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mixed integer linear programming Applied to Adaptive Directional Overcurrent Protection Considering N-1 Contingency

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2023年第3期59卷 2807-2821页

作者： Martin, Klaus Tesser Marchesan, Adriano Cavalheiro Bassi de Araujo, Olinto Cesar Cardoso, Ghendy, Jr. da Silva, Marcelo Freitas Fed Inst Educ Sci & Technol Farroupilha BR-97760000 Jaguari RS Brazil Univ Fed Santa Maria BR-97105900 Santa Maria RS Brazil

This work proposes a methodology for directional overcurrent protection coordination in interconnected transmission systems considering a possible network contingency state. The methodology uses the short-circuit data of the current network topology;however, the maximum load current data in the protection section of each relay is obtained considering the n-1 criterion, already foreseeing the disconnection of some network line. Thus, if a line gets disconnected, other protective devices will not improperly actuate by the redistribution of load currents in the network. The objective is to propose an adaptive protection scheme to redo the coordination for each topological change in the network. To this end, this work considers a smart grid environment with a supervisory system with communication capability between this and the remote devices. To obtain the optimal performance, the coordination problem, originally non-linear and non-convex, is linearized, allowing its formulation as a mixed integer linear programming problem. The methodology is applied to the 8-bus test system in 3 different cases and the 30-bus test system. Results show that the optimal coordination is obtained in a fast computational processing time, showing the suitability of the methodology for real-time application.

关键词： Adaptive protection directional overcurrent relay mixed integer linear programming power system protection power systems

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mixed integer linear programming Based Large Neighborhood Search Approaches for the Directed Feedback Vertex Set Problem 9th

Mixed Integer Linear Programming Based Large Neighborhood Se...

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9th International Conference on Metaheuristics and Nature Inspired Computing (META)

作者： Bresich, Maria Varga, Johannes Raidl, Guenther R. Limmer, Steffen TU Wien Inst Log & Computat Vienna Austria Honda Res Inst Europe GmbH D-63073 Offenbach Germany

ISBN: (纸本)9783031692567;9783031692574

A directed feedback vertex set (DFVS) of a directed graph is a subset of vertices whose removal makes the graph acyclic. Finding a DFVS of minimum cardinality is the goal of the directed feedback vertex set problem, an NP-hard combinatorial optimization problem. We first consider two mixed integer linear programming (MILP) models for this problem, which, when solved with Gurobi, are effective on graphs of small to medium complexity but do not scale well to large instances. Aiming at better scalability and higher robustness over a large variety of graphs, we investigate a large neighborhood search (LNS) in which a destroy operator removes randomly chosen nodes from an incumbent DFVS and one of the MILP models is used for repair. Regarding the destroy operator, finding a best degree of destruction is challenging. A main contribution lies in proposing several selection strategies for this parameter as well as a strategy for choosing the more promisingMILP model for repair. We evaluate the performance of the MILP models and different LNS variants on benchmark instances and compare the approaches to each other as well as to state-of-the-art procedures. Results show that our LNS variants yield clearly better solutions on average than standalone MILP solving. Even though our approaches cannot outperform the state-of-the-art, we gain valuable insights on beneficially configuring such a MILP-based LNS.

关键词： Large neighborhood search Mathematical programming mixed integer linear programming Directed feedback vertex set problem

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A Fast and Accurate Self-Healing Scheme for Intelligent Distribution Networks Using mixed integer linear programming

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IEEE ACCESS 2024年 12卷 21586-21595页

作者： Lu, Jiangang Su, Junni Zhao, Ruifeng Chen, Fengchao Wang, Qiujie Yan, Wei Guangdong Power Grid Co Ltd Elect Power Dispatching & Control Ctr Guangzhou 510600 Peoples R China Guangdong Power Grid Co Ltd Dongguan Power Supply Bur Dongguan 523000 Peoples R China Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Sec Chongqing 400044 Peoples R China

Fast and accurate self-healing after faults is an important way to improve the reliability of distribution networks. To improve the self-healing speed and accuracy of distribution networks, this paper proposes a fast and accurate self-healing scheme for distribution networks using mixed integer linear programming. Firstly, this method constructs a centralized 5G communication network architecture, which can effectively reduce the communication delay during the self-healing process. Secondly, the principle of logical algebraic transformation is utilized to linearize the switch function in the fault location model, thereby achieving equivalent transformation of the fault location model. Thirdly, using the polyhedral approximation method, the mixed integer second-order cone programming of the power supply recovery model is transformed into a mixed integer linear programming. The proposed method was validated by distribution network systems with different nodes in MATLAB/Simulink, and the results showed that the proposed method significantly improved self-healing speed and accuracy.

