Reinforcement Learning in Adaptive Control of Power System Generation

Considering our depleting resources, efficient energy production and transmission is the need of the hour. This paper focuses on the concept of using Reinforcement Learning (RL) to control the power systems unit commitment and economic dispatch problem. The idea of reinforcement learning strives to present an ever optimal system even when there are load fluctuations. This is done by training the agent (system), thereby enriching its knowledge base which ensures that even without manual intervention all the available resources are used judiciously. Also the agent learns to reach long term objective of minimizing cost by autonomous optimization. A model free reinforcement learning method called, Q learning is used to find the cost at various loadings and is compared with the conventional priority list method and the performance improvement due to Q learning is proved.