KEYWORDS: Evolutionary algorithms, Batteries, Power grids, Evolutionary optimization, Mathematical optimization, Statistical analysis, Monte Carlo methods, Algorithm development
Disorderly charging of large-scale electric vehicles will exacerbate the peak-to-valley load difference in the station area, increase the load pressure of grid equipment, and threaten the safe and economic operation of the grid. How to utilize EV V2G technology to reduce the load peak-valley difference to realize the heavy overload management of the station area is a key concern in the future. This paper proposes a two-stage optimal scheduling strategy for EVs in V2G mode counting and prioritizing, establishes a two-layer optimal scheduling model with the objective function of minimizing the standard deviation of loads, minimizing the peak-to-valley difference, and maximizing the revenue of the users' side and the agents, develops its charging and discharging prioritized scheduling plan through the optimization of the charging and discharging distribution time period and the dynamic time-sharing tariff, and employs the quadratic variational difference evolution (SVDE) algorithm to solve the model, and the results are shown in the following figure. The model is solved, and the results show that the proposed model can effectively reduce the load peak-valley difference and realize the win-win benefits for the station governance, user side and charging pile agents.
Energy storage, as a schedulable resource, can be added to the optimal scheduling model of microgrids, which can effectively improve the penetration rate of renewable energy in the grid and improve the reliability and economy of the system. Aiming at grid-connected microgrids, including wind power, photovoltaic, energy storage devices, and micro gas turbines, this paper takes the lowest comprehensive operating cost as the objective function. It takes power balance, distributed power output characteristics, energy storage battery power, and tie line constraints as constraints to establish the optimal configuration model of the microgrid. The Tent chaotic mapping and Cauchy mutation operator method improve the traditional algorithms of the grey wolf, such as the last use of an improved algorithm to solve the model; the simulation results show that the improved grey wolf algorithm has good optimization ability, this model can realize economy optimal micro power grid, the effectiveness of the proposed method was verified.
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