ABSTRACT

The integration of distributed energy resources (DERs), such as solar photovoltaic and battery systems, is revolutionizing power grids, offering new possibilities and challenges. To accurately model the impact of aggregated DER behaviors on power transmission while considering operational constraints, it is essential to develop comprehensive integrated transmission-distribution (T&D) network models. However, creating full-scale models for each distribution system is impractical due to the large number of systems connected to the regional transmission grid. In this project, our main objective is to develop Aiesaa, an Artificial-intelligence (AI) assistant, to transform the process of creating compact and integrated T&D network models. We aim to overcome the labor-intensive nature, scalability and model conversion issues, and communication challenges faced by current co-simulation approaches. Aiesaa will leverage advanced machine learning techniques to streamline three crucial modeling tasks. Firstly, it will assist in scenario classification, allowing human experts to focus on non-critical scenarios where simplified models can be used. Secondly, Aiesaa will employ meta-modeling techniques to select and parameterize reduced-order models for critical scenarios, striking a balance between accuracy and complexity. Lastly, Aiesaa will facilitate human-in-the-loop model integration, ensuring collaboration between AI and experts to achieve optimal model performance and complexity.

By combining the speed and accuracy of AI with the insights and experiences of human experts, Aiesaa will introduce a novel framework for engineering model creation that surpasses existing methodologies. This approach automates routine tasks and workflows, freeing up experts to concentrate on higher-level activities that demand their expertise. Importantly, the human-in-the-loop approach ensures that AI serves as a collaborator rather than a replacement for human professionals. The development of Aiesaa has significant implications for computational efficiency and cost-effectiveness. By reducing model complexity and shortening development time, Aiesaa enables the use of compact integrated T&D models on standalone computing platforms. This reduces reliance on expensive infrastructure, enhances data security, and accelerates simulations. Additionally, Aiesaa reduces the learning curve for modelers, empowering them to focus on higher-level tasks such as engineering system design and future scenarios. Upon completion of the project, we plan to share a prototype of Aiesaa with the research and engineering community, fostering advancements in the field.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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