Improving Power Quality and Reducing Outages in Smart Grids with Load Balancing Approach and Using Distributed Generation

With the rapid evolution of electricity grids into smart, cyber-physical energy systems, traditional paradigms for ensuring power quality and reducing outages are being fundamentally challenged. The integration of distributed generation (DG) resources—such as solar photovoltaics, microturbines, and wind power—alongside increased variability and bidirectional power flows, has rendered classical control and management strategies inadequate. This study proposes a novel, unified approach that synergizes adaptive load balancing algorithms with the real-time coordination of distributed generation, leveraging the latest advances in artificial intelligence and hybrid control architectures to advance smart grid performance. The presented methodology introduces an AI-driven, reinforcement learning-based controller embedded within a hierarchical architecture. At the edge, distributed microcontrollers perform rapid data acquisition and local control, while a centralized optimization engine enables global, system-wide decisions. The core innovation lies in the adaptive load redistribution algorithm, which continuously monitors critical grid parameters and executes targeted load shifting based on evolving operational and forecasted conditions. Simultaneously, a dynamic dispatch model is employed for DG units, utilizing fine-grained renewables forecasting (solar irradiation, wind patterns) and grid status feedback to synchronize generation with consumption—ensuring maximum power quality and stability. Comprehensive validation is conducted through co-simulation environments that combine DigSILENT Power Factory grid modeling with hardware-in-the-loop (HIL) implementations, simulating both urban and rural feeder networks. Case studies feature a mid-scale distribution segment with intensive rooftop solar integration and flexible loads. Results demonstrate that the proposed method substantially outperforms conventional static balancing and uncoordinated DG dispatch; specifically, voltage unbalance is reduced by up to ۳۴%, harmonic distortion (THD) by over ۵۰%, and customer outage duration by ۴۲%. Beyond technical improvements, the hybrid control design enables seamless transition between centralized optimization and decentralized autonomy, ensuring grid resilience even in the face of communication failures or extreme contingencies. This paper's key novelty is the holistic, AI-enabled integration of load management and distributed generation within a scalable, real-world framework. The approach is both adaptive—able to respond to evolving grid states—and extensible, with potential applications in electric vehicle integration and smart microgrid operation. Ultimately, this research marks a significant step toward self-learning, resilient smart grids capable of sustaining high power quality and reliability amidst the increasing complexity of modern energy ecosystems.

کلیدواژه ها:

Smart Grids ، Power