Adaptive Power Management of Large-Scale Electric Vehicle Charging Network

Demand for public charging is growing with the increasing adoption of electric vehicles (EV). Motivated by these developments, the parking industry is fast transitioning from pure play parking to parking with charging; but are facing difficulties to scale charging operations due to high infrastructure costs and grid-level capacity constraints. These challenges can be potentially alleviated by organizing the charging network into clusters, each consisting of a collection of EV chargers. To prevent imbalances between the charging demand across network clusters and available power supply, while being able to service different type of customers with priority for prime users; the power flow in the charging network needs to be managed intelligently. To achieve this, we consider the problem of optimal power allocation to not only satisfy these multiple system objectives, but also scale efficiently to large network sizes involving hundreds of clusters and charging points. We present a solution approach based on mixed-integer linear program (MILP) to overcome the above challenges. Using simulations, we show that MILP performs better than a greedy approach by allocating 12% more power to prime customers while respecting various system constraints; with solution speed ranging from a few seconds (on small networks) to less than a minute (on large networks).