CityLearn: A Tutorial on Reinforcement Learning Control for Grid-Interactive Efficient Buildings and Communities (Tutorials Track)
Kingsley E Nweye (The University of Texas at Austin); Allen Wu (The University of Texas at Austin); Hyun Park (The University of Texas at Austin); Yara Almilaify (The University of Texas at Austin); Zoltan Nagy (The University of Texas at Austin)
Buildings are responsible for up to 75% of electricity consumption in the United States. Grid-Interactive Efficient Buildings can provide flexibility to solve the issue of power supply-demand mismatch, particularly brought about by renewables. Their high energy efficiency and self-generating capabilities can reduce demand without affecting the building function. Additionally, load shedding and shifting through smart control of storage systems can further flatten the load curve and reduce grid ramping cost in response to rapid decrease in renewable power supply. The model-free nature of reinforcement learning control makes it a promising approach for smart control in grid-interactive efficient buildings, as it can adapt to unique building needs and functions. However, a major challenge for the adoption of reinforcement learning in buildings is the ability to benchmark different control algorithms to accelerate their deployment on live systems. CityLearn is an open source OpenAI Gym environment for the implementation and benchmarking of simple and advanced control algorithms, e.g., rule-based control, model predictive control or deep reinforcement learning control thus, provides solutions to this challenge. This tutorial leverages CityLearn to demonstrate different control strategies in grid-interactive efficient buildings. Participants will learn how to design three controllers of varying complexity for battery management using a real-world residential neighborhood dataset to provide load shifting flexibility. The algorithms will be evaluated using six energy flexibility, environmental and economic key performance indicators, and their benefits and shortcomings will be identified. By the end of the tutorial, participants will acquire enough familiarity with the CityLearn environment for extended use in new datasets or personal projects.