School of
Information Technology and Electrical Engineering

Speaker: Mr Lei You
Seminar Date: Tue, 15/10/2019 - 15:00
Venue: 49-502; AEB Seminar Room
Host: Prof Tapan Saha; Dr Hui Ma

Seminar Type:  PhD Confirmation Seminar

Abstract: 

The increasing integration of intermittent renewable energy into the electricity network can cause considerable network congestions. One possible solution to alleviate network congestions is to utilize the dynamic line rating (DLR) to gain the increase of the conductor capacity. However, the integration of DLR into power system operation and planning still faces a number of challenges. The most significant challenges are the spatial-temporal variabilities and forecasting uncertainties of the DLR. In particular, other than the uncertainty of renewable generation, the adoption of DLR can introduce additional uncertainties and impose more stresses on the safe and economical operation of the electricity network.

This research investigates the integration of DLR into the operating and planning of transmission networks considering uncertainties which are congested due to the renewable accommodation. The main task of this research is to develop an efficient framework which can utilize the benefits of DLR for network congestion mitigation while managing the spatial-temporal uncertainties of DLR and renewable generation to keep the power system security.

During the past 12 months, the research work is on the investigation of DLR for the increased integration of wind while managing the correlated DLR and wind generation forecast uncertainties in the power system scheduling. A chance-constrained AC-OPF framework considering the affine corrective control to tackle the uncertainties has been proposed and developed. Firstly, the joint chance constraints are adopted to ensure the whole system’s security against the uncertainties. Then a computational efficient iterative algorithm is developed. The algorithm incorporates the partial linearization of AC power flow and the robust reformulation of joint chance constraints based on polyhedral uncertainty sets. Moreover, the individual chance constraints for more flexible uncertainty management is explored. Sample-based and analytical reformulations are combined together. Case studies demonstrate that the proposed framework and algorithms can effectively handle the correlation between DLR and wind generation, achieve a considerable benefit of DLR, and properly control the operational uncertainty.

The next step research work involves the improvement of the modelling of the uncertainties and the associated optimization framework. In addition, the coordination between DLR and other flexible technologies for improved renewable integration will be studied. In particular, the uncertainty management using  emerging controllable technologies such as HVDC and batteries will be investigated. Furthermore, the optimized deployment of DLR in transmission planning problems considering the long-term variabilities and uncertainties of DLR will be studied.

Biography: 

Mr Lei You received B.Sc. degree in Electrical Engineering from Liaoning University, China in 2015, and M.Sc. degree in Electrical Engineering from South China University of Technology, China in 2018. Currently, he is working toward his PhD degree at the University of Queensland, Australia. His research interests include renewable energy integration, dynamic line rating, forecast uncertainty and power system reliability.