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检索条件"主题词=Mixed integer linear programming"

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Distribution Network Planning Considering loss with new linearization expression 3

Distribution Network Planning Considering loss with new line...

引用

3rd Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Ge, Yang Gao, Wenhao Lian, Xiaoyang Wang, Mingqiang Han, Liqun Zhang, Baoyu Zhou, Tong Dezhou Elect Co Dezhou Shandong Peoples R China Shandong Univ Minist Educ Intelligent Dispatch & Control Jinan Peoples R China

ISBN: (纸本)9781665425513

This paper summarizes the distribution network planning technology equipped with distributed generators (DGs), and proposes a distribution network planning method in which the capacities of DGs and distribution lines are simultaneously optimized. The proposed model tries to minimize the total cost which includes the investment cost and operation cost. In addition, this paper analyzes the influence of network loss, and a new loss linearization method is proposed. Finally the planning model belongs to a mixed integer linear programming problem. To validate its effectiveness and superiority, the proposed distribution network planning model is tested on the IEEE 33-bus distribution network system.

关键词： distribution network planning distributed generator Line capacity planning network loss mixed integer linear programming

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Multiobjective Branch-and-Cut Method with Shared Cutting Plane Information: A Case Study on Optimizing Garbage Collection

Multiobjective Branch-and-Cut Method with Shared Cutting Pla...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Tomoki Kaho Kazutoshi Sakakibara Mikiharu Arimura Shinya Watanabe The Graduate School of Engineering Muroran Institute of Technology Muroran Japan Department of Data Science Faculty of Information Engineering Toyama Prefectural University Toyama Japan Department of Engineering Muroran Institute of Technology Muroran Japan Department of Sciences and Informatics Muroran Institute of Technology Muroran Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

This paper presents an application of the Multi-Objective Branch-and-Bound based on Decomposition (MOBB/D) method for Branch-and-Cut, combining branch-and-bound and cutting-plane methods. Traditionally, multi-objective branch-and-bound methods for multi-objective mixed-integer linear programming problems struggle with the large number of Pareto-optimal solutions. MOBB/D addresses this by decomposing multi-objective problems into single-objective subproblems using weight vectors and applying branch-and-bound to each, with information sharing between nearby subproblems. In this paper, we customize the MOBB/D method for Branch-and-Cut to achieve an efficient search by sharing cutting plane information in MOBB/D. We demonstrate our approach with a case study on garbage collection planning in Muroran City, Hokkaido, Japan. This case study combines facility location and route optimization within a multi-objective framework, balancing the burdens on residents and garbage collection workers. Numerical experiments compare execution times and solutions with and without cutting plane sharing, and visualize the impact of different objective balance preferences on route optimization. The results show substantial efficiency gains and provide practical insights for multi-objective optimization in urban planning.

关键词： Visualization Scalability Roads Urban planning Vectors Real-time systems Planning mixed integer linear programming Computational efficiency Optimization

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Enhanced Power System Restoration Through MILP Black Start Allocation Optimization

Enhanced Power System Restoration Through MILP Black Start A...

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IEEE Kansas Power and Energy Conference

作者： Shaharier Kabir Abu Shufian Riadul Islam Nasif Hannan Md. Saniat Rahman Zishan Shaikh Anowarul Fattah Dept. of EEE American International University-Bangladesh Dhaka Bangladesh Dept. of ECE Lamar University Texas USA Dept. of EEE Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (数字)9798350372403

ISBN: (纸本)9798350372410

Restoring a power system following a blackout is a critical undertaking, necessitating the efficient allocation of Black Start (BS) resources—generators capable of initiating without external power. This research introduces an innovative approach to address a critical challenge in power system restoration—optimal black start allocation. Efficient allocation of resources when power systems face blackouts is crucial for rapid restoration. Leveraging the power of mixed integer linear programming (MILP), this study formulates and solves the black start allocation problem with the primary objective of minimizing allocation costs, all while satisfying many operational constraints. These constraints include ensuring power supply meets demand, adhering to generator output limits and ramping rates, and enforcing time-bound generator startup decisions. Furthermore, it maintains power flow equilibrium and network stability through branch flow and voltage magnitude constraints. This research offers a promising solution to enhance the resilience and reliability of power systems by optimizing the allocation of black start resources. This would reduce downtime ultimately and mitigate the far-reaching impacts of blackouts on society and the economy.

关键词： Voltage Optimal scheduling Power system stability Generators Power system restoration Power system reliability mixed integer linear programming

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Research on the Mechanism of Electric Vehicle Aggregators Participating in Electricity Market

Research on the Mechanism of Electric Vehicle Aggregators Pa...

