As electric vehicles (EV) grow more popular and vehicle-to-grid (V2G) technology advances, large-scale EV aggregations (EVA) have become integral to the power system. However, effectively capturing the distinct idle energy storage characteristics of EVAs across regions and integrating them seamlessly into power system operations remains a challenge. The shortcomings of existing research can be summarized as the follows: Firstly, current methods for assessing the dispatchable regions (DR) of EVs remain inadequate, lacking systematic frameworks and classification methods. Secondly, current multi-level coordinated control strategy often overlooks the holistic nature of coordinated control, which spans multiple levels, including the power grid, garage, and users. Merely considering factors related to EVs and their users is insufficient, as it fails to provide a comprehensive guidance for all coordinated control participants, such as the power grid and garage.
This paper addresses the aforementioned issues by conducting the following works. Firstly, methods for establishing multi-stage electric vehicle dispatchable region (MEVDR) for both EV and EVA are proposed and further investigated. Secondly, the probability density functions of various EV data in different regions and time periods of clustering centers are captured using Gaussian mixture model (GMM). Thirdly, the MEVDR of EVAs in different regions and time periods are established and comprehensively analyzed. Furthermore, the proposed MEVDR model can be used to construct multi-period constraints. Based on this, a vehicles-garage-grid multi-level coordinated control system (VGGMCCS) based on MEVDR can be constructed, which consists of two levels and can therefore be considered a bi-level model. After a thorough analysis, VGGMCCS incorporates two mixed integer programming (MIP) problems, allowing the use of commercial solvers for rapid and efficient problem solving. Finally, in order to provide further validation of the effectiveness of the VGGMCC system based on MEVDR, a comparison was made between the proposed method and the contrasting strategies.
The case study shows that, when compared to two contrasting strategies, the proposed VGGMCCS has been demonstrated to reduce the grid network loss by 12.17% compared to comparative strategy 1 and by 8.69% compared to comparative strategy 2 during peak electricity demand periods. And to reduce users′ average daily charging costs by 7.88% compared to comparative strategy 1, and to increase operators′ revenues by 17.63% compared to comparative strategy 2. Meanwhile, the load fluctuation amplitude of the transformer at the garage node has been significantly reduced. During peak electricity consumption periods, the power fluctuation of transformers under VGGMCCS decreased by 96.36% compared to comparative strategy 1 and by 82.59% compared to comparative strategy 2. Last but not least, VGGMCCS also has a high solution speed, ensuring decision accuracy while quickly responding to dispatching requests from lower-level garages, effectively reducing both the time and economic losses caused by rescheduling requests after EVs are integrated into the power grid. The results show that VGGMCCS can effectively reduce users′ costs, improve the economic benefits of the garage and enhance the operational efficiency of the power grid, while ensuring the long-term stable operation of the power system, thus achieving a win-win situation for users, garage operators and power grid companies.
In summary, this paper provides a thorough establishment and analysis of EVA′s MEVDR across a diverse range of geographical and temporal contexts. Furthermore, when compared to the contrasting strategies, the proposed VGGMCCS promises to enhance both the economic benefits and operational efficiency of the power system significantly.