Introduction of adaptive clonal differential evolution in allocation and sizing of renewable-energy distributed generation units in distribution networks

Renewable-Energy Distributed Generation units (REDGs) that are installed in distribution network offer a lot of advantages in terms of technical, economic and environmental benefits. However, REDGs have both positive and negative effects depending on their sizes and locations. Thus, the purpose of this study is to determine the optimal locations and sizes of REDGs that attains fuel consumption reduction and system reliability improvement while satisfying various constraints and considering relevant uncertainties. To optimize the locations and sizes of REDGs, Adaptive Clonal Differential Evolution (ACDE) is proposed to improve the performance of Clonal Differential Evolution (CDE) by updating the control parameters in an adaptive manner. Previously, CDE with randomized scaling factor was introduced. CDE algorithm is capable of enhancing the exploration and searching ability, hence accelerates the convergence of the algorithm. However, the randomized scaling factor does not guarantee the robustness of the algorithm. Therefore, control parameter adaptation that utilizes collected data is introduced to favour providing information on good parameter values. The proposed algorithm is verified on a 33-bus test system. The comparative studies are carried out and the simulation results show that the proposed algorithm is more stable and robust than CDE.