Inexact stochastic risk-aversion optimal day-ahead dispatch model for electricity system management with wind power under uncertainty. (15th August 2016)

• Record Type: Journal Article
• Authors: Ji, Ling; Huang, Guo-He; Huang, Lu-Cheng; Xie, Yu-Lei; Niu, Dong-Xiao
• Is Part Of: Energy. Volume 109(2016)
• Page Start: 920
• Page End: 932
• Publication Date: 2016-08-15
• Subjects: Day-ahead market -- Bidding strategy -- Uncertainty -- Stochastic robust programming -- Wind power generation; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2016.05.018
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: High penetration of wind power generation and deregulated electricity market brings a great challenge to the electricity system operators. It is crucial to make optimal strategy among various generation units and spinning reserve for supporting the system safety operation. By integrating interval two-stage programming and stochastic robust programming, this paper proposes a novel robust model for day-ahead dispatch and risk-aversion management under uncertainties. In the proposed model, the uncertainties are expressed as interval values with different scenario probability. The proposed method requires low computation, and still retains the complete information. A case study is to validate the effectiveness of this approach. Facing the uncertainties of future demand and electricity price, the system operators need to make optimal dispatch strategy for thermal power units and wind turbine, and arrange proper spinning reserve and flexible demand response program to mitigate wind power forecasting error. The optimal strategies provide the system operators with better trade-off between the maximum benefits and the minimum system risk. In additional, two different market rules are compared. The results show that extra financial penalty for the wind power dispatch deviation is another efficient way to enhance the risk consciousness of decision makers and lead to more conservative strategy. Highlights: An inexact two-stage stochastic robust programming model for electricity system with wind power penetration. Uncertainties expressed as discrete intervals and probability distributions. Demand response program was introduced to adjust the deviation in real-time market. Financial penalty for imbalance risk from wind power generation was evaluated.