Optimal operation of static energy storage in fast-charging stations considering the trade-off between resilience and peak shaving

Enhanced penetration of electric vehicles (EVs) poses several challenges to the power network, such as uncertain peak loads and resilience issues during outages. Both resilience and peak shaving functions can be achieved by using a local static battery energy storage system (BESS) in the charging stations. However, resilience and peakshaving are contradictory, i.e. increasing one will deteriorate the other. Therefore, in this study, a resilience and peak shaving trade-off scheme is proposed to optimally utilize the static BESS. Firstly, a resilience window is formulated to determine the amount of energy to be stored in the BESS for the resilience of EVs in the case of any contingency. During peak hours of the day, more importance is given to peak-shaving, whereas in the off-peak hours, resilience is prioritized. Then, an optimization algorithm is developed to minimize the cost of the system while maintaining resilience and maximizing peak shaving. Using the proposed window method, an optimal window size has been determined via a normalization approach. Simulations have been carried out to show the effectiveness of the proposed scheme by considering the conflicting nature of resilience and peak shaving. With the help of the proposed strategy additional 3.9 % of peak shaving and 3.41 % reduction operational costs is achieved. Moreover, sensitivity analysis has been carried out by considering different factors (market price, size of EV fleet, and BESS size) that can affect the optimal size of the resilience window.

Automated summary

This study proposes a trade-off scheme between resilience and peak shaving, utilizing a local static battery energy storage system (BESS) in charging stations to address the challenges posed by electric vehicles (EVs) to the power network. An optimal window size for storing energy in the BESS is determined to ensure EV resilience during contingencies while prioritizing either peak shaving or resilience based on the time of day. An optimization algorithm is developed to minimize system costs while maintaining resilience and maximizing peak shaving. Simulation results demonstrate the effectiveness of the proposed scheme, achieving additional 3.9% peak shaving and 3.41% reduction in operational costs. Sensitivity analysis is also conducted to consider factors that may impact the optimal size of the resilience window, such as market price, EV fleet size, and BESS size.