使用火虫算法来优化混合可再生能源系统的尺寸,这种系统受到电池退化限制,并考虑到电源和负载的不确定性
Tianmeng Yuan1, Yong Mu1, Tao Wang1
1Tangshan Power Supply Company State Grid Jibei Electric Power Co.Ltd, Tangshan, 063000, Hebei, China.
Heliyon
|April 9, 2024
概括
这项研究优化了使用风能,太阳能和电池存储的混合可再生能源系统,考虑到电池的退化. 火算法将成本降至最低,同时在不确定的条件下确保可靠性.
科学领域:
- 可再生能源系统可再生能源系统
- 优化技术 优化技术
- 储能 储能 储能 储能 储能 储能
背景情况:
- 混合可再生能源系统 (HRES) 对于可持续发电至关重要.
- 整合风力轮机,光伏电池板和电池存储带来了规划方面的挑战.
- 电池退化对HRES的长期性能和成本效益产生重大影响.
研究的目的:
- 为风能,太阳能和电池存储的混合系统制定一个具有成本效益的规划策略.
- 将电池退化限制纳入HRES规划模型.
- 通过随机优化解决需求和可再生能源发电的不确定性.
主要方法:
- 采用了基于场景的随机优化方法.
- 火虫算法被用来解决混合整数非线性问题.
- 电池退化模型被整合到系统规划中.
主要成果:
- 拟议的方案成功地将HRES的建设和维护成本降到最低.
- 数字结果表明,利用可再生资源的混合系统的规划成本较低.
- 与确定性方法相比,随机建模提高了系统可靠性,尽管计划成本略高.
结论:
- 有风能,太阳能和电池存储的混合系统为发电提供了可靠和具有成本效益的解决方案.
- 电池为管理可再生能源电力波动提供了必要的灵活性.
- 火算法有效地为复杂的HRES规划问题找到最佳解决方案,降解模型提供精确的电池行为表示.
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