LVCI approach for optimal allocation of distributed generations and capacitor banks in distribution grids based on moth–flame optimization algorithm

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In recent years, due to growth in electricity demand, distribution networks suffer from increased power losses, decreased voltage levels, and increased power quality problems. The distributed generations (DGs) and shunt capacitor banks (SCBs) are considered the convenient sources of active and reactive power compensation in distribution networks, respectively. Moreover, the optimum allocation of DGs and SCBs plays a sufficient role in improving voltage profile and voltage stability that lead to ameliorate power quality and minimize the system power losses. In this respect, this article provides a novel efficient and vigorous moth–flame optimization (MFO) algorithm for solving the optimization problem of DGs and SCBs allocation. Furthermore, a loss–voltage–cost index (LVCI) approach has been incorporated into the proposed optimization methodology as an effective objective function to enhance the voltage profile and minimize the system power losses and the total annual operating cost. Moreover, the proposed scheme is implemented in two stages. In the first stage, the most candidate buses for installing DGs and SCBs are evaluated using loss sensitivity factors (LSFs). In the second stage, the MFO optimization algorithm is implemented to estimate the optimal placement of DGs and SCBs besides their sizing from the nominated buses based on LVCI as the main objective function. The suggested scheme has been tested on 33-bus and 69-bus IEEE standard radial distribution networks with different load levels. Furthermore, it is applied on a practical case study of Moscow region network that consists of 111-bus radial distribution network under different load levels. To insure the validation and accuracy of the proposed algorithm, the acquired results have been compared with other methods and techniques. The numerical results proved that the suggested optimization scheme has notability with high accuracy to estimate the optimal solution of DGs and SCBs allocation for minimizing the system power losses, enhancing the voltage profile and maximizing the net savings as compared to other techniques.