Charging coordination of Plug-In Electric Vehicles based on the line flow limits and power losses

Abstract

The increased popularity of Plug-In Electric Vehicles (PEVs) over the years has resulted in their large deployment across the traditional Electric Power Grid (EPG). Sudden addition of these highly intermittent, excessive capacity loads along with the increase in generation will result in potential stress, overloading of lines and efficiency degradation of the EPG. To overcome these potential issues, a coordinated PEV charging strategy is proposed in this paper to prevent the overloading of the lines and minimizing the power losses incurred by the random charging of the PEVs. Two factors referred as line flow and power loss sensitivity factors are derived from the Newton Raphson Jacobian to coordinate the charging activities of the PEVs. These sensitivity factors are used to identify the nodes and lines in the EPG that are prone to overload. Charging commands are sent to the identified nodes to assist in a coordinated charging. The proposed methodology isimplemented on a small radial IEEE 6 bus distribution system and its performance is demonstrated.

Electric Vehicle Charging Coordination on Distribution Network by Using Particle Swarm Optimization and Genetic Algorithm

“…There are some issues during PEVs charging coordination caused due to lines and busses inside the EDN can be classified into two types which arevoltage limit [2], [3] and unacceptable power losses [2]. This sectionhighlights the method to improve these issues.…”

“…These will result the line overload and efficiency degradation of grid. In order to overcome line flow and power losses issues, a coordinated PEV charging strategy is proposed in [2]. Thus, it can prevent the line from overloaded and hence minimizing the power losses.…”

Resilient Stochastic Control Strategies in Cyber-Physical Microgrids

“…Once the EVs are accurately modeled, their effects on distribution systems can be analyzed. Some researchers in [30][31][32][33][34] evaluate solutions for power quality problem with EVs. In this work, we model optimal charging and discharging and evaluate the best location for power quality.…”

“…Using a genetic algorithm, the proper place for EV parking lots and optimum capacity for each parking were found to reduce the power loss in [33]. In [34], charging coordination is done based on the line flow limits and power loss. Harmonic problems due to EVs are investigated in [35].…”