INTERNATIONAL JOURNAL of RENEWABLE ENERGY RESEARCH P. V. K. Babu and K. Swarnasri, Vol.10, No.1, March, 2020 Multi-Objective Optimal Allocation of Electric Vehicle Charging Stations in Radial Distribution System Using Teaching Learning Based Optimization Ponnam Venkata K Babu*‡, K. Swarnasri** * Department of EEE, Research Scholar, Acharya Nagarjuna University, Guntur, AP, India ** Department of EEE, RVR & JC College of Engineering, Chowdavaram, Guntur, AP, India (kishore.ponnam@gmail.com, swarnasrik@gmail.com) ‡ Corresponding Author; Ponnam Venkata K Babu, Department of EEE, Research Scholar, Acharya Nagarjuna University, Guntur, AP, India. Tel: +91 9494414529, kishore.ponnam@gmail.com Received: 23.01.2020 Accepted: 04.03.2020 Abstract- In recent times, the adoption rate of Electric Vehicles (EVs) in the transportation sector has been increased significantly across the world towards sustainability. On the other side, the increasing EV load penetration in an electric power sector can cause for the generation-demand imbalance, real power loss increment, poor voltage profile, and consequently voltage stability margin decrement. To mitigate the impact of increasing EV load penetration on radial distribution systems (RDS), it is essential to integrate EV Charging Stations (CSs) at appropriate locations. In this paper, the teaching-learning based optimization (TLBO) algorithm is applied to determine the optimal locations of EV-CSs considering the objectives minimization of real power loss and average voltage deviation index and maximization of voltage stability index. The simulation studies are performed on standard IEEE 33-bus and 69-bus test systems. The results have highlighted the need for optimal allocation of EV-CSs for maintaining the system performance as better as possible even under increased loading conditions due to EV-CSs. Also, TLBO has shown its ability over other heuristic algorithms namely particle swarm optimization (PSO), ant lion optimizer (ALO),flower pollination algorithm (FPA) and cuckoo search algorithm (CSA) by providing the optimal value consistently in solving the complex non-linear multi-objective optimization problem. Keywords Electric vehicles, charging stations, optimal allocation, multi-objective optimization, TLBO algorithm, radial distribution system. 1. Introduction In view of increasing carbon footprints due to conventional energy (CE) generation sources and petroleum- based transportation, sustainable measures such as integration of renewable energy sources (RES) in the energy sector and adoption of electric vehicles (EVs) for transportation have been focused significantly across the world. According to global EV outlook 2019, International Energy Agency (IEA), the E-mobility is expanding at a rapid pace. In 2018, the global electric car fleet was exceeded 5.1 million and the growth was almost double as compared with 2017 statistics. Under this scenario, it is essential to provide the required infrastructure such as charging stations (CSs), parking lots (PLs), battery swapping stations (BSSs), energy storage systems (ESSs) and energy balance using RES etc in the existing electric distribution networks (EDN) and also performance evaluation for better reliable and secured operation. The performance improvement of EDN is handled effectively via optimally allocating renewable based distribution generation (DG) in the past 2 decades. In [1], voltage stability factors (VSFs) and Flower Pollination Algorithm (FPA) are proposed to optimally allocate solar photovoltaic distribution generation (DG) considering loss minimization and voltage stability maximization. In [2], the