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Exact optimization of inter-array dynamic cable networks for Floating Offshore Wind Farms

RENEWABLE ENERGY 2024年 237卷

作者： Perez-Rua, Juan-Andres Lund, Rasmus Sode Verelst, David Robert Abrahamsen, Asger Bech Dykes, Katherine Tech Univ Denmark Dept Wind & Energy Syst Frederiksborgvej 399 DK-4000 Roskilde Denmark

Optimization of inter-array dynamic cables for Floating Offshore Wind Farms (FOWFs) using three integer linear programs and a heuristic is presented. Design optimization of fixed-bottom offshore wind is a challenging research problem but the presence of dynamic components in FOWFs adds new complexity - as the Floating Offshore Wind Turbine (FOWT), the support structure including the station-keeping system, and the floating power cables all experience dynamic movement in reaction to wind, wave and even current forcing. In this study, dynamic modeling for the response of this system is first carried out to assess the risk of potential mechanical interference between movable elements. Subsequently, safety zones constraints are defined in the optimization to ensure minimally safe conditions for operation of the combined FOWT/support-structure/cables system. Likewise, additional constraints including maximum thermal limits, tree topology without branching, and others are incorporated. The programs follow an incremental approach. Model 1 proposes a simple way to avoid mechanical interference, Model 2 adds variables modeling mooring lines anchoring, and Model 3 increases the degrees of freedom through addition of the positioning of the touchdown point where the dynamic and static sections meet at the seabed. The applicability is illustrated through realistic case studies for a reference FOWF in Europe. Results show that: (i) Modern branch-and-cut solvers are able to solve Model 2 getting the global optimum in seconds, and (ii) further cost refining can be obtained after wrapping Model 3 in the heuristic, using Model 2 as the initial design, decreasing the cost of this layout by around 1.5% in few hours through a nonrectilinear topology.

关键词： Floating offshore wind energy Dynamic cables Exact optimization mixed integer linear programming Heuristics Thermal analysis Hydrodynamics

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Sizing of energy storage for spinning reserve and efficiency increase in isolated power systems within a data-driven stochastic unit commitment framework

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JOURNAL OF ENERGY STORAGE 2025年 111卷

作者： Adeyemo, Ayotunde A. Marra, Francesco Tedeschi, Elisabetta Norwegian Univ Sci & Technol NTNU Dept Elect Energy N-7034 Trondheim Norway Equinor Res Ctr Arkitekt Ebbells Veg 10 N-7053 Ranheim Norway Univ Trento Dept Ind Engn Via Sommar 9 I-38123 Trento Italy

This paper proposes a data-driven stochastic unit commitment (SUC) framework for sizing battery energy storage system (BESS) for spinning reserve and efficiency increase in isolated power system. BESS is used to provide spinning reserve to manage the uncertainties in the power system. Additionally, load-shifting demand response (DR) is incorporated in the data-driven SUC model to reduce the size of the BESS required. Also, battery degradation is modelled in the data-driven SUC formulation to make the results as accurate as possible. Furthermore, to reduce the computational burden a novel scenario reduction method based on the Kantorovich distance metric is proposed for selecting scenarios (from the high number of generated scenarios for load and renewable sources) that describe the problem with limited loss of information. The case study is based on an offshore oil and gas platform in the North Sea which has four identical gas turbine generators and is assumed to integrate offshore wind turbines and an offshore solar farm, thus having three sources of uncertainty (load, wind and solar). Five test cases were analyzed: a baseline case without BESS and flexible load (DR), and other cases including degrading BESS and flexible load, either separately or jointly. First, the selected scenarios were analyzed and seen to retain most of the information in the original historical dataset, which validates the accuracy of the proposed scenario reduction method. The results showed that the methodology proposed in this work can be used to get an accurate optimal BESS capacity and that the inclusion of load-shifting DR can help reduce the optimal BESS capacity. Cases where an optimally sized BESS was considered provided cost reductions in the range 2.54 % to 3.68 % and carbon emissions reductions in the range 3.88 % to 4.5 % compared to the baseline case. Finally, the result also showed that battery degradation modelling is important as the cost reduction increased from 2.54 % to 3

