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In a smart grid environment, the demand response simulation and management is a crucial factor. For this purpose, a distributed framework that operates efficiently on the Internet of Things-based smart grid environment is presented in this paper. A universal collaboration software approach is used for exploiting the IoT-based smart grid environment for validation of demand response policies while enabling real-time simulation and management. The structure of the framework may be retained intact while enabling plug-and-play-based demand response policies and emulating the model in real-time power systems in a practical manner. In addition to the smart grid, the smart devices that are connected to the internet at the premise of the customer are integrated with the solution for transmission of actuation commands and retrieval of energy data. These features emphasize the demand response management capability of the proposed framework in a real-time smart grid environment. A demand response policy test case is used for verification of the performance of the proposed model in a smart grid environment [ABSTRACT FROM AUTHOR]

When imaging a target in space, atmospheric turbulence might change the direction of the path of light. This might be caused by the random motion of the turbulent medium leading to drastic distortion of the image. In order to correct the temporally varying blur, reduction spatially, and geometric distortion, a blind deblurring atmospheric turbulence based on the convolutional network is proposed. This includes image reconstruction subnetwork, asymmetric U-net, and feature extraction noise suppression block (FENSB). In the U-net, instead of the traditional convolution layer that is normally used, a deblurring noise suppression block (DNSB) is used. The basic idea behind this methodology is to reduce the noise before deblurring occurs. Hence the aspect of noise suppression before deblurring is carried out by the DNSB block along with FENSB. At the time of convolutional encoding, DNSB and FENSB are used to identify and extract the rich features of the maps and further suppress noise. In order to fuse high-level and low-level features, the subnetwork is skip connected with FENSB thereby improving the integrity of the system during the process of reconstruction. This mechanism of increasing the training difficulty of the network in a step-by-step manner is used to slowly converge into a complex model facilitating better quality of images despite the turbulence. The results of simulation data, as well as that of real data, indicate that it is possible to suppress the noise and restore the image with high efficiency. [ABSTRACT FROM AUTHOR]

Reliability, efficiency, and cost-effectiveness of smart grids are enhanced with power demand softening by means of efficient load management in electric vehicles. In such initiatives, the involvement of EV users may reduce due to the lack of adaptable user-centric approaches. During the connection sessions, the EV charging time is determined using a deep learning algorithm-based smart charging strategy proposed in this paper. Here, the total energy cost of the vehicle is minimized by making charging decisions considering demand time series, pricing, environment, driving, and other auxiliary data. The memorization technique is used for the estimation of the optimal solution of the existing connection sessions in the initial stage. The deep learning models are trained with this existing data and optimal decisions to make suitable decisions in real-time scenarios where car usage or future energy price values are undetermined. A significant reduction in the charging cost is observed by training the neural network with the proposed model. The results obtained are compared to the optimal charging costs computed and are found to be closely similar. [ABSTRACT FROM AUTHOR]