Deriving generation dispatch is essential for efficient and secure operation of electric power systems. This is usually achieved by solving a security-constrained optimal power flow(SCOPF) problem, which is by nature non-convex, nonlinear and thus computational intensive. The state-of-the-art optimization approaches are not able to solve this problem for large-scale power systems within power system operation time window (usually 5 minutes). In this work, we developed supervised learning approaches to determine security-constrained generation dispatch within much shorter time window. More importantly, the physical constrain of only accessing to local measurements and other information in most utilities’ real-time operation can not be ignored for the predictive models.The feasibility and accuracy of utilizing only local features(measurements and grid information in one area) to predict optimal local generation dispatch (dispatch of all generators in the corresponding area) in multi-area power systems has been explored. The results showed optimal local generation dispatch can be predicted with local features with high accuracy, which is comparable to the results obtained with global features.
Revised: February 11, 2020 |
Published: January 17, 2019
Sun Y., X. Fan, Q. Huang, X. Li, R. Huang, T. Yin, and G. Lin. 2019.Local Feature Sufficiency Exploration for Predicting Security-constrained Generation Dispatch in Multi-Area Power Systems. In The 17th IEEE International Conference on Machine Learning and Applications (ICMLA 2018), Orlando, FL, 1283-1289. Piscataway, New Jersey:IEEE.PNNL-SA-137906.doi:10.1109/ICMLA.2018.00208