Abstract
The deterministic methods generally used to solve DC optimal power flow (OPF) do not fully capture the uncertainty information in wind power, and thus their solutions could be suboptimal. However, the stochastic dynamic AC OPF problem can be used to find an optimal solution by fully capturing the uncertainty information of wind power. That uncertainty information of future wind power can be well represented by the short-term future wind power scenarios that are forecasted using the generalized dynamic factor model (GDFM)—a novel multivariate statistical wind power forecasting model. Furthermore, the GDFM can accurately represent the spatial and temporal correlations among wind farms through the multivariate stochastic process. Fully capturing the uncertainty information in the spatially and temporally correlated GDFM scenarios can lead to a better AC OPF solution under a high penetration level of wind power. Since the GDFM is a factor analysis based model, the computational time can also be reduced. In order to further reduce the computational time, a modified artificial bee colony (ABC) algorithm is used to solve the AC OPF problem based on the GDFM forecasting scenarios. Using the modified ABC algorithm based on the GDFM forecasting scenarios has resulted in better AC OPF’ solutions on an IEEE 118-bus system at every hour for 24 h.
Highlights
With the increasing penetration level of wind power, challenges from the variability and uncertainty, more precisely from the stochasticity of wind power [1,2], of wind power to power system reliability have been reported by researchers and system operators [3]
The advantages of the generalized dynamic factor model (GDFM) are as follows: (1) the factor analysis (FA) is used to overcome the “curse of dimensionality”, which happens when we model the high dimensional time series through the vector autoregressive model; (2) the co-movement between time series is used to overcome the curse of dimensionality, so that all scenarios in the GDFM share the spatial and temporal correlation and statistical characteristics that the actual wind power outputs have; and (3) arbitrary numbers of wind power scenarios can be generated from white noise signals
This paper shows that the complex stochastic dynamic AC optimal power flow (OPF) under high wind power penetration can be solved based on the wind power forecasting scenarios of the GDFM
Summary
With the increasing penetration level of wind power, challenges from the variability and uncertainty, more precisely from the stochasticity of wind power [1,2], of wind power to power system reliability have been reported by researchers and system operators [3]. The future wind power forecasting scenarios for 24 h are forecasted to solve the stochastic dynamic AC OPF problem. The computational cost is very high because the MC simulation does not consider the correlations among scenarios, and an extensive number of scenarios needs to be generated in order to fully represent combinations of various wind farms. AC OPF to calculate the optimal dispatch by incorporating the variability and uncertainty of wind power based on wind power forecasting scenarios over 24 h and considering the correlation among scenarios. We forecast the future wind power scenarios as the input to the stochastic OPF to reduce the computational cost by utilizing the common characteristics of correlated scenarios. The forecasted future scenarios through the GDFM can represent the spatial and temporal correlations among wind power and can fully capture the uncertainty information of the wind power.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