关键词： Stochastic unit commitment mixed integer linear programming Lithium Iron Phosphate Battery Battery energy storage system Scenario reduction Multiple uncertainties

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An interregional optimization approach for time series aggregation in continent-scale electricity system models

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ENERGY 2025年 324卷

作者： Brown, Patrick R. Cole, Wesley J. Mai, Trieu Natl Renewable Energy Lab Golden CO 80401 USA

Modeling electric power systems with high shares of weather-dependent resources requires tradeoffs between temporal, spatial, and operational resolution. Many studies perform time series aggregation using clustering algorithms to reduce the temporal dimension, but when modeling continent-scale electricity systems that are large enough to contain multiple independent weather systems, this approach requires large numbers of representative periods to minimize errors in regional wind and solar capacity factors. Here, a new optimization-based approach for representative period selection and weighting is introduced that minimizes regional errors in average renewable capacity factors and electricity demand. The method delivers higher regional fidelity with fewer representative periods than alternative clustering methods when applied to wind, solar, and demand profiles for the contiguous United States. When representative periods are selected from multiple weather years, the optimized method reproduces regional averages with lower error than a complete 365-day time series from any single weather year. The method identifies only representative (as opposed to outlying) periods but can be combined with an iterative "stress period" identification approach to guide efficient decision-making considering both average and high-risk weather conditions.

关键词： Time series aggregation mixed integer linear programming Temporal resolution Representative days Energy system modeling

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MILP Acceleration: A Survey from Perspectives of Simplex Initialization and Learning-Based Branch and Bound

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JOURNAL OF THE OPERATIONS RESEARCH SOCIETY OF CHINA 2025年第1期13卷 1-55页

作者： Huang, Meng-Yu Huang, Ling-Ying Zhong, Yu-Xing Yang, Hui-Wen Chen, Xiao-Meng Huo, Wei Wang, Jia-Zheng Zhang, Fan Bai, Bo Shi, Ling Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Huawei Hong Kong Res Ctr Theory Lab Hong Kong Peoples R China

mixed integer linear programming (MILP) is an NP-hard problem, which can be solved by the branch and bound algorithm by dividing the original problem into several subproblems and forming a search tree. For each subproblem, linear programming (LP) relaxation can be solved to find the bound for making the following decisions. Recently, with the increasing dimension of MILPs in different applications, how to accelerate the solution process becomes a huge challenge. In this survey, we summarize techniques and trends to speed up MILP solving from two perspectives. First, we present different approaches in simplex initialization, which can help to accelerate the solution of LP relaxation for each subproblem. Second, we introduce the learning-based technologies in branch and bound algorithms to improve decision making in tree search. We also propose several potential directions and extensions to further enhance the efficiency of solving different MILP problems.

关键词： MILP acceleration Simplex initialization linear programming mixed integer linear programming Machine learning

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Task Assignment for Multiple Multi-purpose Unmanned Aerial Vehicles Using Greedy Algorithm

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INTERNATIONAL JOURNAL OF AERONAUTICAL AND SPACE SCIENCES 2024年第4期25卷 1380-1394页

作者： Jeon, Ha-Min Lim, Jae-Woo Ryoo, Changkyung Inha Univ Dept Aerosp Engn Incheon South Korea

Unmanned aerial vehicle fleets demand effective task assignment since the task scope of each vehicle depends on the task-specific equipment it carries. Here, we propose real-time task assignment for multiple multipurpose unmanned aerial vehicles using a greedy algorithm. The greedy algorithm does not require complex mathematical modeling and can respond to real-time situational changes due to its fast computation. The proposed algorithm uses the total flight time of all unmanned aerial vehicles as the objective function so that each vehicle completes all tasks within its available battery level. To increase the diversity of solutions, the algorithm is dually computed in the forward and backward directions from the starting and destination points, respectively. We apply the algorithm to water resource management and compare the results with those of mixed integer linear programming and the genetic algorithm. While the flight-time cost is similar across algorithms, computation is 3-8 times faster for the proposed algorithm than for the genetic algorithm. This advantage increases as the number of tasks and/or unmanned aerial vehicles increases. The proposed algorithm's consideration of remaining battery levels when selecting randomly unmanned aerial vehicles also results in a higher average remaining battery level than the other algorithms. These results suggest that the proposed algorithm shows promise and feasibility for application to real-time task assignment scenarios for multiple multipurpose unmanned aerial vehicles.

