Internet of energy-based demand response management scheme for smart homes and PHEVs using SVM

The usage of information and communication technology (ICT) in the power sector has led to the emergence of smart grid (SG). The connected loads in SG are able to communicate their consumption data to the grid using ICT and thus forming a large Internet of Energy (IoE) network. However, various issues such as-increasing demand-supply gap, grid instability, and deteriorating quality of service persist in this network which degrade its performance. These issues can be handled in an efficient way by managing the demand response (DR) of different types of loads. For this purpose, cloud computing can be leveraged to gather the data generated in IoE network and perform analytics to manage DR. Working in this direction, a novel scheme to handle the DR of smart homes (SHs) and plug-in hybrid electric vehicles (PHEVs) is presented in this paper. The proposed scheme is based on analyzing the demand of these users at the cloud server for flattening the overall load profile of grid. This scheme is divided into two hierarchical stages which work as follows. In the first stage, the residential and PHEV users are identified whose demands can be regulated. This task is achieved with the help of a binary-class support vector machine (SVM) which uses Gaussian kernel function to classify these users. In the next stage, the load in SHs is curtailed on the basis of a pre-defined rule-base after analyzing the consumption data of various devices; whereas PHEVs are managed by controlling their charging rates. The efficacy of proposed scheme has been tested on PJM benchmark data and Open Energy Information dataset. The simulation results prove that the proposed scheme is effective in maintaining the overall load profile of SG by managing the DR of SHs and PHEV users. (C) 2018 Elsevier B.V. All rights reserved.