Quadratic Interpolation Optimization for Planning of Renewable Energy-based DG Units in Power System Networks

Abstract

Optimal planning of distributed generation (DG) units within power system networks PSN is crucial for improving the reliability and quality of power delivery. This involves optimizing the size and location of DGs, considering both technical and operational constraints. This study proposes the Quadratic Interpolation Optimization (QIO) method, a mathematically inspired meta-heuristic algorithm, for the optimal planning and allocation of renewable energy (RE)-based DG units. The optimization problem aims to enhance the voltage profile and stability index of the network, minimize power losses, and maximize investment profitability. The QIO algorithm utilizes the principle that any three points can define a quadratic function, efficiently identifying the minimizer of that function and overcoming the limitations of traditional quadratic interpolation methods. To validate the proposed algorithm, simulations were conducted on the standard IEEE 69-bus radial distribution system. Various scenarios were analyzed by varying the type and number of DG units to evaluate their impact on network performance. Compared to existing methods, the QIO demonstrates superior efficiency in optimizing the allocation and sizing of RE-based DG units, achieving faster convergence and reduced computational time.