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Applied Power Electronics Conference and Exposition (APEC)

作者： Ghaffari, Abolfazl Askarzadeh, Alireza Fadaeinedjad, Roohollah Moschopoulos, Gerry Grad Univ Adv Technol Dept Elect & Comp Engn Kerman Iran Grad Univ Adv Technol Inst Sci & High Technol & Environm Sci Dept Energy Management & Optimizat Kerman Iran Western Univ Dept Elect & Comp Engn London ON Canada

ISBN: (纸本)9798350316643

The use of wind energy as an energy resource in distribution networks is increasing. A disadvantage to using wind energy, however, is the emission of flicker in an electrical distribution network (EDN), which may adversely affect power quality during system operation. An effective way to improve power quality is to include the allocation of wind turbines (WTs) in the planning phase. The location and size of WTs can significantly improve the network power quality and electrical performance in an EDN. This paper considers modeling for flicker emission by using a mixed integer non-linear programming (MINLP) model to determine the optimal location and size of WTs. The optimal design of the network associated with WTs generation profile patterns and the variation of load demand during the planning horizon 24-h is also determined. The proposed model is implemented in GAMS and tested on a standard IEEE 33-bus system.

关键词： Power quality-based allocation flicker mitigation wind turbine mixed integer non-linear programming

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Task Classification for Optimal Offloading and Resource Allocation in Vehicular Edge Computing 8

Task Classification for Optimal Offloading and Resource Allo...

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8th IEEE International Conference on Fog and Mobile Edge Computing (FMEC)

作者： Mubashir, Memona Ahmad, Rizwan Saadat, Ahsan Chaudhry, Saqib Rasool Kiani, Adnan K. Alam, Muhammad Mahtab Natl Univ Sci & Technol NUST Islamabad Pakistan Munster Technol Univ Cork Dept Comp Sci Cork T12 P928 Ireland Nottingham Trent Univ Dept Comp Sci Nottingham NG11 8NS England Tallinn Univ Technol Thomas Johann Seebeck Dept Elect EE-12616 Tallinn Estonia

ISBN: (纸本)9798350316971

Vehicular Edge Computing (VEC) aims to extend cloud service provision to the network edge vehicles, thus reducing back-haul network traffic and service latency. The emergence of computation-intensive tasks in connected, cooperative, and autonomous vehicles is inevitable. Vehicular applications and services generate time-sensitive tasks and require near real-time processing and updates. Executing such computationally intensive tasks on vehicles either leads to high computation latency or unavailability of resources. Recently, VEC has evolved as a popular choice for offloading and used for offloading these complex tasks. However, recent works put an upper limit on task offloading for execution. Moreover, executing a small task over a VEC server causes increased energy consumption and communication latency in a highly dynamic topology. Furthermore, due to the limited computational resources and capacity of the VEC server, it cannot process all tasks when the number of requests is high. Therefore, it is required to have an optimal selection for tasks offloading to solve this problem. A task-centric offloading and optimal resource allocation scheme based on task priority has been proposed in this paper. The proposed approach increases the probability of execution of these tasks on the VEC server as compared to other computation-intensive tasks. The numerical results demonstrate that the proposed work not only minimizes the processing delay but also has increased the percentage of tasks offloaded to the VEC server having higher priority in comparison to baselines.

关键词： Vehicular Edge computing Priority-based-offloading Computation resource allocation mixed integer non-linear programming

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Dynamic coal blending under sustainability concerns: a case study from the Indian steel industry

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INTERNATIONAL JOURNAL OF QUALITY & RELIABILITY MANAGEMENT 2023年第9期40卷 2114-2143页

作者： Ahmad, Md Tanweer Firouz, Mohammad Srivastava, Nishit Kumar ICFAI Fdn Higher Educ ICFAI Business Sch Dept Operat & IT Hyderabad India Univ Alabama Birmingham Dept Management Informat Syst Quantitat Methods Birmingham AL USA