关键词： Distribution networks Power supplies Fault location 5G mobile communication Location awareness Fault currents Delays mixed integer linear programming Distribution network fault location fault recovery self-healing mixed integer linear programming 5G communication

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Machine learning augmented branch and bound for mixed integer linear programming

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MATHEMATICAL programming 2024年 1-44页

作者： Scavuzzo, Lara Aardal, Karen Lodi, Andrea Yorke-Smith, Neil Delft Univ Technol Delft Netherlands Cornell Tech Jacobs Technion Cornell Inst New York NY USA Technion IIT New York NY USA

mixed integer linear programming (MILP) is a pillar of mathematical optimization that offers a powerful modeling language for a wide range of applications. The main engine for solving MILPs is the branch-and-bound algorithm. Adding to the enormous algorithmic progress in MILP solving of the past decades, in more recent years there has been an explosive development in the use of machine learning for enhancing all main tasks involved in the branch-and-bound algorithm. These include primal heuristics, branching, cutting planes, node selection and solver configuration decisions. This article presents a survey of such approaches, addressing the vision of integration of machine learning and mathematical optimization as complementary technologies, and how this integration can benefit MILP solving. In particular, we give detailed attention to machine learning algorithms that automatically optimize some metric of branch-and-bound efficiency. We also address appropriate MILP representations, benchmarks and software tools used in the context of applying learning algorithms.

关键词： mixed integer linear programming Branch and bound Machine learning Metrics Benchmarks

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Integral techno-economic design & operational optimization for district heating networks with a mixed integer linear programming strategy

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ENERGY 2024年 308卷

作者： Rojer, Jim Janssen, Femke van der Klauw, Thijs van Rooyen, Jacobus TNO Oudewaalsdorperweg 63 NL-2597 AK The Hague Zuid Holland Netherlands TNO Leeghwaterstr 44 NL-2628 CA Delft Zuid Holland Netherlands

The Netherlands, with over 90% of homes heated by natural gas is currently in the early phases of the heat transition to find alternative solutions towards 2050. According to ambitions of the Dutch government up to 50% of future implemented heating systems in the built environment will use District Heat Networks. The investment-heavy nature for District Heating System (DHS) makes it challenging to establish viable business- cases as supply-side parties require security of demand. However, resident participation is lacking as there is no integral estimate for the cost of heat over the lifetime in the early planning phase. This paper proposes a network integral techno-economic optimization with minimal a-priori assumptions. An integral network cost optimization enables to achieve a considerably more reliable cost of heat in the early planning phase. Both the investments and operational strategy are optimized with a mixed integer linear programming approach that captures the physics as well as the financial choices. linearization of the physics are chosen to have a conservative impact on the costs estimates. The end scenario network is designed where the placement and size of sources, storage components and pipes are optimized together with the operational strategy, e.g. thermal allocation time-series, for all assets. The workflow was applied to a greenfield network for the Dutch municipality of Rijswijk. It was shown that a 18% reduction in Total Cost of Ownership in the primary grid could be achieved by introducing decentralized sources, decentralized storages and seasonal storage.

