Power performance assessment of building energy systems

Power performance;Building energy systems;Demand response performance indicator;Performance indicator;Building load assessment

Makhmalbaf, Atefe ; Augenbroe, Godfried Architecture Brown, Jason Grijalva, Santiago Somasundaram, Sriram Ahmed, Osman ; Augenbroe, Godfried

University:Georgia Institute of Technology

Department:Architecture

关键词: Power performance;    Building energy systems;    Demand response performance indicator;    Performance indicator;    Building load assessment;   

Others  :  https://smartech.gatech.edu/bitstream/1853/55592/1/MAKHMALBAF-DISSERTATION-2016.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

Buildings are the main consumers of electricity across the world. In the past research, the focus has been on evaluating the energy performance of buildings whereas the instantaneous power consumption of systems and aggregated load profiles have received less attention. Today, buildings are involved in the challenges of ‘power grid modernization.’ This is mostly because the increasing diversity of building systems requires a better understanding of their behavior during peak hours and the “demand charges” that are associated with it. Other drivers are the need to lower the carbon footprint of the electricity supply (i.e., reduction of grid as well as building scale emissions) and the growing number of demand response (DR) programs that rely on dynamic adjustments of building systems to support grid stability and resiliency. However, we lack methods, models, and performance measures that support building-grid interaction evaluations. This thesis has developed methods and models needed to study and assess performance of buildings in the electricity system. To achieve this, building thermal models, conventionally used to capture energy consumption are enhanced with electricity characteristics (e.g., voltage). With these models the impact of voltage on load shape of different systems is investigated and a set of quantitative power performance indicators (PIs) defined. These PIs are consequently applied to a variety of building control strategies in the context of DR scenarios. The developed PIs provide the fundamental component needed in decision support and auto-DR systems to quantitatively, systematically, and consistently compare and assess power performance of different building system types in given operation scenarios. This assessment is important for a range of applications. At building level, facility managers can use quantitative performance comparison of control strategies for both energy efficiency and peak reduction decisions. At grid level, our method can be used for power planning and management studies such as load forecasting. In the first part, this thesis demonstrates the feasibility of the thermal enhanced models with electrical characteristics by developing these models and showing how they can be constructed and used for different system types. In the second part, this thesis verifies usability of the performance assessment framework developed for DR and energy management decisions at building level. This is achieved by applying performance indicators defined to a set of scenarios. Results indicate how each performance indicator can support different performance criteria such as power and energy efficiency while maintaining thermal comfort of occupants. These quantitative PIs can be implemented in decision support systems that consider the trade-off between energy efficiency and investments in power management at the building site.