最佳的能源管理,应用负载弹性,整合可再生资源
Mohamed Mustafa Ragab1, Rania A Ibrahim2, Hussein Desouki1
1Arab Academy for Science, Technology & Maritime Transport, Alexandria, Egypt.
Scientific reports
|September 11, 2023
概括
将可再生能源 (RES) 与需求侧管理 (DSM) 整合,优化了智慧城市的能源. 这种方法有效地降低了电力成本,碳排放和峰值与平均值比率 (PAR).
科学领域:
- 电气工程 电气工程
- 智慧城市 智慧城市
- 可再生能源系统可再生能源系统
背景情况:
- 智慧城市的发展面临着基础设施成本和满足能源需求的挑战.
- 整合可再生能源 (RES) 和需求侧管理 (DSM) 为高效能源管理提供了解决方案.
- 平衡产能和负载配置是最大限度地降低成本和环境影响的关键.
研究的目的:
- 通过整合可再生能源和DSM,优化智能城市的能源管理.
- 实现多目标目标,包括降低成本,减轻碳排放和改进负载概况.
- 解决可再生能源发电和负载需求的不确定性.
主要方法:
- 使用海马优化 (SHO) 算法进行可再生能源的最佳分配和大小.
- 实施需求侧管理 (DSM) 策略,用于灵活的负载配置文件.
- 在各种场景下对IEEE 69bus系统进行全面的模拟.
- 对SHO与遗传算法 (GA),灰狼优化 (GWO),鱼优化 (WO) 和斑马优化 (ZO) 的比较分析.
主要成果:
- 海马优化 (SHO) 算法实现了可再生能源的最佳大小和分配.
- 将弹性负载转移与最佳的可再生能源分配相结合,大大降低了功耗损失.
- 该研究表明,负载的峰值与平均值比率 (PAR) 显著降低.
- 与其他优化技术相比,SHO所需的可再生能源要少得多,以获得可比结果.
结论:
- 通过SHO将弹性负载转移与最佳尺寸和分配的可再生能源相结合,为智慧城市的能源管理提供了有效的解决方案.
- 这种综合方法成功地最大限度地减少了总功耗损失,并降低了峰值与平均值 (PAR) 的比率.
- 这些发现突显了SHO在负载和发电不确定性下优化可再生能源整合的效率.
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