PANEL: Power Grid Operations and Planning Under Uncertainty: How Can AI Help Address Existing Challenges?

Sara Eftekharnejad , Syracuse Univ., USA; Chilukuri Mohan; Syracuse Univ., USA; Nancy Min, EcoLong; ; Amarsagar Matavalam, Arizona State U.; Bo Yang, Hitachi America Ltd.; ABSTRACT: Power grid uncertainties have increased dramatically in recent years. The rapid integration of intermittent energy resources such as wind and solar energy systems is expected to increase the grid uncertainties further. These uncertainties challenge the everyday grid operation and planning if not correctly modeled and quantified. In recent years, there has been significant research and development in data-driven modeling of distributed renewable energy resources. Powered by recent advancements in artificial intelligence and machine learning, these data-driven models are especially robust to handle the dynamic nature of intermittent energy resources. In addition to generation uncertainties, extreme weather patterns have led to an increase in failure uncertainties. Hence, modeling and predicting failures in near real-time is more critical than ever in preventing widespread blackouts. However, traditional statistical techniques fail to predict future events under these interdependent uncertainties. As a result, there has been significant interest in recent years to develop more accurate data-driven failure models that are also efficient and fast enough for near real-time decision-making. This panel will discuss various AI-centric research and development efforts to address the existing challenges of power grid uncertainties. These efforts include generation forecast and modeling, cascading failure prediction, and power grid operations and planning under uncertainty. The panelists will discuss how AI could complement traditional power grid analysis techniques to address the existing problems and where AI techniques are limited in addressing those challenges. The panelists will also discuss their outreach and industry collaboration efforts.