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The transition from centralized power grids to microgrids (MGs) has facilitated the increased integration of renewable energy sources (RES) to the power systems. However, the operational cost in MGs is highly dependent on planning decisions. Therefore, the optimal sizing and placement of RES will offer significant savings in operational costs and reduce power losses in the system. This study proposes a new technique for optimal sizing and placement of solar capacity in the electrical network to minimize the operational cost of conventional generators and real power losses. Due to the complexity and non-linearity associated with the problem, a new natural inspired optimization technique called lightning search algorithm (LSA) is developed to find the optimal solution. To assess LSA, differential evolution algorithm is also used to solve the same problem and a comparison is conducted to prove the superiority of LSA in reducing computation time. The optimization problem is applied to IEEE 14- and 30-bus systems with 24-hr load and solar profile. The obtained results verify the effectiveness of the proposed LSA approach to obtain the optimal sizing and placement of RES and reduce operational cost and power losses. [ABSTRACT FROM AUTHOR]

—In this article, a new nature-inspired metaheuristic technique called the differential search algorithm is proposed to solve the optimal power flow problem. The proposed differential search algorithm has been developed and tested under normal and contingency power system conditions. To show the effectiveness of the proposed method, it has been demonstrated on the standard IEEE 30-bus and IEEE 118-bus test systems with different objectives that reflect performance indices of the power system. Obtained results using the proposed technique indicate that the proposed differential search algorithm provides an effective, a robust, and a high-quality solution for the optimal power flow problem. The comparisons of the proposed differential search algorithm results with those reported in the literature reveal the potential and superiority of the proposed algorithm in terms of the optimal solution quality and robustness. [ABSTRACT FROM AUTHOR]