Optimal allocation of EV charging spots and capacitors in distribution network improving voltage and power loss by Quantum-Behaved and Gaussian Mutational Dragonfly Algorithm (QGDA)

•Optimal planning of charging stations (CS) along with capacitors (CAP).•Proposed technique is quantum-behaved Gaussian mutational DA (QGDA).•Parking lot and capacitor allocation is suggested for congestion management.In order to optimally determine the parking lot size, QGDA is utilized. In this paper, a new methodology for optimal planning of charging stations (CS) along with capacitors (CAP) using proposed technique is presented. In order to achieve a better balance between exploration and exploitation for dragonfly algorithm (DA), the quantum-behaved and Gaussian mutation strategies on the performance of DA is used. This is the novelty of our proposed work. Hence, it is named as quantum-behaved Gaussian mutational DA (QGDA). Here, parking lot and capacitor allocation is suggested for congestion management along with reactive power compensation. In order to optimally determine the parking lot size, QGDA is utilized. The effectiveness of the proposed technique is tested on adapted IEEE 34-bus distribution network. The result attained by QGDA technique is compared with existing techniques such as PSO and BBO. With PL, the proposed technique achieves the Ploss and Qloss of 25.61 kW and 25.99KVar. PL and C at same node, the proposed technique achieves the Ploss and Qloss of 31.09 kW and 32.88KVar. PL and C at different node, the proposed technique achieves the Ploss and Qloss of 25.09 kW and 26.98KVar. Furthermore, the proposed results show that an uneven EV charging scenario can cause significant voltage unbalance that goes beyond its allowed limit of 2%.