Abstract

Stabilizing power fluctuations and arranging reserves play a more crucial role in the secure and economic operation of the active distribution network (ADN), as the high penetration rate of uncertain renewable energy sources (RES) increases. In this paper, a multi-timescale ADN optimal dispatching model based on stochastic model predictive control (SMPC) is proposed to track the random fluctuation of RES and address the operation risk. Firstly, load shedding and RES curtailment risk is quantified by conditional value at risk (CVaR) based on the probability distribution and a reserve arrangement strategy is further proposed, taking into account the trade-off between reserve and operation risk costs. Secondly, the copula theory is introduced to establish a high-dimensional prediction error model and capture the spatial-temporal characteristics. Furthermore, typical RES output scenarios for the day-ahead, intra-day and real-time stages are generated according to the scenario method and prediction error curve. Then, a multi-timescale optimal dispatching framework based on SMPC is established, in which the outputs of units are optimized and coordinated under different time scales. Finally, simulation results on a modified IEEE 33-bus power system, demonstrated the superiority of the proposed method in tracking the fluctuation of RES, and also verify the effectiveness of the method to improve the economy of system operation.

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