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Inexact stochastic risk-aversion optimal day-ahead dispatch model for electricity system management with wind power under uncertainty

Ji, Ling, Huang, Guo-He, Huang, Lu-Cheng, Xie, Yu-Lei, Niu, Dong-Xiao
Energy 2016 v.109 pp. 920-932
case studies, consciousness, decision making, electricity, electricity costs, markets, models, power generation, risk, spinning, uncertainty, wind power, wind turbines
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.