An information gap decision theory and improved gradient-based optimizer for robust optimization of renewable energy...

Area of Science:

• Electrical Engineering
• Optimization Theory
• Renewable Energy Systems

Background:

• Radial distribution networks require efficient integration of renewable energy sources.
• Uncertainties in renewable generation and network demand pose significant challenges.
• Existing optimization methods may struggle with complex multi-objective problems.

Purpose of the Study:

• To develop a robust fuzzy multi-objective framework for optimizing dispersed and hybrid renewable energy resources.
• To propose a novel multi-objective improved gradient-based optimizer (MOIGBO) for enhanced convergence.
• To analyze the impact of resource allocation (dispersed vs. hybrid) and uncertainty on network performance.

Main Methods:

• A robust fuzzy multi-objective optimization framework was implemented.
• A novel MOIGBO incorporating Rosenbrock's technique was developed.
• Information Gap Decision Theory (IGDT) was used to determine maximum uncertainty radius (MUR) and system robustness.

Main Results:

• The MOIGBO effectively balanced objectives, achieving optimal solutions on the Pareto front.
• Dispersed renewable energy resource allocation demonstrated superior performance compared to hybrid configurations.
• Maximum system robustness was achieved at 30% uncertainty risk, with specific MURs for resource production and load demand.

Conclusions:

• The proposed MOIGBO is a powerful tool for optimizing renewable energy integration in distribution networks.
• Dispersed renewable energy systems are more effective than hybrid systems for improving network operation under uncertainty.
• The framework provides valuable insights for managing uncertainties in renewable energy forecasting and demand patterns.