Optimal stochastic power flow using enhanced multi-objective mayfly algorithm

Area of Science:

• Electrical Engineering
• Optimization Theory
• Renewable Energy Systems

Background:

• Classical multi-objective optimal power flow (MOOPF) traditionally uses thermal generators.
• Growing demand for renewable energy necessitates MOOPF solutions incorporating wind and solar photovoltaics (PV).
• Predicting intermittent renewable power presents a significant challenge.

Purpose of the Study:

• To address the MOOPF problem with integrated wind and solar energy sources.
• To develop a robust algorithm capable of handling the complexities of renewable energy integration.
• To optimize multiple objectives including fuel cost, emissions, power loss, and voltage deviation.

Main Methods:

• Application of Weibull probability distribution function (PDF) for wind power prediction.
• Utilization of lognormal PDF for solar power availability assessment.
• Implementation of an enhanced multi-objective mayfly algorithm (NSMA-SF) utilizing non-dominated sorting and superiority of feasible solutions.

Main Results:

• The NSMA-SF algorithm was successfully applied to modified IEEE-30 and standard IEEE-57 bus test systems.
• Simulation results demonstrated the algorithm's effectiveness in tackling the MOOPF problem with renewables.
• Performance was analyzed and compared against existing MOOPF methods.

Conclusions:

• The proposed NSMA-SF algorithm provides an effective approach for multi-objective optimal power flow with wind and solar integration.
• The study highlights the importance of accurate renewable power prediction for grid stability.
• The findings contribute to the advancement of smart grid technologies and renewable energy management.