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Adaptive Distributionally Robust Optimization

MANAGEMENT SCIENCE 2019年第2期65卷 604-618页

作者： Bertsimas, Dimitris Sim, Melvyn Zhang, Meilin MIT 77 Massachusetts Ave Cambridge MA 02139 USA Natl Univ Singapore NUS Business Sch Dept Analyt & Operat Res Singapore 119077 Singapore Singapore Univ Social Sci SUSS Business Sch Singapore 599494 Singapore

We develop a modular and tractable framework for solving an adaptive distributionally robust linear optimization problem, where we minimize the worst-case expected cost over an ambiguity set of probability distributions. The adaptive distributionally robust optimization framework caters for dynamic decision making, where decisions adapt to the uncertain outcomes as they unfold in stages. For tractability considerations, we focus on a class of second-order conic (SOC) representable ambiguity set, though our results can easily be extended to more general conic representations. We show that the adaptive distributionally robust linear optimization problem can be formulated as a classical robust optimization problem. To obtain a tractable formulation, we approximate the adaptive distributionally robust optimization problem using linear decision rule (LDR) techniques. More interestingly, by incorporating the primary and auxiliary random variables of the lifted ambiguity set in the LDR approximation, we can significantly improve the solutions, and for a class of adaptive distributionally robust optimization problems, exact solutions can also be obtained. Using the new LDR approximation, we can transform the distributionally adaptive robust optimization problem to a classical robust optimization problem with an SOC representable uncertainty set. Finally, to demonstrate the potential for solving management decision problems, we develop an algebraic modeling package and illustrate how it can be used to facilitate modeling and obtain high-quality solutions for medical appointment scheduling and inventory management problems.

关键词： dynamic programming linear programming nonlinear programming quadratic uncertainty hospitals

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Machine Learning With Neuromorphic Photonics

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2019年第5期37卷 1515-1534页

作者： de Lima, Thomas Ferreira Peng, Hsuan-Tung Tait, Alexander N. Nahmias, Mitchell A. Miller, Heidi B. Shastri, Bhavin J. Prucnal, Paul R. Princeton Univ Dept Elect Engn Princeton NJ 08544 USA NIST Boulder CO 80305 USA Queens Univ Dept Phys Engn Phys & Astron Kingston ON KL7 3N6 Canada

Neuromorphic photonics has experienced a recent surge of interest over the last few years, promising orders of magnitude improvements in both speed and energy efficiency over digital electronics. This paper provides a tutorial overview of neuromorphic photonic systems and their application to optimization and machine learning problems. We discuss the physical advantages of photonic processing systems, and we describe underlying device models that allow practical systems to be constructed. We also describe several real-world applications for control and deep learning inference. Finally, we discuss scalability in the context of designing a full-scale neuromorphic photonic processing system, considering aspects such as signal integrity, noise, and hardware fabrication platforms. The paper is intended for a wide audience and teaches how theory, research, and device concepts from neuromorphic photonics could be applied in practical machine learning systems.

关键词： Deep learning machine learning more-than-Moore computing neuromorphic photonics nonlinear programming optimization photonic hardware accelerator photonic integrated circuits photonic neural networks silicon photonics wavelength-division multiplexing (WDM)

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Optimization procedure to minimize fuel consumption of a four-stroke marine turbocharged diesel engine

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ENERGY 2019年 168卷 897-908页

作者： Tadros, M. Ventura, M. Guedes Soares, C. Univ Lisbon Inst Super Tecn Ctr Marine Technol & Ocean Engn CENTEC Lisbon Portugal

A numerical optimization model is developed to simulate the performance of a large four-stroke marine turbocharged diesel engine along the entire operating range by finding the optimal value of the adjustable parameters: speed of the turbocharger, start angle of injection, intake valve timing and amount of injected fuel. This numerical model assists to find the optimal values of these different parameters together without performing any experimental trials for different engine speeds and loads by minimizing the fuel consumption and by verifying the exhaust emissions limitation. This model couples a nonlinear optimization procedure implemented in Matlab/Simulink and 1D engine simulation software. Firstly, the marine diesel engine model is established and calibrated using data from the manufacturer, and the nonlinear optimization model is constructed using the fitness function, static penalty functions and boundary conditions. The optimizer adopted showed a good computing performance even when running in normal computers. The model is used to calculate the BSFC and to compute the amount of CO2 and NOx emissions for different working conditions. The results are important because in marine diesel engines, the values of the parameters of the turbocharger, injection system and valves timings and thus the amount of BSFC, CO2 and NOx emissions are not provided by the manufacturer along the entire operating range, although these data are required in different marine applications either during the preliminary stage of ship design or to be used in ship routing code. This simulation environment based optimization scheme can be further used to compute the performance of any marine diesel engine and to be a part of a numeric model of the complete propulsion system. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Marine diesel engine Four-stroke Exhaust emissions nonlinear programming Optimization model Fuel economy

