access icon free Energy price forecasting for optimal managing of electric vehicle fleet

© The Institution of Engineering and Technology
Received 30/03/2020, Accepted 15/09/2020, Revised 21/07/2020, Published 07/10/2020

Defining tools and algorithms to support the decision-making process for charging electric vehicles (EVs) is a fundamental theme for the spread of EVs. Utilities can use this approach to incentive or discourage the charge of EVs according to different constraints. In this study, the authors refer the EV clusters or fleets, where there is only one energy buyer for all the clusters. This approach corresponds to an indirect method based on prices to induce behaviours in the management of charging on clusters of EVs. The first actor of the algorithm is an aggregator of EV fleet operators acting as a dealer between the electricity market and consumers. A theoretical game model based on Stackelberg's formulation is proposed to capture the interaction between the fleet operator and the owners/drivers of the EVs. A bi-level optimisation problem arises to represent the game between the agents involved: at the upper level, the aggregator maximises its benefits, while the lower level represents the behaviour of rational drivers as a fleet. The proposed method is applied to actual data obtained observing the behaviour of a car-sharing fleet.

Inspec keywords: electric vehicle charging; pricing; power markets; optimisation; load forecasting; game theory; decision making

Other keywords: game model; electricity market; energy price forecasting; rational driver behaviour; optimal managing; electric vehicle charging; car-sharing fleet; electric vehicle fleet; charging management; fleet operator; EV clusters; energy buyer; EV fleet operators; Stackelberg formulation; bi-level optimisation problem; decision-making process

Subjects: Game theory; Power system management, operation and economics; Automobile electronics and electrics; Optimisation techniques; Power system planning and layout

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