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Hybrid Active-Passive RIS Transmitter Enabled Energy-Efficient Multi-User Communications

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第9期23卷 10653-10666页

作者： Huang, Ao Mu, Xidong Guo, Li Zhu, Guangyu Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Natl Engn Res Ctr Mobile Internet Secur Technol Beijing 100876 Peoples R China Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England

A novel hybrid active-passive reconfigurable intelligent surface (RIS) transmitter enabled downlink multi-user communication system is investigated. Specifically, RISs are exploited to serve as transmitter antennas, where each element can flexibly switch between active and passive modes to deliver information to multiple users. The system energy efficiency (EE) maximization problem is formulated by jointly optimizing the RIS element scheduling and beamforming coefficients, as well as the power allocation coefficients, subject to the user's individual rate requirement and the maximum RIS amplification power constraint. Using the Dinkelbach relaxation, the original mixed-integer nonlinear programming problem is transformed into a nonfractional optimization problem with a two-layer structure, which is solved by the alternating optimization approach. In particular, an exhaustive search method is proposed to determine the optimal operating mode for each RIS element. Then, the RIS beamforming and power allocation coefficients are properly designed in an alternating manner. To overcome the potentially high complexity caused by exhaustive searching, we further develop a joint RIS element mode and beamforming optimization scheme by exploiting the Big-M formulation technique. Numerical results validate that: 1) The proposed hybrid RIS scheme yields higher EE than the baseline multi-antenna schemes employing fully active/passive RIS or conventional radio frequency chains;2) Both proposed algorithms are effective in improving the system performance, especially the latter can achieve precise design of RIS elements with low complexity;and 3) For a fixed-size hybrid RIS, maximum EE can be reaped by setting only a minority of elements to operate in the active mode.

关键词： Multi-user communication energy-efficient design hybrid reconfigurable intelligent surface RIS element scheduling mixed-integer nonlinear programming Multi-user communication energy-efficient design hybrid reconfigurable intelligent surface RIS element scheduling mixed-integer nonlinear programming

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A Superstructure Representation, Generation, and Modeling Framework for Chemical Process Synthesis

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AICHE JOURNAL 2016年第9期62卷 3199-3214页

作者： Wu, WenZhao Henao, Carlos A. Maravelias, Christos T. Univ Wisconsin Dept Chem & Biol Engn Madison WI 53706 USA Univ Wisconsin DOE Great Lakes Bioenergy Res Ctr Madison WI 53706 USA

We present a framework for the efficient representation, generation, and modeling of superstructures for process synthesis. First, we develop a new representation based on three basic elements: units, ports, and conditioning streams. Second, we present four rules based on "minimal" and "feasible" component sets for the generation of simple superstructures containing all feasible embedded processes. Third, in terms of modeling, we develop a modular approach, and formulate models for each basic element. We also present a canonical form of element models using input/output variables and constrained/free variables. The proposed methods provide a coherent framework for superstructure-based process synthesis, allowing efficient model generation and modification. (C) 2016 American Institute of Chemical Engineers

关键词： optimization mixed-integer nonlinear programming

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Decision analysis on generation capacity of a wind park

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2009年第8期13卷 2126-2133页

作者： Kongnam, C. Nuchprayoon, S. Premrudeepreechacharn, S. Uatrongjit, S. Elect Generating Author Thailand Dept Elect Maintenance Nonthaburi 11130 Thailand Chiang Mai Univ Dept Elect Engn Chiang Mai 50200 Thailand

The investment decision on generation capacity of a wind park is difficult when wind studies or data are neither available nor sufficient to provide adequate information for developing a wind power project. Although new measurement is possible but it is definitely time consuming. To determine the optimum capacity, decision analysis techniques are proposed in this paper to cope with uncertainties arising from wind speed distribution and power-speed characteristics. The wind speed distribution is modeled from the measured data, the Rayleigh distribution, and the Weibull distribution. The power-speed curve of a wind turbine from cut-in speed to rated speed is modeled by using linear, parabolic, cubic, and quadratic characteristics. The optimization model is formulated as a mixed-integer nonlinear programming problem. The constraints are considered as interval bounds so that a set of feasible solutions is obtained. The optimum solution can be determined by using the profit-to-cost and profit-to-area ratios as performance metrics of investment. Decision analysis rules are then applied to overcome the uncertainty problem and to refine the investment plan. The proposed procedure has been tested with the wind power project of the Electricity Generating Authority of Thailand. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Decision analysis mixed-integer nonlinear programming Optimization Uncertainty Wind park

