A new bio-inspired optimization algorithm has demonstrated significant potential for reducing costs and improving stability in power systems integrating renewable energy sources. The Boosting Circulatory System-Based Optimization (BCSBO) algorithm, developed by researchers from Texas Tech University, the University of Bologna, and Islamic Azad University, mimics the adaptive behavior of the human circulatory system to navigate the complex decision landscapes of modern electrical grids.
Modern power networks face increasing challenges due to the variable nature of renewable energy. Solar irradiation and wind speed fluctuate unpredictably, creating uncertainty that traditional optimization methods, designed for stable fossil-fuel-based systems, struggle to manage. Many existing heuristic algorithms perform inconsistently under these stochastic conditions, creating an urgent need for more resilient optimization strategies.
The BCSBO algorithm, detailed in a study published in Frontiers of Engineering Management in 2025 (DOI 10.1007/s42524-025-4167-2), enhances an earlier circulatory-inspired framework by equipping computational "blood-mass agents" with more flexible movement rules. This allows them to circulate through solution spaces, escape congestion points, and continuously seek better pathways, much like the biological system optimizes for survival.
Researchers rigorously tested BCSBO using five distinct optimal power flow objectives on standard IEEE 30-bus and 118-bus systems. The algorithm consistently delivered the lowest operational costs across all scenarios, including minimizing fuel cost with valve-point effects (achieving USD 781.86), minimizing generation cost under carbon tax (USD 810.77), addressing prohibited operating zones, reducing network power losses, and limiting voltage deviations. BCSBO outperformed established competitors like Particle Swarm Optimization, Moth–Flame Optimization, Thermal Exchange Optimization, and Elephant Herding Optimization.
Crucially, the team incorporated the inherent uncertainty of wind and solar power by modeling stochastic behavior with Weibull and lognormal distributions. Even under highly variable conditions, the algorithm maintained stability, demonstrating strong robustness for real-world renewable systems. These results illustrate BCSBO's ability to navigate multi-objective, non-convex, and renewable-driven optimization landscapes with exceptional consistency.
The implications for power grid operators and utilities are substantial. By offering a more intelligent way to solve optimal power flow problems, BCSBO provides a practical tool for reducing fuel dependence, improving voltage stability, and integrating solar and wind power without compromising network reliability. For regions deploying large-scale renewable assets, the algorithm's ability to manage uncertainty is particularly valuable as renewable power rapidly reshapes global electricity systems.
Beyond electricity networks, the algorithm's adaptable computational mechanics make it suitable for broader engineering challenges including energy storage scheduling, smart-grid control, transportation logistics, and industry-scale optimization tasks where rapid, accurate, and uncertainty-tolerant decision-making is essential. The research represents a decisive step forward for renewable-era grid optimization, addressing the growing challenge of operating increasingly complex grids with maximum efficiency and minimal cost.
