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T.H. Nabuurs
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Mitigating Electrical Grid Congestion using Thermal Energy Storage for Thermal Demand Shifting in Buildings
A Design-to-Operation Framework for Integrated Building Thermal Energy Storage Systems
As global energy systems transition toward full electrification, peak demand congestion has emerged as a primary bottleneck for decarbonization. Thermal Energy Storage (TES) offers a critical solution for strategic peak shifting; however, the lack of a standardized design workflow often leads to inefficient sizing or failure to maintain operational stability. This thesis develops a multi-zone optimization model that integrates transient thermal load profiles and hydraulic stability requirements into a unified control framework. The model processes dynamic inputs, including real-time grid constraints and variable load demands, against user-defined performance preferences. By utilizing a multi-objective function, the model identifies optimal design solutions that prevent the common pitfall of oversized storage vessels while maintaining occupant thermal comfort boundaries.
The research quantifies a two-layer buffering effect. Firstly, structural thermal inertia acts as a passive buffer that dampens thermal demand peaks through the building's intrinsic heat capacity. Secondly, the active TES system integration acts as a predictive buffer: by utilizing real-time data to either pre-charge the building's structural mass or the active TES system, as defined in the control strategy, the system effectively shifts thermal demand temporally. This way the reliance on the physical thermal storage tank during peak periods is reduced.
The general framework is validated with a case study. Sensitivity analysis for the case study reveals a non-linear relationship between heating capacity and required storage volume, with an turning point at 95 kW; increasing heating capacity from 95 kW to 115 kW yields a 21.08\% reduction in hot tank volume for fixed scheduling and 18.47\% for dynamic Model Predictive Control (MPC) strategies. Furthermore, the analysis demonstrates that cold tank sizing is, at some point, constrained by hydraulic stability requirements, rather than thermal cooling demand. These findings provide a robust methodology for determining optimal storage capacities, ensuring grid-compliant delivery while strictly adhering to system performance constraints. ...
The research quantifies a two-layer buffering effect. Firstly, structural thermal inertia acts as a passive buffer that dampens thermal demand peaks through the building's intrinsic heat capacity. Secondly, the active TES system integration acts as a predictive buffer: by utilizing real-time data to either pre-charge the building's structural mass or the active TES system, as defined in the control strategy, the system effectively shifts thermal demand temporally. This way the reliance on the physical thermal storage tank during peak periods is reduced.
The general framework is validated with a case study. Sensitivity analysis for the case study reveals a non-linear relationship between heating capacity and required storage volume, with an turning point at 95 kW; increasing heating capacity from 95 kW to 115 kW yields a 21.08\% reduction in hot tank volume for fixed scheduling and 18.47\% for dynamic Model Predictive Control (MPC) strategies. Furthermore, the analysis demonstrates that cold tank sizing is, at some point, constrained by hydraulic stability requirements, rather than thermal cooling demand. These findings provide a robust methodology for determining optimal storage capacities, ensuring grid-compliant delivery while strictly adhering to system performance constraints. ...
As global energy systems transition toward full electrification, peak demand congestion has emerged as a primary bottleneck for decarbonization. Thermal Energy Storage (TES) offers a critical solution for strategic peak shifting; however, the lack of a standardized design workflow often leads to inefficient sizing or failure to maintain operational stability. This thesis develops a multi-zone optimization model that integrates transient thermal load profiles and hydraulic stability requirements into a unified control framework. The model processes dynamic inputs, including real-time grid constraints and variable load demands, against user-defined performance preferences. By utilizing a multi-objective function, the model identifies optimal design solutions that prevent the common pitfall of oversized storage vessels while maintaining occupant thermal comfort boundaries.
The research quantifies a two-layer buffering effect. Firstly, structural thermal inertia acts as a passive buffer that dampens thermal demand peaks through the building's intrinsic heat capacity. Secondly, the active TES system integration acts as a predictive buffer: by utilizing real-time data to either pre-charge the building's structural mass or the active TES system, as defined in the control strategy, the system effectively shifts thermal demand temporally. This way the reliance on the physical thermal storage tank during peak periods is reduced.
The general framework is validated with a case study. Sensitivity analysis for the case study reveals a non-linear relationship between heating capacity and required storage volume, with an turning point at 95 kW; increasing heating capacity from 95 kW to 115 kW yields a 21.08\% reduction in hot tank volume for fixed scheduling and 18.47\% for dynamic Model Predictive Control (MPC) strategies. Furthermore, the analysis demonstrates that cold tank sizing is, at some point, constrained by hydraulic stability requirements, rather than thermal cooling demand. These findings provide a robust methodology for determining optimal storage capacities, ensuring grid-compliant delivery while strictly adhering to system performance constraints.
The research quantifies a two-layer buffering effect. Firstly, structural thermal inertia acts as a passive buffer that dampens thermal demand peaks through the building's intrinsic heat capacity. Secondly, the active TES system integration acts as a predictive buffer: by utilizing real-time data to either pre-charge the building's structural mass or the active TES system, as defined in the control strategy, the system effectively shifts thermal demand temporally. This way the reliance on the physical thermal storage tank during peak periods is reduced.
The general framework is validated with a case study. Sensitivity analysis for the case study reveals a non-linear relationship between heating capacity and required storage volume, with an turning point at 95 kW; increasing heating capacity from 95 kW to 115 kW yields a 21.08\% reduction in hot tank volume for fixed scheduling and 18.47\% for dynamic Model Predictive Control (MPC) strategies. Furthermore, the analysis demonstrates that cold tank sizing is, at some point, constrained by hydraulic stability requirements, rather than thermal cooling demand. These findings provide a robust methodology for determining optimal storage capacities, ensuring grid-compliant delivery while strictly adhering to system performance constraints.