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International Joint Conference on Neural Networks

作者： Marco Severini Stefano Squartini Marco Fagiani Francesco Piazza Department of Information Engineering Università Politécnica delle Marche Ancona Italy

ISBN: (纸本)9781479919611

Although smart grids are regarded as the technology to overcome the limits of nowadays power distribution grids, the transition will require much time. Dynamic pricing, a straightforward implementation of demand response, may provide the means to manipulate the grid load thus extending the life expectancy of current technology. However, to integrate a dynamic pricing scheme in the crowded pool of technologies, available at demand side, a proper energy manager with the support of a pricing profile forecaster is mandatory. Although energy management and price forecasting are recurrent topics, in literature they have been addressed separately. On the other hand, in this work, the aim is to investigate how well an energy manager is able to perform in presence of data uncertainty originating from the forecasting process. On purpose, an energy and resource manager has been revised and extended in the current manuscript. Finally, it has been complemented with a price forecasting technique, based on the Extreme Learning Machine paradigm. The proposed forecaster has proven to be better performing and more robust, with respect to the most common forecasting approaches. The energy manager, as well, has proven that the energy efficiency of the residential environment can be improved significantly. Nonetheless, to achieve the theoretical optimum, forecasting techniques tailored for that purpose may be required.

关键词： Energy Management mixed-integer linear programming Dynamic Price Forecasting Neural Networks Extreme Learning Machine Smart Micro Grid Energy management Neural network Learning machines mixed integer linear programming Pricing Forecasting techniques manager resource manager Residential environment life expectancy Demand-Responsive Smart grids Energy efficiency

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Multi-Objective Distribution System Reconfiguration for Reliability Enhancement and Loss Reduction

Multi-Objective Distribution System Reconfiguration for Reli...

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IEEE Power and Energy Society General Meeting

作者： N. G. Paterakis S. F. Santos J. P. S. Catalao A. Mazza G. Chicco O. Erdinc A. G. Bakirtzis UBI INESC-ID and IST Covilha and Lisbon POLITO Yildiz Technical Univ. AUTh

ISBN: (纸本)9781467380416

This paper deals with the radial distribution system reconfiguration problem in a multi-objective scope, aiming to determine the optimal configuration by means of minimization of active power losses and several reliability indices. A novel way to calculate these indices under a mixed-integer linear programming (MILP) approach is provided. Afterwards, an efficient implementation of the ε-constraint method using lexicographic optimization is employed to solve the multi-objective optimization problem, which is formulated as a MILP problem. After the Pareto Efficient solution set is generated, a multi-attribute decision making procedure is used, namely the technique for order preference by similarity to ideal solution (TOPSIS) method, so that a decision maker (DM) can express preferences over the solutions and facilitate the final selection.

关键词： Distribution system reconfiguration Epsilon-constraint method Loss minimization mixed-integer linear programming Multi-objective optimization Reliability loss reduction dispensing system Reconfiguration mixed integer linear programming optimal configuration reliability index Solution Set Preferences

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Optimal maintenance scheduling of a gas engine power plant using generalized disjunctive programming

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AICHE JOURNAL 2014年第6期60卷 2083-2097页

作者： Castro, Pedro M. Grossmann, Ignacio E. Veldhuizen, Patrick Esplin, Douglas Lab Nacl Energia & Geol P-1649038 Lisbon Portugal Univ Lisbon Fac Ciencias Ctr Invest Operac P-1749016 Lisbon Portugal Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA Sasol LTD Sasolburg South Africa

A new continuous-time model for long-term scheduling of a gas engine power plant with parallel units is presented. Gas engines are shut down according to a regular maintenance plan that limits the number of hours spent online. To minimize salary expenditure with skilled labor, a single maintenance team is considered which is unavailable during certain periods of time. Other challenging constraints involve constant minimum and variable maximum power demands. The objective is to maximize the revenue from electricity sales assuming seasonal variations in electricity pricing by reducing idle times and shutdowns in high-tariff periods. By first developing a generalized disjunctive programming model and then applying both big-M and hull reformulation techniques, we reduce the burden of finding the appropriate set of mixed-integer linear constraints. Through the solution of a real-life problem, we show that the proposed formulations are very efficient computationally, while gaining valuable insights about the system. (c) 2014 American Institute of Chemical Engineers AIChE J, 60: 2083-2097, 2014

关键词： planning optimization logic mixed-integer linear programming mathematical modeling

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Index Tracking Using Data-Mining Techniques and mixed-Binary linear programming

Index Tracking Using Data-Mining Techniques and Mixed-Binary...

