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

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.