Abstract:

Massive adoption of shared electric mobility benefits people's daily commute and environment but creates overload issues into the power grid, then further causes challenges to charging service operations and power management. Previous research always focuses on a single optimization process on shared vehicle planning rather than the combination of demand management into day-ahead planning operations. To this end, the authors attempt to propose a mixed integer programming model integrating demand response operations to explore further the impacts of demand response on shared electric vehicle planning operations. The authors first model a two-stage model integrating charging facility location in the first stage and vehicle relocation in the second stage. Moreover, both supply-side and demand-side uncertainties are considered and approximated into tractable form by applying average sample approximation and robust distributional set featuring the entropy knowledge and electric vehicle's multi-level charging duration. The demand response policy is also proposed to reshape the original charging demand into an economical and reliable way to improve operational efficiency and mitigate the power overload issues caused by massive electric vehicle adoption. Further, they conduct a real-world case study in Amsterdam, the Netherlands, to explore the social-operational impacts of vehicle planning optimization model integrating the demand response, robust charging facility planning in three areas: (1) The demand response integration promote electric vehicle planning operations on cost-saving for about 3%. (2) Data richness of serviceability towards charging piles influence all decisions through the shared electric vehicle charging station planning. (3) A trade-off exists between technical progress on charging rate and charging technology stability