面向高比例新能源并网场景的风光-电动车协同调度方法

doi:10.16183/j.cnki.jsjtu.2022.040

上海交通大学学报 ›› 2022, Vol. 56 ›› Issue (5): 554-563.doi: 10.16183/j.cnki.jsjtu.2022.040

所属专题：《上海交通大学学报》“新型电力系统与综合能源”专题（2022年1~6月）

• 新型电力系统与综合能源 • 上一篇下一篇

面向高比例新能源并网场景的风光-电动车协同调度方法

李林晏¹, 韩爽¹, 乔延辉¹, 李莉¹(), 刘永前¹, 阎洁¹, 刘海东²

1.华北电力大学新能源电力系统国家重点实验室; 新能源学院, 北京 102206
2.大唐(赤峰)新能源有限公司,内蒙古自治区赤峰 024000

收稿日期:2022-02-22 出版日期:2022-05-28 发布日期:2022-06-07
通讯作者: 李莉 E-mail:lili@ncepu.edu.cn
作者简介:李林晏(1997-),男,山西省运城市人,硕士生,从事风光出力特性分析、风光互补系统优化调度研究.
基金资助:
雅砻江流域风光水多能互补运行的优化调度方式研究(U1765201)

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

LI Linyan¹, HAN Shuang¹, QIAO Yanhui¹, LI Li¹(), LIU Yongqian¹, YAN Jie¹, LIU Haidong²

1. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources; School of New Energy, North China Electric Power University, Beijing 102206, China
2. China Datang (Chifeng) New Energy Co., Ltd., Chifeng 024000, Inner Mongolia Autonomous Region, China

Received:2022-02-22 Online:2022-05-28 Published:2022-06-07
Contact: LI Li E-mail:lili@ncepu.edu.cn

摘要/Abstract

摘要：

风光-电动汽车协同调度能够有效降低风光出力和电动汽车无序充电等多重不确定性对电力系统的不利影响.现有优化调度模型多以等效负荷波动最小为优化目标,仅考虑了等效负荷的整体波动性,无法衡量风光出力与负荷的匹配度且并未考虑不同出力场景下风光出力的差异性.针对上述问题,提出一种面向高比例新能源并网场景的风光-电动车协同调度方法.构建基于蒙特卡罗模拟的电动汽车无序充电模型;基于风光出力预测数据,构建基于Gap statistic和K-means++算法的风光出力典型日划分模型;以等效负荷方差和负荷追踪系数最小为双优化目标,构建风光-电动汽车协同调度模型,并采用NSGA-II算法求解.结果表明:所提模型能够有效提升风光出力与负荷的匹配度,降低等效负荷波动性,从而缓解风光出力和电动汽车无序充电等多重不确定性对电力系统的不利影响.

关键词: 典型日划分, 源荷匹配度, 风光-电动汽车, 协同调度, NSGA-II算法

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

中图分类号:

TM614
TM 615

李林晏, 韩爽, 乔延辉, 李莉, 刘永前, 阎洁, 刘海东. 面向高比例新能源并网场景的风光-电动车协同调度方法[J]. 上海交通大学学报, 2022, 56(5): 554-563.

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.

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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

面向高比例新能源并网场景的风光-电动车协同调度方法

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

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