AI-enhanced model predictive control in microgrids: systematic review of algorithms, applications, and emerging trends

Yanping Qiao; Wei Li; Donghui Guo

AI-enhanced model predictive control in microgrids: systematic review of algorithms, applications, and emerging trends

Yanping Qiao

Wei Li

Donghui Guo

Vol. 26, No. 4, pp. 831-842, Apr. 2026

10.1007/s43236-026-01312-5

Artificial intelligence

Microgrid model prediction

Control Policy

Distributed generation

Abstract

This study comprehensively reviews model predictive control (MPC) strategies for power converters in microgrids across primary, secondary, and tertiary control levels. Key developments include the integration of artificial intelligence (AI) with MPC to enhance dynamic response and uncertainty handling, particularly through data-driven approaches, which optimize the cost function. The control framework is further refined into AI-based MPC to address challenges such as topology diversity, high photovoltaic penetration, and switching dynamics. Digital twin technology establishes bidirectional mapping between the physical and cyber spaces to support real-time optimization. By combining situational awareness, cloud computing, and AI-driven forecasting, adaptive and self-optimizing microgrid operation is facilitated. However, the increasing complexity of modern microgrids—driven by source-load uncertainty and limited resource flexibility—demands a shift from conventional model-driven methods to a data-physical fusion paradigm. Finally, future research directions are outlined, which highlight the integration of scalable AI-based MPC and the standardization of digital twin technologies.

Statistics

Show / Hide Statistics

Statistics (Past 3 Years)
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.

Cite this article

[IEEE Style]

Y. Qiao, W. Li, D. Guo, "AI-enhanced model predictive control in microgrids: systematic review of algorithms, applications, and emerging trends," Journal of Power Electronics, vol. 26, no. 4, pp. 831-842, 2026. DOI: 10.1007/s43236-026-01312-5.

[ACM Style]

Yanping Qiao, Wei Li, and Donghui Guo. 2026. AI-enhanced model predictive control in microgrids: systematic review of algorithms, applications, and emerging trends. Journal of Power Electronics, 26, 4, (2026), 831-842. DOI: 10.1007/s43236-026-01312-5.