Optimal Coordinated Charging and Routing Scheme of Electric Vehicles in Distribution Grids: Real Grid Cases

The government policies and benefits led to the surge in penetration of Plug-in Hybrid Electric Vehicles (PHEVs) and Battery Electric Vehicles (BEVs) into both public and private sectors. Power Grids are dynamic with load and generation varying, and wide spread adoption of PHEVs can jeopardize and endanger the operation and security of the distribution grids due to overloading and congestion. This paper develops two optimization models, one is for the routing algorithm of Battery Electric Vehicles (BEVs) to find the minimum energy consumption for Personal Rapid Transit (PRT) system in a collegiate environment. The other optimization model is for charging a maximum number of PHEVs interfaced with American Electric Power (AEP) utility system. Mixed Integer Linear Programming (MILP) is used to determine the charging schedules of PHEVs to minimize the power system overloading. MILP is also used to find the optimum charging schedule of BEVs to satisfy the passenger demand of the transit system. Different charging strategies have been developed, and their effects on distribution system voltage profile and losses have been illustrated. We used the real-time data for the PRT routing algorithm. PHEV customers are categorized into different real-world Demand Response (DR) participants capable of flattening the load curve. A time series simulation of a distribution feeder test system is performed to show the feasibility of the proposed methods. Case studies on AEP system and West Virginia University transportation system were carried out. The simulation results demonstrate the effectiveness of our proposed optimization framework in reducing system peak load and satisfying the demand while utilizing minimum number of BEVs.

Automated summary

This study developed optimization models for PHEVs and BEVs, using MILP algorithm to design charging schedules to reduce power system overloading. The simulation results demonstrate the effectiveness of the proposed optimization framework in reducing system peak load.