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IEEE International Conference on Industrial Engineering and Engineering Management

作者： Oliver Strub Philipp Baumann Department of Business Administration University of Bern Bern Switzerland Department of Industrial Engineering and Operations Research UC Berkeley Berkeley USA

ISBN: (纸本)9781467380676

Index tracking has become one of the most common strategies in asset management The index-tracking problem consists of constructing a portfolio that replicates the future performance of an index by including only a subset of the index constituents in the portfolio. Finding the most representative subset is challenging when the number of stocks in the index is large. We introduce a new three-stage approach that at first identifies promising subsets by employing data-mining techniques, then determines the stock weights in the subsets using mixed-binary linear programming, and finally evaluates the subsets based on cross validation. The best subset is returned as the tracking portfolio. Our approach outperforms state-of-the-art methods in terms of out-of-sample performance and running times.

关键词： Cross validation data mining index tracking mixed-integer linear programming principal component analysis

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Decision models for capacity planning in a regeneration environment

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2014年第23期52卷 7007-7026页

作者： Eickemeyer, Steffen C. Herde, Felix Irudayaraj, Pratik Nyhuis, Peter Leibniz Univ Hannover Inst Prod Syst & Logist Hannover Germany Leibniz Univ Hannover Inst Prod Management Hannover Germany Georgia Inst Technol Predictix LLC Atlanta GA 30332 USA

This paper presents an approach to capacity planning and coordination for the regeneration of complex investment goods. In order to capture the specifics of the regeneration environment, Bayesian networks are utilised to improve the accuracy of the workload forecast and mathematical models are proposed for the long-, medium- and short-term capacity planning and coordination. The long- and medium-term models maximise the total profit through the optimum determination of the quantity of goods to be regenerated in-house or at the sites of external vendors, the number of regenerated goods to be stored and the extent to which penalties should be tolerated for delayed regeneration orders. The short-term model determines the optimal allocation of human and machine resources to different orders. The models are validated through the use of real-world data supplied by an industrial partner. Sensitivity analyses are conducted in order to gain insights into the model. The application of the models leads to significant improvements for the industry partner due to the reduction in regeneration costs as a result of implementing varying plant capacities over the course of a year.

关键词： MRO capacity planning mixed-integer linear programming

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A revised method for robust optimal design of energy supply systems based on minimax regret criterion

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ENERGY CONVERSION AND MANAGEMENT 2014年 84卷 196-208页

作者： Yokoyama, Ryohei Fujiwara, Koji Ohkura, Masashi Wakui, Tetsuya Osaka Prefecture Univ Dept Mech Engn Naka Ku Sakai Osaka 5998531 Japan

A robust optimal design method of energy supply systems under uncertain energy demands is revised so that it can be applied to systems with complex configurations and large numbers of periods for variations in energy demands. First, a robust optimal design problem is described by using the minimax regret criterion and considering the hierarchical relationship among design variables, energy demands, and operation variables, which is followed by a solution method. Especially, a novel solution method is proposed for efficiently evaluating an upper bound for the optimal value of the maximum regret. Then, a method of comparing two energy supply systems under uncertain energy demands is proposed by utilizing a part of the revised robust optimal design method. Finally, through a case study on a gas turbine cogeneration system for district energy supply, the validity and effectiveness of the revised optimal design method and features of the robust optimal design are clarified. In addition, the gas turbine cogeneration system is compared with a conventional energy supply system using the proposed comparison method. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Energy supply systems Uncertainty Robust optimal design Minimax regret criterion Multilevel linear programming mixed-integer linear programming

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Optimal train routing and scheduling for managing traffic perturbations in complex junctions

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2014年 59卷 58-80页

作者： Pellegrini, Paola Marliere, Gregory Rodriguez, Joaquin Univ Lille Nord France Ifsttar ESTAS F-59666 Villeneuve Dascq France

Real-time traffic management in railway aims to minimize delays after an unexpected event perturbs the operations. It can be formalized as the real-time railway traffic management problem, which seeks for the best train routing and scheduling in case of perturbation, in a given time horizon. We propose a mixed-integer linear programming formulation for tackling this problem, representing the infrastructure with fine granularity. This is seldom done in the literature, unless stringent artificial constraints are imposed for reducing the size of the search space. In a thorough experimental analysis, we assess the impact of the granularity of the representation of the infrastructure on the optimal solution. We tackle randomly generated instances representing traffic in the control area named triangle of Gagny, and instances obtained from the real timetable of the control area including the Lille-Flandres station (both in France) and we consider multiple perturbation scenarios. In these experiments, the negative impact of a rough granularity on the delay suffered by trains is remarkable and statistically significant. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Real-time railway traffic management problem mixed-integer linear programming Track-circuit Complex junction Routing Scheduling

