Boosting energy flexibilities for the smart grid energy sharing network with optimal management of urban energy, charging and traffic resources

Carbon neutrality strategies put higher requirements on urban energy efficiency and grid operations. Although existing energy-sharing frameworks work on demand response and grid balancing, few studies investigated the co-impact of EVs and internal grids on electric-traffic networks, the dual roles of EVs as mobile storage and flexible loads, and the comparison between single and multiple objective controls. This work introduces an integrated EV-grid energy-sharing framework, implementing a predictive control for peak shaving and proposing a smart EV charging system. The key objectives include boosting energy flexibility, improving grid stability, and better utilizing road and public charging station (PCS) resources. Python-TRNSYS co-simulation is adopted for the smart management of EV charging and discharging behaviours. The result shows that cases with 60 % renewable penetrations have the best technical performance of 0.516. Higher renewable penetrations will perform better in the relative net present value, benefiting from the feed-in tariff. The rented grid capacity of 300 kW will achieve the lowest annual operational costs due to the effective peak shaving control and lower grid rental fees. The annual shaved power and shifted energy can reach more than 2900 kW and 2.1 × 10⁵ kWh for cases with 20 % renewable penetrations during on-peak periods. Among all control objectives, cost-based and PCS-based controls tend to utilize more PCS and road resources due to lower charging unit costs, and multi-objective control will balance the energy charged in building charging station and PCS.