Autonomous Agent for Microgrid Management

Our team proposes an autonomous agent to optimize microgrid management by
controlling power distribution, managing battery usage, and directing energy
flow to ensure efficient and reliable energy delivery based on real-time needs.

Reinforcement learning can be used to train this agent to make decisions based
on varying input conditions and utilizes a reward function to optimize it to pursue
the best outcome decision.

Proposed Model Inputs:

• Weather data
• Geospatial data
• Energy market data
• Microgrid constraints: maximum battery load, energy available from the grid, etc.

Proposed Model Outputs:

• Battery charging schedule
• Energy import and export recommendations
• Energy shortage warnings and alerts
• Energy production and demand forecasts
• Grid stability predictions