Genetic Algorithm Based Coordination and Optimization Model for Generation Grid Load Storage in Active Distribution Networks

Researchers propose a hybrid optimization framework combining fuzzy logic and genetic algorithms to manage generation, storage, and load coordination in active distribution networks. Tested on IEEE-69 power systems with high renewable energy penetration, the approach reduces technical constraints while maintaining similar investment costs compared to deterministic methods.

This technical paper addresses a critical challenge in modern power grid management: optimizing distributed energy resources under inherent uncertainty. As renewable energy sources become increasingly prevalent, traditional deterministic optimization models struggle to account for weather variability and unpredictable consumer demand patterns. The proposed fuzzy genetic algorithm framework bridges this gap by incorporating probabilistic reasoning into evolutionary optimization, allowing grid operators to make more robust scheduling decisions despite incomplete information.

The research builds on established computational techniques—genetic algorithms have long been used for power system optimization, while fuzzy logic excels at modeling real-world ambiguity. The novelty lies in their integration through penalty factors that simultaneously minimize operational costs and constraint violations. Simulation results on the IEEE-69 test network demonstrate tangible benefits: reduced technical constraints without proportional increases in capital expenditure, suggesting the framework's practical viability.

For the energy sector, this work has implications for grid modernization, particularly in regions with aggressive renewable energy targets. Utility operators and grid planners could leverage such optimization tools to improve dispatch efficiency, reduce curtailment losses, and defer costly infrastructure upgrades. The framework's ability to function under uncertain parameters makes it especially valuable for decentralized energy systems where real-time data collection remains challenging.

Future validation on larger, more complex grids and integration with actual market price signals would strengthen the framework's applicability. Additionally, comparative studies against other uncertainty-handling methods (robust optimization, stochastic programming) would clarify its competitive advantages. As distribution networks become increasingly active with distributed generation and storage, computational frameworks addressing inherent uncertainties will become essential infrastructure components.

• →Hybrid fuzzy-genetic algorithm framework effectively coordinates distributed energy resources under weather and demand uncertainty.
• →Simulation results show reduced technical constraints compared to traditional deterministic optimization without proportional cost increases.
• →Fuzzy logic enables the model to avoid infeasible solutions during network adaptation and planning scenarios.
• →Framework provides scientific basis for uncertainty assessment in modern distribution network planning and operations.
• →Approach addresses critical gap as renewable energy penetration increases in active distribution networks globally.