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IEEE General Meeting Power& Energy Society

作者： Zhongqi Zhao Yiyun Yao Lei Fan Fei Ding Departments of Electrical and Computer Engineering and Engineering Technology University of Houston Houston TX USA National Renewable Energy Laboratory Denver CO USA

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Evaluating Photovoltaic Hosting Capacity (PVHC) is an essential step in the process of integrating solar energy into power grids, particularly when focusing on the distribution network (DN) as the primary integration target. PVHC needs to be investigated, especially in cases where the grids are unbalanced, and their operational conditions vary spatially and temporally. This motivation prompted us to propose a scalable model tailored to this application. In this paper, we applied linearization to the alternating current optimal power flow (AC-OPF) and solar inverters, transforming the original problem into a mixed-integer linear programming (MILP) problem. Additionally, we accounted for the battery energy storage system (BESS) as a time-coupling factor for calculating PVHC. We then compared the PVHC results between the IEEE-13 bus and SMART-DS San Francisco (SFO) cases and discussed the extent to which BESS can enhance the PVHC of a DN. Furthermore, we designed a web-based graphical visualization for the SFO case, enabling user interaction with raw data and simulation results on a map through a graphical user interface (GUI). In summary, our results and findings provide valuable insights for future three-phase unbalanced AC-OPF PVHC practices and their visualization.

关键词： Photovoltaic systems Simulation Power system dynamics Data visualization Solar energy Inverters Power grids mixed integer linear programming Load flow Graphical user interfaces

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Evaluation of CO2 Emissions by Water Pumping Scheduling with Water Demand Response

Evaluation of CO2 Emissions by Water Pumping Scheduling with...

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Power and Energy Systems Engineering (CPESE), International Conference on

作者： Shuhei Sugimura Akihisa Kaneko Yasuhiro Hayashi Takayuki Tanabe Department of Advanced Science and Engineering Waseda University Tokyo Japan Advanced Collaborative Research Organization for Smart Society Waseda University Tokyo Japan MEIDENSHA CORPORATION Tokyo Japan

ISBN: (数字)9798350377439

ISBN: (纸本)9798350377446

In recent years, the amount of the photovoltaic (PV) system has been rapidly increasing from the viewpoint of carbon neutral. There are growing needs for efforts to reduce CO 2 emission by effectively utilizing surplus electricity from PVs and promoting local production for local consumption. In order to address these issues, this study focused on the electric demand control of water supply pumps in the water distribution system, which is infrastructure equipment for consumers. By controlling the start/stop of the water pump considering PV generation condition, it is expected to contribute to the reduce CO 2 emission from the electricity used by the pump. Therefore, this paper proposes an optimal operation planning method for water pumps to reduce CO 2 emission using a mixed integer linear programming problem. The control targets are the start/stop of the water pumps and amount of water demand response (DR) in each time-slot. The water DR also improves the flexibility of the water distribution system. The effectiveness of proposed method and water DR is evaluated in terms of reduction of CO 2 emission and the operation of water pump. As a result, water DR improved flexibility of water distribution system and reduced CO 2 emission because operating more pumps on sunny days and fewer pumps on rainy days.

关键词： Electricity Prevention and mitigation Production Carbon neutral Systems engineering and theory Water pumps Demand response Planning mixed integer linear programming Voltage control

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Building Equitable Student Project Groups - A Simulation Study to Assess Heuristic Assignment Methods

Building Equitable Student Project Groups - A Simulation Stu...

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Simulation Winter Conference

作者： Matthew Dabkowski Stephen Gillespie Ian Kloo Devon Compeau Mai Tran Dept. of Systems Eng. (DSE) United States Military Academy (USMA) West Point NY USA

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

This paper explores methods for forming equitable project groups in a class by minimizing the range in mean cumulative quality point averages (CQPAs) across the groups. Using simulation, it forms representative classes of various sizes from real-world CQPA data, and it compares the approximate solutions of seven heuristic assignment methods to the globally optimal solutions found with mixed integer linear programming. Finding a near optimal heuristic is useful as exact methods can be computationally expensive or require specialized knowledge or software. Simulation results indicate that the Alternating Convergence heuristic consistently outperforms the other six heuristics, generating near optimal solutions with low computational cost. This suggests the Alternating Convergence heuristic is a practical method for building project groups when equitability in scholastic achievement supports one's pedagogical philosophy or objectives.

