Uncertainty-Aware Management of Smart Grids Using Cloud-Based LSTM-Prediction Interval

Area of Science:

• Smart Grid Technology
• Artificial Intelligence in Energy Systems
• Distributed Computing

Background:

• Smart grid power management faces challenges due to the stochastic nature of renewable energy sources and consumer demand.
• Optimizing social welfare in power grids requires efficient coordination between generation and consumption units.
• Existing frameworks often struggle with real-time uncertainty analysis and decentralized computation.

Purpose of the Study:

• To introduce an uncertainty-aware cloud-fog computing framework for smart grid power management.
• To develop a multiagent-based system that optimizes social welfare by treating consumers and generators as agents.
• To integrate deep learning for accurate prediction intervals of stochastic sources and enable decentralized computation.

Main Methods:

• A multiagent-based algorithm where consumers set prices for their demand and generators participate in a social welfare optimization problem.
• Deep learning for distributive uncertainty analysis, calculating prediction intervals for loads, wind turbines (WTs), and photovoltaics (PVs).
• Deployment of fog computing for rapid calculations and local storage, complemented by cloud services for large-scale data processing and virtual applications.

Main Results:

• The proposed framework effectively handles uncertainty from stochastic sources like WTs and PVs by providing a 'preparation range' for agent power consumption/generation.
• Fog and cloud computing infrastructure enables efficient, decentralized computation and data management for real-time power management.
• Performance evaluation on smart grid test systems demonstrates the framework's capability to achieve optimal outcomes rapidly across various grid scales.

Conclusions:

• The uncertainty-aware cloud-fog framework provides a robust and efficient solution for smart grid power management.
• The multiagent approach with deep learning-based uncertainty analysis enhances grid stability and economic efficiency.
• This framework offers a scalable and timely solution for optimizing power management in modern smart grids.