Call For Paper Volume: V, Issue: 06 | JUNE 2026 | International Journal of Advanced Trends in Engineering and Management (IJATEM)
Volume | Issue | | Paper ID: ICNGECT_2026_009 | DOI: https://doi.org/10.59544/ikju4155/icngect26p9

AC Bio-Inspired Micro-Grid Architecture for Renewable-Rich Urban Distribution Systems

G.Jaya Bharathi, R.Pragadeesh, D.Naresh Anand

As cities move rapidly toward high levels of renewable energy, the weaknesses of traditional urban AC distribution networks are becoming hard to ignore. These systems were originally designed for large, centralized power plants and long-distance transmission [9], [10]. Today, they must handle rooftop solar, distributed wind, bidirectional power flow, and rising demand [1], [2]. The result is higher copper losses, repeated voltage conversions, reactive power circulation, and inefficient AC–DC–AC stages. Simply upgrading existing components is no longer enough the structure of the grid itself needs to evolve. This paper proposes a new approach: An AC Bio-Inspired Micro-Grid (BIMG) architecture modeled after the ventilation and transport networks of plant leaves. In a leaf, energy and nutrients are distributed through a finely branched structure that minimizes transport distance while maintaining uniform supply and resilience [7], [8]. There is no single distant control point; instead, regulation happens locally and efficiently across the network. Applying this principle to power systems, the proposed architecture localizes renewable generation near load centers, aggregates distributed sources through a DC layer, and uses grid-forming invert to Synthesize stable medium-voltage AC [3], [4]. Instead of behaving like a rigid radial system, the network operates more like a living structure decentralized, adaptive, and efficient. A first-principle analytical loss model based on three-phase power flow equations is developed to compare the proposed system with a conventional urban network. For a 70 MW case study, the traditional architecture shows total system losses of approximately 19.6%, while the bio-inspired MG reduces losses to 6.27% nearly a 68% reduction. These improvements come from shorter electrical paths, better power factor control, and fewer conversion stages.