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Economical-environmental-technical optimal power flow solutions using a novel self-adaptive wild geese algorithm with
Pavel Trojovský1, Eva Trojovská2, Ebrahim Akbari2
1Department of Mathematics, Faculty of Science, University of Hradec Králové, Rokitanského 62, 500 03, Hradec Králové, Czech Republic. pavel.trojovsky@uhk.cz.
Scientific Reports
|February 20, 2024
Summary
This study presents an enhanced self-adaptive wild goose algorithm (SAWGA) for optimizing power flow in electrical grids with renewable energy. SAWGA efficiently reduces costs and improves convergence, outperforming traditional methods.
Area of Science:
- Electrical Engineering
- Computer Science
- Optimization Algorithms
Background:
- Optimal Power Flow (OPF) problems are complex, especially with renewable energy integration.
- Traditional algorithms struggle with OPF due to high complexity and local optima.
- Existing methods face challenges in optimizing economical-environmental-technical objectives.
Purpose of the Study:
- To introduce an enhanced self-adaptive wild goose algorithm (SAWGA) for solving OPF problems.
- To address uncertainties in power systems with integrated solar photovoltaic (PV) and wind power (WT) units.
- To improve cost reduction and convergence speed in OPF solutions.
Main Methods:
- Developed SAWGA by incorporating four optimizers into the classical wild goose algorithm.
- Applied SAWGA to optimize OPF models on IEEE 30-bus and 118-bus electrical networks.
- Simulated systems with conventional thermal units alongside PV and WT units.
Main Results:
- SAWGA demonstrated superior performance in optimizing OPF compared to traditional WGA and other algorithms.
- Achieved significant reductions in overall fuel consumption costs.
- Showcased faster and more efficient convergence towards optimal solutions.
Conclusions:
- SAWGA effectively manages OPF challenges and optimizes various objective functions.
- The algorithm exhibits a robust ability to find global or near-global optimal settings.
- SAWGA offers a superior approach for total cost reduction and rapid convergence in power systems.
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