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Generalized hp Pseudospectral-Convex programming for Powered Descent and Landing

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2019年第7期42卷 1562-1570页

作者： Sagliano, Marco DLR German Aerosp Ctr D-28359 Bremen Germany

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Computational Method for Optimal Guidance and Control Using Adaptive Gaussian Quadrature Collocation

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2019年第9期42卷 2026-2041页

作者： Dennis, Miriam E. Hager, William W. Rao, Anil, V Univ Florida Dept Mech & Aerosp Engn Gainesville FL 32611 USA Univ Florida Dept Math Gainesville FL 32611 USA

A method is described for computational optimal guidance and control using adaptive Gaussian quadrature collocation and sparse nonlinear programming. The method employs adaptive Legendre-Gauss-Radau (LGR) quadrature collocation using a mesh truncation and remapping procedure at the start of each guidance cycle, thereby retaining only the mesh points associated with the unexpired horizon. Using this approach, the starting mesh for a guidance cycle is smaller than the starting mesh used on the previous guidance cycle. The nonlinear programming problem is then solved on the reduced mesh corresponding to the unexpired horizon to generate the control that is used on the current guidance cycle. It is noted that the mesh points on the unexpired horizon are well placed for rapidly solving the LGR quadrature collocation approximation of the optimal control problem. The method developed in this paper is demonstrated on two challenging aerospace optimal control problems. For both examples, the dynamics are simulated on each guidance cycle using a perturbed dynamic model in the absence and presence of a computation time delay. The results of this study demonstrate that the method developed in this paper is viable as a computational method for optimal guidance and control.

关键词： Computational Methods Hamilton Jacobi Bellman Equation nonlinear programming Reusable Launch Vehicle Flight Path Angle Angle of Attack Bank Angle MATLAB Earth Powered Descent Guidance

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Solar-Sail Trajectory Design to Planetary Pole Sitters

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2019年第6期42卷 1402-1412页

作者： Vergaaij, Merel Heiligers, Jeannette Delft Univ Technol Fac Aerosp Engn Dept Astrodynam & Space Missions NL-2629 HS Delft Netherlands

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Robust model for optimal allocation of renewable energy sources, energy storage systems and demand response in distribution systems via information gap decision theory

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第4期13卷 511-520页

作者： Hooshmand, Ehsan Rabiee, Abbas Univ Zanjan Dept Elect Engn Zanjan Iran

Considering increasing distributed energy resources and responsive loads in smart grid paradigm, this study proposes a new approach for robust hourly energy scheduling of distribution systems at the presence of severe uncertain renewable energy sources (RES). Wind and photovoltaic power generations are considered as the RESs. The aim is to minimise the total energy procurement cost, while considering the participation of RESs, by their optimal allocation in the network. The inherent uncertainty of RESs is handled via information gap decision theory. One of the features of the proposed model is to consider the impact of demand response and energy storage system as the effective tools to reduce unintended costs due to uncertainty of RESs. Also, the proposed model handles the uncertainty of multiple RESs in a way that maximum tolerable uncertainty of RESs is achieved for a given worsening of total energy procurement cost. The proposed model is formulated as a mixed integer nonlinear optimisation problem and is implemented in general algebraic modelling system environment. The model is applied on the IEEE standard 33-bus radial test system, and the obtained results substantiate that the utilisation of ESS and DR can reduce the impact of RESs' uncertainty on the energy cost.

关键词： decision theory demand side management smart power grids distributed power generation IEEE standards energy storage distribution networks optimisation renewable energy sources photovoltaic power systems integer programming nonlinear programming robust model optimal allocation energy storage system demand response distribution systems information gap decision theory distributed energy resources responsive loads smart grid paradigm robust hourly energy scheduling severe uncertain renewable energy sources photovoltaic power generations total energy procurement cost inherent uncertainty multiple RESs maximum tolerable uncertainty general algebraic modelling system environment radial test system energy cost RES uncertainty mixed integer nonlinear optimisation problem IEEE standard 33-bus radial test system

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A Competitive Swarm Optimizer-Based Technoeconomic Optimization with Appliance Scheduling in Domestic PV-Battery Hybrid Systems

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COMPLEXITY 2019年第1期2019卷

作者： Wang, Bo Li, Yanjing Yang, Fei Xia, Xiaohua Huazhong Univ Sci & Technol Artificial Intelligence & Automat Sch Minist Educ Image Proc & Intelligent Control Key Lab Wuhan 430074 Hubei Peoples R China Wuhan Univ Sch Elect Engn & Automat Dept Artificial Intelligence & Automat Wuhan 430072 Hubei Peoples R China Univ Pretoria Dept Elect Elect & Comp Engn ZA-0028 Pretoria South Africa

A technoeconomic optimization problem for a domestic grid-connected PV-battery hybrid energy system is investigated. It incorporates the appliance time scheduling with appliance-specific power dispatch. The optimization is aimed at minimizing energy cost, maximizing renewable energy penetration, and increasing user satisfaction over a finite horizon. nonlinear objective functions and constraints, as well as discrete and continuous decision variables, are involved. To solve the proposed mixed-integer nonlinear programming problem at a large scale, a competitive swarm optimizer-based numerical solver is designed and employed. The effectiveness of the proposed approach is verified by simulation results.