关键词： District heating network Design and operational optimization Asset sizing Seasonal thermal energy storage Decentralized thermal energy storage mixed integer linear programming

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A multi-period multiple parts mixed integer linear programming model for AM adoption in the spare parts supply Chain

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2024年第5期37卷 550-571页

作者： Mecheter, Asma Pokharel, Shaligram Tarlochan, Faris Tsumori, Fujio Qatar Univ Dept Mech & Ind Engn Doha Qatar Kyushu Univ Fac Engn Dept Aeronaut & Astronaut Fukuoka Japan Qatar Univ Dept Mech & Ind Engn POB 2713 Doha Qatar

This research proposes a multi-period multiple parts mixed-integer linear programming optimization model for the trade-off analysis of spare parts supply through computer numerical control (CNC) manufacturing and additive manufacturing (AM). The multiple spare parts have different characteristics such as volume, shape size, and geometry complexity. The model focuses on minimizing lead times and thus reducing downtime costs. Scenario analyses are developed for some parameters to assess the robustness of the model. The analysis shows that the mix between AM-based spare parts and CNC-based spare parts is sensitive to changes in demand. For the given data, the findings demonstrate that AM is cost-effective with spare parts having high geometry complexity while CNC-based manufacturing is economically feasible for spare parts with low geometry complexity and large sizes. The proposed model can support decision-makers in selecting the optimal manufacturing method for multiple spare parts having different characteristics and attributes. The paper concludes with limitations and future directions.

关键词： Additive manufacturing supply chain spare parts optimization mixed integer linear programming conventional manufacturing

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A mixed integer linear programming Model for Resolution of the Antenna-Satellite Scheduling Problem

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第1期60卷 463-473页

作者： Linares, Lorena Vazquez, Rafael Perea, Federico Galan-Vioque, Jorge Univ Seville Dept Aerosp Engn Fluids Mech ETSI Seville 41092 Spain Univ Seville Appl Math 2 EPS Seville 41004 Spain Univ Seville Appl Math II ETSI Seville 41092 Spain

This article deals with one of the types of "Satellite Range Scheduling" problems arising in Earth Observation Satellite operations, Antenna-Satellite Scheduling. Given a set of satellites, a set of available antennas and a time horizon, the problem consists of designing an operational plan that assigns satellites to antennas in an optimal fashion. Extending a previous integer linear programming (ILP) model (shortening model, with only integer variables), we propose a mixed ILP (MILP) (shaving model, which includes both continuous and integer variables), to more efficiently solve this problem. After computing the passes generated by the satellites' windows of visibility from the antennas, the optimal planner is able to cancel a pass, move it to another antenna, or shorten its duration, in order to avoid scheduling conflicts. In contrast to the shortening model, which used intersections between passes to determine the best schedule, the shortening operation is now referred to as shaving, since the shaving model can arbitrarily adjust the duration of a pass in a razor-like fashion, giving the model its name. Computational results obtained in tests over realistic scenarios prove that the shaving model outperforms the shortening model, producing fewer cancellations, smaller shaved times, and a fairer distribution of cancelled passes among satellites, with much shorter preprocessing times.

关键词： Satellites Satellite antennas Task analysis Computational modeling Genetic algorithms Optimal scheduling Antennas Antenna-satellite allocation deconfliction mixed integer linear programming optimal satellite scheduling satellite range scheduling

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Dynamic Fleet Configuration Model for Optimizing Earthmoving Operations Using mixed integer linear programming

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JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT 2024年第11期150卷

作者： Khallaf, Zaid Alshibani, Adel Alsawafy, Omar Mohammed, Awsan Bubshait, Abdulaziz King Fahd Univ Petr & Minerals Appl Res Ctr Metrol Stand & Testing Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Dept Architectural Engn & Construct Management Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Construct & Bldg Mat Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Dept Ind & Syst Engn Dhahran 31261 Saudi Arabia

Practitioners in construction management primarily focus on two key indicators of project success: total cost and completion time. Heavy equipment and machinery play pivotal role in determining these measures, representing significant cost elements in various heavy construction projects such as road construction. Consequently, there is a pressing need for an efficient approach to determining the optimal scheduling of these heavy resources to minimize costs and shorten completion times. This paper proposes an innovative approach to address this challenge by introducing a mixed integer linear programming (MILP) model. The aim is to identify the optimal configuration for heavy equipment in earthmoving operations. The dynamic nature of the configuration process is adopted, enabling daily updates to the schedule based on the contractor's available resources. Moreover, environmental considerations are integrated into the decision-making process, ensuring a comprehensive approach to project optimization. To demonstrate the superiority of the developed model, three case projects from the literature have been solved. The proposed model led to a significant improvement in project cost, with an average enhancement of 25%, and in completion time, with an average improvement of 50% compared with the literature case studies. This paper presents a novel MILP model designed to optimize earthmoving operations, focusing on dynamic fleet configurations and emission costs. Unlike existing models, this approach provides daily fleet setups for multiple cut and fill sites, considering the contractor's available resources. It calculates optimal soil quantities to be moved, monitors soil levels at sites, and estimates daily trips between them. In the realm of project bidding and management, this model offers valuable insights and practical applications. It empowers project managers with a robust tool for optimizing fleet configurations during bid preparation, enabling contractors to determi

