Revenue analysis of a lightweight V2G electricity trader based on real-life energy demand patterns

Abstract

This paper analyzes the revenue of a microgrid employing the lightweight vehicle-togrid energy trade coordinator, focusing on such parameters as seasonal rates, number of participating electric vehicles, stay time, and amount to sell. The heuristic method cuts down the vast search space for the complex optimization problem by by iteratively matching the time slot having the least available electricity. The analysis result, obtained from a real-life demand pattern, reveals that on the winter rate, 100 EVs can sell all of their energy, while for the 200-EV case, they can sell and double the revenue in 12 out of 20 days. Next, on the summer rate, in which the peak rate interval is extended, the revenue increases by 1.5 times. At the same time, the 200-EV case sometimes brings less benefit than the 100-EV case for the given parameter set. As the target brokering system responsively finds a near-optimal solution until a certian bound, it can be further scalable with a module-based distributed brokering mechanism.