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Algorithmic Approach for Improved mixed-integer Reformulations of Convex Generalized Disjunctive Programs

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INFORMS JOURNAL ON COMPUTING 2015年第1期27卷 59-74页

作者： Trespalacios, Francisco Grossmann, Ignacio E. Carnegie Mellon Univ Pittsburgh PA 15213 USA

In this work, we propose an algorithmic approach to improve mixed-integer models that are originally formulated as convex generalized disjunctive programs (GDPs). The algorithm seeks to obtain an improved continuous relaxation of the mixed-integer linear and mixed-integer nonlinear programming (MILP/MINLP) model reformulation of the GDP while limiting the growth in the problem size. There are three main stages that form the basis of the algorithm. The first one is a presolve, consequence of the logic nature of GDP, which allows us to reduce the problem size, find good relaxation bounds, and identify properties that help us determine where to apply a basic step. The second stage is the iterative application of basic steps, selecting where to apply them and monitoring the improvement of the formulation. Finally, we use a hybrid reformulation of GDP that seeks to exploit both of the advantages attributed to the two common GDP-to-MILP/MINLP transformations, the Big-M, and the Hull reformulation. We illustrate the application of this algorithm with several examples. The results show the improvement in the problem formulations by generating models with improved relaxed solutions and relatively small growth of continuous variables and constraints. The algorithm generally leads to reduction in the solution times.

关键词： generalized disjunctive programming mixed-integer nonlinear programming

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Optimization of a peer-to-peer system for efficient content replication

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2009年第2期196卷 423-433页

作者： Cervellera, Cristiano Caviglione, Luca CNR Ist Studi Sistemi Intelligenti Automaz I-16149 Genoa Italy

This paper introduces a framework for the optimization of a peer-to-peer (p2p) based content replication system, aiming at actively exploiting the presence of a centralized component that represents a recent trend in content delivery architectures. To this purpose, we formalize a real-time mixed-integer nonlinear programming problem over a discrete time dynamic system, and propose a hybrid random/nonlinear programming scheme that allows to find good solutions while remaining computationally feasible. Two performance indexes. representing different objectives of the content replication process (e.g., speed vs. improved resistance against node failures), are discussed. Simulative tests are presented to prove the effectiveness of the proposed solution, with respect to typical strategies adopted by existing systems. (C) 2008 Elsevier B.V. All rights reserved.

关键词： OR in telecommunications Peer-to-peer systems mixed-integer nonlinear programming

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A comparative study of multi-objective optimization methodologies for molecular and process design

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COMPUTERS & CHEMICAL ENGINEERING 2020年 136卷 106802-000页

作者： Lee, Ye Seol Graham, Edward J. Galindo, Amparo Jackson, George Adjiman, Claire S. Imperial Coll London Ctr Proc Syst Engn Inst Mol Sci & Engn Dept Chem Engn South Kensington Campus London SW7 2AZ England

