The authors propose an algorithm for solving reactive power planning problems. The optimization approach is based on a recursive mixed-integer programming technique using an approximation method. A fundamental feature...
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The authors propose an algorithm for solving reactive power planning problems. The optimization approach is based on a recursive mixed-integer programming technique using an approximation method. A fundamental feature of this algorithm is that the number of capacitor or reactor units can be treated as a discrete variable in solving large-scale VAr (volt-ampere reactive) planning problems. Numerical results have verified the validity and efficiency of the algorithm.< >
This paper presents an application of a customized linear programming (LP) based model predictive control strategy to the paper machine cross direction (CD) control problem. The objective of CD control is to maintain ...
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This paper presents an application of a customized linear programming (LP) based model predictive control strategy to the paper machine cross direction (CD) control problem. The objective of CD control is to maintain flat profiles of variables of interest by minimizing worst case deviations from setpoints (defects). These control problems can have as many as 200 actuators (inputs) and 400 sensor measurements (outputs). This large size coupled with the stringent real-time requirement of computing a control move in a few seconds poses a very challenging control problem. Computational results that demonstrate the effectiveness of this strategy will be presented. For typical disturbances this algorithm can compute provably optimal control moves for a 400 input x 400 output control problem in approximately 5 s versus approximately 90 s for a generic LP algorithm on a HP 9000/770 workstation. (C) 1999 Elsevier Science Ltd. All rights reserved.
With the increasing share of renewable energy sources in microgrids, systems enhancing the power flexibility at the demand side have become mandatory in microgrid architecture. Thus, the electrical energy storage syst...
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With the increasing share of renewable energy sources in microgrids, systems enhancing the power flexibility at the demand side have become mandatory in microgrid architecture. Thus, the electrical energy storage system has been mostly integrated into microgrids although its capital and operating costs are very high. Hence, diversifying microgrid energy storage based on the end-energy usage can improve the reliability, operating cost, and power demand flexibility of the microgrid. The hereby study integrates an absorption chiller and electrical heat pump coupled to a heat storage into a renewable energy resources-based microgrid and develops its centralized energy management model for both off-grid and on-grid operation modes. The model considers the current energy level of energy storages in the microgrid, current, and forecasted information state to compute the vector-valued decision minimizing a realistic microgrid operating cost. The operating cost includes the energy purchasing/generation, heating/electrical storage, and penalty cost due to load shedding. Hence, such an objective function leads to maximizing the consumption of the generated renewable power, minimizing the energy storage and load shedding cost. The load shedding has been allowed to increase the flexibility in microgrid energy management. However, a high penalty has been imposed on each electrical, heating, or cooling load shedding decision. The decision vector components are thermal and electrical power flow between each energy source to loads and energy storage systems. The problem has been modeled as a linear programming with forecasted multi-parametric inputs at different prediction horizons. The effect of the charging/discharging rate and capacity of energy storages, coefficient of performance of absorption chiller and heat pump, prediction horizon, and energy level of storage devices provided to the myopic energy management model has been evaluated for better integration of renewable source
The improved primal simplex (IPS) was recently developed by Elhalaloui et al. to take advantage of degeneracy when solving linear programs with the primal simplex. It implements a dynamic constraint reduction based on...
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The improved primal simplex (IPS) was recently developed by Elhalaloui et al. to take advantage of degeneracy when solving linear programs with the primal simplex. It implements a dynamic constraint reduction based on the compatible columns, i.e., those that belong to the span of a given subset of basic columns including the nondegenerate ones. The identification of the compatible variables may however be computationally costly and a large number of linear programs are solved to enlarge the subset of basic variables. In this article, we first show how the positive edge criterion of Raymond et al. can be included in IPS for a fast identification of the compatible variables. Our algorithm then proceeds through a series of reduction and augmentation phases until optimality is reached. In a reduction phase, we identify compatible variables and focus on them to make quick progress toward optimality. During an augmentation phase, we compute one greatest normalized improving direction and select a subset of variables that should be considered in the reduced problem. Compared with IPS, the linear program that is solved to find this direction involves the data of the original constraint matrix. This new algorithm is tested over Mittelmann's benchmark for linear programming and on instances arising from industrial applications. The results show that the new algorithm outperforms the primal simplex of CPLEX on most highly degenerate instances in which a sufficient number of nonbasic variables are compatible. In contrast, IPS has difficulties on the eleven largest Mittelmann instances. (C) 2015 Elsevier B.V. All rights reserved.
This paper develops an interactive method for multiple objective linear programming problems. The decision maker's capability to evaluate the trade-off vector is affected by the number of criteria involved in the ...
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This paper develops an interactive method for multiple objective linear programming problems. The decision maker's capability to evaluate the trade-off vector is affected by the number of criteria involved in the trade-off vector. In this paper, the problem considered has too many criteria for the decision maker to properly evaluate the trade-off vector when all the criteria are considered simultaneously. Hence partitioning of the criteria set is recommended to facilitate easier interaction with the decision maker. A linear additive utility function is assumed.
