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

As more distributed energy resources become part of the demandside 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.