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International Conference on Power and Energy Technology (ICPET)

作者： Zhu Shunbo Wang Wen Bian Pengfei Shi Tianchen An Guangxiang Li Bo Li Liang Yang Ye State Grid Zhejiang Electric Vehicle Service Co. Ltd. State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China State Grid Smart Internet of Vehicles Company Ltd. Beijing China Beijing Huadian Energy Internet Research Institute Co. Ltd. Beijing China

ISBN: (数字)9798350375855

ISBN: (纸本)9798350375862

The current ownership of Electric Vehicles (EVs) in China is experiencing rapid growth, which presents new challenges to the distribution of electric grid loads. At present, the pricing strategies and demand response mechanisms of EV charging aggregators are of great practical importance, as they guide EV users to charge their vehicles during off-peak hours and alleviate the grid load during peak hours. This paper presents a leader-follower game model in which the aggregator, acting as the leader, sets electricity prices to encourage EV users to charge during off-peak periods, thereby reducing the pressure on the electricity supply during peak periods. In addition, a mixed integer linear programming method is employed to solve the model, ensuring global optimality of the strategy. Empirical evidence indicates that electricity prices should be adaptively adjusted in accordance with seasonal fluctuations and changes in demand in order to maximize the aggregator's revenue and minimize the user's charging costs. This approach is conducive to the deployment of electric vehicles and the advancement of smart grids.

关键词： Costs Electricity Games Pricing Transforms Electricity supply industry Electric vehicle charging mixed integer linear programming Smart grids Load modeling

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Supplier Selection and Order Allocation for Assembly Products using Multi-Stage Stochastic Bi-Objective Approach

Supplier Selection and Order Allocation for Assembly Product...

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IEEE International Conference on Industrial Engineering and Engineering Management

作者： Ashutosh Kumar Thakur Indrajit Mukherjee Shailesh J. Mehta School of Management Indian Institute of Technology Bombay Mumbai India

ISBN: (数字)9798350386097

ISBN: (纸本)9798350386103

This study addresses supplier selection and order allocation (SS&OA) strategy for multi-component assembly products considering demand uncertainties and disruption risks. We propose a novel multi-stage stochastic bi-objective mixed-integer linear programming (MILP) model leveraging the augmented- $\varepsilon$-constraint 2 method (AUGMECON2) to optimize total cost and purchasing value. The model incorporates disruptions, allows flexible order fulfilment, and integrates a comprehensive supplier evaluation process considering qualitative and quantitative criteria. Furthermore, an optimisation-based simulation procedure, based on a rolling planning horizon framework, is used to approximate the solution. The findings reveal diminishing returns on purchasing value with increasing costs. The optimal sourcing strategy depends on minimum order quantity (MOQ). Multiple sourcing excels under MOQ due to demand responsiveness, while dual sourcing offers superior cost efficiency without MOQ. Additionally, multiple sourcing outperforms in managing inventory levels. Sensitivity analysis confirms these trends and the impact of Bill-of-Materials complexity, supplier count, and demand variability on cost and service level. Finally, this research provides valuable insights for manufacturers in dynamic environments, enabling them to optimize SS&OA decisions and achieve improved supply chain performance.

关键词： Costs Uncertainty Sensitivity analysis Supply chains Stochastic processes programming Planning mixed integer linear programming Resource management Assembly

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Cycle-Configuration: A Novel Graph-theoretic Descriptor Set for Molecular Inference

Cycle-Configuration: A Novel Graph-theoretic Descriptor Set ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Bowen Song Jianshen Zhu Naveed Ahmed Azam Kazuya Haraguchi Liang Zhao Tatsuya Akutsu Graduate School of Informatics Kyoto University Kyoto Japan Department of Mathematics Quaid-i-Azam University Islamabad Pakistan Graduate School of Advanced Integrated Studies in Human Survavibility Kyoto University Kyoto Japan Institute for Chemical Research Kyoto University Kyoto Japan

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

In this paper, we propose a novel family of descriptors of chemical graphs, named cycle-configuration (CC), that can be used in the standard "two-layered (2L) model" of mol-infer, a molecular inference framework based on mixed integer linear programming (MILP) and machine learning (ML). Proposed descriptors capture the notion of ortho/meta/para patterns that appear in aromatic rings, which has been impossible in the framework so far. Computational experiments show that, when the new descriptors are supplied, we can construct prediction functions of similar or better performance for all of the 27 tested chemical properties. We also provide an MILP formulation that asks for a chemical graph with desired properties under the 2L model with CC descriptors (2L+CC model). We show that a chemical graph with up to 50 non-hydrogen vertices can be inferred in a practical time.

关键词： Computational modeling Biological system modeling Machine learning mixed integer linear programming Bioinformatics Chemicals Standards

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Considering Disturbance Scenarios in Clustered Inertia-Aware Generation Expansion Planning

Considering Disturbance Scenarios in Clustered Inertia-Aware...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Oussama Alaya Taiseer Alhaj Ali Benedikt Jahn Hendrik Lens Faculty 4 IFK Power Generation and Automatic Control Group University of Stuttgart

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

The transition to a carbon-neutral power system requires replacing conventional generation with distributed renewable energy (RE) sources. This poses challenges related to reduced system inertia and large and long-distance power transmission in the system. This paper presents a novel generation expansion planning (GEP) methodology called inertia-aware clustered generation expansion planning (I-CGEP). The I-CGEP approach integrates spatial clustering and generator clustering to optimize discrete generation expansion options while avoiding overestimation of system inertia. The model incorporates advanced Rate of Change of Frequency (RoCoF) constraints that, in addition to standard constraints following predefined outages, also account for the expected power imbalance resulting from large disturbance scenarios such as regional islanding. The entire problem is formulated as a mixed-integer linear programming (MILP) problem. The proof of concept of the methodology is demonstrated using the open European transmission system model PyPSA-Eur. The results show its suitability for maintaining the required RoCoF under different scenarios.

