Designing Grid-Interactive Smart Communities using CityLearn
The decarbonization of buildings presents new challenges for the reliability of the electrical grid as a result of the intermittency of renewable energy sources and increase in grid load brought about by end-use electrification. To restore reliability, grid-interactive efficient buildings can provide flexibility services to the grid through demand response. Residential demand response programs are hindered by the need for manual intervention by customers. To maximize the energy flexibility potential of residential buildings, an advanced control architecture is needed. Reinforcement learning is well-suited for the control of flexible resources as it is able to adapt to unique building characteristics compared to expert systems. Yet, factors hindering the adoption of RL in real-world applications include its large data requirements for training, control security and generalizability. This talk will cover some of our recent work addressing these challenges. We proposed the MERLIN framework and developed a digital twin of a real-world 17-building grid-interactive residential community in CityLearn. We show that 1) independent RL-controllers for batteries improve building and district level KPIs compared to a reference RBC by tailoring their policies to individual buildings, 2) despite unique occupant behaviors, transferring the RL policy of any one of the buildings to other buildings provides comparable performance while reducing the cost of training, 3) training RL-controllers on limited temporal data that does not capture full seasonality in occupant behavior has little effect on performance. Although, the zero-net-energy (ZNE) condition of the buildings could be maintained or worsened as a result of controlled batteries, KPIs that are typically improved by ZNE condition (electricity price and carbon emissions) are further improved when the batteries are managed by an advanced controller.
Zoltan Nagy, PhD
Dr. Nagy is an assistant professor in the Department of Civil, Architectural, and Environmental Engineering at The University of Texas at Austin, directing the Intelligent Environments Laboratory since 2016. A roboticist turned building engineer, his research interests are in smart buildings and cities, renewable energy systems, control systems for zero emission building operation, and the application of machine learning and artificial intelligence for the built environment for a sustainable energy transition. He has received the Outstanding Researcher Award from IBPSA-USA in 2022, several Best Paper awards from the CISBAT conference, Building & Environment journal, as well as a Highest Cited Paper award from Applied Energy. He organized and chaired the first workshop on Reinforcement Learning for energy management in buildings and cities (RLEM’20) at ACM BuildSys’20.