Time-of-Use tariff rates estimation for optimal demand-side management using electric vehicles

The exponential growth of electric vehicles (EVs) has raised the electricity burden that may resolve through demand-side management (DSM). DSM restructure the power system that allows sustainable development without substantial expansion in the smart grid (SG). DSM with EVs is in the preliminary stage, relying on existing advanced metering infrastructure (AMI) to enable diverse motivational techniques. Amongst various schemes, Time-of-Use (ToU) price-based mechanism is the most accepted, where tariff rates vary with the day timing. However, determining the tariff rates is significant to motivate EV prosumers for efficient DSM. The paper proposes a methodology for ToU tariff estimation to provide optimal DSM using EVs with big data technology. The NoSQL database allows the accumulation of historical and real-time data with the computational environment for electric power. A novel mathematical model calculates the tariff rates using EVs' peak and off-peak contribution coefficients. Besides, conditional prioritization is presented based on EVs' State-of-Charge (SoC) to mitigate the simultaneous charging of numerous EVs. In the simulation, the aggregator (AG) manages the data from multiple internet-of-thing (IoT) based smart net meters with the proposed computational facility. Results demonstrated with realistic data have effectively reduced the peak consumption by 6%-7% with an elasticity of 0.45.

Automated summary

The exponential growth of electric vehicles has led to an increased electricity burden, which can be addressed through demand-side management. This paper proposes a methodology for estimating Time-of-Use tariffs using big data technology to enable optimal demand-side management using electric vehicles. Simulation results show that the proposed method effectively reduces peak consumption by 6%-7%.