Advanced AI approaches for the modeling and optimization of
These advancements underscore the critical role of AI-driven and optimization-based approaches in enhancing the efficiency, resilience, and cost-effectiveness of modern microgrid systems.
Frontiers | Multi-objective coordinated control and optimization for
Guo et al. (2025) proposed a three-objective scheduling strategy for islanded microgrids based on an improved MOPSO algorithm. By enhancing parameter adjustment strategies and
Microgrid Controls | Grid Modernization | NLR
Expertise in distributed optimization and control of adaptable power systems that can be applied to microgrid distributed energy resources dispatch Power hardware-in-the-loop testing of
Distributed Optimal Control for Grid-Forming and Grid-Feeding
Abstract: This paper proposes a distributed optimal control for grid-forming (GFM) and grid-feeding (GFE) converters in an islanded direct current (DC) microgrid. An optimization problem is first
A comprehensive review of microgrid control methods: Focus on AI
Effective control systems are essential for ensuring smooth integration, managing energy storage systems, and maintaining microgrid safety. In this study, a review of recent control methods
Hybrid Intelligent Control System for Adaptive Microgrid Optimization
Effective control strategies are essential for optimizing MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control
Role of optimization techniques in microgrid energy management
Microgrids (MG) are low voltage, small scale electricity grids that comprises a wide variety of distributed energy resources (DER) that can operate in a controlled and coordinated manner to
A review of control strategies for optimized microgrid operations
To maximize energy source utilization and overall system performance, various control strategies are implemented, including demand response, energy storage management, data
A review of control strategies for optimized microgrid operations
Integrating diverse renewable energy sources into the grid has further emphasized the need for effec-tive management and sophisticated control strategies. This review explores the crucial role of control
A Reinforcement Learning Approach for Optimal Control in
Microgrids (MGs) provide a promising solution by enabling localized control over energy generation, storage, and distribution. This paper presents a novel reinforcement learning (RL)-based