Abstract

This paper presents a new optimization technique based on quantum computing principles to solve a security constrained power system economic dispatch problem (SCED). The proposed technique is a population-based algorithm, which uses some quantum computing elements in coding and evolving groups of potential solutions to reach the optimum following a partially directed random approach. The SCED problem is formulated as a constrained optimization problem in a way that insures a secure- economic system operation. Real Coded Quantum-Inspired Evolution Algorithm (RQIEA) is then applied to solve the constrained optimization formulation. Simulation results of the proposed approach are compared with those reported in literature. The outcome is very encouraging and proves that RQIEA is very applicable for solving security constrained power system economic dispatch problem (SCED). possibly discontinuous. Both continuous and discrete variables may be involved in the problem. In this paper, a new optimization method is presented. The developed method is novel in the concept of searching mechanism to find the optimum. The proposed Real Coded Quantum-Inspired Evolution Algorithm (RQIEA) is based on quantum computing principles which incorporate the ideas stemmed from the behavior of quantum computing elements encapsulated in a structured evolution process. Quantum computing is based on several phenomena of the quantum world that are fundamentally different from those encountered in classical computing. Such phenomena are complex probability amplitudes, quantum interference, quantum parallelism, quantum entanglement and the unitary nature of quantum evolution. The main objective of this study is to introduce the proposed Real Coded Quantum-Inspired Evolution Algorithm to the subject of power system dynamic security with the most economical operating conditions. In this study RQIEA is employed to solve the security constrained economic dispatch problem (SCED). This new approach will be used to minimize the system objective cost function satisfying a set of constraints.

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