AUTHOR=Liu Yang , Yu Haidong , Wang Feng , Huang Min , Shi Junqing , Liu Wenbin , Wu Ying , Li Lisheng , Liu Minglin 

TITLE=Electric vehicle scheduling strategy based on dynamic adjustment mechanism of time-of-use price

JOURNAL=Frontiers in Smart Grids

VOLUME=Volume 4 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/smart-grids/articles/10.3389/frsgr.2025.1554251

DOI=10.3389/frsgr.2025.1554251

ISSN=2813-4311

ABSTRACT=As the grid-connected capacity of distributed photovoltaic (PV), energy storage, electric vehicle (EV), and other devices gradually increases, new source-load equipment becomes an important demand response (DR) resource in the distribution network (DN). To fully utilize the DR's capability for EVs and other devices and reduce the system operating costs and line network loss, this article presents a DR scheduling strategy for EVs based on a time-of-use (TOU) price dynamic adjustment mechanism. First, a fuzzy C-mean (FCM) clustering algorithm is used to calculate the typical operating curves of PV and electrical load and their optimal number of classifications. The deterministic scenarios express the PV's output characteristics and the users' electricity consumption characteristics. Second, a dynamic adjustment mechanism of TOU price is proposed based on the load operation curve of the DN, and the interactive price-incentive signal for DR within the DN is formulated. Finally, a DR scheduling strategy for EVs in the DN that considers the economic cost of system operation and line network loss is proposed. CPLEX in MATLAB is employed to simulate the cases. After applying the TOU price dynamic adjustment mechanism proposed, the peak total load and peak–valley load difference decreased by 6.9% and 33.8%, respectively, compared to implementing fixed electricity prices. At the same time, the operating revenue of the distribution network increased by 13.2%, and the line network loss decreased by 12.9%. The analysis results demonstrate that the proposed EV DR scheduling strategy can realize the price guidance and orderly scheduling of EVs and reduce the operation cost and line network loss in the DN.