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Electric vehicle (EV) demand is increasing day by day raising one of the major challenges as the lack of charging infrastructure. To reduce the carbon footprint, countries are pushing for the rapid growth of the renewable energy to be used as the source of charging station. In this paper, an optimized battery energy storage system (BESS) integrated with solar PV in a charging station is designed for the overall benefit of the system. Particle swarm optimization (PSO) is used to determine the optimal cost of the battery based on the parking area capacity, PV generation capacity, the load connected to the solar PV system and the availability of the EVs. An optimum rate of charging / dis-charging of the BES will ensure a prolonged life cycle of the battery. Also, artificial neural network (ANN) is used to predict day ahead PV generation and load demand, and hence the battery power, in order to design the storage capacity of BES to satisfy the maximum demand.

Wind energy is one of the fastest growing energy resources of alternative power generation across the globe. Maximum number of generators in wind farm is provided with Doubly Fed Induction Generator (DFIG) due to its many advantages. The effects on small signal stability of system with distributed additional load, equipped with DFIG based wind farms, is presented here. The additional load added in the existing system is made same in value as the power generated by the wind farm. Different distributions of additional load are considered to determine the small signal stability of system with varying wind power penetration. The position of the negative real part of eigenvalues of system matrix helps to determine the best location for the integration of wind power for particular load distribution of additional load among different load buses. A new sensitivity based index has been introduced here to determine the maximum penetration level in terms of the system small signal stability. WSCC 3-machine 9-bus system is used for the study which is carried out in MATLAB.