The need to consider multiple objectives in molecular design, whether based on techno-economic, environmental or health and safety metrics is increasingly recognized. There is, however, limited understanding of the suitability of different multi-objective optimization (MOO) algorithm for the solution of such design problems. In this work, we present a systematic comparison of the performance of five mixed-integer non-linear programming (MINLP) MOO algorithms on the selection of computer-aided molecular design (CAMD) and computer-aided molecular and process design (CAMPD) problems. The five methods are designed to address the discrete and nonlinear nature of the problem, with the aim of generating an accurate approximation of the Pareto front. They include: a weighted sum approach without global search phases (SWS), a weighted sum approach with simulated annealing (WSSA), a weighted sum approach with multi level single linkage (WSML), the sandwich algorithm with MLSL (SDML) and the non dominated sorting genetic algorithm-II (NSGA-II). The algorithms are compared systematically in two steps. The effectiveness of the global search methods is evaluated with SWS, WSSA and WSML. WSML is found to be most effective and a comparative analysis of WSML, SDML and NSGA-II is then undertaken. As a test set of these optimization techniques, two CAMD and one CAMPD problems of varying dimensionality are formulated as case studies. The results show that the SDML provides the most efficient generation of a diverse set of Pareto points, leading to the construction of an approximate Pareto front close to exact Pareto front. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Computer-aided molecular design Computer-aided molecular and process design Multi-objective optimization mixed-integer nonlinear programming Global optimization

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Multilevel Field Development Optimization Under Uncertainty Using a Sequence of Upscaled Models

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MATHEMATICAL GEOSCIENCES 2017年第3期49卷 307-339页

作者： Aliyev, Elnur Durlofsky, Louis J. BP Amer Inc Houston TX 77079 USA Stanford Univ Dept Energy Resources Engn Stanford CA 94305 USA

The robust optimization of reservoir performance under geological uncertainty typically requires the simulation of multiple geological realizations at each iteration of the optimization run. This results in high computational expense, particularly when simulation models are highly resolved and many realizations are employed to characterize geological uncertainty. In recent work we introduced a multilevel optimization procedure that uses a sequence of upscaled models to accelerate field development optimization. The core optimizer is a particle swarm optimization-mesh adaptive direct search (PSO-MADS) hybrid technique. Coarse-scale models are constructed from the fine-grid geological characterization using an accurate global transmissibility upscaling procedure. In this paper we extend the multilevel framework to enable efficient optimization under uncertainty. New treatments include the use of a sample validation procedure for realization selection and the use of the standalone MADS optimizer (rather than PSO-MADS) after the first optimization stage. Numerical results are presented for two example systems, and for each case optimization over both ten and 100 realizations is performed. For ten-realization cases we achieve comparable results, and speedups of a factor of 10 or more, relative to the conventional single-level optimization procedure. Speedups are estimated to be even more substantial for 100 realization cases, for which conventional optimization is not practical. We also investigate the application of a multilevel Monte Carlo approach as an alternative to our proposed techniques for optimization under uncertainty. Although this method is faster than the conventional approach, it is not as efficient as the multilevel procedures developed in this work.

关键词： Multilevel optimization Upscaling Derivative-free optimization Petroleum field development mixed-integer nonlinear programming Optimization under uncertainty Multilevel Monte Carlo

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A generalized short-term unit commitment approach for analyzing electric power and natural gas integrated systems

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ELECTRIC POWER SYSTEMS RESEARCH 2019年 172卷 63-76页

作者： Tovar-Ramirez, C. A. Fuerte-Esquivel, C. R. Martinez Mares, A. Sanchez-Garduno, J. L. Univ Michoacana Morelia Michoacan Mexico

This paper proposes a generalized short-term unit commitment approach for optimizing the hourly thermal generation scheduling by considering the existing interdependence between natural gas and electricity infrastructures. This generalized approach is formulated as a mixed-integer nonlinear optimization problem where a multi-period optimal gas and alternating current power flow model is incorporated into the thermal unit commitment problem. Contrary to all other proposals that ignore the reactive power dispatch and alternating current electricity network constraints for solving the unit commitment problem that considers the integrated electricity-natural gas system, the proposed solution approach simultaneously co-optimizes the active and reactive power scheduling and dispatch, while taking into account the set of physical and operational constraints associated with each infrastructure. This set of constraints includes reactive power generation limits, nodal voltage magnitude limits and dynamic gas flow constraints. The solution is obtained by decomposing this generalized optimization problem into two mutually connected optimization subproblems: one spatially separable related to the unit commitment problem and the other temporally separable associated with the unified co-optimization of both energy systems, which are sequentially solved until the same hourly generation dispatch in both subproblems is obtained. Hence, this solution is optimal for the unit commitment problem and the optimal gas and power flow problem. Study cases on two multi-energy systems are presented to numerically illustrate the suitability and main characteristics of the proposed approach.

