FLEXIBLE OPTIMIZATION ALGORITHM FOR THE OPTIMAL POWER FLOW PROBLEM
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Optimal Power Flow (OPF) is a large-scale and nonlinear programming problem with a non-convex feasible region minimizing the total production cost of the system subject to some technical constraints and power balance equalities. With increasing complexity in the control of power systems, this work is motivated to develop an optimization algorithm with an efficient centralizing scheme. Since the problem is non-convex, solving it is computationally heavy, time consuming and hence making it difficult to converge to a global solution. A modified version of the Gauss-Seidel Algorithm which is experimentally proven to have a fast rate of convergence with less error is implemented. The framework comprises of an algorithm for the sequential optimization of the non-convex problem that is AC OPF. The AC OPF problem is mathematically modelled to be solved by an interior point method based MATLAB solver called fmincon. Although the objective function is a convex quadratic function, the non-convexity of the power balance and the power flow constraints needed to be approximated. A linearization ‘restriction’ scheme is used to convexify the constraints in this paper. One of the features of the framework is that the degree of updating and hence acceleration towards optimal solution can be chosen by the user. This allows for the rise of new algorithms that also has an important feature of these algorithms is that the exact solution need not be calculated saving on computation time and memory.