Alvarez Malebran, Ricardo Javier

Power systems around the globe are undergoing a transformation characterized by a massive deployment of converter-interfaced generation (CIG) to effectively combat climate change. However, achieving a seamless transition from current power systems dominated by synchronous generators (SGs) to future ones with high levels of CIG requires overcoming several technical challenges. From a frequency stability perspective, reduced system inertia increases the frequency nadir after a loss of generation thereby endangering frequency stability. In this context, this paper proposes a novel methodology for determining control requirements to impose on CIG as their penetration in the network increases. Results of a case study based on the Chilean grid projected for the year 2046 show that, if only grid-following converters without frequency control capability are deployed, a maximum CIG penetration level of 75% can be achieved without threatening frequency stability. The Chilean system can reach a 99% CIG penetration, provided that the remaining CIGs are deployed in grid-following with frequency support capability. Finally, we show that if the last SG is replaced with a grid-forming converter, the system can still sustain frequency stability and exhibits a good dynamic performance. These results demonstrate that, at least from a frequency stability viewpoint, achieving a 100% based CIG system is technically possible. The proposed methodology can be used by energy regulators to define the control requirements necessary to impose on CIG for achieving renewable energy targets in a secure way. Although the obtained results are particular for the Chilean system, the proposed methodology can be applied to any power system

The increasing uptake of converter-interfaced generation (CIG) is changing power system dynamics, rendering them extremely dependent on fast and complex control systems. Regularly assessing the stability of these systems across a wide range of operating conditions is thus a critical task for ensuring secure operation. However, the simultaneous simulation of both fast and slow (electromechanical) phenomena, along with an increased number of critical operating conditions, pushes traditional dynamic security assessments (DSA) to their limits. While DSA has served its purpose well, it will not be tenable in future electricity systems with thousands of power electronic devices at different voltage levels on the grid. Therefore, reducing both human and computational efforts required for stability studies is more critical than ever. In response to these challenges, several advanced simulation techniques leveraging artificial intelligence (AI) have been proposed in recent years. AI techniques can handle the increased uncertainty and complexity of power systems by capturing the non-linear relationships between the system’s operational conditions and their stability without solving the set of algebraic-differential equations that model the system. Once these relationships are established, system stability can be promptly and accurately evaluated for a wide range of scenarios. While hundreds of research articles confirm that AI techniques are paving the way for fast stability assessments, many questions and issues must still be addressed, especially regarding the pertinence of studying specific types of stability with the existing AI-based methods and their application in real-world scenarios. In this context, this article presents a comprehensive review of AI-based techniques for stability assessments in power systems. Different AI technical implementations, such as learning algorithms and the generation and treatment of input data, are widely discussed and contextualized. Their practical applications, considering the type of stability, system under study, and type of applications, are also addressed. We review the ongoing research efforts and the AI-based techniques put forward thus far for DSA, contextualizing and interrelating them. We also discuss the advantages, limitations, challenges, and future trends of AI techniques for stability studies.