. The solution obtained from the distributed optimization of the subsystems is compared to both a decentral and a system-wide solution, showing that the distributed solution lies close to the global optimum of the process.

关键词： Energy consumption Costs Automation Approximation algorithms mixed integer linear programming Task analysis Optimization

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Using mixed integer linear programming with Stochastic Models for Monte Carlo Investment Analysis in the District Heating Sector

Using Mixed Integer Linear Programming with Stochastic Model...

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作者： Laakso, Juhana Aalto University

学位级别：硕士

The energy sector is transitioning from heavy reliance on fossil fuels to focus on renewable energy production. This transition requires decarbonization of district heating. For Fortum, one of the largest heat producers in the world, this is an important strategic focus. To achieve decarbonization of district heating systems in a profitable and low risk way, advanced tools for decision making and risk analysis are required. To answer for this need, this thesis aims to investigate how advanced MILP models created with the Fortum MONA tool can be used in a Monte Carlo simulation-based investment analysis process. To capture uncertainties in key input variables as accurately as possible, stochastic models for temperature and electricity spot prices are developed. An investment analysis process is created and tested by conducting a case study on a large heat pump investment in the Fortum Espoo district heating system. The results of the case study indicate that the developed investment analysis process succeeds in conveying a comprehensive view on investment risk and profitability, and that MONA models can successfully be used in this type of analysis. However, multiple challenges are identified, which must be overcome in order for the analysis process to be taken into wide use in Fortum. The greatest challenges are related to computation time and issues with local processing. To tackle these challenges, it is recommended that in the future, cloud computing should be utilized to run the process. In addition, development of more stochastic models for uncertain variables is suggested, in order to capture all relevant uncertainties related to an investment.

关键词： Monte Carlo simulation investment analysis district heating stochastic modelling mixed integer linear programming

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A Novel Framework Based on mixed-integer linear programming to Restore a Disintegrated Power Grid after Cascading Failures

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IEEE LATIN AMERICA TRANSACTIONS 2021年第4期19卷 669-678页

作者： Beyza, Jesus Yusta, Jose M. Ruiz-Paredes, Hector F. Garcia-Paricio, Eduardo Inst Tecnol Morelia Programa Grad & Invest Ingn Elect Morelia 58120 Michoacan Mexico Univ Zaragoza Dept Ingn Elect Zaragoza 50009 Aragon Spain

Electric power systems are prone to disturbances and contingencies, which can trigger cascading failures with severe consequences for society. These undesirable events could disintegrate the electrical infrastructure in areas with disconnected elements. In this article, we propose a novel procedure to restore a collapsed power grid composed of multiple islands and isolated assets. The framework developed identifies the power lines to be closed during the electrical network recovery stages. In the latter, link overload limits and generation thresholds are taken into account. Two disintegrated networks based on the well-known IEEE 57-bus test system are built to demonstrate the performance of our proposal. In summary, this methodology provides a solution to recover the power system optimally and reliably.

关键词： Power system protection Power system faults Power grids mixed integer linear programming IEEE transactions Art Silicon compounds cascading failures power system security Resilience vulnerability

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Optimal Configuration of Integrated Energy Systems Based on mixed integer linear programming

Optimal Configuration of Integrated Energy Systems Based on ...

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Image Processing, Electronics and Computers (IPEC), Asia-Pacific Conference on

作者： Xianbo Du Zhengyong Wu Lei Zou Yiming Tang Chao Fang Chaoqun Wang Jiangsu Frontier Electric Technology Co. Ltd. Nanjing China Zhejiang University Hangzhou China

ISBN: (纸本)9781728190198

The research on the configuration and optimization strategy of the integrated energy system is of great significance and value for ensuring the regional energy balance, reducing the investment cost of the system and improving the economy of operation, as it is an efficient and low-carbon form of energy utilization. In this paper, an optimal configuration method of integrated energy system based on mixed integer linear programming is proposed that abstracts all kinds of equipment with the same output energy type into a parallel structure with input and output functions, which can realize synchronous optimization of equipment type, capacity and operation strategy. Then, auxiliary variables and constraints are introduced for the non-linear function terms in the model which are equivalent to the mixed integer linear programming problems that are easy to solve. Finally, the comprehensive energy system to be built in a park is optimized and the effectiveness of this method is verified by the test results of an example.

