Economic operation of electric vehicle parking lot based on vehicle-to-grid function

Qi, Jiwen

Economic operation of electric vehicle parking lot based on vehicle-to-grid function

Qi, Jiwen

Permalink

Publication Type:: Thesis
Issue Date:: 2024

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download thesisAdobe PDF (8.43 MB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Qi, Jiwen
dc.date.accessioned	2025-06-02T22:50:18Z
dc.date.available	2025-06-02T22:50:18Z
dc.date.issued	2024
dc.identifier.uri	http://hdl.handle.net/10453/187591
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	In recent years, the rapid development of electric vehicles (EVs) has drawn increasing attention to the field, particularly in relation to the vehicle-to-grid (V2G) function and its economic operation challenges. This research aims to delve into the V2G function and the economic operation and planning of EV parking lots. A model is developed for an EV parking lot equipped with V2G, renewable energy sources, and energy storage system. Various charging modes and uncertainties, such as electricity market prices and solar radiation, are considered. The model classifies EVs based on parking duration and adjusts charging prices dynamically using a linear price-demand relationship. Scenario generation in MATLAB validates the model’s effectiveness, demonstrating superior profitability compared to two alternative models across multiple cases. Further analysis incorporates distributed energy resources and examines the parking lot’s participation in spot and Frequency Control Ancillary Services (FCAS) markets. Uncertainty in market prices, solar irradiance, and wind speed is forecasted using long short-term memory models. EV behavior, including arrival times and state of charge, is simulated via Monte Carlo methods. An Information Gap Decision Theory-based approach is proposed to optimize V2G incentives under uncertain conditions, yielding the highest profit when participating in both FCAS and spot markets. A hybrid multi-agent bi-level optimization framework integrates a deep reinforcement learning (DRL)-based virtual power plant (VPP) with lower-level EV parking lot models, using mixed-integer linear programming. The VPP dynamically adjusts prices in response to market conditions, with lower-level models maximizing profits and providing feedback to the upper-level for enhanced learning. Results highlight the sensitivity of the pricing strategy to changes in the lower bounds, with significant impacts on system profitability.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/187591/1/thesis.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2024 Jiwen Qi
dc.rights	au.edu.uts.lib/cph
dc.title	Economic operation of electric vehicle parking lot based on vehicle-to-grid function	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

In recent years, the rapid development of electric vehicles (EVs) has drawn increasing attention to the field, particularly in relation to the vehicle-to-grid (V2G) function and its economic operation challenges. This research aims to delve into the V2G function and the economic operation and planning of EV parking lots. A model is developed for an EV parking lot equipped with V2G, renewable energy sources, and energy storage system. Various charging modes and uncertainties, such as electricity market prices and solar radiation, are considered. The model classifies EVs based on parking duration and adjusts charging prices dynamically using a linear price-demand relationship. Scenario generation in MATLAB validates the model’s effectiveness, demonstrating superior profitability compared to two alternative models across multiple cases. Further analysis incorporates distributed energy resources and examines the parking lot’s participation in spot and Frequency Control Ancillary Services (FCAS) markets. Uncertainty in market prices, solar irradiance, and wind speed is forecasted using long short-term memory models. EV behavior, including arrival times and state of charge, is simulated via Monte Carlo methods. An Information Gap Decision Theory-based approach is proposed to optimize V2G incentives under uncertain conditions, yielding the highest profit when participating in both FCAS and spot markets. A hybrid multi-agent bi-level optimization framework integrates a deep reinforcement learning (DRL)-based virtual power plant (VPP) with lower-level EV parking lot models, using mixed-integer linear programming. The VPP dynamically adjusts prices in response to market conditions, with lower-level models maximizing profits and providing feedback to the upper-level for enhanced learning. Results highlight the sensitivity of the pricing strategy to changes in the lower bounds, with significant impacts on system profitability.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/187591