Solving bi-objective economic-emission load dispatch of diesel-wind-solar microgrid using African vulture...

Area of Science:

• Electrical Engineering
• Optimization Techniques
• Renewable Energy Systems

Background:

• Microgrids require reliable power, increasingly integrating renewable energy sources (RESs).
• Optimal microgrid sizing involves balancing cost and emissions, a complex bi-objective optimization problem (Economic-Emission Load Dispatch - EELD).
• Metaheuristic approaches are commonly used for solving EELD problems.

Purpose of the Study:

• To explore advanced metaheuristic methods for the EELD problem in microgrids.
• To propose and apply the African Vulture Optimization Algorithm (AVOA) for microgrid EELD.
• To analyze the impact of individual RESs on microgrid cost and emissions.

Main Methods:

• The African Vulture Optimization Algorithm (AVOA), inspired by vulture behavior, was employed.
• AVOA was validated on standard test systems before application to a Jaisalmer, India microgrid with diesel, wind, and solar sources.
• Statistical tests (ANOVA, Wilcoxon, robustness, TukeyHSD) were used to assess performance and superiority.

Main Results:

• AVOA demonstrated superior performance in solving the microgrid EELD problem.
• Significant reductions in cost (5.25%) and emissions (33.09%) were achieved compared to other methods.
• The study analyzed RES impact across three generation scenarios, using viability scores and performance indices.

Conclusions:

• AVOA is highly effective for microgrid EELD, offering substantial cost and emission reductions.
• The algorithm's exploration and exploitation characteristics contribute to its success.
• AVOA provides a statistically validated, superior solution for optimizing microgrid power systems.