Electric vehicles can serve as energy storage. The energy storage system is composed of multiple mobile electric vehicle energy storage units. Compared with other peak shaving methods, electric vehicles participate in the power grid mainly has the advantages of considerable capacity, fast response, high efficiency, and low investment. Using spare electric vehicles to feedback electric energy to the grid during peak power consumption can reduce the load fluctuation. Based on the study of electric vehicles as energy storage auxiliary power grid peak shaving, this paper fully considers the charging and discharging parameters of electric vehicles, and establishes a control model based on power grid peak shaving participated by the electric vehicles. Take a certain type of EV as an example to study this paper. Simulated annealing algorithm (SA), differential evolution algorithm (DE) and immune algorithm (IA) are used to simulate and solve the model respectively. Combined with the analysis of the actual operating load data of a local power grid, the peak-valley difference of the auxiliary power grid can be significantly reduced after considering the charging and discharging parameters of electric vehicles, The validity of the model and the selection of the best algorithm are verified.
KEYWORDS: Optical storage, Monte Carlo methods, Solar energy, Power grids, Photovoltaics, Batteries, Mathematical optimization, Genetic algorithms, Computer simulations, Statistical analysis
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.
KEYWORDS: Frequency modulation, Solar energy, Photovoltaics, Modulation frequency, Power grids, Control systems, Frequency response, Solar cells, Optical storage, Lithium
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.
KEYWORDS: Solar energy, Optical storage, Batteries, Power grids, Control systems, Particle swarm optimization, Photovoltaics, Particles, Computer simulations, Process control
The orderly control strategy of the optical storage charging station can significantly reduce the impact of the disordered charging of many vehicles on the power grid. However, users' response to orderly charging will cause inconvenience to some extent. Therefore, this paper proposes a coordinated control strategy of optical storage charging station based on peak-valley period, taking the minimum total load of charging station and the minimum charging cost of users within the user charging period as the objective function, and adopts the bee colony algorithm to comprehensively analyze the impact of different peak-valley period division and different user responsiveness on the power grid impact. The effectiveness of this strategy is verified by an example analysis. This method significantly reduces the impact of the optical storage charging station on the power grid.
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