Multi-time-scale robust economic dispatching method for the power system with clean energy

The Journal of Engineering

Qiaobo Liu¹  Xiaofu Xiong¹  Jinxin Ouyang¹  Jizhong Zhu²  Jin Zou²  Peizheng Xuan²  Pingping Xie² 

[1] Chongqing University;SEPRI, China Southern Power Grid;

关键词: power generation dispatch;    load forecasting;    hybrid power systems;    power generation economics;    power generation scheduling;    robust constraints;    robust level;    confidence level increases;    IEEE 9-bus system;    multitime scheduling method;    multitime-scale robust economic dispatching method;    large-scale integration;    energy presents;    power system economic dispatch;    multitime-scale robust economic dispatch strategy;    multisource hybrid power system;    variable confidence level;   

DOI  :  10.1049/joe.2018.8623

来源: DOAJ

【 摘 要 】

Clean energies such as wind energy and solar energy have increased very fast in order to meet the environmental requirements. However, due to the uncertainties of wind and solar energy, the large-scale integration of new energy presents a great challenge to the power system economic dispatch. Thus, a multi-time-scale robust economic dispatch strategy of a multi-source hybrid power system based on the variable confidence level is proposed. The deterministic constraints of each time scale are transformed into robust constraints that take the uncertainty into account. Meanwhile, the robust level whose confidence level increases with the shortening of time scale is set to improve the scheduling conservative degree step by step. The selection principle of the robust level of each time scale is also presented. The proposed approach is applied to an IEEE 9-bus system. The calculation results are compared with those from the traditional multi-time scheduling method and show the effectiveness of the paper, which can reduce the uncertainty impact of wind, solar, and load forecast, as well as achieve a great balance of security, economic, and environmental benefits.

【 授权许可】

Unknown