CityLearn v2: Energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities

TL;DR

CityLearn v2 introduces a comprehensive simulation environment for benchmarking and developing control strategies for distributed energy resources in grid-interactive communities, emphasizing resilience, occupant comfort, and carbon-awareness.

Contribution

It extends the original CityLearn platform by incorporating detailed load profiles and multi-agent control capabilities for energy management and occupant-centric objectives.

Findings

Demonstrated reinforcement learning for battery and vehicle-to-grid control

Showcased thermal comfort management during heat pump modulation

Enabled benchmarking of diverse control algorithms in virtual communities

Abstract

As more distributed energy resources become part of the demand-side infrastructure, it is important to quantify the energy flexibility they provide on a community scale, particularly to understand the impact of geographic, climatic, and occupant behavioral differences on their effectiveness, as well as identify the best control strategies to accelerate their real-world adoption. CityLearn provides an environment for benchmarking simple and advanced distributed energy resource control algorithms including rule-based, model-predictive, and reinforcement learning control. CityLearn v2 presented here extends CityLearn v1 by providing a simulation environment that leverages the End-Use Load Profiles for the U.S. Building Stock dataset to create virtual grid-interactive communities for resilient, multi-agent distributed energy resources and objective control with dynamic occupant feedback. This work details the v2 environment design and provides application examples that utilize reinforcement learning to manage battery energy storage system charging/discharging cycles, vehicle-to-grid control, and thermal comfort during heat pump power modulation.