Advancing Renewable Electricity Consumption With Reinforcement Learning
This addresses the problem of grid stability and carbon reduction for energy systems, but appears incremental as it applies existing RL methods to a known bottleneck.
The paper tackles the challenge of intermittence in renewable energy by proposing a reinforcement learning-based electricity pricing agent that shifts customer demand to periods of high renewable generation, aiming to reduce carbon emissions.
As the share of renewable energy sources in the present electric energy mix rises, their intermittence proves to be the biggest challenge to carbon free electricity generation. To address this challenge, we propose an electricity pricing agent, which sends price signals to the customers and contributes to shifting the customer demand to periods of high renewable energy generation. We propose an implementation of a pricing agent with a reinforcement learning approach where the environment is represented by the customers, the electricity generation utilities and the weather conditions.