PurposeIncreasing scarcity of natural resources and the adverse effects of unsustainable practices call for more and more efficient management strategies in the energy industry. The quality of the coke plays a significant role in the quality and durability of the output steel which is produced using the energy from the coal. This paper aims to investigate the dynamic coal blending problem under overall cost and coke quality constraints in the steel industry within a periodic cycle of ***/methodology/approachConsidering the variability of the natural properties over a periodic cycle, this study proposes a multi-period mixed-integer non-linear programming formulation to optimize the total blending costs while taking various coke quality constraints into account. Besides, this study applies factorial design to investigate about the significant effect of coal proportions as well as improvement into the overall cost of *** this case study, utilizing real data from a coal blending facility in India, through a factorial design, the authors obtain optimal desirable levels of coal proportions and their criticality levels towards the total cost of blending (TCB) or objective function. This analysis reflects the role of the coke quality constraints in the objective function value while characterizing the price of sustainability for the case study among other critical ***/valueObjective function (or TCB) includes basic coal cost, movement cost and environmental costs during the coal and coke processing at a coke-oven and blast furnace of steel industry. The price of sustainability provides managerial insights on that sacrifices the industry has to make in order to become more "sustainable".

关键词： Dynamic coal blending mixed integer non-linear programming Multi-period problem Factorial design Price of sustainability

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Optimal drone deployment for cost-effective and sustainable last-mile delivery operations

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2024年第0期

作者： Kumar, Gaurav Tanvir, Oqais Kumar, Akhilesh Goswami, Mohit Indian Inst Technol Dept Ind & Syst Engn Kharagpur W Bengal India Univ British Columbia Fac Forestry Ind Engn Res Grp Vancouver BC Canada Indian Inst Management Dept Operat & Qty Tech Raipur Chhattisgarh India

Delivery by drones holds significant potential to solve issues (such as high costs, access to remote areas, etc.) faced in last-mile delivery operations, particularly in the e-commerce industry. Still, it involves complex issues such as multi-trip operations, energy estimation, and battery recharge planning. A sound drone delivery problem entails an optimal drone deployment plan with routing details at the lowest possible cost. To this end, this study focuses on formulating a delivery problem that involves multi-trip drone routing, energy optimization, and travel time optimization problems where energy consumption by drones is modeled as a non-linear function. We develop a mixed integer non-linear programming model as an integrated optimization model. This model aims to: (a) maximize revenue by meeting demand completely without leaving idle drones, (b) optimize energy use by drones, and (c) minimize the required drone fleet size for an optimal plan. The proposed model is solved using the Gurobi Solver, which employs data supplied by a well-known e-commerce firm. We introduce a two-phase heuristic solution methodology to tackle larger networks' complexities. This method consists of the clustering phase (K-means clustering method) and the optimization phase. The robustness of the developed mathematical modeling is demonstrated by testing with varied large problem instances. The evaluation shows that expanding destination options boosts drone demand until saturation, necessitating more drones. Efficient route planning and fleet adjustments are crucial for meeting rising demand and satisfying customers amidst dense clustering. This model helps e-commerce manage daily last-mile drone deliveries and anticipate future growth.

关键词： last-mile delivery drone deployment plan drone routing, mathematical optimization mixed integer non-linear programming drone fleet sizing

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An integrated model for airport runway assignment and aircraft trajectory optimisation

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 160卷

作者： Barea, Adrian de Celis, Raul Cadarso, Luis Rey Juan Carlos Univ Aerosp Syst & Transport Res Grp Camino Molino 5 Fuenlabrada 28942 Madrid Spain

Air traffic management of terminal manoeuvring area involves high complexity as air traffic converges to airports. In addition, air traffic is currently experiencing a remarkable growth despite the COVID19 pandemic effects. This trend, which is expected to continue in the mid and near future, motivates the development of methodologies that improve the efficiency and automatisation of air traffic management processes to efficiently prevent bottlenecks in current airports instead of expanding or building new facilities, which usually implies higher costs. Specifically, runway assignment is of capital importance for the correct exploitation of current airports capacity. In this paper, a mixed integer non -linear model is presented which deals with aircraft approach and landing operations. It integrates decisions regarding runway assignment and trajectory optimisation. Since this problem is difficult to be solved, a Benders decomposition is proposed. The master model deals with runway assignment, resulting in a mixed integer linear programming model. The submodel deals with the trajectory determination problem, resulting in a nonlinear programming model that minimises a combination of fuel consumption and aircraft delay while complying with operational constraints. In addition, a rolling horizon approach is employed for real -size case studies, which systematically optimises operations within 30-min intervals. Computational results on real-world problem instances of Madrid-Barajas airport are reported. Our solutions are found to be tractable and robust in the face of data variations.

