通过前一天排程进行最佳能源管理,考虑可再生能源和智能电网中的需求响应
Lyu-Guang Hua1, Hisham Alghamdi2, Ghulam Hafeez3
1Power China Hua Dong Engineering Corporation Limited, Hangzhou 311122, China.
ISA transactions
|October 8, 2024
概括
本研究介绍了一种新的智能电网 (SPG) 优化模型,使用多目标风力驱动优化 (MOWDO) 来提高能源效率和可持续性. 该模型有效地平衡了经济,环境和舒适因素,优于现有的算法.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 环境科学 环境科学
背景情况:
- 智能电网 (SPG) 需要高效的能源管理,但面临来自不确定分布式发电 (DG) 和负载的挑战.
- 现有的优化模型与可再生能源和消费者需求的随机性质作斗争.
研究的目的:
- 为 SPG 开发一种新的优化模型,解决 DG 和负载中的不确定性.
- 通过同时优化经济,环境和用户舒适性的目标来改善能源管理.
主要方法:
- 利用概率方法来建模 DG 和负载的不确定的行为.
- 使用模糊机制的多目标风驱动优化 (MOWDO) 技术.
- 综合混合需求响应 (HDR) 和电池储能系统 (BESS).
主要成果:
- 与多目标鸟群优化 (MOBSO) 算法相比,MOWDO模型表现出更高的性能.
- 在运营成本,用户舒适度和减少污染排放方面取得了更好的结果.
- 在各种配置中验证了有效性,包括独立和并发的多目标优化.
结论:
- 拟议的概率MOWDO模型为优化不确定性下的SPG提供了一个强大的解决方案.
- 混合需求响应和BESS集成增强了电网稳定性和可持续性.
- 该模型在智能电网中平衡经济,环境和舒适性目标方面取得了重大进展.
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