中圖分類號： TP33 文獻(xiàn)標(biāo)識碼： A DOI：10.16157/j.issn.0258-7998.200179 中文引用格式： 金商鶴，張宇，王育飛，等. 電動汽車接入微網(wǎng)優(yōu)化調(diào)度模型建立及其算例[J].電子技術(shù)應(yīng)用，2021，47(1)：82-85，107. 英文引用格式： Jin Shanghe，Zhang Yu，Wang Yufei，et al. Establishment of optimal scheduling model for electric vehicles connected to microgrid and calculation examples[J]. Application of Electronic Technique，2021，47(1)：82-85，107.
Establishment of optimal scheduling model for electric vehicles connected to microgrid and calculation examples
Jin Shanghe1，Zhang Yu1,2，Wang Yufei1，Shi Shanshan2，Wang Haojing2
1.Shanghai University of Electric Power，Shanghai 200090，China； 2.State Grid Shanghai Electric Power Research Institute，Shanghai 200437，China
Abstract： In order to solve the problem of fluctuation of wind-power output and disordered charging of electric vehicles connected to the power grid, a two-stage optimal dispatching model of electric vehicles connected to the micro grid by classification was established according to the sensitivity of electric vehicle users to the incentive factors, and a case study was carried out. The research results show that compared with disordered charging, the two-stage dispatching micro grid of electric vehicles significantly reduces the value in load peak and valley periods, the energy storage unit can basically meet the operation demand of the micro grid, the wind-energy utilization rate is up to 95.43%, and the connection line exchange power is only 24.2 kW, which significantly reduces the impact of wind-energy fluctuations of the micro grid on the large power grid. With the increase of the proportion of class III electric vehicles, the utilization rate of wind-power increased obviously. An example shows that the proposed two-stage optimal dispatching model can effectively reduce the power purchased from the micro grid, which improves the utilization rate of wind and wind, and improves the influence of micro grid power fluctuations on the large power grid.
Key words : access micro network；electric cars；scheduling model；wind utilization rate；the example analysis
