No abstract

With the increase in new energy power generation and the continuous augment in the penetration rate of electric vehicles, it is of crucial importance to use electric vehicles as energy storage devices to promote the consumption of new energy. Aiming at the uncertainty of electric vehicles, this paper proposes a method based on multiobjective optimization for electric vehicle-supercapacitor hybrid energy storage system to track PV project output. The hybrid system consists of electric vehicles and supercapacitor. Electric vehicles and supercapacitors supplement the deviation of PV actual power and predicted power by charging and discharging. The electric vehicle is regarded as a nonspecific way to label a piece of equipment that can store energy. First and foremost, on the basis of traditional method of predicting PV output, a PSO-BP prediction method based on PCA is proposed to improve the accuracy of PV output prediction. Secondarily, according to the different characteristics of electric vehicles and supercapacitors, the empirical mode decomposition (EMD) method is used to decompose the deviation that the hybrid energy storage system needs to bear with the purpose of initially allocating the energy. Furthermore, a multiobjective optimization model is established for the precise energy distribution of the hybrid energy storage system, and the NSGA-III algorithm is used to solve it. Ultimately, the data of a domestic PV power station are used for simulation. After optimized control, the result shows that the standard deviation of the system output is reduced from 1967 to 75.77. The research in this article provides a theoretical basis for the application of electric vehicle virtual energy storage technology in the field of auxiliary new energy grid connection.

Under the "double carbon" goal, the proportion of new energy in China's power system is increasing year by year, and the power grid is facing huge challenges under the "double high" power system. Photovoltaic power generation has a strong volatility. After grid connection, it has no ability to provide support for the frequency stability of the system. On the contrary, it will bring uncontrollable interference to the grid. Especially when the power grid has serious frequency fluctuations, the ability and time of frequency control are more difficult to meet the system requirements. The above problems are huge challenges for PV grid connection. The energy storage has the characteristics of fast response, high climbing speed and accurate action. In order to improve the impact of photovoltaic grid connection on the system frequency, introducing energy storage to assist the primary frequency modulation of photovoltaic stations can enhance the friendliness of photovoltaic power generation. Finally, this paper studies the primary frequency modulation control strategy of photovoltaic station assisted by energy storage. Through simulation, the curves of energy storage in different situations and the amount of action are obtained.

The orderly control strategy adopted in the charging station can decrease the peak-valley difference (PVD) and promote the normality and safety of the charging station in the electrical power grid. However, the orderly control strategy will lead to inconvenience for EV’s owners to charge and limit its large-scale application. Therefore, it is an urgent problem to reasonably optimize the charging and discharging process of electric vehicles in the optical storage charging station. The research in this paper is mainly aimed at making the grid side meet expectations, and making the EV’s owners charge the lowest. In this paper, aiming at the minimum PVD of the grid and the lowest EV’s owners charging cost, taking into account the maximum load and EV’s owners charging time of the charging station, the genetic algorithm is used to solve the problem, and the coordinated control model for optimizing the charging and discharging behavior of electric vehicles in the optical storage charging station is established. The feasibility of the strategy is verified by an example analysis. The method significantly reduces the PVD of the power grid.

No abstract