Applied Chemical Engineering (Transferred)

To address the adverse impacts due to rapid growth of electric vehicles (EVs), a robust planning framework is developed in this paper for optimal deployment of EV charging stations and solar energy resources in the distribution network. Uncertainty modeling of EV is done using probability density function considering stochastic parameters extracted from real National Household Travel Survey (NHTS)datasheet. Considering solar irradiance as the uncertainty parameter, a practical Photovoltaic (PV) model is developed using beta probability function. To solve the problem of optimal allocation of EV charging stations and PV in the distribution network, proposed Teaching Learning Based Optimization algorithm is used. The problem is formulated to minimize the power loss reduction index and the voltage deviation index while considering system constraints. Here this proposed approach is tested to Indian 28 bus rural distribution network and standard IEEE 69 bus system in MATLAB. Also to assess the efficiency of the proposed technique, it is compared with three different algorithms, i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) in terms of convergence characteristics and computational time. The system indices, i.e., voltage profile, line loss, voltage stability and the penetration level of EV charging station are improved after simultaneously optimally deploying EV charging station and PV units both in rural and standard 69 bus distribution networks. Different case studies were conducted and it was observed that deployment of EV charging station in the network leads to deterioration of voltage profile, voltage stability and line loss. The simulation outcome further reveals that the addition of PV panels concurrently with EV charging stations enhances the system performances and the penetration level of EV charging station in the network.

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