Abstract
A Virtual Power Plant (VPP) is a network of distributed power generating units, flexible power consumers, and storage systems. A VPP balances the load on the grid by allocating the power generated by different linked units during periods of peak load. Demand-side energy equipment, such as Electric Vehicles (EVs) and mobile robots, can also balance the energy supply-demand when effectively deployed. However, fluctuation of the power generated by the various power units makes the supply power balance a challenging goal. Moreover, the communication security between a VPP aggregator and end facilities is critical and has not been carefully investigated. This paper proposes an AI-enabled, blockchain-based electric vehicle integration system, named AEBIS for power management in a smart grid platform. The system is based on an artificial neural-network and federated learning approaches for EV charge prediction, in which the EV fleet is employed as a consumer and as a supplier of electrical energy within a VPP platform. The evaluation results show that the proposed approach achieved high power consumption forecast with $\mathit {R^{2}}$ score of 0.938 in the conventional training scenario. When applying a federated learning approach, the accuracy decreased by only 1.7%. Therefore, with the accurate prediction of power consumption, the proposed system produces reliable and timely service to supply extra electricity from the vehicular network, decreasing the power fluctuation level. Also, the employment of AI-chip ensures a cost-efficient performance. Moreover, introducing blockchain technology in the system further achieves a secure and transparent service at the expense of an acceptable memory and latency cost.
Highlights
In recent years, the utilization of renewable resources in the energy matrix has been increasing
EVALUATION METHODOLOGY The data set for the power consumption prediction contains the features of weather, geography, and user information as explained in the previous section
The results indicate that our FLbased network could provide an accurate prediction for Electric Vehicles (EVs) power consumption, which is necessary for either upcoming electricity supply or EV reservation
Summary
The utilization of renewable resources in the energy matrix has been increasing. Variants of renewable resource providers, e.g., wind power [2], photovoltaic power [3], and hydro-power [4], serve as the power suppliers and transmit electrical energy from the generating sites to a power grid [5]. The power grid distributes electric power to all the consumers, including residential areas, hospitals, commercial areas, administrative areas, and Electric Vehicle (EV) fleets. Aiming to achieve efficient dispatch and utilization of renewables, the Virtual Power Plant (VPP) has been proposed to act as an intermediary between distributed energy resources (DERs), power grid, controllable loads, and EVs [6]–[9]. The current VPP demonstrations target efficient integration and distribution of resources.
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