论文标题
数据驱动的关键绩效指标和用于构建能源灵活性的数据集:审查和观点
Data-Driven Key Performance Indicators and Datasets for Building Energy Flexibility: A Review and Perspectives
论文作者
论文摘要
通过短期需求端管理(DSM)和储能技术,能源灵活性现在被视为平衡不同能源网格中波动供应与建筑物的能源需求的主要关键。考虑到不断增长的可再生能源生产的间歇性以及建筑物中电力需求的日益增长的动力时,这一点尤其重要。本文对(1)数据驱动的能源灵活性指标(KPI)进行了整体审查,用于操作阶段的建筑物以及(2)可用于测试能量灵活性KPI的开放数据集。该评论从91个最近出版物中确定了81个数据驱动的KPI。这些KPI根据其类型,复杂性,范围,主要利益相关者,数据要求,基线要求,解决方案和受欢迎程度进行了分类和分析。此外,收集并评估了330个建筑数据集。其中,有16人被认为足以具有建筑物执行需求响应或建筑物到网格(B2G)服务的功能。分析了DSM策略,构建范围,网格类型,控制策略,所需的数据功能以及这些选定的16个数据集的可用性。这篇综述揭示了未来解决现有文献局限性的机会:(1)开发新的数据驱动方法,以专门评估现有建筑物的不同能源灵活性策略和B2G服务; (2)开发无基线KPI,可以根据易于访问的建筑传感器和仪表数据计算出来; (3)致力于促进建筑能源灵活性,例如设计公用事业计划,标准化能源灵活性量化和验证过程; (4)策划数据集,具有适当描述以进行能源灵活性评估。
Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is especially important when considering the intermittent nature of ever-growing renewable energy production, as well as the increasing dynamics of electricity demand in buildings. This paper provides a holistic review of (1) data-driven energy flexibility key performance indicators (KPIs) for buildings in the operational phase and (2) open datasets that can be used for testing energy flexibility KPIs. The review identifies a total of 81 data-driven KPIs from 91 recent publications. These KPIs were categorized and analyzed according to their type, complexity, scope, key stakeholders, data requirement, baseline requirement, resolution, and popularity. Moreover, 330 building datasets were collected and evaluated. Of those, 16 were deemed adequate to feature building performing demand response or building-to-grid (B2G) services. The DSM strategy, building scope, grid type, control strategy, needed data features, and usability of these selected 16 datasets were analyzed. This review reveals future opportunities to address limitations in the existing literature: (1) developing new data-driven methodologies to specifically evaluate different energy flexibility strategies and B2G services of existing buildings; (2) developing baseline-free KPIs that could be calculated from easily accessible building sensors and meter data; (3) devoting non-engineering efforts to promote building energy flexibility, such as designing utility programs, standardizing energy flexibility quantification and verification processes; and (4) curating datasets with proper description for energy flexibility assessments.