CHARGING OF PLUGIN ELECTRICAL VEHICLE USING MATLAB

Stage  1: PEV charging in a conventional distribution grid

Task 1: Understanding basic battery charging parameters.

In Task 1, we dealt with the charging process of a single PEV to familiarize with the basic concepts about battery charging. We selected different values of the available power from the grid, the battery charging rate and capacity, the initial state of charge and the user arrival and departure times in order to evaluate when the user is actually satisfied in terms of state of charge at the departure time.

 

Task 2: A real case of study: multiple charging requests in a conventional electrical grid (The “superdumb” approach).

In Task 2 we simulated the evolution of a local node of the distribution grid in which six PEVs have to be charged by using the so-called “superdumb” approach. In this case, the electrical grid is a conventional one, without any additional feature to support specific procedures or strategies to manage the vehicles charging. No communication is then possible between the PEVs and the utility in order to adapt the charging process to the grid load conditions. This means that the charging process of a PEV starts when the vehicle is plugged in and it is performed at the maximum charging rate allowed by the battery (provided that enough power is available from the grid at the time of plug-in). In the case when the power request exceeds the available power at that time, the charging request is rejected and the user cannot charge his battery. We performed several analyses by considering different realistic user profiles in terms of battery capacity, charging rate, arrival and departure times.

Task 3: Simple managing strategies for multiple charging requests in an electrical grid (The “dumb” approach).

In Task 3 we used the “dumb” approach, which is still a First-Come-First-Served strategy in which the charging process of a PEV is performed at the maximum charging rate, provided that the grid is capable of supplying the required power. However, in the “dumb” scenario, users can communicate to the grid manager their arrival and their (expected) departure times, the required energy to complete the charge as well as the instant at which they are actually fully charged. Thanks to this information exchange, a kind of negotiated charging can be implemented: if a user cannot be served at the time of arrival, its charging request is not completely rejected, but just queued until sufficient power becomes available (obviously, if power is available before the user departure time).