This book chapter presents a flexible approach to incorporate mathematical models of FACTS devices into the Power Flow (PF) and the Optimal Power Flow (OPF) analysis tools, as well as into the standard OPF Market-Clearing (OPF-MC) procedure. The proposed approach uses the Matlab Optimization Toolbox because it allows to easily: (a) implement a given optimization model, (b) include different objective functions using distinct equality and inequality constraints and (c) modify and reuse an optimization model that has been previously implemented. The conventional OPF model is the main core of the proposed approach, which is easily implemented and adapted to include the mathematical models of FACTS devices. The resulting implementation of the OPF model featuring FACTS devices can be easily modified and adjusted to obtain the implementation of both the PF and the OPF-MC models which includes such devices. It should be mentioned that with the flexible approach proposed here, the complexity as well as the implementation time of optimized models featuring embedded FACTS devices is significantly reduced, since it is not necessary to define the expressions associated with the hessian matrix and the gradient vector. The flexibility and reliability of the proposed approach are demonstrated by means of several study cases using test as well as real power systems.

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The current requirements of the industry present challenges that must faced daily in the different productive sectors. The use of the cutting-edge technology and the efficient handling of large volumes of information allow the development of new computational tools that improve accuracy and optimize calculation’s times in pipe design. In this work the application is presented of the Piping Systems Analysis Program (PSAP), same that was developed as a graphical interface in the computer language MATLAB®; this computational application determines the size of the commercial pipe based on the pressure difference, elevation, water temperature and volume flow as critical design parameters. The algorithm of solution uses skills of Big Data for the precise handling of large volumes of information, getting accurate results at reduced times without compromising the reliability of pipe systems; this way optimizes the design process compared to traditional analytical methods.