ORIGINAL RESEARCH article
Front. Smart Grids
Sec. Smart Grid Technologies
Volume 4 - 2025 | doi: 10.3389/frsgr.2025.1554251
Electric Vehicle Scheduling Strategy Based on Dynamic Adjustment Mechanism of Time-of-Use Price
Provisionally accepted
Yang Liu1,2Haidong Yu1,2Min Huang1,2
Junqing Shi3*Wenbin Liu1,2Xiangyu Wen1,2Ying Wu1,2Lisheng Li1,2
Feng Wang1,2Minglin Liu4- 1State Grid Shandong Electric Power Research Institute, Jinan, China
- 2Shandong Smart Grid Technology Innovation Center, Jinan, China
- 3School of Electrical Engineering, Shandong University, Jinan, China
- 4State Grid Shandong Electric Power Company, Jinan, China
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As the grid-connected capacity of distributed photovoltaic (PV), energy storage, electric vehicle (EV), and other device gradually increases, the new source-load equipment becomes an important demand response (DR) resource in the distribution network (DN). To fully utilize the DR capability of EVs and other devices and reduce the system operating costs as well as line network loss, this paper presents a DR scheduling strategy for EVs based on a time-of-use (TOU) price dynamic adjustment mechanism. Firstly, 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 output characteristics of PV and the electricity consumption characteristics of users. Secondly, 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 DN that takes into account 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 in this paper, compared with the implementation of fixed electricity prices, the peak total load and peak valley load difference decreased by 6.9% and 33.8%; 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.
Keywords: Electric vehicles1, demand response2, optimal scheduling3, clustering algorithm4, time-of-use price model5
Received: 01 Jan 2025; Accepted: 21 Apr 2025.
Copyright: © 2025 Liu, Yu, Huang, Shi, Liu, Wen, Wu, Li, Wang and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Junqing Shi, School of Electrical Engineering, Shandong University, Jinan, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.