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52nd North American Power Symposium (NAPS)

作者： Huang, Hao Panyam, Varuneswara Narimani, Mohammad Rasoul Layton, Astrid Davis, Katherine R. Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA Texas A&M Univ Dept Mech Engn College Stn TX 77843 USA

ISBN: (纸本)9781728181929

Power systems are susceptible to natural threats including hurricanes and floods. Modern power grids are also increasingly threatened by cyber attacks. Existing approaches that help improve power system security and resilience may not be sufficient;this is evidenced by the continued challenge to supply energy to all customers during severe events. This paper presents an approach to address this challenge through bio-inspired power system network design to improve system reliability and resilience against disturbances. Inspired by naturally robust ecosystems, this paper considers the optimal ecological robustness that recognizes a unique balance between pathway efficiency and redundancy to ensure the survivability against disruptive events for given networks. This paper presents an approach that maximizes ecological robustness in transmission network design by formulating a mixed-integer nonlinear programming optimization problem with power system constraints. The results show the increase of the optimized power system's robustness and the improved reliability with less violations under N - x contingencies.

关键词： Power Networks Design mixed-integer nonlinear programming Power System Resilience

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A Branch and Bound Algorithm to Solve Nonconvex MINLP Problems via Novel Division Strategy: An Electric Power System Case Study 17

A Branch and Bound Algorithm to Solve Nonconvex MINLP Proble...

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17th IEEE International Conference on Environment and Electrical Engineering (IEEE EEEIC) / 1st IEEE Industrial and Commercial Power Systems Europe Conference (IEEE I and CPS Europe)

作者： de Jesus Delgado, Marcos Andre Pourakbari-Kasmaei, Mahdi Rider, Marcos Julio UFMT Dept Math Rondonopolis MT Brazil UNESP Dept Elect Engn Ilha Solteira SP Brazil Univ Estadual Campinas Dept Syst & Energy Campinas SP Brazil

ISBN: (纸本)9781538639177

This paper presents two Branch and Bound algorithms (B&B) for solving mixed-integer nonlinear programming (MINLP) problems with nonconvex search space. The main advantage of the proposed algorithms, comparing with the commonly used B&B algorithms, is using an innovative way of variables' separation and subproblems' division while, if necessary, one more variable is used in the separation process. This approach allows circumventing the probable difficulties caused by nonlinearity and nonconvexity. This paper aims at addressing the following issues of how to: 1) deal with nonlinear programming problems, 2) detect the infeasibility of the resulted NLP problems, and 3) deal with the nonconvexity of the problem. In order to show the applicability, the proposed algorithms are applied to one of the most complicated problems in power system, the long-term static transmission expansion planning, which is modeled as an MINLP problem. Several case studies such as Garver 6-bus, IEEE 24-bus, South Brazilian 46-bus, Bolivian 57-bus, and the Colombian 93-bus are conducted to reveal the effectiveness and shortcoming of the proposed algorithms. Results show that the proposed algorithms can find the best-known solutions for most of the aforementioned systems with a significant reduction in the number of subproblems.

关键词： branch and bound algorithm mixed-integer nonlinear programming transmission expansion planning

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A nonlinear model for a capacitated stochastic transportation network using multiple linear regression

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INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2016年第4期11卷 252-261页

作者： Shiripour, Saber Mahdavi, Iraj Mahdavi-Amiri, Nezam Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar Iran Sharif Univ Technol Fac Math Sci Tehran Iran

Here, a capacitated location-multi-allocation-routing problem with intelligent stochastic travel times is considered. In our study, the concept of intelligent stochastic travel times incurs two issues: (1) consideration of some random factors in computing the travel times and (2) impact of the traveling population on these random factors simultaneously. Here, a deterministic factor of road length and two random factors of the time spent in traffic and the number of accidents are considered. It is assumed that the time spent in traffic is distributed based on an exponential distribution function and the other factor is distributed based on a Poisson distribution function. Also, the capacities of server nodes and arcs for accepting the population are assumed to be limited. Using multiple linear regression, we formulate the problem as a mixed-integer nonlinear programming model. The objective is to find appropriate locations as server locations among the candidate locations, allocate the existing population in each demand node to server locations and determine the movement path of each member to reach its corresponding server with respect to the simultaneous change of the stochastic travel times so that the expected total transportation time is minimized. Also, to investigate the validation and behavior of the proposed probabilistic model, a network example is provided and computational results are analyzed.

