This paper presents a collaborative planning model for the active distribution network (ADN) and electric vehicle (EV) charging stations, taking into account the vehicle-to-grid (V2G) function and reactive power support of EVs in different regions. This is an important contribution as it addresses the growing concern of integrating EVs into the power grid in a way that maximizes their benefits.
The authors propose a sequential decomposition method to solve the problem, which involves breaking down the holistic problem into two sub-problems. Subproblem I focuses on optimizing the charging and discharging behavior of autopilot electric vehicles (AEVs) using a mixed-integer linear programming (MILP) model. This is essential for efficiently utilizing the available capacity of charging stations and managing the EVs’ interaction with the grid. By carefully modeling the charging and discharging processes, the authors can address concerns regarding grid stability and congestion caused by high EV penetration.
Subproblem II, on the other hand, utilizes a mixed-integer second-order cone programming (MISOCP) model to plan the ADN, retrofit or construct V2G charging stations (V2GCS), and integrate multiple distributed generation resources (DGRs). This is an intriguing approach as it considers not only the installation of V2GCS but also the installation of DGRs, which can increase the resilience and environmental friendliness of the ADN. Moreover, by employing a MISOCP model, the authors can efficiently solve this complex optimization problem.
One notable aspect of this study is that it analyzes the impact of bi-directional active-reactive power interaction of V2GCS on ADN planning. This is crucial because bi-directional power flows can significantly affect the voltage stability of the distribution network. By considering this interaction, the authors are able to design an ADN that can handle the additional power flows from EVs without compromising the grid’s stability and quality of supply.
The presented model was tested on the 47 nodes ADN in Longgang District, Shenzhen, China, as well as the IEEE 33 nodes ADN. The results demonstrate that the proposed decomposition method significantly improves the speed of solving large-scale problems while maintaining accuracy, even with low AEV penetration. This is an encouraging finding as it shows the scalability and effectiveness of the proposed model in real-world scenarios.
In conclusion, this paper offers a collaborative planning model for integrating EVs into the power grid, considering V2G functionality and reactive power support. The sequential decomposition method effectively solves this complex optimization problem, leading to improved planning and management of ADNs and EV charging stations. With the increasing adoption of EVs, research like this is critical in enabling a smooth and efficient integration of electric vehicles into our energy systems.