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Optimal Guidance and Control with Nonlinear Dynamics Using Sequential Convex programming

JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2020年第4期43卷 633-644页

作者： Foust, Rebecca Chung, Soon-Jo Hadaegh, Fred Y. Univ Illinois Dept Aerosp Engn 104 South Wright St Urbana IL 61801 USA CALTECH Dept Aerosp Grad Aerosp Labs Aerosp Pasadena CA 91125 USA CALTECH Jet Prop Lab 4800 Oak Grove Dr Pasadena CA 91109 USA

This paper presents a novel method for expanding the use of sequential convex programming (SCP) to the domain of optimal guidance and control problems with nonlinear dynamics constraints. SCP is a useful tool in obtaining real-time solutions to direct optimal control, but it is unable to adequately model nonlinear dynamics due to the linearization and discretization required. As nonlinear program solvers are not yet functioning in real-time, a tool is needed to bridge the gap between satisfying the nonlinear dynamics and completing execution fast enough to be useful. Two methods are proposed, SCP with nonlinear dynamics correction (SCPn) and modified SCPn (M-SCPn), which mixes SCP and SCPn to reduce runtime and improve algorithmic robustness. Both methods are proven to generate optimal state and control trajectories that satisfy the nonlinear dynamics. Simulations are presented to validate the efficacy of the methods as compared with SCP.

关键词： Nonlinear Dynamics Trajectory Optimization mixed integer linear programming Quadrotor Motion Planning Numerical Integration Multi Agent System Robots MATLAB Pseudospectral Methods

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An exact microgrid formation model for load restoration in resilient distribution system

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2020年 116卷

作者： Zhu, Junpeng Yuan, Yue Wang, Weisheng Hohai Univ Coll Energy & Elect Engn Nanjing 211100 Jiangsu Peoples R China China Elect Power Res Inst State Key Lab Operat & Control Renewable Energy & Beijing 100192 Peoples R China

The increase in extreme weather affects the distribution system, leading to system collapse and interruption power supply to loads. Microgrid formation and operation in island mode is a resilient solution when major faults happen in the distribution system. This paper proposes a novel microgrid formation model considering power loss and voltage constraints in the power balance and operation feasibility. The introduction of binary variables to represent the reconfigured network and the adoption of non-linear equations to represent the exact power flow will lead the microgrid formation model to be a mixed integer nonlinear program (MINLP). This paper proposes an iteration based linear approximation of the MINLP, the exactness of which can be guaranteed by the convergence of the iteration process. Numerical results based on IEEE 33 and PE&G 69 bus standard distribution systems demonstrate the effectiveness of the developed method.

关键词： Microgrid formation Load restoration Resilient distribution system mixed integer linear programming linear approximation

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Stochastic energy management for a renewable energy based microgrid considering battery, hydrogen storage, and demand response

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2022年 30卷

作者： Eghbali, Nazanin Hakimi, Seyed Mehdi Hasankhani, Arezoo Derakhshan, Ghasem Abdi, Babak Islamic Azad Univ Elect Engn Dept Damavand Branch Damavand Iran Islamic Azad Univ Renewable Energy Res Ctr Damavand Branch Damavand Iran Florida Atlantic Univ Coll Engn & Comp Sci Boca Raton FL 33431 USA

This paper presents a stochastic management algorithm to address the optimal operation of smart microgrids (MGs) in the high presence of stochastic renewable energy resources. The proposed management algorithm aims to find a day-ahead optimal operation of the renewable energy resources, including wind turbine, photovoltaic (PV) unit, Fuel Cell (FC), electrolyzer, microturbine, and energy storage that simultaneously considers the participation of the smart homes in demand response programs. The presented stochastic management is formulated as a mixed-integer linear programming (MILP) problem solved through a stochastic programming approach. Two different energy storage devices, i.e., battery and hydrogen storage tank, are modeled, and their operations are compared. Various uncertainties (e.g., wind speed, solar irradiance, demand, and electricity price) are modeled, where the scenario-based approach is applied to find a finite number of scenarios. The efficiency of the presented algorithm is verified in four scenarios to evaluate the effect of hydrogen storage and demand response program. Finally, it is shown that applying both hydrogen storage and demand response program can benefit the MG from the economic perspective and results in a significant reduction in the total cost. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Battery Demand response Hydrogen storage mixed integer linear programming Smart microgrid Stochastic energy management Stochastic programming

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A Matheuristic Approach to the Open Shop Scheduling Problem with Sequence-Dependent Setup Times*

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IFAC-PapersOnLine 2022年第10期55卷 2167-2172页

作者： Erica Pastore Arianna Alfieri Claudio Castiglione Gaia Nicosia Fabio Salassa Dipartimento di Ingegneria Gestionale e della Produzione Politecnico di Torino Italy Dipartimento di Ingegneria Universitè degli Studi “Roma Tre” Italy

This paper deals with an open shop scheduling problem in which sequence-dependent setup times are present. In open shops there are no restrictions on the processing route of each job, so the decision regards not only the sequencing of jobs on each machine, but also the sequencing of operations (machines) for each job. These type of problems typically arise in application contexts where the order in which the operations are executed is irrelevant. In this work a novel heuristic approach based on mathematical programming, i.e., a matheuristic, is developed and its performance is assessed through a computational study on open shop benchmark instances.

