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Optimal and Secure Operation of Transmission Systems
Published in Antonio Gómez-Expósito, Antonio J. Conejo, Claudio Cañizares, Electric Energy Systems, 2017
José Luis Martínez Ramos, Víctor Hugo Quintana
First versions of the OPF were developed in the beginning of 1960s as a consequence of the introduction of new nonlinear programming techniques [6,7]. Since then, the new versions of the OPF have followed the evolution of the mathematical programming techniques, with many variants because of the peculiarities of the OPF problem. The great variety of applied optimization techniques (Ref. [8] constitutes an useful survey of OPF algorithms) goes from the generalized reduced gradient [6], to Newton's method [9], sequential quadratic programming [10], sequential linear programming [11], and, more recently, interior point methods [12]. The majority of the currently commercialized OPFs use one of the last four techniques.
Damping ratio maximization in thickness direction using viscoelastic and structural materials based on constrained layer damping
Published in Engineering Optimization, 2022
Seita Inozume, Tatsuhito Aihara
The optimization problem in Equations (14) to (16) is a nonlinear optimization problem. There are many methods that solve the nonlinear optimization problem called the nonlinear programming (NLP) problem, such as sequential linear programming (SLP) and sequential quadratic programming (SQP). A primary drawback of some SQP algorithms is that the first-order eigenvector sensitivity must be evaluated before obtaining the second-order eigenvalue sensitivity (Alvin 1997; Wang 1991; Nelson 1976). This increases the associated computational cost. In SLP, linear approximations of nonlinear optimization problems are solved sequentially, using established linear programming (LP) techniques such as the dual–simplex method. Thus, the stage of updating design variables in Figure 1 is performed using SLP in this study.
Sequential linear programming and particle swarm optimization for the optimization of energy districts
Published in Engineering Optimization, 2019
Elisa Riccietti, Stefania Bellavia, Stefano Sello
Sequential Linear Programming (SLP) is an iterative method to find local minima of nonlinear constrained optimization problems by solving a sequence of linear programming problems, (Robinson 1972; Byrd et al.2003). The adopted SLP method is close to the one presented in (Robinson 1972), but is equipped with a trust-region approach to promote the global convergence. Moreover a bound-constraints handling strategy is adopted, which is similar to the one used in (Herty et al.2007) where an SLP approach without trust-region is presented for equality constrained problems. The proposed approach is also different from those presented in (Fletcher and de la Maza 1989; Byrd et al.2003) as second-order information is not used.