Fuzzy genetic algorithm cuts grid costs while handling renewable uncertainty

A new paper on arXiv presents a computational framework that merges fuzzy logic with genetic algorithms to optimize the coordination of generation, grid connection, load, and energy storage in active distribution networks. The authors, including Jinlu Zhang and colleagues, address the challenge of system uncertainties caused by weather variability and fluctuating user demand. They model renewable energy generation, load curves, and market prices using fuzzy set representations, and then apply a genetic algorithm that introduces fuzzy elements into system management. The algorithm adjusts penalty factors to improve stability and seeks better scheduling or power dispatch solutions. The fitness function penalizes uncertainty and constraint violations to optimize expected operational costs, producing feasible and economical results even when parameters are unknown.

Simulation results on the IEEE-69 power system—a standard benchmark with high renewable penetration and storage—show that the fuzzy genetic algorithm strategy effectively reduces technical constraints compared to deterministic optimization schemes. Total investment remains at a similar level. The fuzzy reasoning optimization also avoids unreasonable or impossible network adaptation choices. This approach provides a scientific basis for uncertainty assessment in distribution network planning, offering grid operators a tool to make more robust decisions under unpredictable conditions. The paper is 9 pages with 4 figures and is available on arXiv.