Distributionally robust unit commitment with an adjustable uncertainty set and dynamic demand response. (1st January 2023)

• Record Type: Journal Article
• Authors: Qing, Ke; Huang, Qi; Du, Yuefang; Jiang, Lin; Bamisile, Olusola; Hu, Weihao
• Is Part Of: Energy. Volume 262:Part A(2023)
• Publication Date: 2023-01-01
• Subjects: Adjustable uncertainty set -- Demand response -- Unit commitment -- Renewable energy -- Dynamic scheduling; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.125434
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: To address the uncertainty of renewable energy in unit commitment, an adjustable uncertainty set of renewable energy is introduced and unit commitment is scheduled to guarantee the safe operation of the power system in this set. However, the operational risk arises when renewable energy falls out of the adjustable uncertainty set and this risk should be evaluated to determine the adjustable uncertainty set. In this paper, a distributionally robust optimization approach is proposed to calculate the risks of load shedding and renewable energy curtailment at the lower and upper bounds of the adjustable uncertainty set. After the evaluation of the operational risk, the day-ahead unit commitment with the adjustable uncertainty set is determined together with the demand response reserve in the reduction of the operational risk. In real time, the dynamic demand response is proposed to further reduce the operational risk. The proposed distributionally robust method with the dynamic demand response for dealing with the uncertainty of renewable energy is verified on the IEEE 6-bus, 30-bus, and 118-bus systems. Simulation results show that the proposed method reduces the cost of the unit commitment and the operational risk. Highlights: The adjustable uncertainty set and demand response are used to tackle the uncertainty. A distributionally robust optimization approach is proposed to evaluate the risk. A dynamic demand response scheduling is proposed to reduce the operational risk.