关键词： Task assignment Greedy algorithm mixed integer linear programming Genetic algorithm Multipurpose unmanned aerial vehicle Water resource management

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Optimizing spot intensity with lower bound constraints for IMPT: Exposing shortcomings and introducing an enhanced strategy

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MEDICAL PHYSICS 2024年第10期51卷 7523-7544页

作者： Han, Dong Tong, Jingdong Yang, Yu Liu, Hongcheng Liang, Xiaoying Yaddanapudi, Sridhar Park, Chunjoo Tan, Jun Furutani, Keith Beltran, Chris Lu, Bo Univ Florida Dept Ind & Syst Engn Gainesville FL 32611 USA Mayo Clin Florida Dept Radiat Oncol Jacksonville FL 32224 USA

BackgroundIntensity Modulated Proton Therapy (IMPT) is a sophisticated radiation treatment allowing for precise dose distributions. However, conventional spot selection strategies in IMPT face challenges, particularly with minimum monitor unit (MU) constraints, affecting planning quality and *** study introduces an innovative Two-Stage mixed integer linear programming (MILP) method to optimize spot intensity in IMPT with Lower Bound (LB) constraints. This method seeks to improve treatment planning efficiency and precision, overcoming limitations of existing *** approach evaluates prevalent IMPT spot selection strategies, identifying their limitations, especially concerning MU constraints. We integrated LB constraints into a MILP framework, using a novel three-phase strategy for spot pool selection, to enhance performance over traditional heuristic methods and L1 + L infinity strategies. The method's efficacy was tested in eight study cases, using Dose-Volume Histograms (DVHs), spot selection efficiency, and computation time analysis for benchmarking against established *** proposed method showed superior performance in DVH quality, adhering to LB constraints while maintaining high-quality treatment plans. It outperformed existing techniques in spot selection, reducing unnecessary spots and balancing precision with efficiency. Cases studies confirmed the method's effectiveness in producing clinically feasible plans with enhanced dose distributions and reduced hotspots, especially in cases with elevated LB *** Two-Stage MILP strategy signifies a significant advancement in IMPT treatment planning. By incorporating LB constraints directly into the optimization process, it achieves superior plan quality and deliverability compared to current methods. This approach is particularly advantageous in clinical settings requiring minimum spot number and high MU LB constraints, offering the potential for

关键词： intensity modulated proton therapy lower bound constraints mixed integer linear programming spot intensity optimization treatment planning

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Evaluation of effect of providing balancing reserves to power system in Japan through operational control of water pumps

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ENERGY REPORTS 2024年 11卷 3424-3435页

作者： Akimoto, Yushi Negishi, Shintaro Ikegami, Takashi Tokyo Univ Agr & Technol Grad Sch Bio Applicat & Syst Engn 2-24-16 Naka Cho Koganei Shi Tokyo 1848588 Japan Kanagawa Univ Dept Elect Elect & Informat Engn 3-27-1 RokkakubashiKanagawa ku Yokohama 2218686 Japan

In Japan, renewable energy is being rapidly introduced. Hence, surplus power and the required balancing reserves will increase. In this study, to deal with this problem, we focus on a water distribution system (WDS), especially water pumps, which can contribute to supply-demand balancing by shifting their operation. To evaluate the current potential of water pumps in Japan, we modeled them and simulated their operation, in which they can shift their pumping schedule to reduce surplus power and provide balancing reserves to the power system, adhering to constraints such as the demand for water and the storage of water reservoirs. We then developed a numerical model that simultaneously optimizes the operation of generators and water pumps throughout Japan, and simulated their operation in 2040. The results showed that despite increasing their power consumption, pumps shifted their pumping schedule to contribute to the entire power system and reduced fossil generator fuel costs and renewable energy output curtailment.