关键词： Dynamic fleet configuration Earthmoving optimization Construction management Fleet management mixed integer linear programming

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Modeling Favorable Locations for Biogas Plants that Generate Electricity from Dairy and Beef Cattle Manure through mixed integer linear programming

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BIORESOURCES 2024年第3期19卷 4517-4530页

作者： Yilmaz, Halil Ibrahim Gonbe, Yalcin Isparta Univ Appl Sci Dept Agr Struct & Irrigat TR-32260 Isparta Turkiye

mixed integer linear programming (MILP) is known as a type of programming that can combine continuous variables, integer variables, and (0-1) variables in the same algorithm and generate fiffing results for the data. Using this technique, it is possible to model and solve complex problems in many different fields such as economics, biology, engineering, etc. In the present study, a regional planning model was developed using MILP technique for the conversion of manure from dairy and beef cattle into biogas and electrical energy. For this regional planning study, considering the locations of future facilities, data on dairy and beef cattle in the Isparta province of TOrkiye were used. According to the model written and solution outputs, to utilize all manure obtained from dairy and beef cattles in Isparta, 5 biogas plants with a total manure processing capacity of approximately 522,000 tons should be built in different districts. It is possible to produce a total of approximately 21,000,000 m 3 of biogas and 38,500 MW of electricity per year in these biogas plants. This electrical energy obtained can meet 3.83% of the annual electricity consumption of Isparta province.

关键词： Optimization Biogas mixed integer linear programming Manure management Planning model

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Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, mixed integer linear programming, and Onsset Method

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ENERGIES 2024年第12期17卷

作者： Kabe, Moyeme Bokovi, Yao Sedzro, Kwami Senam Takouda, Pidename Lare, Yendoube Univ Lome CERME Ctr Excellence Reg Maitrise Elect Elect Engn 01 BP 1515 Lome Togo Univ Lome Lab Solar Energy 01 BP 1515 Lome Togo CERME Dept Elect Engn Lab Res Engn Sci LARSI 01 BP Lome 1515 Togo Natl Renewable Energy Lab Golden CO 80401 USA EPL Ecole Polytech Lome 01 BP 1515 Lome Togo

Optimal planning and design of microgrids are priorities in the electrification of off-grid areas. Indeed, in one of the Sustainable Development Goals (SDG 7), the UN recommends universal access to electricity for all at the lowest cost. Several optimization methods with different strategies have been proposed in the literature as ways to achieve this goal. This paper proposes a microgrid installation and planning model based on a combination of several techniques. The programming language Python 3.10 was used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. Four levels of study were carried out. The first level consisted of simulating the model obtained with a cluster, which is considered based on the elbow and k-means clustering method as a case study. The second level involved sizing the microgrid with a capacity of 40 kW and optimizing all the resources available on site. The example of the different resources in the Togo case was considered. At the third level, the work consisted of proposing an optimal connection model for the microgrid based on voltage stability constraints and considering, above all, the capacity limit of the source substation. Finally, the fourth level involved a planning study of electrification strategies based mainly on microgrids according to the study scenario. The results of the first level of study enabled us to obtain an optimal location for the centroid of the cluster under consideration, according to the different load positions of this cluster. Then, the results of the second level of study were used to highlight the optimal resources obtained and proposed by the optimization model formulated based on the various technology costs, such as investment, maintenance, and operating costs, which w

关键词： microgrids optimization k-means clustering mixed integer linear programming onsset

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