Optimizing dispatch factor in smart energy networks using cloud-based computational resources

Area of Science:

• Electrical Engineering
• Computer Science
• Energy Systems

Background:

• The modern power grid is increasingly integrating eco-friendly energy sources and advanced matrix technologies.
• Cloud computing offers computational power and adaptability for real-time energy dispatch and grid management.

Purpose of the Study:

• To present a comprehensive framework for cloud-based load management technologies.
• To focus on the dispatch factor as a key parameter for energy dispatch decisions.
• To optimize the dispatch of distributed energy resources.

Main Methods:

• Formulation of a constrained optimization objective function for power distribution networks.
• Development of a novel algorithm for evaluating parametric values within the objective function.
• Creation of a framework for leveraging cloud computational platforms for resource management.

Main Results:

• A methodology for efficient energy dispatch was developed, integrating machine learning, optimization algorithms, and real-time data analytics.
• The proposed methodology dynamically adjusts the dispatch factor for improved energy distribution.
• Implementation on the Google Cloud Platform demonstrated the effectiveness of the proposed approach.

Conclusions:

• The research validates the effectiveness of the proposed cloud-based methodology for optimized energy dispatch.
• The framework facilitates efficient integration and management of distributed resources in evolving power grids.
• Continuous adaptation of the dispatch factor is key to advanced grid operations.