关键词： Integrated gas and electricity networks mixed-integer nonlinear programming Power systems operation Security-constrained unit commitment

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Solving Chance-Constrained Optimization Problems with Stochastic Quadratic Inequalities

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OPERATIONS RESEARCH 2016年第4期64卷 939-957页

作者： Lejeune, Miguel A. Margot, Francois George Washington Univ Washington DC 20052 USA Carnegie Mellon Univ Tepper Sch Business Pittsburgh PA 15213 USA

We propose a new and systematic reformulation and algorithmic approach to solve a complex class of stochastic programming problems involving a joint chance constraint with random technology matrix and stochastic quadratic inequalities. The method is general enough to apply to nonconvex as well as nonseparable quadratic terms. We derive two new reformulations and give sufficient conditions under which the reformulated problem is equivalent. The second reformulation provides a much sparser representation of the feasible set of the chance constraint and offers tremendous computational advantages. This new reformulation method can be used for linear stochastic inequalities and will also significantly improve the solution of such joint chance-constrained problems. We provide general and easily identifiable conditions under which the base reformulations can be linearized. We show that the size of the reformulated problems, in particular, their number of binary variables and quadratic mixed-integer terms, does not grow linearly with the number of scenarios used to represent uncertainty. We propose two new nonlinear branch-and-bound algorithms for the nonconvex quadratic integer reformulations. We present detailed empirical results, comparing the various reformulations and several algorithmic ideas that improve the performance of the mixed-integer nonlinear solver Couenne for solving these problems. Guidelines on how to tune the solver and to select reformulations are presented. The test instances are epidemiology and disaster management facility location models and cover the three types of stochastic quadratic inequalities, namely, product of two decision variables that are both binary, binary and continuous, or both continuous.

关键词： stochastic programming mixed-integer nonlinear programming Boolean programming nonlinear branch-and-bound algorithm quadratic stochastic inequality joint probabilistic constraint random technology matrix epidemiology facility location

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Disjunctive linear separation conditions and mixed-integer formulations for aircraft conflict resolution

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第2期296卷 520-538页

作者： Dias, Fernando H. C. Hijazi, Hassan Rey, David UNSW Sydney Sch Civil & Environm Engn Sydney NSW 2052 Australia Los Alamos Natl Lab POB 1663 Los Alamos NM 87545 USA

We address the aircraft conflict resolution problem in air traffic control. We introduce new mixed-integer programming formulations for aircraft conflict resolution with speed, heading and altitude control which are based on disjunctive linear separation conditions. We first examine the two-dimensional aircraft conflict resolution problem with speed and heading control represented as continuous decision variables. We show that the proposed disjunctive linear separation conditions are equivalent to the classical nonlinear conditions for aircraft separation. Further, we characterise conflict-free trajectories based on aircraft velocity bounds and propose a simple pre-processing algorithm to identify aircraft pairs which are either always conflict-free, or which cannot be separated using speed and heading control only. We then incorporate altitude control and propose a lexicographic optimisation formulation that aims to minimise the number of flight level changes before resolving outstanding conflicts via two-dimensional velocity control. The proposed mixed-integer programming formulations are nonconvex, and we propose convex relaxations, decomposition methods and constraint generation algorithms to solve the two-dimensional and lexicographic optimisation formulations to guaranteed optimality. Numerical experiments on four types of conflict resolution benchmarking instances are conducted to test the performance of the proposed mixed-integer formulations. Further, the proposed method is compared against two benchmarks based on state-of-the-art approaches for the aircraft conflict resolution problem. Our numerical results show that the proposed method largely outperforms both benchmarks in terms of runtime and is able to solve significantly more instances to global optimality. (c) 2021 Published by Elsevier B.V.

关键词： Global optimisation mixed-integer nonlinear programming Convex relaxation Conflict resolution Air traffic control

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