CityLearn v2: An OpenAI Gym environment for demand response control benchmarking in grid-interactive communities

As more distributed energy resources become part of the demand-side infrastructure, it is important to quantify the energy flexibility they provide, as well as identify the best control strategies to accelerate their real-world adoption. CityLearn provides an environment for benchmarking of simple and advanced control algorithms in virtual grid-interactive communities. The updated CityLearn v2 environment introduced here extends the v1 environment to provide load shedding flexibility through heating ventilation and air conditioning power control coupled with a data-driven temperature dynamics model. The updated environment also includes the functionality to assess the resiliency of control algorithms during power outage events.