关键词： Scheduling Open Shop Sequence-dependent setup times Matheuristics mixed integer linear programming

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A simple layout optimization formulation for load-carrying tensegrity structures

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2020年第6期62卷 2935-2949页

作者： Nanayakkara, K. I. U. He, Linwei Fairclough, Helen E. Gilbert, Matthew Univ Sheffield Dept Civil & Struct Engn Mappin St Sheffield S1 3JD S Yorkshire England

Traditional tensegrity structures comprise isolated compression members lying inside a continuous network of tension members. In this contribution, a simple numerical layout optimization formulation is presented and used to identify the topologies of minimum volume tensegrity structures designed to carry external applied loads. Binary variables and associated constraints are used to limit (usually to one) the number of compressive elements connecting a node. A computationally efficient two-stage procedure employing mixed integer linear programming (MILP) is used to identify structures capable of carrying both externally applied loads and the self-stresses present when these loads are removed. Although tensegrity structures are often regarded as inherently 'optimal', the presence of additional constraints in the optimization formulation means that they can never be more optimal than traditional, non-tensegrity, structures. The proposed procedure is programmed in a MATLAB script (available for download) and a range of examples are used to demonstrate the efficacy of the approach presented.

关键词： Layout optimization Tensegrity structures mixed integer linear programming

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Optimal producer well placement and multiperiod production scheduling using surrogate modeling

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COMPUTERS & CHEMICAL ENGINEERING 2022年 165卷

作者： Moolya, Akash Rodriguez-Martinez, Alejandro Grossmann, Ignacio E. Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA TotalEnergies Integrated Asset Modeling Pau France

The main objective of this paper is to determine the optimal placement of oilfield producer wells while accounting for surface infrastructure constraints. To accomplish this objective, we replace a first-principles optimization model with a hybrid approach, which draws the correlations for oil production from data obtained by rigorous simulations of grid discretized reservoir models. We use these correlations to build a Multiperiod mixed integer linear programming (MILP) problem with the production constraints and maximization of the Net Present Value (NPV) as the objective function. A computationally efficient multiperiod MILP model is proposed to evaluate the performance of this methodology with a surrogate correlation for the oil production rate built in ALAMO. This set of equations takes into account the distance of the producer well from the injection well, the depth of the producer well, and the time of operation. This model allows higher levels of grid discretization and longer time horizons of interest. In order to address systems where high spatial resolution is necessary and computational expense is high, we introduce a methodology of Spatial Aggregation and Disaggregation. In such method, we first simplify the problem through aggregation to then decompose the obtained solution, producing highly resolved well placement strategies. With this approach, it is shown that the optimal well placement locations can be obtained with order of magnitude reductions in computational expense.

关键词： Optimal well placement Oil production planning mixed integer linear programming Surrogate model Spatial aggregation

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A mathematical programming model for optimal fleet management of electric car-sharing systems with Vehicle-to-Grid operations

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JOURNAL OF CLEANER PRODUCTION 2022年 368卷

作者： Prencipe, Luigi Pio van Essen, J. Theresia Caggiani, Leonardo Ottomanelli, Michele de Almeida Correia, Goncalo Homem Polytech Univ Bari Dept Civil Environm Bldg & Land Engn & Chem Bari Italy Delft Univ Technol Delft Inst Appl Math Delft Netherlands Delft Univ Technol Dept Transport & Planning Delft Netherlands

Electric car-sharing systems have attracted large attention in recent years as a new business model for achieving both economic and environmental benefits in urban areas. Among different types, the one considered in this paper is the so-called one-way car-sharing system whereby a user can begin and end a trip at any station of the system. At the same time, the Vehicle-to-Grid (V2G) concept is emerging as a possible innovative solution for smart power grid control. A management system that combines car-sharing system operations and V2G technology is a recent challenge for academia and industry. In this work, a mixed integer linear programming formulation is proposed to find the optimal management of electric vehicles in a one-way car-sharing system integrated with V2G technology. The proposed mathematical model allows finding the optimal start-of-day electric vehicles distribution that maximizes the total revenue obtained from system users and V2G profits through daily electric vehicles charging/discharging schedules. These schedules are based on mean daily users' electric vehicles requests and electricity prices. The model can be applied to evaluate the possible average daily profitability of V2G operations. In order to test the model performance, we applied it to a small-size test network and a real-size test network (the Delft network in the Netherlands). Under the model assumptions, the adoption of V2G technology allows to fully cover the daily charging costs due to users' trips and to obtain V2G profits by taking advantage of electric vehicles unused time without significantly reducing the satisfied car-sharing system demand. Most of the energy purchased to charge the electric vehicles batteries is provided back to the grid during energy peak load demand, creating benefits also for energy providers.