关键词： Renewable energy sources Islanding Europe Power transmission Generators Planning Smart grids Safety mixed integer linear programming Standards

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Integrated Infrastructure Strategy for Sustainable Electrical Vehicles Charging Stations and Transportation Networks

Integrated Infrastructure Strategy for Sustainable Electrica...

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IEEE Power India Conference

作者： Digambar Singh Govind Manoj Fozdar Department of Electrical Engineering Malaviya National Institute of Technology Jaipur India

ISBN: (数字)9798350367065

ISBN: (纸本)9798350367072

The adoption of Electric Vehicles(EVs) is growing because of their environment-friendly properties and more efficient, With the increasing shift toward EVs, the need for a robust and well-coordinated charging infrastructure has become critical. The current lack of sufficient Fast Charging Stations (FCS) poses a significant challenge to supporting widespread EV usage. Proper planning of charging stations, both in terms of location and capacity, is essential to accommodate rising EV demand. Complicating this process is the interdependence between Transportation Networks (TN) and Power Distribution Networks (PDN), which requires a unified approach for efficient large-scale EV integration. This paper introduces a coordinated planning approach for integrating EV charging stations within both TN and PDN frameworks. A detailed optimization model is presented to identify the optimal locations and capacities for charging stations based on EV traffic patterns and power network capabilities. The traffic flow across the transportation network is modelled using an unconstrained traffic assignment method, while the PDN is represented through a linearized power flow model. To capture the complex interactions between TN and PDN, the model employs a mixed-integer linear programming (MILP) framework that incorporates constraints from both networks. Through simulations on coupled traffic and power networks, the proposed approach demonstrates its effectiveness in optimizing EV charging infrastructure, facilitating a smoother transition to electric mobility.

关键词： Optimization models Transportation Traffic control Probabilistic logic Electric vehicle charging Planning Power systems mixed integer linear programming Reliability Traffic congestion

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Procurement Cost Minimization of an Energy Community with Biogas, Photovoltaic and Storage Units

Procurement Cost Minimization of an Energy Community with Bi...

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14th IEEE Madrid PowerTech Conference (IEEE POWERTECH)

作者： Pulazza, Giorgia Orozco, Camilo Borghetti, Alberto Tossani, Fabio Napolitano, F. Univ Bologna Dept Elect Elect & Informat Engn Bologna Italy

ISBN: (纸本)9781665435970

The paper presents a day-ahead scheduling procedure of a local energy community (LEC) that includes one or more producers equipped with a biogas power plant. The other participants may own photovoltaic units, battery energy systems (BESs), and loads. The aim of the scheduling, which essentially concerns biogas units and BESs, is the minimization of the daily energy procurement cost of the LEC, including the fuel cost. The scheduling procedure also provides the prices of the internal transactions. In particular, the paper shows the impact of the biogas power production and analyzes how it affects the prices of the transactions between the LEC participants. Several case studies are presented, characterized by different scenarios of LEC self-consumption, number of dispatchable units, and fuel consumption. Both a centralized and a distributed optimization model, based on the alternating direction method of multipliers (ADMM), have been implemented and compared.

关键词： Biogas day-ahead scheduling energy management system local energy community mixed integer linear programming alternating direction method of multipliers

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Distributed Planning of Multi-Agent Systems with Coupled Temporal Logic Specifications

Distributed Planning of Multi-Agent Systems with Coupled Tem...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Buyukkocak, Ali Tevfik Aksaray, Derya Yazicioglu, Yasin Univ Minnesota Dept Aerosp Engn & Mech Minneapolis MN 55455 USA Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

In this paper, we investigate the multi-agent mission planning and control problem under coordination constraints such as collision avoidance and spatial coherence. We propose a sequential trajectory planning algorithm that is run by each agent in a distributed manner. Mission requirements including servicing specific regions, avoiding static and dynamic obstacles (other agents), and flying close to the other agents are incorporated into the problem as Signal Temporal Logic (STL) constraints. The planning problem under STL constraints is formulated as a mixed integer linear Program and an off-the-shelf tool is used to find the high-level trajectories. The resulting high-level trajectories are then tracked by the agents via Model Predictive Control. Illustrative simulation and experiment results are presented in addition to comparing the proposed method with a centralized solution.

关键词： Multi Agent Coordination Model Predictive Control mixed integer linear programming Quadrotor Cyber Physical System MATLAB Robots Operating Systems Motion Planning Optimal Control

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