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

12/18/2020

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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 electricity demand and demand response has the potential for reducing peaks of electricity by about 20 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.

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

MERLIN: Multi-agent offline and transfer learning for occupant-centric energy flexible operation of grid-interactive communities using smart meter data and CityLearn

Advancing Renewable Electricity Consumption With Reinforcement Learning

Gym-ANM: Open-source software to leverage reinforcement learning for power system management in research and education

BEAR: Physics-Principled Building Environment for Control and Reinforcement Learning

Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution Systems

Combined Peak Reduction and Self-Consumption Using Proximal Policy Optimization

Predicting vehicles parking behaviour in shared premises for aggregated EV electricity demand response programs

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

Related Research

MERLIN: Multi-agent offline and transfer learning for occupant-centric energy flexible operation of grid-interactive communities using smart meter data and CityLearn

Advancing Renewable Electricity Consumption With Reinforcement Learning

Gym-ANM: Open-source software to leverage reinforcement learning for power system management in research and education

BEAR: Physics-Principled Building Environment for Control and Reinforcement Learning

Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution Systems

Combined Peak Reduction and Self-Consumption Using Proximal Policy Optimization

Predicting vehicles parking behaviour in shared premises for aggregated EV electricity demand response programs