As renewable power rapidly reshapes global electricity systems, engineers face a growing challenge of operating increasingly complex grids with maximum efficiency and minimal cost. A new bio-inspired optimization approach called the Boosting Circulatory System-Based Optimization (BCSBO) algorithm mimics the adaptive behavior of the human circulatory system to navigate difficult decision landscapes in power networks integrating wind and solar energy. Modern electrical networks have evolved into dynamic ecosystems where renewable energy brings both opportunity and uncertainty, with solar irradiation fluctuating by the hour and wind speed swinging without warning. Traditional optimization methods designed for stable, fossil-fuel-based systems struggle with nonlinear constraints, valve-point effects, or prohibited operating zones, while many existing heuristic algorithms stagnate or perform inconsistently under stochastic renewable conditions.
A team of researchers from Texas Tech University, the University of Bologna, and Islamic Azad University has unveiled this high-performance optimization method designed for modern power grid complexities. Published in Frontiers of Engineering Management in 2025, the BCSBO algorithm strengthens an earlier circulatory-inspired framework and delivers superior performance across multiple optimal power flow scenarios. Through extensive testing on standard IEEE 30-bus and 118-bus systems, the team demonstrates how BCSBO outperforms leading algorithms in reducing operational cost and enhancing renewable integration. The upgraded algorithm is modeled on the biological logic of blood flow, equipping "blood-mass agents" with flexible, adaptive movement rules that allow them to circulate through solution space, escape congestion points, and continuously seek better pathways.
The algorithm was rigorously evaluated using five distinct optimal power flow objectives: minimizing fuel cost with valve-point effects, minimizing generation cost under carbon tax, addressing prohibited operating zones, reducing network power losses, and limiting voltage deviations. Across all tests, BCSBO delivered the lowest operational costs, achieving USD 781.86 in the base cost scenario and 810.77 under carbon-tax conditions, beating well-established competitors like Particle Swarm Optimization, Moth–Flame Optimization, Thermal Exchange Optimization, and Elephant Herding Optimization. The team incorporated the inherent uncertainty of wind and solar power by modeling stochastic behavior with Weibull and lognormal distributions, with the algorithm maintaining stability even under highly variable conditions.
The authors emphasize that BCSBO represents a decisive step forward for renewable-era grid optimization, noting that power networks are no longer governed by predictable and static conditions. Their enhanced circulatory-inspired design allows the algorithm to adapt dynamically, avoid stagnation, and deliver reliable decisions even when renewable output is highly uncertain. By offering a more intelligent and robust way to solve optimal power flow problems, BCSBO provides grid operators with a powerful tool for the renewable transition that can help utilities reduce fuel dependence, improve voltage stability, and integrate solar and wind power without compromising network reliability.
