Paper #3
Reputation-based Fair Power Allocation to Plug-in Electric Vehicles in the Smart Grid
Abstract – We present a reputation-based framework for allocating power to plug-in electric vehicles (EVs) in the smart grid. In this framework, the available capacity of the distribution network measured by distribution-level phasor measurement units is divided in a proportionally fair manner among connected EVs, considering their demands and self-declared deadlines. To encourage users to estimate their deadlines more precisely and conservatively, a weight is assigned to a each deadline based on the user’s reputation, which comprises two kinds of evidence: deadlines declared before and after the actual departure times in the recent past. Assuming reliable communication between sensors installed in the network and charging stations, we design a decentralized algorithm which allows the users to independently compute their fair share based on signals received from upstream sensors without sharing their private information, e.g., their deadline, with a central scheduler. We prove that this algorithm achieves quadratic convergence under specific conditions and evaluate it empirically on a test distribution network by comparing it with a centralized algorithm which solves the same optimization problem, a decentralized gradient-projection algorithm with linear convergence, and earliest-deadline-first and least-laxity-first scheduling policies. Our results corroborate that the proposed algorithm can track the available capacity of the network despite changes in the demands of homes and other inelastic loads, improves a fairness metric, and increases the overall allocation to users who have a better reputation.
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