Profit-aware Online Vehicle-to-Grid Decentralized Scheduling under Multiple Charging Stations
For operators of electric vehicle charging networks, this work provides a practical scheduling method that improves profit and grid load balance, though the gains are incremental.
This paper proposes an online decentralized greedy algorithm for vehicle-to-grid scheduling across multiple charging stations, maximizing overall profit for both demand and supply entities. Simulations show the algorithm outperforms an alternative strategy in profit and load flatness, and reveals an optimal number of stations for profit maximization.
Fluctuations in electricity tariffs induced by the sporadic nature of demand loads on power grids has initiated immense efforts to find optimal scheduling solutions for charging and discharging plug-in electric vehicles (PEVs) subject to different objective sets. In this paper, we consider vehicle-to-grid (V2G) scheduling at a geographically large scale in which PEVs have the flexibility of charging/discharging at multiple smart stations coordinated by individual aggregators. We first formulate the objective of maximizing the overall profit of both, demand and supply entities, by defining a weighting parameter. We then propose an online decentralized greedy algorithm for the formulated mixed integer non-linear programming (MINLP) problem, which incorporates efficient heuristics to practically guide each incoming vehicle to the most appropriate charging station (CS). The better performance of the presented algorithm compared to an alternative allocation strategy is demonstrated through simulations in terms of the overall achievable profit and flatness of the final electricity load. Moreover, the results of simulations reveal the existence of optimal number of deployed stations at which the overall profit can be maximized.