A data-aided robust approach for bottleneck identification in power transmission grids for achieving transportation electrification ambition: a case study in New York state

Qianzhi Zhang; Yuechen Sopia Liu; H.Oliver Gao; Fengqi You

Advances in Applied Energy (Jul 2024)

A data-aided robust approach for bottleneck identification in power transmission grids for achieving transportation electrification ambition: a case study in New York state

Qianzhi Zhang,
Yuechen Sopia Liu,
H.Oliver Gao,
Fengqi You

Affiliations

Qianzhi Zhang: Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa 35406, USA; Systems Engineering, Cornell University, Ithaca, 14853, NY, USA
Yuechen Sopia Liu: Department of Civil and Environment Engineering, Cornell University, Ithaca 14853, New York, USA
H.Oliver Gao: Systems Engineering, Cornell University, Ithaca, 14853, NY, USA; Department of Civil and Environment Engineering, Cornell University, Ithaca 14853, New York, USA; Corresponding authors.
Fengqi You: Systems Engineering, Cornell University, Ithaca, 14853, NY, USA; Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca 14853, New York, USA; Corresponding authors.

Journal volume & issue: Vol. 14
p. 100173

Abstract

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As the enthusiasm for electric vehicles passes the range anxiety and other tests, large-scale transportation electrification becomes a prominent topic in research and policy discussions. In consequence, the public attention has shifted upstream and holistically towards the integration of large-scale transportation electrification to power systems. This paper proposes a method to identify bottlenecks in power transmission systems to accommodate large-scale and stochastic electric vehicles charging demands. First, a distributionally robust chance-constrained direct current optimal power flow model is developed to quantify the impacts of stochastic electric vehicles charging demands. Subsequently, an agent-based model with the trip-chain method is applied to obtain the spatiotemporal distributions of electric vehicles charging demands for both light-duty electric vehicles and medium and heavy-duty electric vehicles. The first two moments of those distributions are extracted to build an ambiguity set of electric vehicles charging demands. Finally, a 121-bus synthetic transmission network for New York State power systems is used to validate the future transportation electrification in New York State from 2025 to 2035. Results show that the large-scale transportation electrification in New York State will account for approximately 13.33 % to 16.79 % of the total load demand by 2035. The transmission capacity is the major bottleneck in supporting New York State to achieve its transportation electrification. To resolve the bottlenecks, we explore some possible solutions, such as transmission capacity expansion and distributed energy resources investment. Wind power shows an advantage over solar energy in terms of total operational costs due to better peak alignment between wind power and electric vehicles charging demand.

Published in Advances in Applied Energy

ISSN: 2666-7924 (Online)
Publisher: Elsevier
Country of publisher: United Kingdom
LCC subjects: Social Sciences: Industries. Land use. Labor: Special industries and trades: Energy industries. Energy policy. Fuel trade
Website: https://www.journals.elsevier.com/advances-in-applied-energy

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