A new multi-timescale optimal scheduling model considering wind power uncertainty and demand response. (May 2023)

• Record Type: Journal Article
• Authors: Xu, Haiyan; Chang, Yuqing; Zhao, Yong; Wang, Fuli
• Is Part Of: International journal of electrical power & energy systems. Volume 147(2023)
• Publication Date: 2023-05
• Subjects: Wind power scheduling -- Demand response -- Multi-timescale -- Robust optimization -- Stochastic chance-constrained programming; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2022.108832
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A model combining demand response and multi-timescale scheduling is proposed. The model integrates the advantages of day-ahead, intraday and real-time schedulings. The model overcomes the uncertainty caused by large-scale wind power grid connections. The model can ensure the security of the power system and reduce the total cost. Abstract: When wind power is connected to a power grid, intermittency and uncertainty increase the difficulty of power system dispatching and operation. A multi-timescale optimal scheduling model based on wind power uncertainty and demand response are proposed to address the uncertainty of wind power integration. The demand response program is first implemented on the customer side to adjust the electricity consumption pattern and reduce the peak-valley load difference. Then, the robust optimization based on extreme scenarios is used in the day-ahead scheduling stage according to the characteristic that the wind power prediction accuracy gradually improves step by step with the refinement of timescales so that the decision variable meets requirements in all scenarios. In the intraday scheduling stage, the method based on stochastic chance-constrained programming is used, and the out-of-bounds phenomenon is allowed in extreme cases. During real-time scheduling, the deviation remaining after intraday scheduling is corrected. Finally, a 10-unit system is used as an example to demonstrate the feasibility and effectiveness of the proposed scheduling model. The simulation results show that the total economic cost, reserve cost and wind power curtailments of the real-time scheduling model are reduced by 18.21%, 66.06% and 66.57%, respectively, compared with the day-ahead scheduling model. In addition, compared with the intraday scheduling model, the wind power curtailments of the real-time scheduling model are reduced by 20.00%, which proves the necessity of multi-timescale coordinated scheduling.