Browsing by Author "Castro, Carlos Alberto"

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    AC transmission expansion planning considering uncertainty
    (Institute of Electrical and Electronics Engineers Inc., 2019) Castro, Carlos Alberto
    © 2019 IEEE. Taking into account uncertainty into the Transmission Expansion Planning (TEP) problem is an important and complex issue. So far, the literature has proposed only simplified models in order to solve this problem. In this research work, a formulation and solution method to solve the Transmission System Expansion Planning problem considering uncertainty and using the AC network model is proposed. A simheuristic technique that combines simulation with an Ant Colony Optimization algorithm is used. The Montecarlo Simulation (MS) technique to handle the stochastic behavior of demand and generation, and the Ant Colony Optimization algorithm for continuous domains (ACOR) to find the optimal solution of the problem were employed. The results were obtained for different test scenarios on the Garver 6-bus and the IEEE 24-bus test systems.
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    Transmission expansion planning considering the impact of distributed generation
    (Institute of Electrical and Electronics Engineers Inc., 2019) Matute Alvarado, Nelson Esteban; Torres Contreras, Santiago Patricio; Castro, Carlos Alberto
    Distributed Generation (DG) is a very important alternative to the traditional approach of centralized generation and plays a major role not only in electric distribution systems but also in transmission systems. The incidence of DG in the electrical system (sub-transmission and/or distribution) could defer the addition of new transmission circuits and reduce transmission network losses, representing potential economical savings. This paper studies the economic impact of DG on the Transmission Expansion Planning (TEP) problem including also the cost of transmission network losses. A long-term deterministic static transmission expansion planning using the mathematical AC model is presented. DG is modeled as the summation of each type of small-scale generation technology concentrated in the load node. The proposed TEP approach provides information on the optimal combination of transmission circuits and DG in load nodes. The problem, formulated using the AC model, corresponds to a full non convex, non-linear mixed-integer programming (MINLP) problem. Performance comparisons between Particle Swarm Optimization (PSO) and Artificial Fish Swarm Algorithm (AFSA), to solve the problem, are shown. Garver 6 - bus and IEEE 24 - bus test systems are used to evaluate this TEP approach.