关键词： Philosophical considerations Simulation Buildings Software mixed integer linear programming Computational efficiency Convergence

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Active Distribution Network Energy Storage Planning Model for Reliability Requirements

Active Distribution Network Energy Storage Planning Model fo...

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Power and Electrical Technology Conference (APET), Asia

作者： Yaqi Sun Wenchuan Wu Haotian Zhao Yujie Sheng Department of Electrical Engineering State Key Laboratory of Power Systems Tsinghua University Beijing China State Grid Fujian Electric Power Dispatching and Control Center State Grid Fujian Electric Power Co. Ltd Fuzhou China

ISBN: (数字)9798350367690

ISBN: (纸本)9798350367706

The integration of renewable energy sources into the power grid introduces significant volatility, which presents new challenges to maintaining reliable power supply. This increased volatility amplifies the demand for energy storage solutions within distribution networks. To address these challenges, this paper first develops an analytical reliability assessment model and embeds it into an energy storage configuration model. Then the model is converted into a mixed-integer linear programming (MILP) model for efficient solution. Case studies demonstrate that this approach can be used for energy storage planning according to different reliability requirements.

关键词： Analytical models Renewable energy sources Power supplies Distribution networks Reliability engineering Power grids Planning mixed integer linear programming Active distribution networks Energy storage

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Coordinated EV Charging Attacks to Cause Transmission Line Overloads

Coordinated EV Charging Attacks to Cause Transmission Line O...

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International Youth Conference on Energetics (IYCE)

作者： Mahdi Bahrami Louis Wehenkel Montefiore Institute Department of EE&CS University of Liège Liège Belgium

ISBN: (数字)9798350372380

ISBN: (纸本)9798350372397

This paper proposes a model for one of the most important cyber-attacks against an electric vehicle (EV) ecosystem. In the considered attack, the aim is to disrupt the normal operation of the transmission system (TS) by causing a spike in EV load demand in order to induce multiple branch overloads in the TS. We thus formulate a decision-making problem aiming at inducing line overloads by manipulating EV charging prices in different regions, while considering limited resources of the cyber-attacker and a simplified model of the TS. This problem is expressed from the attackers’ perspective as a mixed-integer linear programming (MILP) problem of maximizing the total magnitude of branch overloads. To illustrate the proposed model, it is applied to the Roy Billinton Test System. The simulation results show that this attack strategy could cause some problems in the TS.

关键词： Power transmission lines Simulation Ecosystems Decision making Electric vehicle charging mixed integer linear programming Cyberattack

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Multi-UGV Task Reallocation for Sensor and Actuator Faults

Multi-UGV Task Reallocation for Sensor and Actuator Faults

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Youngwoo An Yongsoon Eun Department of Electrical Engineering & Computer Science DGIST Daegu Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

This paper proposes a task reallocation method for multi Unmanned Grounded Vehicle (UGV) systems in sensor and actuator fault situations. The proposed method formulates the task reallocation problem as a mixed-integer linear programming (MILP) using the distance between UGVs and task priority. When a UGV sensor or actuator fault occurs, the proposed method solves the MILP and finds the optimal task reallocation solution. The solution ensures continuing high-priority tasks for the multi-UGV systems. The proposed method is validated in simulation and experiment.

关键词： Actuators Costs Automation Control systems mixed integer linear programming

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Optimal Shared Energy Storage Capacity Configuration in Multi-energy Microgrids Considering Battery Lifetime Loss Based on Relaxation Techniques

Optimal Shared Energy Storage Capacity Configuration in Mult...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Hualong Liu Wenyuan Tang Department of Electrical and Computer Engineering North Carolina State University Raleigh USA

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

Installing shared battery energy storage systems (BESSs) in multi-energy microgrids (MEMGs) with the high penetration of inverter-based resources can effectively promote renewable consumption; nevertheless, how to configure the BESS capacity is an important issue. Therefore, we propose an optimal BESS capacity configuration method considering BESS lifetime loss based on stochastic programming and relaxation techniques. First, we present a basic framework of the shared BESS. Second, we consider the BESS lifetime loss based on the rain-flow counting algorithm, and the uncertainties of renewable generation and loads based on stochastic programming. Third, we propose a bilevel program for optimal shared BESS configuration in MEMGs, and then we convert the bi-level program into a single level mixed integer linear programming (MILP) model using relaxation techniques, the Karush-Kuhn-Tucker (KKT) conditions, and the Big-M method. Finally, we verify the validity of our models through conducting case studies. The results show that the shared BESS can effectively reduce the configured capacity and cost of the BESS.