关键词： Intelligent stochastic travel times multiple linear regression multi-allocation mixed-integer nonlinear programming

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Exploring the convex transformations for solving non-convex bilinear integer problems

Exploring the convex transformations for solving non-convex ...

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European Symposium on Computer Aided Process Engineering-9 (Escape-9)

作者： Harjunkoski, I Pörn, R Westerlund, T Abo Akad Univ Dept Chem Engn Proc Design Lab FIN-20520 Turku Finland Abo Akad Univ Dept Math FIN-20520 Turku Finland

In the present paper some methods to transform a non-convex bilinear mixed-integer programming problem are considered in more detail. The methods have been applied in solving the trim-loss problem with MINLP methods and even if the transformations lead to a global optimal solution, there are some variable properties that affect the solution performance. The properties are explored by systematically solving a set of example problems with modified transformation techniques and analyzing the results.

关键词： mixed-integer nonlinear programming convex transformation trim-loss problem

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Joint Resource Segmentation and Transmission Rate Adaptation in Cloud RAN with Caching as a Service 17

Joint Resource Segmentation and Transmission Rate Adaptation...

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17th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Tang, Jianhua Quek, Tony Q. S. Tay, Wee Peng Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing Peoples R China Singapore Univ Technol & Design Singapore Singapore Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9781509017492

By introducing Caching as a Service (CaaS) in Cloud radio access network (C-RAN), the joint resource segmentation and transmission rate adaptation problem is investigated in this paper. Specifically, in the baseband unit (BBU) pool of C-RAN, we optimally segment computation and storage resources to different types of virtual machines (VMs), and in the remote radio heads (RRHs), we adjust the beamformers to obtain the cache-based adaptive rate (CBAR). We aim to minimize the system cost, which includes server cost, VM cost and wireless transmission cost. The joint optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem, which contains.. l(0)-norm terms in the objective function and nonconvex constraints. We propose a three-step solution approach, i.e., a general smooth function approximation step, a weighted minimum mean square error (WMMSE) reformulation step and an integer recovery step. Simulation results show that our proposed integer recovery algorithms recover the integer variable values effectively.

关键词： C-RAN Caching as a Service resource segmentation joint transmission mixed-integer nonlinear programming

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LaGO: a (heuristic) Branch and Cut algorithm for nonconvex MINLPs

LaGO: a (heuristic) Branch and Cut algorithm for nonconvex M...

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5th Europe Workshop on Advances in Continuous Optimization

作者： Nowak, Ivo Vigerske, Stefan Humboldt Univ Dept Math D-10099 Berlin Germany Lufthansa Syst Berlin Berlin Germany

We present a Branch and Cut algorithm of the software package LaGO to solve nonconvex mixed-integer nonlinear programs (MINLPs). A linear outer approximation is constructed from a convex relaxation of the problem. Since we do not require an algebraic representation of the problem, reformulation techniques for the construction of the convex relaxation cannot be applied, and we are restricted to sampling techniques in case of nonquadratic nonconvex functions. The linear relaxation is further improved by mixed-integer-rounding cuts. Also box reduction techniques are applied to improve efficiency. Numerical results on medium size test problems are presented to show the efficiency of the method.

关键词： global optimization branch and bound branch and cut outer approximation mixed-integer nonlinear programming

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A MINLP Model to Optimize Battery Placement and Operation in Smart Grids

A MINLP Model to Optimize Battery Placement and Operation in...

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IEEE-Power-and-Energy-Society Innovative Smart Grid Technologies Conference (ISGT)

作者： dos Santos, Caio Cavalheiro, Ellen Bartmeyer, Petra Lyra, Christiano Univ Estadual Campinas Dept Syst & Energy Sao Paulo Brazil

ISBN: (纸本)9781728131030

This paper proposes a mixed-integer nonlinear programming model (MINLP) to address the battery energy storage system placement problem (BESP). The solution provides the battery energy storage system (BES) operational policy and a solution for the BESP in order to achieve the reduction of energy losses cost. The model considers AC power flow and voltage magnitude constraints;the battery equations are included into the model in order to provide its daily operation. It also considers a photovoltaic energy plant connected to the distribution feeder. The computational studies evaluate the energy costs and the maximum voltage in each bus of a 34-bus test feeder. The results show that the BES policy can reduce up to 24% of the costs with energy losses depending on the result of BESP;besides, the BES operation allows a reduction in the maximum voltage for all scenarios.

