A Wind-Solar-Electric Vehicles Coordination Scheduling Method for High Proportion New Energy Grid-Connected Scenarios

doi:10.16183/j.cnki.jsjtu.2022.040

Abstract

Abstract:

Wind-solar-electric vehicles coordinated optimization scheduling can effectively reduce the adverse effects of multiple uncertainties of wind-solar output and disorderly charging of electric vehicles on the power system. Most of the existing optimization scheduling models take the minimum equivalent load fluctuation as the optimization objective, which, only considering the overall fluctuation of equivalent load, cannot measure the matching degree of output-load, and do not consider the difference of output in different output scenarios. Therefore, a wind-solar-electric vehicles coordination scheduling method for high proportion new energy grid-connected scenarios is proposed. First, the disordered charging model of electric vehicles by Monte Carlo simulation is constructed. Then, a wind-solar output typical day classification model using Gap statistical and K-means++ is constructed based on the forecasting data of wind and solar power. Finally, taking the minimum equivalent load variance and load tracking coefficient as the double optimization objectives, a wind-solar-electric vehicles coordination optimization scheduling model is established, and the NSGA-II algorithm is used to solve it. The results demonstrate that the proposed model can effectively improve the matching degree of wind-solar output and load, and reduce the fluctuation of equivalent load, so as to reduce the adverse effects of multiple uncertainties of wind-solar output and disorderly charging of electric vehicles on the power system.

Key words: typical day, output-load matching, wind-solar-electric vehicles, coordination scheduling, NSGA-II algorithm

CLC Number:

TM614
TM 615

LI Linyan, HAN Shuang, QIAO Yanhui, LI Li, LIU Yongqian, YAN Jie, LIU Haidong. A Wind-Solar-Electric Vehicles Coordination Scheduling Method for High Proportion New Energy Grid-Connected Scenarios[J]. Journal of Shanghai Jiao Tong University, 2022, 56(5): 554-563.

Figures/Tables 6

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典型日	优化目标	优化调度结果
典型日	优化目标	F₂	F₁/MW²	F₃/MW	α/%
4	单目标	0.10	3395218.02	16110.13	95.06
	双目标	0.06	3099184.40	13797.68	95.04
5	单目标	0.11	2012967.13	14637.51	95.19
	双目标	0.07	1832434.89	14463.81	95.03
9	单目标	0.08	6138278.51	24960.08	95.01
	双目标	0.05	5561544.85	23093.18	95.00
12	单目标	0.08	740009.18	13849.89	95.19
	双目标	0.05	475042.78	11241.12	95.03
15	单目标	0.09	1481212.29	20593.76	95.01
	双目标	0.04	973148.80	9973.25	95.00