Abstract
The structural mass of a building provides inherent thermal storage capability. Through sector coupling, the building mass can provide additional flexibility to the electric power system, using, for instance, combined heat and power plants or power-to-heat. In this work, a mathematical model of building inertia thermal energy storage (BITES) for integration into optimized smart grid control is introduced. It is shown how necessary model parameters can be obtained using generalized additive modeling (GAM) based on measurable building data. For this purpose, it is demonstrated that the ceiling surface temperature can serve as a proxy for the current state of energy. This allows for real-world implementation using only temperature sensors as additionally required hardware. Compared with linear modeling, GAM enable improved modeling of the nonlinear characteristics and interactions of external factors influencing the storage operation. Two case studies demonstrate the potential of using building storage as part of a virtual power plant (VPP) for optimized smart grid control. In the first case study, BITES is compared with conventionally used hot water tanks, revealing economic benefits for both the VPP and building operator. The second case study investigates the potential for savings in CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emission and grid connection capacity. It shows similar benefits when using BITES compared to using battery storage, without the need for hardware investment. Given the ubiquity of buildings and the recent advances in building control systems, BITES offers great potential as an additional source of flexibility to the low-carbon energy systems of the future.
Highlights
In the pursuit of low-carbon energy supply, a combination of various storage technologies is required to provide flexibility at different temporal and spatial scales in energy systems
A mathematical model of the storage is required to enable the integration of building inertia thermal energy storage (BITES) into optimized smart grid control
Extending prior work [5], a more general mathematical model is formulated, and respective parameters are obtained with generalized additive models based on measurable building data
Summary
In the pursuit of low-carbon energy supply, a combination of various storage technologies is required to provide flexibility at different temporal and spatial scales in energy systems. Both papers propose a methodology for utilizing BITES in real-world applications and providing the thermal storage potential as flexibility for coupled electric and thermal energy systems. While this central idea and the mathematical formulation of the BITES model for integration into mixed-integer linear programming (MILP) optimization problems remain similar to the conference version, this paper significantly extends the modeling, parameter estimation, and evaluation processes. In the earlier version of this work [5], a multiple linear regression model is used to determine the BITES parameters describing the buildings’ state of energy (SoE) This leads to several constraints with regards to the external factors that influence the SoE.
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