{'fraction_non_alphanumeric': 0.01282051282051282, 'fraction_numerical': 0.0004578754578754579, 'mean_word_length': 4.969945355191257, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Distributing Power to Electric Vehicles on a Smart Grid

Electric vehicles create a demand for additional electrical power. As the popularity of electric vehicles increases, the demand for more power can increase more rapidly than our ability to install additional generating capacity. In the long term we expect that the supply and demand will become balanced. However, in the interim the rate at which electric vehicles can be deployed will depend on our ability to charge these vehicles in a timely manner. In this paper, we investigate fairness mechanisms to distribute power to electric vehicles on a smart grid. We simulate the mechanisms using published data on the current demand for electric power as a function of time of day, current driving habits for commuting, and the current rates at which electric vehicles can be charged on standard home outlets. In the simulations we assume that there is sufficient excess power, over the current demand to charge all the electric vehicles, but that there is not sufficient power to charge all the vehicles simultaneously during their peak demand. We use information collected on the smart grid to select which vehicles to charge during time intervals. The selection mechanisms are evaluated based upon the fraction of the vehicles that are forced to leave late in order to acquire sufficient charge, and the average time that those vehicles are delayed. We also compare the techniques with conventional pricing mechanisms that shift demand by charging less during off peak hours. We have found that simple strategies that only use measurements on battery levels and arrival times to select the vehicles that will be charged may delay a significant fraction of the vehicles by more than two hours when the excess capacity available for charging vehicles exceeds their requirements by as much as a factor of three. However, when we can use reliable information on departure times and driving distances for the electric vehicles, we can reduce the delays to a few minutes when the capacity available for charging exceeds their requirements by as little as 5%. Keywords-Fair Allocation, Min-max Fairness, Electric Vehicles, Smart Grid