Optimization of electric vehicle facility deployment and charging strategy for smart grid
Creators: name: LIU, Chen
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 400805
subject: Electrical energy transmission, networks and systems

description: The smart grid has been the subject of much systematic investigation in recent years with the aim of achieving a balance between the environment and economics. Renewable energy sources have a pivotal role in the energy trading market, and traditional powerplants are no longer the only power sources in the grid. With the integration of renewable energy sources, a new energy trading regulation is required to improve marketing efficiency and fairness in the energy supply side. The rapid growth in electric vehicles (EVs) has had a considerable impact on electricity consumption, and a sudden increase in EV charging requests could lead to a substantial blackout risk in the smart grid.

This thesis focuses on the feasibility of improving integration between renewable energy sources and EV charging/discharging strategies in the smart grid. A power system operator could apply the proposed method. This work is divided into three parts, which are summarized below.

First, the multi-energy pricing problem in the electricity market is due to the introduction of a new two-stage stochastic game model, where the stochastic Stackelberg-Cournot-Nash (SSCN) equilibrium is applied to characterize the optimal energy bidding strategy of the forward market and optimal energy trading strategy of the spot market. The sampling average approximation (SAA) technique is used to address the stochastic game model in a distributed way. The aggressive bidding behavior in the forward market is controlled by a risk measurement based on conditional value-at-risk (CVaR). Moreover, an accelerated best-response dynamics-based algorithm is developed to solve the renewable energy system optimization problems, where the optimal parameters of acceleration and convergence analysis are presented by a heavy ball method. The proposed optimization model is simulated using real-world data, and the evaluation results confirm that the power generators could reduce the risk of overbidding and that they improve the efficiency and fairness of electricity marketing.

Second, the optimization objective is subject to EV drivers, and energy price is considered to minimize their driver discomfort (including queuing time, charging rate, and time for charging). This problem is formulated using bilevel optimization programming, and the solution involves the awareness of charging demand variations at different periods during the day. An online recommendation of EV chargers (EVCs) to EV drivers is provided, which is essential to achieve the optimization objective. For this purpose, an approximate algorithm is proposed with an approximation ratio. Also, the cost function of photovoltaic (PV) system expansion is modeled and the temperature and illumination intensity are considered. The optimal solar panel expansion is achieved by applying the peak load shaving technique. The empirical study uses real datasets from Shenzhen City, one of the largest cities in China, and the extensive tests verify the superiority of the proposed methods from all aspects. The proposed method could increase profit for EVC investors and usability for EV drivers. It could also improve economic welfare when gasoline cars are replaced by EVs.

Third, based on the distribution of EV charging stations and renewable energy systems, the EV charging/discharging problem is modeled using non-cooperative game theory. The payoff function of this model maximizes the revenue of discharging activity and minimizes the overall generation cost by decreasing electricity prices in peak hours. The proposed non-cooperative game coordinates the EV charging/discharging strategy with the concept of a sharing economy to shift/shave peak load. The battery charging/discharging problem is formulated using a bilevel programming model, and the objective is to minimize the EV charging cost with the maximal battery station operation revenue. To obtain the optimized solution, the proposed bilevel optimization problem is divided into two subproblems, and an alternating algorithm is applied to solve them. The simulation shows that the rescheduled charging activities can shift to avoid peak load, while the peak load can be shaved by discharging, i.e. the energy generation cost will be reduced in peak load hours by charging/discharging activities.