An interval-parameter fuzzy linear programming with stochastic vertices (IFLPSV) method is developed for water resources management under uncertainty by coupling interval-parameter fuzzy linear programming (IFLP) with...
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An interval-parameter fuzzy linear programming with stochastic vertices (IFLPSV) method is developed for water resources management under uncertainty by coupling interval-parameter fuzzy linear programming (IFLP) with stochastic programming (SP). As an extension of existing interval parameter fuzzy linear programming, the developed IFLPSV approach has advantages in dealing with dual uncertainty optimization problems, which uncertainty presents as interval parameter with stochastic vertices in both of the objective functions and constraints. The developed IFLPSV method improves upon the IFLP method by allowing dual uncertainty parameters to be incorporated into the optimization processes. A hybrid intelligent algorithm based on genetic algorithm and artificial neural network is used to solve the developed model. The developed method is then applied to water resources allocation in Beijing city of China in 2020, where water resources shortage is a challenging issue. The results indicate that reasonable solutions have been obtained, which are helpful and useful for decision makers. Although the amount of water supply from Guanting and Miyun reservoirs is declining with rainfall reduction, water supply from the South-to-North Water Transfer project will have important impact on water supply structure of Beijing city, particularly in dry year and extraordinary dry year.
A decision-support tool (ORES) in the form of a linear program is developed to determine the optimal investment and operating decisions for residential energy systems. It shows how energy conversion units such as a co...
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A decision-support tool (ORES) in the form of a linear program is developed to determine the optimal investment and operating decisions for residential energy systems. It shows how energy conversion units such as a cogeneration fuel cell, a heat pump, a boiler, photovoltaic panels and solar thermal collectors can be combined with energy storage devices, consisting of a battery and a hot water tank, to drive down total yearly energy costs and CO2 emissions while meeting space heat, hot water and electricity needs. Under the assumption of perfect demand and production forecasts and depending on how the dwelling is allowed to exchange electricity with the grid, cost reductions between 5 and 60% are possible, whereas emissions can be cut by 45-90% with respect to a base case. Stochastic programming is used effectively to reduce the sensitivity to uncertainty in weather parameters. The resulting cost increase is limited to 1.2%. Decision rules are implemented to account for unforeseen variations in electric load. If it is assumed that peak loads can occur at any instant of the optimization horizon, energy costs rise by 9%, which in off-grid scenarios, are driven by the installation of an about 50% bigger battery system. (C) 2016 Elsevier Ltd. All rights reserved.
For decision making problems involving uncertainty, both stochastic programming as an optimization method based on the theory of probability and fuzzy programming representing the ambiguity by fuzzy concept have been ...
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For decision making problems involving uncertainty, both stochastic programming as an optimization method based on the theory of probability and fuzzy programming representing the ambiguity by fuzzy concept have been developing in various,ways. In this paper, we focus on multiobjective linear programming problems with random variable coefficients in objective functions and/or constraints. For such problems, as a fusion of these two approaches, after incorporating fuzzy goals of the decision maker for the objective functions, we propose an interactive fuzzy satisficing method for the expectation model to derive a satisficing solution for the decision maker. An illustrative numerical example is provided to demonstrate the feasibility of the proposed method. (C) 2002 Elsevier Science B.V. All rights reserved.
The linear programming model for die casting processes provides the optimal production plan that maximizes the average efficiency of melting furnaces. In this article, we address supplementary production in linear pro...
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The linear programming model for die casting processes provides the optimal production plan that maximizes the average efficiency of melting furnaces. In this article, we address supplementary production in linear programming scheduling of die casting processes. Since a real die casting campaign is subject to a variety of potential malfunctions, there may exist defective items among final cast products, thus failing to match the quantities of customer orders. The objective of this study is to propose a novel scheme of preventing product shortage by manufacturing additional cast products associated with the result of the linear programming model. In particular, supplementary production is practicable by utilizing the residue of molten alloy and cast scraps or remnants available in each shift. The sequence of casting shifts is also adjusted for facilitating this supplementary production. The proposed scheme does not alter any part of the original scheduling result of the linear programming model. A numerical experiment is conducted using real foundry data to validate integrity and performance of the proposed scheme.
We propose a novel privacy-preserving random kernel approximation based on a data matrix A is an element of R-mxn whose rows are divided into privately owned blocks. Each block of rows belongs to a different entity th...
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We propose a novel privacy-preserving random kernel approximation based on a data matrix A is an element of R-mxn whose rows are divided into privately owned blocks. Each block of rows belongs to a different entity that is unwilling to share its rows or make them public. We wish to obtain an accurate function approximation for a given y is an element of R-m corresponding to each of the m rows of A. Our approximation of y is a real function on R-n evaluated at each row of A and is based on the concept of a reduced kernel K(A,B'), where B' is the transpose of a completely random matrix B. The proposed linear-programming-based approximation, which is public but does not reveal the privately held data matrix A, has accuracy comparable to that of an ordinary kernel approximation based on a publicly disclosed data matrix A.
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