An Optimized Smart Grid Solution for Charging and Discharging Services in Cloud Computing using Genetic Algorithm with ANN
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2019 by IJCTT Journal|
|Year of Publication : 2019|
|Authors : Harpreet Singh, Pushpraj Kaushik|
|DOI : 10.14445/22312803/IJCTT-V67I5P132|
MLA Style:Harpreet Singh, Pushpraj Kaushik"An Optimized Smart Grid Solution for Charging and Discharging Services in Cloud Computing using Genetic Algorithm with ANN" International Journal of Computer Trends and Technology 67.5 (2019):193-201.
APA Style: Harpreet Singh, Pushpraj Kaushik (2019) An Optimized Smart Grid Solution for Charging and Discharging Services in Cloud Computing using Genetic Algorithm with ANN International Journal of Computer Trends and Technology, 67(5), 193-201.
Smart Grid (SG) scheduling is a most important research apprehensive in grid computing, a skill that enables resource virtualization, on require provisioning and resource allotment between organizations. In the SG, there is no need to be concerned about the whereabouts from where the vehicles energy you are using comes from, yet when you plug your vehicles into the SG system it acquires the energy, which the profession needs to acquire it done. SG scheduling is the strength of character of the optimal use of such a magnificent system. On the way to minimize the energy consumption and plug-in time, an optimization technique based SG solution performed for charging and discharging services in cloud computing environment using the concept of Artificial Neural Network (ANN), which focuses on the efficiency of scheduling in SG environment by utilizing Artificial Intelligence (AI) algorithms to address the smart usage of grid resources and to ensure an overall good balance in the system with healthy reduction in plug-in time and motivate the users to get a comfortable level of quality of service. On the additional side, the appearance of Electric Vehicles (EVs) promises to capitulate numerous benefits to both energy and transportation industry divisions. In this research try to solve the problem of plug-in EVs at public supply stations (EV-PSS) using the Genetic Algorithm (GA) with ANN architecture for SG as an optimization technique which helps to optimized the vehicles properties using fitness function and also it is a novel approach to solve these type of issues based on cloud computing. For real supply energy scenario, an extensive simulation performed and compared it with other relevant datasets (recent datasets) to propel the need to provide intelligent systems in SG based cloud-computing environment. The observations based on GA with ANN, the various result parameters improved as compare to previous datasets such as approx. 0.3% in Plug-in Time, approx. 0.7% in charging & discharging Time, approx. 0.5% in Energy Demand. Further, Simulation results demonstrate the effectiveness of the improved results when considering real EVs charging-discharging loads at peak-hours periods.
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Smart Grid, Electric Vehicles, Artificial Intelligence, Energy management, Genetic Algorithm, Artificial Neural Network (ANN), Cloud Computing.