Peak Load Management in EV Charging Through Intelligent Rotational Strip Packing

The deployment of electric vehicles (EV) faces significant challenges worldwide, particularly in the realms of charging infrastructure and energy optimization. The charging process itself is often time-intensive and inefficient, requiring several hours to fully charge a vehicle, leading to reduced user convenience. Energy optimization is another critical concern, encompassing both EV energy consumption and the main grid’s capacity to support large-scale EV adoption. Inefficient energy management and load scheduling in EVs may lead to increased peak load consumption resulting in higher energy losses and reduced performance. In this work, we address this challenging issue of EV charging management to optimize peak load consumption. Given a set of EVs, we determine their optimal non-preemptive charge scheduling within a specific time period that results in minimizing the peak load. To achieve this, we formulate the problem as a two-dimensional strip-packing problem with rotation and propose an intelligent greedy algorithm, which provides efficient solutions in this problem setup. To evaluate the proposed mechanism, we consider randomly generated data reflecting real-world scenarios. We compare the results with some of the existing algorithms and have observed an average 14.08% – 29.97% gain in terms of peak shaving. Further, the results obtained also depict the efficiency and scalability of the proposed solution.