Joint Planning of Active Distribution Network and EV Charging Stations Considering Vehicle-to-Grid Functionality and Reactive Power Support

TL;DR

This paper introduces a collaborative planning model for active distribution networks and EV charging stations, incorporating vehicle-to-grid functions and reactive power support, using a decomposition approach for efficient large-scale problem solving.

Contribution

It presents a novel sequential decomposition method for joint ADN and EV station planning, considering V2G and reactive power, with validated results on real and standard test systems.

Findings

Decomposition improves solution speed for large-scale problems.

V2G and reactive power support impact ADN planning.

Model effective in real and simulated networks.

Abstract

This paper proposes a collaborative planning model for the active distribution network (ADN) and electric vehicle (EV) charging stations that fully considers the vehicle-to-grid (V2G) function and reactive power support of EVs in different regions. This paper employs a sequential decomposition method based on the physical characteristics of the problem, breaking down the holistic problem into two sub-problems for solution. Subproblem I optimizes the charging and discharging behavior of the autopilot electric vehicles (AEVs) using a mixed-integer linear programming (MILP) model. Subproblem II uses a mixed-integer second-order cone programming (MISOCP) model to plan the ADN and retrofit or construct V2G charging stations (V2GCS), as well as multiple distributed generation resources (DGRs). The paper also analyzes the impact of the bi-directional active-reactive power interaction of V2GCS on ADN planning. The presented model was tested in the 47 nodes ADN in Longgang District, Shenzhen, China, and the IEEE 33 nodes ADN, demonstrating that decomposition can significantly improve the speed of solving large-scale problems while maintaining accuracy with low AEV penetration.