Probabilistic spatiotemporal scenario generation method for dynamic optimal power flow in distribution networks

Abstract

Due to the intricate intertwining of spatiotemporal characteristics and the substantial computational complexity caused by high dimensionality, the simultaneous consideration of both spatial and temporal correlations is a non-trivial problem in power system scheduling with variable renewable energy sources. This paper proposes a novel probabilistic spatiotemporal scenario generation (PSTSG) method for the dynamic optimal power flow problem in distribution networks, which generates probabilistic scenarios considering the spatial and temporal correlations among random variables simultaneously. First, static scenarios are generated by Latin hypercube sampling, and the probability of each static scenario is determined by copula-importance sampling theory, which is proposed to ensure the static scenarios yield the same probabilistic characteristics as the random variables. Next, a probability-based scenario reduction technique is implemented to retain effective scenarios and lower the computational burden. Then, multiple linear regression generalizes the static scenarios into dynamic ones with the same probabilities. Finally, a spatiotemporal scenario-based stochastic optimization is proposed for the dynamic optimal power flow problem, which considers uncertainties in variable renewable energy generation and aims to balance the output of controllable generators, renewable energy curtailment, and load shedding. Numerical simulations in a modified IEEE 33-node distribution network show that the proposed approach outperforms the existing methods that do not simultaneously consider spatial and temporal correlations in terms of computational efficiency and accuracy, proving that the proposed PSTSG method can efficiently capture the spatial and temporal correlation so as to make the optimal scheduling decision.