Electrical Vehicle Charging Impact on Distribution Feeder Model and Mitigation Techniques

Electric Vehicle (EV) charging is one of the largest growing electricity demand sectors that is being added into the electric grid. The bulk electric system, which will carry the majority of the current load, is a specific infrastructure which is regularly monitored for load changes. In contrast, distribution systems do not have the same supervision and therefore can be treated as a black box. The distribution system is important for stability of the grid and in order to predict how much EVs will impact the main grid, a simulator for a distribution line was created to determine substation transformer loading and line loading. In addition, four charging cases for the EVs were created to investigate different charging scenarios. Finally, load mitigation techniques were investigated to offer potential solutions for the overloading of aged infrastructure

iDR: Consumer and Grid Friendly Demand Response System

Peak demand is a major challenge for power utilities across the world. Demand Response (DR) is considered to be effective in addressing peak demand by altering consumption of end consumers, so as to match supply capability. However, an efficient DR system needs to respect end consumer convenience and understand their propensity of participating in a particular DR event, while altering the consumer demand. Understanding such preferences is non-trivial due to the large-scale and variability of consumers and the infrastructure changes required for collecting essential (smart meter and/or appliance specific) data. In this paper, we propose an inclusive DR system, iDR, that helps an electricity provider to design an effective demand response event by analyzing its consumers’ house-level consumption (smart meter) data and external context (weather conditions, seasonality etc.) data. iDR combines analytics and optimization to determine optimal power consumption schedules that satisfy an electricity provider’s DR objectives - such as reduction in peak load - while minimizing the inconvenience caused to consumers associated with alteration in their consumption patterns. iDR uses a novel context-specific approach for determining end consumer baseline consumptions and user convenience models. Using these consumer specific models and past DR experience, iDR optimization engine identifies -(i) when to execute a DR event, (ii) who are the consumers to be targeted for the DR, and (iii) what signals to be sent. Some of iDR’s capabilities are demonstrated using real-world house-level as well as appliance-level data