关键词： Renewable energy sources Uncertainty Costs Battery energy storage system Stochastic processes Microgrids programming Power electronics mixed integer linear programming Load modeling

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Learn Branching Policies with an Attention-based Model

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Procedia Computer Science 2023年 221卷 1416-1422页

作者： Qian Pei Sheng-Jie Chen

The branch-and-bound method is an exact tree search method in mixed integer linear programming. Recently, learning branching policies become a popular topic, but most existing machine learning methods only consider problems with similar structures, and pay little attention to heterogeneous problems. The parameterized search tree method demonstrates promising performance on heterogeneous problems, but there is still room for improvement when it generalizes to other problems. In this paper, we propose an attention-based branching framework, which can exploit information among candidate variables. In this way, we can take full advantage of the properties exploited from the parameterized branch-and-bound search tree, resulting in a better branching policy. Our proposed model is evaluated on multiple mixed integer linear programming instances and attains remarkable performance within a given time limit.

关键词： mixed integer linear programming branch-and-bound algorithm branching policy machine learning

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Genetic Algorithms and Dynamic programming for High-Dimensional Optimization

Genetic Algorithms and Dynamic Programming for High-Dimensio...

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Digital Analysis and Processing, Intelligent Computation (DAPIC), International Conference on

作者： Jingyao Wang Yixuan Yang Linsen Song University of International Business and Economics Beijing China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

This study develops an advanced dynamic optimization model that leverages mixed-integer linear programming (MILP) alongside the Genetic Algorithm (GA) to tackle complex resource allocation and configuration challenges under a multitude of constraints. By incorporating uncertain variables and system constraints, the model adeptly handles multidimensional data, employing intelligent optimization algorithms to enhance its precision and robustness. It uniquely considers the interdependencies among parameters, such as complementarity and substitutability effects, ensuring a more accurate representation of real-world dynamics. To validate its efficacy, sensitivity analysis is conducted, showcasing the model's stability and adaptability across various scenarios. The findings underscore the potential of combining MILP and GA in addressing high-dimensional complex systems, providing a solid scientific foundation for future applications and refinements in optimization modeling techniques. This research not only advances the theoretical understanding but also offers practical insights into optimizing resource management in diverse fields.

关键词： Adaptation models Sensitivity analysis Heuristic algorithms Optimization models Robustness Dynamic programming mixed integer linear programming Resource management Optimization Genetic algorithms

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Mathematical formulations for the scheduling of unrelated parallel machines considering machine availability and eligibility

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 284卷

作者： Uribe, Alejandro Otamendi, Urtzi Artetxe, Arkaitz Rivera, Juan Carlos Univ EAFIT Sch Appl Sci & Engn Carrera 49 N 7 Sur 50 Medellin 05001 Antioquia Colombia Vicomtech Fdn Basque Res & Technol Alliance Donostia San Sebastian 20009 Spain Univ Basque Country UPV EHU Dept Comp Sci & Artificial Intelligence Donostia San Sebastian 20009 Spain

Job scheduling in manufacturing companies faces potential challenges due to varying job durations per machine, working calendar structures, and preventive maintenance. This must be considered to avoid unplanned outages and respect planned downtime. This research contrasts two literature-derived models with a novel approximation to solve the Unrelated Parallel Machine scheduling problem (UPM), incorporating machine availability and eligibility constraints. The implemented models address the optimization of two major metrics: the makespan, and the completion time of the last shift used. Minimizing these measurements is crucial to maximize system efficiency and reduce high-demand industries' operating costs. The models differ in their structure and how they were conceptualized. Model 1 uses sequence-dependent variables, offering detailed job sequencing but increasing problem size as job counts rise. Model 2 simplifies by assigning jobs to machines and shifts without sequencing, improving computational efficiency but compromising detail. Model 3, the novel proposal of this research, balances detail and efficiency by incorporating start-time-dependent variables, suitable for large-scale problems. Tests were performed varying the number of jobs, machines, shift lengths, and mean job durations. The results show that Models 2 and 3 outperform at makespan minimization, obtaining an optimal solution to almost all benchmark instances within an hour. Model 1 struggles with scalability, achieving less than 0.5% GAP in 69 out of 96 small instances and failing for larger setups. For the cases where the minimization of the completion time of the last operating shift is considered, all models improve computational times and GAPs. These findings guide the selection of scheduling models based on available computational resources and detailed planning needs in high-demand industrial environments.

关键词： UPM Scheduling Shifts Makespan mixed integer linear programming

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