Document Type

Journal Article

Publication Title

Sustainable Energy Technologies and Assessments

Volume

76

Publisher

Elsevier

School

School of Engineering

Publication Unique Identifier

10.1016/j.seta.2025.104294

Comments

Qays, M. O., Ahmad, I., Habibi, D., & Moses, P. (2025). Long-term techno-economic analysis considering system strength and reliability shortfalls of electric vehicle-to-grid-systems installations integrated with renewable energy generators using hybrid GRU-classical optimization method. Sustainable Energy Technologies and Assessments, 76, 104294. https://doi.org/10.1016/j.seta.2025.104294

Abstract

Higher penetration of Electric Vehicle-to-Grid charging stations (EV2GCSs) in power grids integrated with renewable energy generators (REGs) result in system-instabilities raising the risk of blackout issues. Although techno-economic feasibility of EV2GCSs has been identified within the literature addressing power outage and financial concerns as a contemporary solution, no long-term techno-economic analysis has been investigated considering system strength and reliability shortfalls before the EV2GCSs installation. Therefore, this research aims to evaluate the long-term (2025–2045) techno-economic analysis of EV2GCSs into REGs-integrated grid systems by accounting for system strength and reliability factors. A novel optimization framework is developed, combining Gated Recurrent Units (GRU) and classical optimization method to achieve optimal size and location of EV2GCSs. The GRU model predicts the energy usage of the EV2GCSs, which is further applied in the classical optimization model to achieve optimal results that analyze long-term techno-economic performance. The obtained results are compared with the existing optimization approaches and non-optimal scenarios, where the proposed research showed that net present value and net profit values can be improved by 19.62% and 13.85% respectively, using the developed optimization framework. Results also show that the system strength level and system reliability can be enhanced approximately 11.829% and 8.671% respectively.

DOI

10.1016/j.seta.2025.104294

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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