Optimal planning of electric vehicle charging stations and distributed generators with network reconfiguration in smart distribution networks considering uncertainties

This paper proposes an optimal planning technique for placing the multiple renewable energy (RE) based distributed generators (DGs), Distribution Static Compensators (DSTATCOMs), and electric vehicle charging stations (EVCSs) in the radial distribution network (RDN) considering the related uncertainties. This approach gives optimal placement and sizes for DGs and DSTATCOMs as well as a number of electric vehicles (EVs) that can be charged at the EVCSs by considering the network reconfiguration (NR). The optimal allocation of EVCSs fulfills the power demand from EVs at various locations and minimizes the negative impact on the power network. The RE-based DGs considered for this work are solar photovoltaic (PV) and wind. The uncertainties related to RE-based DGs and EVCSs have been modeled by using the probabilistic-based two-point estimate method (2PEM). The best locations and sizes are identified by optimizing the individual objectives that is active power losses and voltage stability index (VSI) using the teaching learning based optimization (TLBO) algorithm. Then both objectives are optimized by using the non-dominated sorting-based TLBO algorithm. Furthermore, the optimal planning approach is implemented on IEEE 33 and 69 bus test systems to demonstrate the suitability, practicality, and efficiency of the proposed optimal planning strategy. The obtained results reveal that the proposed technique is beneficial for determining the optimal locations for DGs, DSTATCOMs, and EVCSs without affecting the grid stability. The proposed planning approach can search better network structure with reduced power losses and voltage deviation, enhanced voltage profile, and improved voltage stability.