关键词： Water pump Water distribution system Balancing reserves Supply-demand analysis mixed integer linear programming

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A bottom-up multi-objective optimisation approach to dynamic facility layout planning

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第3期62卷 626-643页

作者： Perez-Gosende, Pablo Mula, Josefa Diaz-Madronero, Manuel Univ Politecn Valencia Res Ctr Prod Management & Engn CIGIP Alcoy Spain Univ Politecn Salesiana Ind Engn Dept Guayaquil Ecuador

Dynamic facility layout planning (DFLP) involves determining an appropriate arrangement scheme of the elements making up the production system for each time period into which the planning horizon is divided. When formulating the problem as an optimisation model, using the traditional top-down approach is usual, which firstly determines the block layout (BL) and then the detailed layout (DL) of each work cell. However by this approach, the BL generates area constraints in the detailed phase, which sometimes limit its implementation. In this context, the present paper presents a multi-objective mixed integer non-linear programming (MOMINLP) model that allows the problem to be addressed by considering an alternative approach, known in the literature as the bottom-up approach. The proposed model, called bottom-up mDFLP, considers three objective functions: (1) minimise the total material handling cost (TMHC) and the total rearrangement cost (TRAC);(2) maximise the total closeness rating (TCR) between departments;(3) maximise the area utilisation ratio (AUR). The original MOMINLP is transformed into a more computationally efficient multi-objective mixed integer linear programming (MOMILP) model. The proposed model is applied and validated in a case study of a company in the metal-mechanic sector with 12 departments for three 4-month periods.

关键词： Facility layout facility planning multicriteria decision making non-linear programming mixed integer linear programming

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A matheuristic-oriented iterated greedy algorithm for multi-mode resource-constrained project scheduling problem under uncertainty

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 193卷

作者： Gao, Zheng Zhang, Liping Yu, Puliang Zhang, Zikai Li, Zixiang Wuhan Univ Sci & Technol Minist Educ Key Lab Met Equipment & Control Technol Wuhan Peoples R China

Multi-mode resource-constrained project scheduling problem is one of the most important branches of combinatorial optimization problems. The aim is to find a feasible solution consisting of activity start time and execution mode so that the project makespan is minimized. However, there are a lot of uncertain events, that bring great challenges to project scheduling. We first propose a mixed integer linear programming model where the activity duration is uncertain. Then two uncertain parameters are introduced to describe the disturbance degree of uncertain activity duration and the allowable violation degree of constraints respectively, and the MILP model with uncertain activity duration is converted to its deterministic robust counterpart model. A matheuristic local optimization approach is proposed to balance computational time with the optimization of the computational result. A matheuristic-oriented iterated greedy algorithm that combines a matheuristic local optimization approach and an iterated greedy algorithm is proposed to solve this problem in this paper. Experimental results indicate that the robust counterpart model can obtain robust optimal solutions for small-scale instances in accepted computational time. Meanwhile, the proposed matheuristic-oriented iterated greedy algorithm can obtain robust near-optimal solutions for large-scale instances and is superior to other compared algorithms.

关键词： Project scheduling Uncertainty Robust counterpart model mixed integer linear programming Matheuristic

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Multi-Agent Task Allocation with Interagent Distance Constraints

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2024年第2期21卷 168-177页

作者： Choi, Euihyeon Chang, Woohyuk Korea Adv Inst Sci & Technol Dept Aerosp Engn 291 Daehak Ro Daejeon 34141 South Korea Agcy Def Dev Aerosp Technol Res Inst Yuseong POB 35 Daejeon 34186 South Korea

In this paper, we consider the task allocation problem for autonomous agents where the distances between any two agents should always be less than a predetermined value. To easily tackle the problem, we introduce a novel concept referred to as a virtual task that helps to decouple the task allocation from the path planning in the joint task allocation and path planning problem. An overall task allocation procedure with two steps is proposed. After the virtual tasks are generated with preliminary clustering in the first step, tasks are clustered into groups and allocated to agents in the second step. To reduce the computational complexity of optimal clustering and task allocation, we propose a suboptimal method called the cluster-level traveling salesman problem (TSP). Numerical results show that the cluster-level TSP suffers only little performance degradation compared to the optimal method, even though its computational costs are dramatically reduced.

关键词： mixed integer linear programming Autonomous Technology Robots Task Allocation Path Planning Traveling Salesman Problem Inter-Agent Distance Constraints Multi-Agent System

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