关键词： nonlinear programming

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Robust optimization for a nonlinear switched time-delay system with noisy output measurements using hybrid optimization algorithm

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2019年第16期356卷 9730-9762页

作者： Yuan, Jinlong Xie, Jun Liu, Chongyang Teo, Kok Lay Huang, Ming Fan, Houming Feng, Enmin Xiu, Zhilong Dalian Maritime Univ Sch Sci Dept Math Dalian 116026 Liaoning Peoples R China Dalian Maritime Univ Coll Transportat Engn Dalian 116026 Liaoning Peoples R China PLA Dalian Naval Acad Dept Basics Teaching & Res Off Math Dalian 116018 Liaoning Peoples R China Shandong Inst Business & Technol Sch Math & Informat Sci Yantai 264005 Peoples R China Curtin Univ Dept Math & Stat Perth WA 6102 Australia Tianjin Univ Finance & Econ Coordinated Innovat Ctr Computable Modeling Manag Tianjin 300222 Peoples R China Dalian Univ Technol Sch Math Sci Dalian 116024 Liaoning Peoples R China Dalian Univ Technol Sch Life Sci & Biotechnol Dalian 116024 Liaoning Peoples R China

This paper considers the problem of using noisy output data to estimate unknown switching times and unknown system parameters arising in 1,3-PD batch production process of glycerol induced by Klebsiella pneumonia. We formulate the problem as a robust optimization problem in which the unknown quantities are decision variables to be chosen optimally, with the cost function penalizing the mean and variance of the relative error between the output of the system and the measured actual noisy system output. This problem is governed by a switched time-delay system subject to continuous state inequality constraints arising from engineering specifications. A new time-scaling transformation and constraint transcription technique are used then to convert this resulting problem into a sequence of approximate subproblems. A hybrid optimization algorithm combined genetic algorithm with gradient-based method is developed to solve these resulting subproblems. Finally, we explore the correctness of the optimal switching times and system parameters obtained, as well as the effectiveness of the proposed algorithm. (C) 2019 Published by Elsevier Ltd on behalf of The Franklin Institute.

关键词： nonlinear programming Robust optimization Switched time-delay system Noisy output data Hybrid optimization algorithm

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Flexibility of controllable power transformers for managing wind uncertainty using robust adjustable linearised optimal power flow

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IET RENEWABLE POWER GENERATION 2019年第2期13卷 262-272页

作者： Nikoobakht, Ahmad Aghaei, Jamshid Farahmand, Hossein Lakshmanan, Venkatachalam Korpas, Magnus Higher Educ Ctr Eghlid Dept Elect Engn Eghlid Iran Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Norwegian Univ Sci & Technol NTNU Dept Elect Power Engn NO-7491 Trondheim Norway

As renewable energy sources (RESs) penetration increases in the power system, the transmission system operators face new challenges to ensure system reliability and flexibility while ensuring high utilisation of uncertain RES generation. Controllable transformers with on-load tap changers and phase shifting capability are the promising flexibility tools to keep the system acceptable security and flexibility levels by controlling the voltage levels and energy flow. The AC optimal power flow (AC OPF) with detailed modelling considerations such as the bus voltage magnitude by including these devices is challenging. This study develops the AC OPF model to propose a robust flexibility optimisation framework for daily scheduling problem with uncertain wind energy sources. Nevertheless, the proposed formulation representation is an intractable mixed integer nonlinear programming (MINLP) while it includes AC grid constraints and the augmented modelling of the mentioned transformers. Accordingly, the proposed MINLP problem has been converted into a mixed-integer linear program where a certain level of solution accuracy can be achieved for the available time budget. The effectiveness of the proposed method is demonstrated using a modified 6-bus and IEEE 118-bus test systems.

关键词： wind power plants wind power power generation control optimal control load flow control robust control on load tap changers power generation reliability power system security power generation scheduling integer programming nonlinear programming power grids power control voltage control controllable power transformers wind uncertainty management robust adjustable linearised optimal power flow renewable energy sources penetration power system transmission system operators system reliability system flexibility uncertain RES generation on-load tap changers phase shifting capability system acceptable security level system acceptable flexibility level voltage level control energy flow control AC optimal power flow model AC OPF model bus voltage magnitude robust flexibility optimisation framework daily scheduling problem uncertain wind energy sources intractable mixed integer nonlinear programming MINLP problem AC grid constraints modified 6-bus test system modified IEEE 118-bus test system

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