预测性加热负载管理和住宅部门的能源灵活性分析,使用原型灰色盒模拟方法:应用于北克省的一个实验房子
Matin Abtahi1, Andreas Athienitis1, Benoit Delcroix2
1Department of Building, Civil and Environmental Engineering, Centre for Zero Energy Building Studies, Concordia University, Montréal, QC, Canada.
概括
本研究引入了一种新的方法,利用基于经济模型的预测控制 (EMPC) 在北克的Flex-D电价下优化家庭供暖. 这种方法可以在温度变化最小的情况下,将每日加热成本降低约30%.
科学领域:
- 建筑能源系统 建筑能源系统
- 控制理论 控制理论 控制理论
- 可持续的能源管理 可持续的能源管理
背景情况:
- 住宅能源消耗是整体能源需求的重要贡献者.
- 使用时间 (TOU) 电价,就像北克的Flex-D费率一样,激励非高峰期的能源使用.
- 有效的加热负载管理对于降低能源成本和环境影响至关重要.
研究的目的:
- 为住宅建筑开发原型灰盒模型.
- 实施基于经济模型的预测控制 (EMPC) 算法,用于加热负载管理.
- 根据静态使用时间关税 (Flex-D) 评估EMPC的有效性.
主要方法:
- 使用现场测量 (11R6C网络) 开发灰盒建筑模型.
- 在模型创建中应用启发式分层划分区域的方法.
- 模拟EMPC以优化电加热输入序列.
主要成果:
- 与参考运行相比,EMPC显示每日加热成本的潜在降低约为30%.
- 从设定点观察室内空气温度的最小平均偏差.
- 该研究发现,电价信号对EMPC业绩的影响比天气预报的不确定性更大.
结论:
- 建议的方法有效地优化了根据Flex-D关税的加热负载管理.
- 在不影响热舒适的情况下,EMPC为住宅供暖提供了显著的成本节约.
- 强调EMPC对天气预报不确定性的稳定性,价格信号是主要的驱动因素.
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