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

Abstract

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. However, research in RL for demand response has been lacking the level of standardization that propelled the enormous progress in RL research in the computer science community. To remedy this, we created CityLearn, an OpenAI Gym Environment which allows researchers to implement, share, replicate, and compare their implementations of RL for demand response. Here, we discuss this environment and The CityLearn Challenge, a RL competition we organized to propel further progress in this field.

under revision

References in corpus (4)

• A Centralised Soft Actor Critic Deep Reinforcement Learning Approach to District Demand Side Management through CityLearn
• Reinforcement Learning Testbed for Power-Consumption Optimization
• Advancing Renewable Electricity Consumption With Reinforcement Learning
• Learning a Distributed Control Scheme for Demand Flexibility in Thermostatically Controlled Loads

Cited by in corpus (5)

• MERLIN: Multi-agent offline and transfer learning for occupant-centric energy flexible operation of grid-interactive communities using smart meter data and CityLearn
• Real-world challenges for multi-agent reinforcement learning in grid-interactive buildings
• Methods for Mitigating Uncertainty in Real-Time Operations of a Connected Microgrid
• Quantifying the benefit of load uncertainty reduction for the design of district energy systems under grid constraints using the Value of Information
• PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems