Hardware-in-the-Loop Simulation of Controlled and Uncontrolled EV Charging in a Distribution Grid

The charging of a rapidly increasing number of EVs places significant strain on electrical distribution grids. Controlled (’smart’) charging of EVs offers a solution to this problem by shifting the charging of vehicles to the most optimal moments in time. Smart charging is not a completely new research topic; it has a high Technology Readiness Level and some commercial implementations already exist. There is a large amount of literature available that compares the effect of controlled and uncontrolled EV charging on the distribution grid. However, most of this literature is based on pure software simulations using power flow analysis and mathematical models of EV charging behaviour. Experimentally validating these results would require a large number of EVs and a controllable distribution grid, which is costly and unpractical. Therefore, this thesis presents a compromise; a hardware-in-the-loop (HIL) testbed in which the distribution grid is software-based but the EV and EV Supply Equipment (EVSE) is hardware-based. In this report, the design and implementation of the testbed is discussed, as well as results from HIL simulations that compare the effect of controlled and uncontrolled EV charging in a distribution grid. The low-cost HIL testbed presented in this thesis is based upon an OPAL-RT OP5700 Digital Real-Time Simulator (DRTS) which runs a Newton-Raphson power flow analysis of a Dutch distribution grid. The DRTS also interfaces with all other hardware in the testbed. This includes a power amplifier which generates a three-phase grid at the voltage calculated by the power flow analysis. A commercially available EVSE is connected to this power amplifier and supplies power to an emulated EV which consists of communication hardware and two bidirectional back-to-back DC power supplies. These power supplies act as an AC load. Various communication protocols were implemented to exchange information between the different systems in a manner that closely represents commercial EVs. A smart charging algorithm which determines the optimal charging current setpoints in real-time, based on external factors like local load power consumption, solar irradiance and energy prices, was implemented into the testbed. The testbed has subsequently been used to study the effect of uncontrolled and controlled charging on the simulated distribution grid for a total of eight different scenarios. Of these scenarios, one consists of traditional uncontrolled charging and the other seven are with the smart charging algorithm activated. These include firstly a base case, then two scenarios with an inaccurate solar irradiance and local load forecast, a further two scenarios with non-ideal EV charging behaviour, and lastly two scenarios with respectively centralized and decentralized curtailment implemented. In all scenarios, one EV was implemented as hardware and three others as software, so that a comparison could be made. Even under non-ideal conditions, smart charging is found to reduce costs, grid overloads, and voltage deviations with respect to uncontrolled charging. The implemented centralized curtailment is shown to be more effective than decentralized and further reduces voltage deviations in the grid but at a slightly higher cost than without this curtailment. Lastly, based on observations made in the HIL simulation results, recommendations for future work and improvement of the smart charging algorithm are given