关键词： Battery energy storage battery placement mixed-integer nonlinear programming voltage magnitude control

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Primal and Dual Bounds for Optimal Transmission Switching

Primal and Dual Bounds for Optimal Transmission Switching

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2014 Power Systems Computation Conference (PSCC)

作者： Coffrin, Carleton Hijazi, Hassan L. Lehmann, Karsten Van Hentenryck, Pascal NICTA Canberra ACT 2601 Australia Australian Natl Univ Canberra ACT 2601 Australia NICTA Melbourne Vic 3010 Australia

ISBN: (纸本)9788393580132

It has been suggested that Optimal Transmission Switching (OTS) may reduce generator dispatch costs by as much as 10%, saving millions of dollars annually. However, this conclusion has been deduced primarily from studies using the DC power flow approximation on two power networks derived from the IEEE 118-bus and RTS-96 cases. This paper is a systematic study of the OTS problem. Various OTS formulations are considered for computing primal and dual bounds on a variety of power networks. The results demonstrate that the DC power flow model is inadequate for solving the OTS problem, and that mixed-integer nonlinear optimization techniques are instrumental in finding high-quality primal and dual bounds. The paper also indicates that, on a variety of benchmarks, transmission line switching may bring generation cost reductions between 0% to 29%.

关键词： Optimal Transmission Switching Optimal Power Flow AC Power Flow DC Power Flow mixed-integer nonlinear programming

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AN ASYNCHRONOUS PARALLEL HYBRID OPTIMIZATION APPROACH TO SIMULATION-BASED mixed-integer nonlinear PROBLEMS

AN ASYNCHRONOUS PARALLEL HYBRID OPTIMIZATION APPROACH TO SIM...

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23rd European Modeling and Simulation Symposium / International Mediterranean and Latin American Modeling Multiconference

作者： Fowler, K. R. Kopp, T. Orsini, J. Griffin, J. D. Gray, G. A. Clarkson Univ Dept Math & Comp Sci Potsdam NY 13676 USA SAS New York NY USA Sandia Natl Labs Livermore CA 94550 USA

ISBN: (纸本)9788890372445

To address simulation-based mixed-integer problems, a hybrid algorithm was recently proposed that combines the global search strengths and the natural capability of a genetic algorithm to handle integer variables with a local search on the real variables using an implementation of the generating set search method. Since optimization is guided only by function values, the hybrid is designed to run asynchronously on a parallel platform. The algorithm has already been shown to perform well on a variety of test problems, and this work is a first step in understanding how the parallelism and local search components influence the search phase of the algorithm. We show that the hybridization can improve the capabilities of the genetic algorithm by using less function evaluations to locate the solution and provide a speed-up analysis on a standard mixed-integer test problem with equality and inequality constraints.

关键词： genetic algorithm pattern search asynchronous mixed-integer nonlinear programming

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Mathematical programming formulations for the optimal placement of imperfect detectors with applications to flammable gas detection and mitigation systems

Mathematical programming formulations for the optimal placem...

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作者： Benavides Serrano, Alberto Jose Texas A&M University

学位级别：Ph.D.

The placement of detectors in mitigation systems is a difficult problem usually addressed in the industry via qualitative and semiquantative approaches. Simplifications are used to circumvent difficulties regarding problem size, parameter uncertainty, and lack of information concerning leak development. Given recent improvement of consequence modeling tools, the use of a stochastic mixed-integer Linear programming (MILP) formulation (SP) was previously proposed to quantitatively approach this problem. This formulation minimizes the expected damage over a large set of gas leak scenarios while assuming perfect detectors. In reality gas detectors are prone to false positives and false negatives. Two solutions are usually implemented in the process industries. First, additional confirmation from several detectors (i.e., voting) is required before emergency actions are triggered in order to avoid false positives. Second, in order to avoid false negatives, the unavailability of the detectors is considered in the placement strategy. Unavailability corresponds to the probability that the detector will not be able to perform its intended function when required. In the first part of this dissertation, two problem formulations were developed and validated to address the issue of imperfect detectors: minimization of expected damage considering unavailability (SP-U) and minimization of the expected damage considering unavailability and voting (SP-UV). SP-U and SP-UV placement results were compared with those obtained assuming perfect detectors. Results demonstrate that explicit consideration of unavailability and voting effects alters the final detector placement. Quantitative risk can be significantly higher if we neglect these issues when solving for the optimal placement. Furthermore, SP-UV placement results were compared with those of four existing approaches for gas detector placement using three different performance metrics in accordance to the objectives of gas detection

关键词： Gas Detection Process Safety Detector Placement Stochastic programming mixed-integer Linear programming mixed-integer nonlinear programming Unavailability Reliability Backup Coverage Voting Thesis

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