关键词： Trajectory optimisation Runway assignment Airport operations mixed integer non-linear programming Benders decomposition

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An emission constraint capacitor placement and sizing problem in radial distribution systems using modified competitive swarm optimiser approach

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INTERNATIONAL JOURNAL OF AMBIENT ENERGY 2021年第11期42卷 1228-1251页

作者： Das, Soumyabrata Malakar, Tanmoy Natl Inst Technol Dept Elect Engn Silchar India

This paper presents an environment friendly and cost-effective solution procedure to solve the Optimal Capacitor Locations and Sizing (OCLS) problem for power distribution systems. The OCLS problem is formulated as a two-stage mixed integer non-linear programming problem. In the first stage, a new Relative Emission Index based formulation is introduced to select the probable candidate buses for the capacitors. In the second stage, the optimum locations and sizes for the capacitor are determined using basic Competitive Swarm Optimiser (CSO) algorithm. Further, a variant of CSO called 'Opposition based CSO (OCSO)' is proposed and implemented in this work to get improved results. Due to the introduction of opposition based learning phase, three-fourths of the swarm are updated in each cycle in the proposed method, unlike CSO. The annual cost is considered as an objective function in this problem. The capacitor locations and VAr outputs are taken as binary and discrete control variables respectively. The CSO and its proposed modified version 'OCSO' are tested on standard 69 and 118 bus RDS networks with constant power load as well as with practical nonlinear loads. The results obtained with the proposed method are validated by comparing with other reported results in the state-of-art literature and those obtained by other methods.

关键词： Competitive swarm optimiser emission distribution system mixed integer non-linear programming optimum capacitors location and sizing

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Layout Optimization for Photovoltaic Panels in Solar Power Plants via a MINLP Approach

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2023年 1-14页

作者： Mignoni, Nicola Carli, Raffaele Dotoli, Mariagrazia Polytech Bari Dept Elect & Informat Engn I-70125 Bari Italy

Photovoltaic (PV) technology is one of the most popular means of renewable generation, whose applications range from commercial and residential buildings to industrial facilities and grid infrastructures. The problem of determining a suitable layout for the PV arrays, on a given deployment region, is generally non-trivial and has a crucial importance in the planning phase of solar plants design and development. In this paper, we provide a mixed integer non-linear programming formulation of the PV arrays' layout problem. First, we define the astronomical and geometrical models, considering crucial factors such as self-shadowing and irradiance variability, depending on the geographical position of the solar plant and yearly time window. Subsequently, we formalize the mathematical optimization problem, whose constraints' set is characterized by non-convexities. In order to propose a computationally tractable approach, we provide a tight parametrized convex relaxation. The resulting optimization resolution procedure is tested numerically, using realistic data, and benchmarked against the traditional global resolution approach, showing that the proposed methodology yields near-optimal solutions in lower computational time. Note to Practitioners-The paper is motivated by the need for efficient algorithmic procedures which can yield near-optimal solutions to the PV arrays layout problem. Due to the strong non-convexity of even simple instances, the existing methods heavily rely on global or stochastic solvers, which are computationally demanding, both in terms of resources and run-time. Our approach acts as a baseline, from which practitioners can derive more elaborate instances, by suitably modifying both the objective function and/or the constraints. In fact, we focus on the minimum set of necessary geometrical (e.g., arrays position model), astronomical (e.g., irradiance variation), and operational (e.g., power requirements) constraints which make the overall problem ha

关键词： Optimization Layout Sun Observers Mathematical models Azimuth Photovoltaic systems Photovoltaic solar power plants solar array layout non-convex optimization convexification parametrization mixed integer non-linear programming

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A comparison among optimization software to solve bi-objective sectorization problem

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HELIYON 2023年第8期9卷 e18602页

作者： Teymourifar, Aydin Univ Catolica Portuguesa Ctr Estudos Gestao & Econ Catolica Porto Business Sch Porto Portugal

In this study, we compare the performance of optimization software to solve the bi-objective sectorization problem. The used solution method is based on an approach that has not been used before in the literature on sectorization, in which, the bi-objective model is transformed into single-objective ones, whose results are regarded as ideal points for the objective functions in the bi-objective model. Anti-ideal points are also searched similarly. Then, using the ideal and anti-ideal points, the bi-objective model is redefined as a single-objective one and solved. The difficulties of solving the models, which are basically non-linear, are discussed. Furthermore, the models are linearized, in which case how the number of variables and constraints changes is discussed. Mathematical models are implemented in Python's Pulp library, Lingo, IBM ILOG CPLEX Optimization Studio, and GAMS software, and the obtained results are presented. Furthermore, metaheuristics available in Python's Pymoo library are utilized to solve the models' single-and bi-objective versions. In the experimental results section, benchmarks of different sizes are derived for the problem, and the results are presented. It is observed that the solvers do not perform satisfactorily in solving models;of all of them, GAMS achieves the best results. The utilized metaheuristics from the Pymoo library gain feasible results in reasonable times. In the conclusion section, suggestions are given for solving similar problems. Furthermore, this article summarizes the managerial applications of the sectorization problems.

