Multiple-Cut Benders Decomposition for Wind-hydro-thermal Optimal Scheduling with Quantifying of Various Types of Reserves

Due to the uncertainty of wind power and load as well as reserve price fluctuation, a new challenge is brought to the optimization of system reserve. Existing models of reserve optimization based on the deterministic method are not able to accurately quantify the reserve demand, which is caused by the uncertainty of wind power and load. This paper proposes a novel quantification method, which is based on the Discrete Fourier Transform (DFT) and Parsevals' theorem, to quantify the reserve demand of various reserves types. Such quantification results are introduced to the coordinated optimization scheduling model of wind-hydro-thermal power system. And to manage the risk produced by the price uncertainty of various reserve types, the multiple Conditional Value-at-Risk (CVaR) indicators are encompassed in the model. The proposed model is converted to a mixed integer linear programming model and solved by multiple-cut Benders decomposition algorithm with Jensen's inequality. Simulation results verify the effectiveness of the reserve demand quantitative method, as well as the advantages of the model in risk management and economic benefits. Besides, the maximum prediction error tolerance basing on the optimized results of large-scale system verifies the rationality and applicability of the proposed method and model.