Multi-objective pathfinder algorithm for multi-objective optimal power flow problem with random renewable energy...

Area of Science:

• Electrical Engineering
• Optimization Theory
• Renewable Energy Systems

Background:

• The multi-objective optimal power flow (MOOPF) problem involves balancing competing objectives like cost, emissions, and grid stability.
• Integrating intermittent renewable energy sources (wind, solar, tidal) introduces significant uncertainty into power systems.
• Accurate modeling of renewable energy variability is crucial for reliable grid operation.

Purpose of the Study:

• To develop a robust MOOPF model incorporating generation cost, emissions, real power loss, and voltage deviation.
• To address the uncertainty of renewable energy sources using probabilistic distribution models.
• To propose an advanced optimization algorithm for solving the complex MOOPF problem.

Main Methods:

• Utilized Weibull, lognormal, and Gumbel distributions to model wind, solar, and tidal energy intermittency.
• Integrated four energy sources (including renewables) into the IEEE-30 test system for realistic simulations.
• Developed a multi-objective pathfinder algorithm (MOPFA) employing elite dominance and crowding distance for optimization.

Main Results:

• MOPFA effectively minimized the four key optimization objectives, achieving a well-distributed Pareto front with diverse solutions.
• The proposed model demonstrated feasibility in reducing emissions and other performance indicators compared to existing methods.
• Statistical tests confirmed MOPFA's superior multi-objective optimization performance, accuracy, and speed.

Conclusions:

• The developed MOOPF model and MOPFA provide a powerful tool for optimizing power systems with significant renewable energy penetration.
• MOPFA significantly outperforms other multi-objective algorithms in solving complex power flow optimization problems.
• The fuzzy decision system effectively selected a compromise solution, enhancing practical applicability.