For this reason, a two-stage algorithm called the Multi-Objective Group Search Optimizer with Pre-Exploration (MOGSOPE) is proposed to efficiently achieve the optimal solution under wind
The feature selection algorithm, multi-objective NSGA-III, identifies the optimal subset features from wind energy datasets.
We validate the methodology using a SWT2.3-93 wind turbine with a rated power of 2.3 MW. We compare the reinforcement learning control to classic controls to show that they are
Experimental results show that, compared to reference open-source controller tuning, the proposed strategy reduces the standard deviations of output power, tower-top fore-aft displacement,
This paper proposes a method that combines meta reinforcement learning with multi-agent reinforcement learning to solve the multi-objective two-stage robust optimization of wind/PV/thermal
In this paper, a multi-objective generation expansion planning (MOGEP) approach for integrating large-scale wind power was proposed, which optimizes three objectives simultaneously, namely the
To enhance system efficiency and economic feasibility, a model of a wind power-integrated hybrid energy storage system with battery and hydrogen was developed using TRNSYS.
In the real world, the OPF problem is multiobjective and the trade-off between multiple objects gives better optimal conditions for operation. In [9], the authors introduced a modified JAYA
The widespread utilization of renewable energy sources, such as wind and solar energy, plays a crucial role in achieving the dual-carbon goal. However, the unce.
Under the current context of the large-scale integration of wind and solar power, the coupling of hydropower with wind and solar energy brings significant impacts on grid stability.
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