The energy consumption in Abu Dhabi has tripled within the last 10 years. Much attention has been devoted to the challenge of increasing generation capacity to meet this demand; yet, of equal concern is the increased load on the system and the extent to which this will stress the transmission network and increase susceptibility to dynamic instabilities.
Power system stability is one of the most important issues for secure and reliable network operation. Any failure to address stability concerns can lead to widespread blackouts and even system collapse. This problem is particularly relevant in the UAE context given the stated objective of the Abu Dhabi and UAE governments to significantly increase the penetration of renewable energy (RE). This will result in greater spatial distribution and temporal variability in electricity generation, both of which would impose additional strain and stability threats on existing networks. In addition, the recent completion of GCC interconnections between the UAE, Oman, and KSA has significantly changed the stability profile of the UAE power system and raised concerns about inter-area oscillations (IAOs). IAOs are a threat to wide-area stability, which are very difficult to detect using conventional Energy Management Systems and could lead to sweeping blackouts that occur within minutes. Existing tools for power system stability assessment are mainly based on “what if” scenarios about system disturbances, and may be inadequate given the complexity of these challenges due to the uncertainty of RE power generation and nonlinearities of power system.
Therefore, the primary goal of this research project is to develop a prototype for a commercial (offline/online) tool (for the power system operator) that will facilitate effective power system stability assessment, visualization, and enhancement (SAVE). The SAVE tool will utilize real-time measurements and full system observability to reliably determine stability margins in the presence of uncertainty resulting from RE power generation and load demand, and will enable small and large signal stability assessments. In this context, advanced methods of individual invariance and functions will be developed and combined with Artificial Intelligence (AI) algorithm, along with data analytics based classification engine to precisely estimate the domain of stability and to predict the system transient stability margins. Furthermore, a visualization system will be developed to support human-in-the-loop classification, diagnosis of the transmission system events, and the visualization of system stability. In addition, power system stability enhancement and the IAOs problems at the interconnections will be investigated, focusing on two key aspects: the real-time detection of IAOs and the development of an advanced damping controller. Finally, the SAVE tool will be implemented and validated for TRANSC network operation. This project is conducted with the active collaboration and support from TRANSCO and Manitoba Hydro International.