关键词： V2G One-way electric car-sharing Electric vehicles mixed integer linear programming Shared mobility

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Towards CAD-Based Mathematical Optimization for Additive Manufacturing – Designing Forming Tools for Tool-Bound Bending 4th

Towards CAD-Based Mathematical Optimization for Additive Man...

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4th International Conference on Uncertainty in Mechanical Engineering, ICUME 2021

作者： Reintjes, Christian Reuter, Jonas Hartisch, Michael Lorenz, Ulf Engel, Bernd Chair of Technology Management University of Siegen Unteres Schloss 3 Siegen57072 Germany Chair of Forming Technology Institute of Production Engineering University of Siegen Breite Straße 11 Siegen57076 Germany

ISBN: (纸本)9783030772550

The trend towards flexible, agile, and resource-efficient production systems requires a continuous development of processes as well as of tools in the area of forming technology. To create load-adjusted and weight-optimized tool structures, we present an overview of a new algorithm-driven design optimization workflow based on mixed-integer linear programming. Loads and boundary conditions for the mathematical optimization are taken from numerical simulations. They are transformed into time-independent point loads generating physical uncertainty in the parameters of the optimization model. CAD-based mathematical optimization is used for topology optimization and geometry generation of the truss-like structure. Finite element simulations are performed to validate the structural strength and to optimize the shape of lattice nodes to reduce mechanical stress peaks. Our algorithm-driven design optimization workflow takes full advantage of the geometrical freedom of additive manufacturing by considering geometry-based manufacturing constraints. Depending on the additive manufacturing process, we use lower and upper bounds on the diameter of the members of a truss and the associated yield strengths. An additively manufactured flexible blank holder demonstrates the algorithm-driven topology design optimization. © 2021, The Author(s).

关键词： Additive manufacturing Adjustable forming tool surfaces Lightweight forming tools mixed integer linear programming Tool-bound bending

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A fast solution approach to solve the generator maintenance scheduling and hydropower production problems simultaneously

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Procedia Computer Science 2022年 207卷 3808-3817页

作者： Geofrey Glangine Sara Seguin Kenjy Demeester Rio Tinto Aluminium 1955 boulevard Mellon Saguenay G7S 4L2 Quebec Canada

The Generator Maintenance Scheduling Problem (GMSP) is a problem that combines a hydropower optimization problem with a scheduling problem. Both problems are known to be hard to solve and combining them leads to an even more challenging mathematical problem. Since the hydropower production functions are nonlinear, hyperplane curve fitting is used to linearize each power production function. The goal of the GMSP is to find an optimal schedule plan to decide when to shut down generators for maintenance. Therefore, one production function needs to be formulated per generator combinations leading to a rather large number of constraints. This paper demonstrates that the complexity of the problems is linked to the number of hyperplanes selected to formulate the power production functions. To accelerate the resolution of the problem, a new heuristic based on the mean square algorithm is presented to reduce the number of hyperplanes required. This heuristic substantially reduces the number of constraints and the solving time is almost ten times faster. Numerical results show that the energy produced and the generated maintenance plannings are similar for both mathematical formulations, more precisely with one hyperplane for each generator combination versus a reduced number of hyperplanes.

关键词： Hydropower scheduling problem maintenance scheduling problem hydropower production functions stochastic programming mixed integer linear programming heuristic

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An Optimization Model and Decision Support System of Operating Room Scheduling in a Teaching Hospital

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淡江理工学刊 2022年第5期25卷 883-891页

作者： Innaka Rizki Meliana Pasca Purwoko Azizah Aisyati Yusuf Priyandari Cucuk Nur Rosyidi

Department of Surgery (DOS) contributes to a major portion of hospital's expenditures and revenues. The costs come from resources which are involved in a surgery. These resources must be used optimally through proper scheduling. Recently, there are still many hospitals schedule the operating rooms manually which will take relatively longer time. Whereas the scheduling of the operating room can be done through developing a mathematical model. We consider several aspects in the model of this research, including the number of operating rooms, working hours, the available number of anesthetic machines, and utilization of each room. We propose a mixed integer linear programming (MILP) model to solve the operating room scheduling problem and based on the model, a decision support system (DSS) is developed to allow the hospital generates a better schedule with a shorter time. The results of sensitivity shows that the model is sensitive to the changes of surgery duration and the available number of anesthetic machines.

关键词： Decision support system mixed integer linear programming Operating rooms Scheduling

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