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Transmission expansion planning in presence of electric vehicles at the distribution level
Date Issued
2021-03
Date Available
2021-09-28T12:09:56Z
Abstract
The planning of the transmission network is an issue that, over the years, has received much attention, particularly due to the impact that this infrastructure has on the safe and reliable functioning of electrical systems. The search for solutions addressing climate change has led to several changes in the functioning of electrical systems, particularly concerning the increasing integration of renewable electricity production. However, in recent years, changes in the load side of the electrical system have also emerged. In particular, electric mobility has been developing, and a high penetration of electric vehicles (EVs) is expected in near future. This consumption is supplied by the distribution system but will impact the transmission network. Naturally, the amount of energy used by EVs is subject to uncertainties, which makes the problem complex. Those uncertainties cannot be easily modeled using statistical distributions because of the reduced history of available information. The transmission system operator (TSO) needs an efficient tool to analyze the adequacy of the transmission network to supply the distribution networks with high penetration of EVs. In this paper, a methodology based on symmetric/constrained fuzzy power flow is proposed to find the optimal investment policy at the transmission level while satisfying the technical constraints. The concept of dual variables provided by Lagrange multipliers, the natural result of the nonlinear optimization problem, is used to obtain the most promising reinforcement options considering the actual structure of the transmission network. The proposed model is tested on an IEEE 14-bus system.
Type of Material
Journal Article
Publisher
Wiley
Journal
International Transactions on Electrical Energy Systems
Volume
31
Issue
3
Copyright (Published Version)
2020 Wiley
Language
English
Status of Item
Peer reviewed
ISSN
2050-7038
This item is made available under a Creative Commons License
File(s)
No Thumbnail Available
Name
Submit of TSO-DSO-R1-V3_Rev_PM-AS (1).docx
Size
1.57 MB
Format
Unknown
Checksum (MD5)
420b95c71a502a90df28da84dc69f171
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