Optimal Generator Start-Up Sequence Strategy Considering Renewable Energy Participation

Abstract

During power system restoration, it is important to use black start units (BSUs) to provide cranking power and quickly restore non-black start units (NBSUs) to maximize system generation capacity. Centralized renewable plants have advantages of lower cranking power and lager ramping rate, and their participation in generators start-up can speed up the system recovery process. In this work, a generator start-up sequence (GSUS) optimization strategy that considers the participation of renewable energy sources is proposed. First, according to the special nature of the non-black unit’s power curve, GSUS model is represented as a continuous-time mixed integer linear programming, with the consideration of critical recovery paths. Then, the uncertainties of renewable energy are modeled by fitting the probability distribution of prediction errors, and the correlations between new energy sources are considered by Gaussian-copula functions. Several representative scenarios are extracted by clustering. Finally, a continuous-time stochastic optimization model of GSUS considering renewable energy uncertainty and correlation is developed. It is verified on an IEEE 39-bus system that the proposed method can efficiently determine the optimal generator start-up sequence and critical recovery paths. Meanwhile, the continuous-time model is much faster than the discrete-time model.