Distribution network planning considering active response of EVs and DTR of cables and transformers
Abstract With increasing electricity demand and large‐scale stochastic charging of electric vehicles (EVs), distribution networks face inevitable shortage of transfer capability, bringing new challenges to distribution network planning (DNP). Dynamic thermal rating (DTR), which evaluates the equipme...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-04-01
|
| Series: | High Voltage |
| Online Access: | https://doi.org/10.1049/hve2.70024 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract With increasing electricity demand and large‐scale stochastic charging of electric vehicles (EVs), distribution networks face inevitable shortage of transfer capability, bringing new challenges to distribution network planning (DNP). Dynamic thermal rating (DTR), which evaluates the equipment rating based on actual meteorological conditions and equipment thermal state, can enhance the equipment transfer capability to meet the increasing load demand. In this paper, we propose a model considering the active response of EVs, and a bi‐level DNP model incorporating the DTR of cables and transformers, in the upper level, the Prim algorithm is embedded into the particle swarm optimisation (PSO) algorithm to obtain an initial grid topology; in the lower level, types of cables and transformers as well as the installation of DTR equipment are determined, second‐order cone (SOC) relaxation and linearisation of the variables product are then carried out to meet the non‐linear constraints of cables and transformers, and the upper and lower models are solved in an iterative manner. Case studies demonstrate that the implementation of DTR effectively enhances the transfer capability of cables and transformers, saving 4.8% investment cost while ensuring 96% uplift of power supply. Besides, with 90% active response rate of EVs, total cost can be further reduced. |
|---|---|
| ISSN: | 2397-7264 |