Vehicle-to-Grid (V2G) describes a system in which plug-in electric vehicles, such as Battery Electric Vehicles (BEV), Plug-in Hybrid Electric Vehicles (PHEV) communicate with the power grid to sell demand response services by either returning electricity to the grid or by throttling their discharging rate.V2G storage capabilities can also enable EVs to store and discharge electricity generated from renewable energy sources such as solar and wind. When the vehicles are parked in a parking-slot, the power can be sent from Vehicle to the DC bus-bar or to the grid. From each Electric-vehicle the voltage level and State of Charge (SOC) information is uploaded to the CLOUD on a regular basis. Fuzzy-Logic shall determine the participation factor/ranking of each vehicle and is communicated to the CLOUD. The Central Controller will receive the uploaded data and depending upon the load requirement the best vehicle(s), to be connected to the grid, is/are shortlisted. The selection information is then informed to the respective electric vehicle, through cloud, so that power is sent from V2G.

Load forecasting is of vital importance for any power system. It helps in taking many decisions regarding energy purchasing and generation, maintenance, etc. Further, load forecasting provides information which is able to be used for energy interchange with other utilities. Over the years, a number of methods have been proposed for load forecasting. This paper focuses on short term load forecasting by using a hybrid model of neural networks and fuzzy logic.

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In deregulated market environment, congestion management plays an important role in power system operation. An approach of applying Demand Response (DR) programs has been used for transmission line congestion management in a deregulated power system. In this paper DR is modelled considering Time of Use (TOU), Critical Peak Pricing (CPP) using MATPOWER software. The paper evaluates DR effects on the generating companies, consumers, merchandising surplus, power system security and operating cost in addition to the congestion management. The proposed models are implemented on IEEE-14 bus system.