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An Optimal Microgrid Operations Planning Using Improved Archimedes Optimization Algorithm
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AI
DOI：10.1109/ACCESS.2022.3185737.png)
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Abstract
En 中文
More new energy sources have been incorporated into a microgrid model with parameter space growing exponentially, causing optimization scheduling as a nonlinear issue to become more complex and difficult to calculate. This study suggests an improved Archimedes optimization algorithm (IAOA) increases optimal performance for the microgrid operations planning issue. A multiobjective function about optimization planning issues is constructed with relevant economic costs and environmental profits for a microgrid community system (MCS). The IAOA is implemented based on the Archimedes optimization algorithm (AOA) by adding reverse learning and multi-directing strategies to avoid the local optimum trap when dealing with complicated situations. The experimental results of the suggested approach on the CEC2017 test suite and microgrid operations planning problem are compared to the various algorithms in the identical condition scenarios to evaluate the recommended approach performance. Compared findings reveal that the suggested IAOA outperforms the various algorithms in comparison, practical solution, and high feasibility.
Keywords:
Microgrids
Planning
Optimization
Power generation
Costs
Generators
Wind turbines
Microgrid operations planning
archimedes optimization algorithm
microgrid community system
improved archimedes optimization algorithm
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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