International Journal of Advances in Electrical Engineering
Optimization of hybrid renewable energy systems using quantum-inspired algorithms
Author(s): Amina N Wekesa, Samuel K Odhiambo, Grace M Mutua and Peter O Nyang’au
Abstract:
The study explores the application of quantum-inspired optimization algorithms (QIOA) for enhancing the performance, efficiency, and reliability of hybrid renewable energy systems (HRES) comprising photovoltaic (PV), wind, and battery storage components. Conventional metaheuristic algorithms, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE), often struggle with complex, nonconvex optimization landscapes and uncertain renewable variability. To address these limitations, a quantum-inspired optimization framework was developed that integrates quantum principles superposition, tunneling, and probabilistic rotation operators within classical computational structures. Using a simulated hybrid microgrid model in MATLAB/Simulink with stochastic solar and wind inputs, the QIOA approach was benchmarked against classical methods for key performance indicators, including Levelized Cost of Energy (LCOE), Net Present Cost (NPC), reliability, and renewable energy fraction.
Results from fifty independent simulations revealed that QIOA significantly reduced both LCOE and NPC while improving convergence rate and reliability (≈99%), outperforming traditional algorithms across all tested metrics. Statistical evaluation using bootstrap confidence intervals and Wilcoxon signed-rank tests confirmed the robustness and significance of these improvements. Additionally, QIOA demonstrated superior resistance to environmental and load uncertainties, maintaining consistent performance under stress conditions. These outcomes validate the hypothesis that quantum-inspired optimization offers a more powerful, globally convergent, and computationally efficient method for HRES optimization than existing metaheuristics.
The research provides both theoretical and practical contributions: it establishes the foundation for integrating quantum computation principles into classical energy optimization and proposes real-world applications for energy planners, microgrid operators, and smart grid designers. The findings highlight the feasibility of incorporating QIOA into renewable energy management platforms, thereby promoting the design of sustainable, cost-effective, and resilient energy infrastructures. This study ultimately demonstrates that quantum-inspired computation represents a promising pathway toward intelligent, uncertainty-resilient optimization for next-generation renewable energy systems.Pages: 59-64 | Views: 273 | Downloads: 86
Download Full Article: Click Here
