基于最佳自适应式启发式算法,以灵活负载的能源优化为基础,使用智能电网中的需求响应
Hisham Alghamdi1, Lyu-Guang Hua2, Ghulam Hafeez3
1Department of Electrical Engineering, College of Engineering, Najran University, Najran, Saudi Arabia.
PloS one
|November 5, 2024
概括
本研究介绍了用于智能电网需求响应负载调度的最佳自适应风驱动优化 (OAWDO) 方法. 在保持用户满意度的同时,OAWDO有效地将电力成本和峰值与平均需求比率降至最低.
科学领域:
- 智能电网和能源优化
- 能源管理中的人工智能
- 能源系统中的消费者行为
背景情况:
- 需求响应 (DR) 对于智能电网的能源优化至关重要,为公用事业公司和消费者带来好处.
- 实施DR面临挑战,包括管理时间变化的定价,可再生能源间歇性和设备需求.
- 在确保客户满意的同时,尽量减少峰值与平均需求比率 (PADR) 和电力成本是关键目标.
研究的目的:
- 使用先进的优化技术开发和实施家用电器负载调度控制器 (LSC).
- 模拟和解决复杂的负载调度问题,考虑各种电网和消费因素.
- 为了最大限度地降低电力成本和PADR,并减轻反弹峰值的影响,而不会影响用户的舒适性.
主要方法:
- 采用了最佳自适应风驱动优化 (OAWDO) 算法,一种随机方法,用于负载调度.
- 在LSC模型中集成了时间变化的定价,可再生能源,电器需求,电池存储和电网约束.
- 结合倾斜块关税与实时变化的定价,以防止反弹峰值.
主要成果:
- 该OAWDO算法有效地安排家用电器以最大限度地降低成本和PADR.
- 在MATLAB中进行的模拟显示了OAWDO在基因算法 (GA),鱼优化算法 (WOA),火优化算法 (FFOA) 和风驱优化 (WDO) 上的优势.
- 与其他方法相比,OAWDO实现了电力成本,PADR和反弹峰值形成的显著降低.
结论:
- 拟议的基于OAWDO的LSC是智能电网需求响应负载调度的有效解决方案.
- 这种方法成功地平衡了经济效益和用户舒适性,解决了能源优化方面的关键挑战.
- OAWDO提供了一种强大而有效的方法来管理能源需求和供应不确定性.
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