[UC04] Transformer Capacity Analysis for EV Charging
Identifying how much EV Charging infrastructure can be accommodated given transformer load capacity at given times.
The Utility Problem
As electricity gains a more significant share of overall energy consumption through the electrification of transportation, heating and cooking, the electricity distribution grid experiences more strain. One of the specific culprits is a rapid rise in (Electric Vehicle) EVs and the consequent need for EV charging. In the distribution grid, each of the grid elements has specific properties. For instance, transformers have a certain “capacity” (or kVA rating) which dictates the maximum electrical load they can support at any given time. This, along with the existing electrical load, restricts how many new EV chargers (as well as other loads) can be installed “downstream” of a given transformer without causing it to “overload .”Load is typically not directly measured at the transformer but at each consumer location downstream of it (often modelled via meters). Therefore existing transformer load needs to be aggregated from all these loads.
Moreover, it is a dynamic quantity modelled as a time series. During “peak periods” (when the load is high), there will be less available capacity for additional EV chargers, whereas during “off-peak periods,” more EV chargers can be supported. For the given use case, it is necessary to answer the question: “how many new EV chargers can be accommodated?” for both of these scenarios.
Results from such analysis provide valuable insights to multiple departments in the utility. Grid planners will be better able to understand the need for an increased load capacity in specific areas of the service area. Asset managers will also benefit from such information as they can manage bottlenecks on the transformer asset side. Lastly, the customer account management department will get information about where additional permits for EV chargers can be issued without upgrading the grid infrastructure. All this can help save significant investment costs by optimizing the existing grid.
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