混乱的自适应正弦共弦多目标优化算法,以解决微电网最佳能源调度问题
N Karthik1, Arul Rajagopalan2, Mohit Bajaj3,4,5
1Department of Electrical and Electronics Engineering, Hindustan Institute of Technology and Science, Chennai, Tamilnadu, India.
Scientific reports
|August 16, 2024
概括
一个新的混乱自适应正弦共弦算法 (CSASCA) 优化了微电网调度,平衡成本和排放. 这种可再生能源解决方案比传统方法提供了更高的性能.
科学领域:
- 可再生能源系统可再生能源系统
- 优化算法 优化算法
- 环境工程 环境工程
背景情况:
- 越来越关注可再生能源的可靠性,效率和环境效益.
- 微电网 (MG) 需要有效的调度,以平衡运营成本和排放.
- 现有的优化方法在处理MGs中的多目标问题时面临挑战.
研究的目的:
- 引入一个新的短期微型电网调度的多目标框架.
- 开发混沌自适应正弦共弦算法 (CSASCA) 以尽量减少成本和污染.
- 评估CSASCA在各种微电网运营场景中的表现.
主要方法:
- 为微电网调度制定一个多目标优化问题的制定.
- 开发和实施混乱自适应正弦共弦算法 (CSASCA).
- 在优化过程中整合模糊逻辑,以加强决策.
- 通过三个不同的运营场景进行绩效评估,并与传统的SCA进行比较.
主要成果:
- 卡萨斯卡实现了优越的帕雷托最佳解决方案,有效平衡降低成本和减排.
- 最佳值显示了成本 (例如,情景2中的98.203欧元) 和排放 (例如,情景1中的337.28公斤) 的显著改善.
- 在勘探,融合,约束处理和参数灵敏度方面,CSASCA的表现优于传统的SCA.
结论:
- CSASCA是解决微电网调度中的复杂多目标优化问题的强大而有效的工具.
- 算法的混乱的自我适应机制显著提高了优化性能.
- CSASCA为改善可再生能源系统的经济和环境效率提供了一个强大的解决方案.
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