IoT Remote Control of Plug-in Electric Vehicle Charging Loads for Smart Energy Management of Virtual Power Plants

The grid integration of plug-in electric vehicles (PEVs) requires a comprehensive analysis and effective control strategies to prevent any violation of the electrical constraints of the power systems. When renewable energy sources (RESs) are available, they can be used to supply the energy demand of the PEVs. In this study, a photovoltaic (PV) power plant and IoT based remote control of PEV charging are proposed as a virtual power plant (VPP). Particularly, the concept for the use of LoRaWAN to remotely monitor and control the PEV charging loads is presented. Moreover, a hierarchical VPP control (VPPC) system based on the model predictive control (MPC) method is proposed for the optimal energy management (EM) of the VPP. The EM of the VPP is scheduled based on the predicted information initially and based on the optimal values for the EM and the intermittency of PV generation, the charging rate of the PEVs is modified during the real-time process. The results of the case studies show that the proposed VPPC can minimize the energy costs to the VPP and satisfy the energy balancing for the VPP entities while fulfilling the charging demand of the PEVs under system uncertainties.