关键词： Computers Computational modeling Image processing Conferences mixed integer linear programming Planning Optimization

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Solver-Based mixed integer linear programming (MILP) Based Novel Approach for Hydroelectric Power Generation Optimization

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IEEE ACCESS 2020年 8卷 174880-174892页

作者： Kumar, Jasvinder Khalil, Ihsan Ullah Ul Haq, Azhar Perwaiz, Aqib Mehmood, Khalid Neelam Jhelum Hydropower Co Water & Power Dev Author Muzaffarabad 13100 Pakistan Natl Univ Sci & Technol NUST Coll Elect & Mech Engn Rawalpindi 44000 Pakistan De Montfort Univ Emerging Technol Res Ctr EMTERC Leicester LE1 9BH Leics England

In Pakistan, hydroelectric power is one of the reliable sources of electricity with a capacity of 8,713 MW, which is 29% of the total energy mix. Hence, with such a vast resource capacity of hydroelectric power, its optimization and dispatch planning will have a great significance. This research work discusses the importance of hydroelectric power generation planning for both storage and run-of-river (ROR) hydropower plants, as well a;a solver-based optimization technique is proposed for the first time to resolve the intricate job of generation planning for hydroelectric power plants in MATLAB. A mathematical-optimization model is also developed, which uses a mixed-integer linear-programming (MILP) algorithm, based on the objective function of profit maximization, which considers a random varying revenue plan as model input. Three hydropower generators of different capacities and efficiencies are considered for the optimization problem. MILP based solution is proposed for both storage and ROR hydropower plants with two dispatch schedules, i.e., Normal dispatch schedule and optimum dispatch schedule. The objective functions are solved, and the profit (in dollars) from each dispatch schedule is calculated and compared. The preliminary optimization results show an increase of $22,000 and $29,130 in the profit for storage and ROR hydropower plants, respectively, which is 19% more than average income. Hence, ensuring the credibility of the proposed algorithm for maximizing the revenue ($), is aimed to facilitate and assist better planning for electric power producers.

关键词： Power systems Optimization Hydroelectric power generation Planning Uncertainty Reliability Power generation planning hydroelectric power system MATLAB mixed integer linear programming mathematical optimization model water storage dams run-of-river

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Optimizing Energy Systems Using mixed-integer linear programming

Optimizing Energy Systems Using Mixed-Integer Linear Program...

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Emerging Power Technologies (ICEPT), International Conference on

作者： Murtaza Khan Syed Shan-e-Abbas Asif Gulraiz Department of Electrical Engineering DHA Suffa University Karachi Pakistan

ISBN: (数字)9798331536657

ISBN: (纸本)9798331536664

Dealing distributed energy resources (DERs) such as electric vehicles (EVs), photovoltaic (PV) systems, and energy storage systems (ESSs) in modern energy networks poses several challenges. mixed-integer linear programming (MILP) offers a full-bodied optimization method capable of handling both discrete and continuous variables while adhering to complex constraints. This research offers an MILP-driven energy management basis designed to lower the operational costs improve renewable energy usage, and maintain grid stability. MILP is active to list DER activities, including ESS operations and EV involvement, under various supply-demand scenarios. The simulation results demonstrate that the framework effectively reduces costs, mitigates peak demand, and improves grid reliability. This study highlights MILP's scalability and reliability as a solution to the multi-layered challenges of energy system optimization.

关键词： Photovoltaic systems Costs Power system stability Virtual power plants Electric vehicles Stability analysis mixed integer linear programming Reliability Thermal stability Energy storage

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Analysis of the Economic Feasibility of Power Substitution Potential at the Industrial Park Level Based on mixed-integer linear programming and Its Application in a Pharmaceutical Park

Analysis of the Economic Feasibility of Power Substitution P...

