Efficient optimization technique for multiple DG allocation in distribution networks

Abstract In the last few decades, interest in the integration of Distributed Generators (DGs) into distribution networks has been increased due to their benefits such as enhance power system reliability, reduce the power losses and improve the voltage profile. These benefits can be increased by determining the optimal DGs allocation (location and size) into distribution networks. This paper proposes an efficient optimization technique to optimally allocate the multiple DG units in distribution networks. This technique is based on Sine Cosine Algorithm (SCA) and chaos map theory. As any random search-based optimization algorithm, SCA faces some issues such as low convergence rate and trapping in local solutions during the exploration and exploitation phases. This issue can be addressed by developing Chaotic SCA (CSCA). CSCA is mainly based on the iterative chaotic map which used to update the random parameters of SCA instead of using the random probability distribution. The iterative chaotic map is applied for single and multi-objective SCA. The proposed technique is validated using two stranded IEEE radial distribution feeders; 33 and 69-nodes. Comprehensive comparison among the proposed technique, the original SCA, and other competitive optimization techniques are carried out to prove the effectiveness of CSCA. Finally, a complete study is performed to address the impact of the intermittent nature of renewable energy resource on the distribution system. Hence, typical loads and generation (represented in PV power) profiles are applied. The result proves that the CSCA is more efficient to solve the optimal multiple DGs allocation with minimum power loss and high convergence rate.