CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy Management

Understand

Rapid urbanization, increasing integration of distributed renewable energy resources, energy storage, and electric vehicles introduce new challenges for the power grid.

• In the US, buildings represent about 70% of the total electricity demand and demand response has the potential for reducing peaks of electricity by about 20%.
• Unlocking this potential requires control systems that operate on distributed systems, ideally data-driven and model-free.
• For this, reinforcement learning (RL) algorithms have gained increased interest in the past years.