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A new approach for maintenance scheduling of generating units in electrical power systems based on their operational hours

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COMPUTERS & OPERATIONS RESEARCH 2014年 50卷 61-79页

作者： Fattahi, M. Mahootchi, M. Mosadegh, H. Fallahi, F. Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Iran Niroo Res Inst Tehran Iran

Generator maintenance scheduling (GMS) determines the outage periods of generating units in a oneyear or two-year planning horizon for regular safety inspection. This paper introduces a new practical GMS for centralized electrical power systems in which, in contrary to previous studies, the outage periods are scheduled based on operational hours of units. Predefined minimum and maximum operating hours of units after a maintenance outage define the beginning and ending of their maintenance windows, respectively. In addition, unit commitment (UC) as a short-term planning is considered in the GMS with hourly time scale. Therefore, this problem becomes more complex than periodic maintenance scheduling. In this paper, a novel mixed-integer linear programming (MILP) model for the problem is developed. However, due to the problem intractability, two different solution algorithms on the basis of ant colony optimization (ACO) and simulated annealing (SA) are extended for the GMS problem. Both algorithms use some developed heuristics and feasibility rules, namely UC heuristic, for solving the UC problem. Numerical results indicate that the solution algorithms perform well compared to the exact solution of the MILP model obtained using CPLEX solver. Also, the solution algorithms as well as the UC heuristic are examined carefully. To demonstrate the performance of the proposed algorithms, two test systems containing 26 and 36 generating units are investigated. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Generator maintenance scheduling Unit commitment mixed-integer linear programming Ant colony optimization Simulated annealing Heuristics

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Many-to-many location-routing with inter-hub transport and multi-commodity pickup-and-delivery

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2014年第3期236卷 863-878页

作者： Rieck, Julia Ehrenberg, Carsten Zimmermann, Juergen Tech Univ Clausthal Inst Management & Econ Operat Res Grp D-38678 Clausthal Zellerfeld Germany

In this paper, we consider a variant of the many-to-many location-routing problem, where hub facilities have to be located and customers with either pickup or delivery demands have to be combined in vehicle routes. In addition, several commodities and inter-hub transport processes are taken into account. A practical application of the problem can be found in the timber-trade industry, where companies provide their services using hub-and-spoke networks. We present a mixed-integer linear model for the problem and use CPLEX 12.4 to solve small-scale instances. Furthermore, a multi-start procedure based on a fix-and-optimize scheme and a genetic algorithm are introduced that efficiently construct promising solutions for medium- and large-scale instances. A computational performance analysis shows that the presented methods are suitable for practical application. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Location-routing Inter-hub transport Multi-commodity pickup-and-delivery mixed-integer linear programming Fix-and-optimize Genetic algorithm

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Outer Approximation and Outer-Inner Approximation Approaches for Unit Commitment Problem

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IEEE TRANSACTIONS ON POWER SYSTEMS 2014年第2期29卷 505-513页

作者： Han, Daolan Jian, Jinbao Yang, Linfeng Guangxi Univ Coll Elect Engn Nanning 530004 Peoples R China Guangxi Univ Nationalities Coll Sci Nanning Peoples R China Yulin Normal Univ Sch Math & Informat Sci Yulin Peoples R China Guangxi Univ Coll Comp & Elect Informat Nanning 530004 Peoples R China

This paper proposes a new separable model for the unit commitment (UC) problem and three deterministic global optimization methods for it ensuring convergence to the global optimum within a desired tolerance. By decomposing a multivariate function into several univariate functions, a tighter outer approximation methodology that can be used to improve the outer approximations of several classical convex programming techniques is presented. Based on the idea of the outer approximation (OA) method and the proposed separable model, an outer-inner approximation (OIA) approach is also presented to solve this new formulation of UC problem. In this OIA approach, the UC problem is decomposed into a tighter outer approximation subproblem and an inner approximation subproblem, where the former leads to a better lower bound than the OA method, and the later provides a better upper bound. The simulation results for systems of up to 100 units with 24 h are compared with those of previously published methods, which show that the OIA approach is very promising due to the excellent performance. The proposed approaches are also applied to the large-scale systems of up to 1000 units with 24 h, and systems of up to 100 units with 96 h and 168 h.

关键词： Inner approximation mixed-integer linear programming outer approximation separable model unit commitment

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