关键词： Multi-objective optimization Sectorization mixed integer non-linear programming GAMS CPLEX Lingo Python Pulp Pymoo GA NSGA-II

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Transmission Expansion Planning Considering Wind Power and Load Uncertainties

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ENERGIES 2022年第19期15卷 7140-7140页

作者： Xie, Yilin Xu, Ying Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Harbin Inst Technol Sch Elect Engn & Automat Harbin 150001 Peoples R China

Due to the rapidly increasing power demand worldwide, the development of power systems occupies a significant position in modern society. Furthermore, a high proportion of renewable energy resources (RESs) is an inevitable trend in further power system planning, due to traditional energy shortages and environmental pollution problems. However, as RESs are variable, intermittent, and uncontrollable, more challenges will be introduced in transmission expansion planning (TEP). Therefore, in order to guarantee the security and reliability of the power system, research related to TEP with the integration of RESs is of great significance. In this paper, to solve the TEP problem considering load and wind power uncertainties, an AC TEP model solved by a mixed integer non-linear programming (MINLP) is proposed, the high-quality optimal solutions of which demonstrate the accuracy and efficiency of the method. Latin hypercube sampling (LHS) is employed for the scenario generation, while a simultaneous backward reduction algorithm is applied for the scenario reduction, thus reducing the computational burden. Through this method, the reserved scenarios can effectively reflect the overall trends of the original distributions. Based on a novel worst-case scenario analysis method, the obtained optimal solutions are shown to be more robust and effective.

关键词： transmission expansion planning AC model Latin hypercube sampling scenario reduction mixed integer non-linear programming

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AI-based Robust Convex Relaxations for Supporting Diverse QoS in Next-Generation Wireless Systems 41

AI-based Robust Convex Relaxations for Supporting Diverse Qo...

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41st IEEE International Conference on Distributed Computing Systems (ICDCS)

作者： Chan, Steve Krunz, Marwan Griffin, Bob Vit Tall Orlando FL 32835 USA Univ Arizona Tucson AZ 85721 USA

ISBN: (纸本)9781665449328

Supporting diverse Quality of Service (QoS) requirements in 5G and beyond wireless systems often involves solving a succession of convex optimization problems, with varied approaches to optimally resolve each problem. Even when the input set is specifically designed/architected to segue to a convex paradigm, the resultant output set may still turn out to be nonconvex, thereby necessitating a transformation to a convex optimization problem via certain relaxation techniques. This transformation in itself may spawn yet other nonconvex optimization problems, highlighting the need/opportunity to utilize a Robust Convex Relaxation (RCR) framework. In this paper, we explore a particular class of Convolutional Neural Networks (CNNs), namely Deep Convolutional Generative Adversarial Network (DCGANs), to solve not only the QoS-related convex optimization problems but also to leverage the same RCR mechanism for tuning its own hyperparameters. This approach gives rise to various technical challenges. For example, Particle Swarm Optimization (PSO) is often used for hyperparameter reduction/tuning. When implemented on a DCGAN, PSO requires converting continuous/discontinuous hyperparameters to discrete values, which may result in premature stagnation of particles at local optima. The involved implementation mechanics, such as increasing the inertial weighting, may spawn yet other convex optimization problems. We introduce a RCR framework that capitalizes upon the feed-forward structure of the "You Only Look Once" (YOLO)-based DCGAN. Specifically, we use a squeezed Deep Convolutional-YOLO-Generative Adversarial Network (DC-YOLO-GAN), hereinafter referred to as a Modified Squeezed YOLO v3 Implementation (MSY3I), combined with convex relaxation adversarial training to improve the bound tightening for each successive neural network layer and to better facilitate the global optimization via a specific numerical stability implementation within MSY3I.

关键词： Quality of Service 5G Networks nonconvex Optimization Convex Relaxation Particle Swarm Optimization Deep Convolutional Generative Adversarial Networks Numerical Implementation You Only Look Once mixed integer non-linear programming Robust Convex Relaxation Framework

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