Optimal allocation strategy of photovoltaic- and wind turbine-based distributed generation units in radial distribution networks considering uncertainty

This paper proposes an improved version of the artificial ecosystem-based optimization (AEO) algorithm called artificial ecosystem-based optimization–opposition-based learning (AEO-OBL), with the aim of improving the performance of the original AEO. In addition, it is utilized for determining the optimal allocation of distributed generation (DG) units in radial distribution networks (RDNs) with the aim of minimizing power and energy losses. The stochastic nature of renewable DGs such as wind turbine and photovoltaic generation is taken in consideration using appropriate probability models. The Loss Sensitivity Index is used to assess the most suitable busses for the integration of DG units in the RDN. AEO is nature-inspired optimization algorithm which imitates the flow of energy in an ecosystem on earth. In the proposed AEO-OBL, the search ability and the balance between the exploration and exploitation phases in the original AEO are enhanced. In the AEO-OBL, five efficient strategies are used to avoid falling on a local optimal: (1) enhanced linear weight coefficient a, (2) production operator, (3) modified consumption operator, (4) modified decomposing operator and (5) opposition-based learning (OBL). The performance of the proposed technique is validated on IEEE 33-bus and 85-bus RDNs. To emphasize the superiority of the proposed technique, the results are compared with the original AEO, Henry gas solubility optimizer (HGSO) and Harris hawks optimization (HHO) algorithm results. Besides, the developed algorithm is compared with other optimization algorithms in literature that solved the same problem. The outcomes indicate a better performance of AEO-OBL relative to other algorithms. Accordingly, AEO-OBL can be a very suitable algorithm in solving the problem of optimal DG allocation in RDNs.