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Electrical Automation and Artificial Intelligence (ICEAAI), International Conference on

作者： Yunfeng Li Yutao Xu Yong Xiao Qihui Feng Yun Zhao Yuelang Zhang Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid CSG Electric Power Research Institute Guangzhou China Electric Power Research Institute of Guizhou Power Grid Co. Ltd. Guiyang China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

To address the lack of rapid and economical methods for assessing the power substitution potential of enterprises and industrial parks under the dual-carbon goals, this study employs a mixed-integer linear programming (MILP) approach to construct a year-round, 8760-hour simulation and optimization model of the energy system at the industrial park level. A quantitative analysis framework for power substitution potential is proposed. Through a practical analysis of power substitution in a pharmaceutical park in Guizhou, it was found that compared with the traditional gas boiler solution, the optimized power substitution scheme using high-temperature heat pumps could save the target park approximately 95,000 RMB in annual energy costs, reduce unit energy cost by about 3%, and decrease unit energy-related carbon emissions by about 4%. This study provides a scientific method and simulation framework for quantitatively analyzing power substitution potential in industrial parks.

关键词： Energy consumption Analytical models Costs Heat pumps Statistical analysis Wind power generation Boilers mixed integer linear programming Pharmaceuticals Load modeling

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A mixed integer linear programming support vector machine for cost-effective feature selection

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KNOWLEDGE-BASED SYSTEMS 2020年 203卷

作者： Lee, In Gyu Zhang, Qianqian Yoon, Sang Won Won, Daehan SUNY Binghamton Dept Syst Sci & Ind Engn Binghamton NY 13902 USA Waseda Univ Fac Commerce Tokyo Japan

In the era of big data, feature selection is indispensable as a dimensional reduction technique to lower data complexity and enhance machine learning performances. However, traditional feature selection methods mainly focus on classification performances, while they exclude the impact of associated feature costs;e.g., price, risk, and computational complexity for feature acquisition. In this research, we extend the l(1) norm support vector machine (l(1)-SVM) to address the feature costs, by incorporating a budget constraint to preserve classification accuracy with the least expensive features. Furthermore, we formulate its robust counterpart to address the uncertainty of the feature costs. To enhance computational efficiency, we also develop an algorithm to tighten the bound of the weight vector in the budget constraint. Through the experimental study on a variety of benchmark and synthetic datasets, our proposed mixed integer linear programming (MILP) models show that they can achieve competitive outcomes in terms of predictive and economic performances. Also, the algorithm that tightens the budget constraint helps to curtail computational complexity. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Feature selection Support vector machine mixed integer linear programming Robust optimization Feature cost Cost uncertainty

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Master Surgical Scheduling of Multi-Surgeon Surgical Schedules Using a mixed-integer linear programming Model and Genetic Algorithms

Master Surgical Scheduling of Multi-Surgeon Surgical Schedul...

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Robotics, Automation and Artificial Intelligence (RAAI), IEEE International Conference on

作者： Wanyok Atisattapong Podcharapol Pongpatcharapun Department of Mathematics and Statistics Faculty of Science and Technology Thammasat University Pathum Thani Thailand

ISBN: (数字)9798331520038

ISBN: (纸本)9798331520045

The purpose of this work is to develop a master surgical scheduling for a case study of a large-scale public hospital in Thailand using a mixed-integer linear programming model (MILP) and a genetic algorithm (GA). Due to the high volume of operations that must be handled every day, our main objective is to create a schedule that maximizes OR utilization, minimizes room overtime, and reduces idle time between surgical patients. The data from Thammasat University Hospital were analyzed and simulated to generate a timetable from daily to weekly schedule. Our results show that an exact solution can be obtained by MILP for a simple problem while a large CPU run time is incurred for the complex problems. GA provided the optimal solution for a complex large-scale problem. The schedule generated by the proposed GA is more flexible and the staff can improve it to obtain more practical timetable in real-life situations, such as increasing the number of surgeries, setting a time block for specific surgical specialties, and handling emergency cases.

关键词： Schedules Hospitals Computational modeling Surgery linear programming Scheduling mixed integer linear programming Personnel Robots Genetic algorithms

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