Optimizing urban-scale energy use with uncertainty from occupant behaviour

在居住者行为不确定的情况下优化城市规模的能源使用

• 批准号: RGPIN-2020-06804
• 负责人: Ouf, Mohamed
• 金额: $ 2.26万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=718519
• 关键词: Optimizing; urban; scale; energy; use

Energy use in urban areas accounts for over 70% of global green house gas (GHG) emissions. Therefore, thorough understanding of their energy supply and demand patterns is critical for achieving any substantial reductions in energy use and related emissions. Energy demand, particularly in buildings, is subject to several sources of uncertainty; among which occupant behaviour is one of the least understood and accounted for. The applicant's doctoral and post-doctoral research introduced new approaches to evaluate and quantify occupant behaviour's effect on energy use uncertainties in buildings at both the design and operational stages. However, the propagation of these uncertainties and their effect on temporal and spatial energy use patterns at the district and wider urban-scales are still largely unknown. Accounting for these uncertainties is crucial for urban-scale energy systems (e.g. district heating/cooling, micro grids with distributed renewable energy generation), especially as new strategies are explored to manage building-grid interactions (e.g. battery or thermal storage and demand response). The long-term objective of this research program is to design and operate resilient net-zero energy communities that optimize energy use in buildings and their interaction with the grid, while considering uncertainty from occupant behaviour. Over the next five years, the applicant and his students will mainly focus on demand-side uncertainties in urban energy systems by 1) quantifying the impact of occupant behaviour on urban-scale energy use, and 2) developing urban-scale energy simulation workflows that integrate occupant-related uncertainty. The overall research program aims to address matching energy supply and demand at different temporal and spatial scales, which is essential for integrating distributed renewable energy sources and improving energy efficiency in urban areas. Outcomes of this research program will lead to optimizing urban energy systems by leveraging inter-building diversity to achieve significant reductions in Canada's energy consumption and related GHG emissions. It builds upon the applicant's expertise in statistical modelling and building simulations to position Canada as a leader in the nascent research field of urban-scale energy modelling. Overall, this research program represents an important step towards designing smart and resilient communities, which are key to meeting Canada's long-term goal of cutting 80% of GHG emissions by 2050 and eventually becoming a carbon neutral economy.

城市地区的能源使用占全球温室气体排放的70%以上。因此，彻底了解它们的能源供应和需求模式对于实现能源使用和相关排放的任何实质性减少至关重要。能源需求，特别是建筑物的能源需求，受到几个不确定因素的影响；其中，居住者的行为是最不被理解和解释的。申请人的博士和博士后研究引入了新的方法来评估和量化居住者行为对建筑在设计和运营阶段能源使用不确定性的影响。然而，这些不确定性的传播及其在区域和更广泛的城市尺度上对时空能源使用格局的影响在很大程度上仍然是未知的。考虑到这些不确定性对于城市规模的能源系统（例如区域供热/制冷，具有分布式可再生能源发电的微电网）至关重要，特别是在探索管理建筑-电网相互作用的新策略（例如电池或储热和需求响应）时。