Optimal day-ahead scheduling frameworks for e-mobility ecosystem operation with drivers preferences under uncertainties

Tookanlou, Mahsa Bagheri (2022) Optimal day-ahead scheduling frameworks for e-mobility ecosystem operation with drivers preferences under uncertainties. Doctoral thesis, Northumbria University.

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Abstract

Distribution networks are envisaged to host significant number of electric vehicles (EVs) and potentially many charging stations (CSs) in the future to provide charging as well as vehicle-to-grid (V2G) services to the electric vehicle owners. A high number of electric EVs in the transportation sector necessitates an advanced scheduling framework for e-mobility ecosystem operation as a whole in order to overcome range anxiety and create a viable business model for CSs. Thus, the future e-mobility ecosystem will be a complex structure with different stakeholders seeking to optimize their operation and benefits. The main goal of this study is to develop a comprehensive day-ahead grid-to vehicle (G2V) and V2G scheduling framework to achieve an economically rewarding operation for the ecosystem of EVs, CSs and retailers using a comprehensive optimal charging/discharging strategy that accounts for the network constraints. To do so, a non-cooperative Stackelberg game, which is formed among the three layers, is proposed. The leader of the Stackelberg game is the retailer and the first and second followers are CSs and EVs, respectively. EV routing problem is solved based on a cost-benefit analysis rather than choosing the shortest route. The proposed method can be implemented as a cloud scheduling system that is operated by a non-profit entity, e.g., distribution system operators or distribution network service providers, whose role is to collect required information from all agents, perform the day-ahead scheduling, and ultimately communicate the results to relevant stakeholders. To facilitate V2G services and to avoid congestion at CSs, two types of trips, i.e., mandatory and optional trips, are defined and formulated. Also, EV drivers’ preferences are added to the model as cost/revenue threshold and extra driving distance to enhance the practical aspects of the scheduling framework. The stochastic nature of all stakeholders’ operation and their mutual interactions are modelled by proposing a three-layer joint distributionally robust chance-constrained (DRCC) framework. The proposed stochastic model does not rely on a specific probability distribution for stochastic parameters. To achieve computational tractability, the exact reformulation is implemented for double-sided and single-sided chance constraints (CCs). Furthermore, the impact of temporal correlation of uncertain PV generation on CSs operation is considered. To solve the problem, an iterative process is proposed to solve the non cooperative Stackelberg game and joint DRCC model by determining the optimal routes and CS for each EV, optimal operation of each CS and retailers, and optimal V2G and G2V prices. Extensive simulation studies are carried out for a e-mobility ecosystem of multiple retailers and CSs as well as numerous EVs based on real data from San Francisco, the USA. The simulation results shows the necessity and applicability of such a scheduling method for the e-mobility ecosystem.

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