论文标题
通过增强学习来推动可再生电力消耗
Advancing Renewable Electricity Consumption With Reinforcement Learning
论文作者
论文摘要
随着可再生能源在当前的电能混合中的份额上升，它们的间歇性被证明是无碳发电的最大挑战。为了应对这一挑战，我们提出了一个电力定价代理，该电力代理将价格信号发送给客户，并为将客户需求转移到高可再生能源的时期。我们建议采用使用加强学习方法的定价代理，在该方法中，环境由客户，发电的实用程序和天气条件代表。
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.
