Based on the assumption that the microgrid adopts the grid-connected mode, this study proposes a bi-level robust optimization framework for interconnected system coordination to address
This paper presents a novel hybrid modified-Gray Wolf Optimization-Sine Cosine Algorithm-Cuckoo Search Algorithm (MGWOSCACSA algorithm) as an optimization tool to minimize
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
To further enhance the efficiency of solving the economic dispatch model, this study combines chaotic mapping and dynamic opposition-based learning with the traditional Grey Wolf
Incorporation of emission reduction as a core optimization objective, ensuring that energy-scheduling decisions support both economic performance and environmental sustainability.
This study investigates the integration of wind turbines, an electrolyzer, and a hydrogen-compatible micro gas turbine (MGT), with a focus on enhancing operational efficiency and
This research aligns with this national goal. Beyond techno-economic optimization, microgrid (MG) electrification is widely recognized as a key solution for delivering reliable and low
This study highlights the importance of dynamic demand response strategies and grid participation for sustainable and cost-effective microgrid management.
As the utilization of renewable energy (RE) sources has increased significantly, the uncertainty of wind and solar has posed a series of challenges to the optimization scheduling for multi-energy microgrid
This research addresses pressing environmental concerns by proposing a novel optimization framework for combined economic and emissions dispatch (CEED) in microgrids, aiming
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