一个基于集群智能的负载转移策略,用于清洁和经济的微电网运行
Srikant Misra1, Bishwajit Dey2, P K Panigrahi1
1Department of EEE, GIET University, Gunupur, Odisha, India.
ISA transactions
|February 4, 2024
概括
需求侧管理 (DSM) 通过转移电力需求来优化微电网能源使用. 这种混合智能方法将发电成本降低10-13%并减少排放,平衡经济和环境因素.
科学领域:
- 电气工程 电气工程
- 能源系统 能源系统
- 人工智能的人工智能
背景情况:
- 微电网负载需求波动,导致使用时间 (TOU) 电价.
- 需求侧管理 (DSM) 是一种在高峰和低谷时间管理负载的经济策略.
- 优化微电网需要平衡发电成本和环境影响.
研究的目的:
- 提出一种新的DSM方法,使用混合智能为LV电网连接的微电网.
- 在降低发电成本和减少DERs污染物排放之间达成妥协.
- 在各种电网策略和定价模型下评估DSM方法的有效性.
主要方法:
- 为DSM方法开发了一种混合智能技术.
- 五个不同的场景检查了不同的电网参与策略,电力市场定价和需求响应机制.
- 环境受约束的经济调度被用来平衡成本和排放.
主要成果:
- 拟议的DSM技术在降低成本方面证明了适用性和有效性.
- 在DSM中,客户参与率为30-40%,导致发电成本减少了10-13%.
- 环境限制的经济调度提供了产能成本和排放之间的优越平衡.
结论:
- 开发的DSM方法适用于LV电网连接的微电网.
- 混合智能技术有效降低发电成本和环境影响.
- 通过统计分析验证的提议的DSM政策,与现有方法相比,提供了更好的解决方案.
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