LAPSE:2023.29760
Published Article
LAPSE:2023.29760
Optimal Pricing of Vehicle-to-Grid Services Using Disaggregate Demand Models
April 13, 2023
The recent revolution in electric mobility is both crucial and promising in the coordinated effort to reduce global emissions and tackle climate change. However, mass electrification brings up new technical problems that need to be solved. The increasing penetration rates of electric vehicles will add an unprecedented energy load to existing power grids. The stability and the quality of power systems, especially on a local distribution level, will be compromised by multiple vehicles that are simultaneously connected to the grid. In this paper, the authors propose a choice-based pricing algorithm to indirectly control the charging and V2G activities of electric vehicles in non-residential facilities. Two metaheuristic approaches were applied to solve the optimization problem, and a comparative analysis was performed to evaluate their performance. The proposed algorithm would result in a significant revenue increase for the parking operator, and at the same time, it could alleviate the overloading of local distribution transformers and postpone heavy infrastructure investments.
Keywords
demand-side management, discrete choice theory, electric vehicle charging, genetic algorithms, Particle Swarm Optimization, revenue management, vehicle-to-grid
Suggested Citation
Latinopoulos C, Sivakumar A, Polak JW. Optimal Pricing of Vehicle-to-Grid Services Using Disaggregate Demand Models. (2023). LAPSE:2023.29760
Author Affiliations
Latinopoulos C: Department of Civil and Environmental Engineering, Imperial College London, London SW7 2BU, UK [ORCID]
Sivakumar A: Department of Civil and Environmental Engineering, Imperial College London, London SW7 2BU, UK [ORCID]
Polak JW: Department of Civil and Environmental Engineering, Imperial College London, London SW7 2BU, UK
Journal Name
Energies
Volume
14
Issue
4
First Page
1090
Year
2021
Publication Date
2021-02-19
Published Version
ISSN
1996-1073
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Original Submission
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PII: en14041090, Publication Type: Journal Article
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LAPSE:2023.29760
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doi:10.3390/en14041090
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Apr 13, 2023
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CC BY 4.0
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