{"title":"Advanced decentralized control framework for voltage stability and proportional power sharing in hybrid AC-DC microgrid","authors":"Atul S. Dahane, Rajesh B. Sharma","doi":"10.1016/j.ref.2026.100809","DOIUrl":null,"url":null,"abstract":"<div><div>A massive integration of hybrid AC-DC microgrids into modern power systems has paved the way for adequate robust decentralized control strategies. Proposed advanced decentralized control framework for hybrid AC-DC microgrids presents a solution to highly significant system voltage stability and proportional power sharing issues. The architecture combines Drop Control with Virtual Impedance, Model Predictive Control (MPC), Adaptive Droop-Based Power Sharing, and commonly Event-Triggered Control with Distributed Consensus Algorithm (DCA) for power dispatching in decentralized controllers. The proposed system operates very well and automatically adapts itself under dynamic circumstances for the distributed coordination without relying on a centralized architecture. The MPC model predicts a cost-optimized horizon for minimizing voltage deviation, while adaptive droop adjusts coefficients in real-time according to availability and load demand. Further, iterated local exchanges allow DCA to deliver distributed coordination, complementing the event-triggered logic that permits reductions by over 40% in updates for continuous-time methods. Simulation studies using a hybrid IEEE 39-bus system reveal that voltage deviation would always remain within ±1.2%, while accuracy in power sharing would remain above 97%, with response times under 32 ms, and control convergence within 50 ms. When benchmarked against existing methods of comparison, these performance parameters are still at least 20% better, which indicates strong improvements in scalability, efficiency, and responsiveness. Thus, the proposed framework holds the promise of becoming a credible response towards the next generation of adaptive and intelligent microgrids.</div></div>","PeriodicalId":29780,"journal":{"name":"Renewable Energy Focus","volume":"57 ","pages":"Article 100809"},"PeriodicalIF":5.9000,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Renewable Energy Focus","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1755008426000013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/1/7 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
A massive integration of hybrid AC-DC microgrids into modern power systems has paved the way for adequate robust decentralized control strategies. Proposed advanced decentralized control framework for hybrid AC-DC microgrids presents a solution to highly significant system voltage stability and proportional power sharing issues. The architecture combines Drop Control with Virtual Impedance, Model Predictive Control (MPC), Adaptive Droop-Based Power Sharing, and commonly Event-Triggered Control with Distributed Consensus Algorithm (DCA) for power dispatching in decentralized controllers. The proposed system operates very well and automatically adapts itself under dynamic circumstances for the distributed coordination without relying on a centralized architecture. The MPC model predicts a cost-optimized horizon for minimizing voltage deviation, while adaptive droop adjusts coefficients in real-time according to availability and load demand. Further, iterated local exchanges allow DCA to deliver distributed coordination, complementing the event-triggered logic that permits reductions by over 40% in updates for continuous-time methods. Simulation studies using a hybrid IEEE 39-bus system reveal that voltage deviation would always remain within ±1.2%, while accuracy in power sharing would remain above 97%, with response times under 32 ms, and control convergence within 50 ms. When benchmarked against existing methods of comparison, these performance parameters are still at least 20% better, which indicates strong improvements in scalability, efficiency, and responsiveness. Thus, the proposed framework holds the promise of becoming a credible response towards the next generation of adaptive and intelligent microgrids.