Fig. 1: Simulation approach to quantify the costs of reinforcing distribution grids.
From: How grid reinforcement costs differ by the income of electric vehicle users
This flowchart illustrates the elements of our simulation model approach labeled by numbers in the order of execution as described in the following. For both neighborhood types, we first populate the grids with the related income group (above- or below-average-income households) by sampling from the household distributions of each neighborhood and grid type. We then assign the related electric vehicle (EV) adoption level and model choice by randomly allocating EVs depending on the income group’s EV adoption and model choice distribution. As a third step, we model the driving patterns for each EV depending on the income group by a time-inhomogeneous Markov chain simulation. Next, we derive the EV charging loads resulting from the EV driving patterns based on the charging probability as well as future car trips planned. We empirically sample representative household electricity load profiles on a household level. We consolidate the EV charging loads and household electricity load profiles by adding up all loads at the house level. Via a power flow analysis, we analyze if overloads occur and reinforce the respective overloading grid element. Lastly, we calculate grid reinforcement costs for each grid and neighborhood type to resolve these overloads.
