#1ASHRAE 2023 ASHRAE WINTER CONFERENCE ATLANTA, Feb 4-8 | AHR Expo, Feb 6-8 Seminar 21 - Optimizing Design and Controls for Thermal Energy Storage at the Building and Community Scale Optimizing phase change composite thermal energy storage using the thermal Ragone framework Nelson James National Renewable Energy Laboratory [email protected] ONREL Transforming ENERGY#2Learning Objectives • Describe how thermal energy storage can result in a lower levelized cost of storage than battery energy storage • Demonstrate how additive manufacturing can result in advanced heat exchanger design and manufacturing for thermal energy storage devices Explain how thermal power requirements impact the optimal design needs. of thermal energy storage heat exchangers Explain the benefits of using optimization to inform distribution and control of mixed types of cool thermal storage across a connected community ASHRAE is a Registered Provider with the American Institute of Architects Continuing Education Systems. Credit earned on completion of this program will be reported to ASHRAE Records for AIA members. Certificates of Completion for non-AIA members are available on request. This program is registered with the AIA/ASHRAE for continuing professional education. As such, it does not include content that may be deemed or construed to be an approval or endorsement by the AIA or any material of construction or any method or manner of handling, using, distributing, or dealing in any material or product. Questions related to specific materials, methods, and services will be addressed at the conclusion of this presentation.#3Outline • Thermal Ragone Framework • Design Optimization Finite Difference Model and Results Approximate Models • Model Comparisons Conclusions#4Thermal Ragone Framework Fluid outlet temperature (°C) ++ 16 1Wm¹K¹ 14 Cutoff temperature 12 10 00 09 40 W m¹ K¹ 1C 200 160 Specific power (W kg) 120 80 40 1WmK¹ 20 40 W m¹K¹ 1Wm1K1 10 W m¹ K-1 1 W m¹K¹ 10 W m¹ K¹ 2 2.5 W m¹ K¹ 5Wm1K1 20 W m¹ K¹ -1 10 2.5 W m¹ K-1 20 W m¹ K-1 40 W m¹ K-1 5W m¹K¹ 40 W m¹ K¹ 0 I 100 80 60 40 20 20 6 0 10 20 30 40 50 State of charge (%) Specific energy (W h kg¯¹) Woods et al. 2021 1 C 0 60 60 • Ragone plots have been recently applied to PCM thermal energy storage devices to illustrate the tradeoffs between power requirements and energy storage capacity • A cutoff temperature can be defined to determine when the useful storage capacity has been depleted • Material and geometric properties like the PCM thermal conductivity and spacing of the heat transfer fluid channels can have significant impacts on the shape of the Ragone plot for a particular storage device#5T fin Device Optimization High temperature fluid inlet Liquid PCM Thermal load Solid PCM Woods et al. 2021 Operational Power requirement (C-rate) Fluid temperature difference Cutoff Temperature Initial storage temperature Geometry Heat transfer fluid tube spacing Tube thickness Porosity of composite additives Material Tfout Low temperature fluid outlet PCM latent heat capacity PCM transition temperature PCM and composite additive densities Thermal conductivities of PCM and composite additives Material Costs Design Objectives Maximize Volumetric Energy Density [kWh/m³] Minimize Upfront Costs [$/kWh] Minimize Levelized Costs of Storage [$/kWh]#6Finite Difference Model liquid solid Phase Change Composite • • A 2D transient numerical model using the finite-difference approach representing a planar thermal energy storage device. Captures the progression of phase change during the discharge process and the effect that has on the heat transfer fluid outlet temperature Assumes the PCM is static, and conduction is the only mode of heat transfer in the composite • Performed a parametric assessment (>14,000 combinations) to capture the influence of device parameters on the performance of a storage device in a space cooling application. • C-rates: 1/6 - 3 Transition Temperature: 1°C -9°C PCM layer thickness: 1cm - 20 cm · dhij WHX [k dy kdx dt Mi.j dx (Tij-1+ Tij+1-2Ti,j) + (Ti−1,j + Ti+1j − 2Tij) - dy • Porosity of conductivity additives: 80% - 100% Woods et al. 2021#7Finite Difference Model - Results kWh kWh = m³ effective m³ nominal Geometry Dependent Nominal Volumetric Energy Density (kWh/m³) 0.95 Porosity (-) 0.85 50 50 40 40 30 30 20 20 110 0.8 0 0.02 0.04 0.06 0.08 0.1 Thickness (m) . Charge Utilization Geometry-Material-Operation Dependent porosity Charge Utilization (%) 10 Tcutoff - Tphase Change (°C) 7 James et al., 2022 4 thickness 0.25 1.5 2.5 C - Rate (1/hr) 100 80 60 60 60 40 40 20 20 0#810 Tcutoff - Tphase Change (°C) Finite Difference Model - Results 7 4 Effective Specific Energy Density (kWh/m³) porosity thickness 0.25 1.5 C - Rate (1/h) 2.5 50 50 40 40 30 30 20 20 10 10 10 Tcutoff - Tphase Change (°C) 4 0 James et al., 2022 Inverse Capital Costs (kWh/$) porosity 0.03 thickness 0.25 1.5 C - Rate (1/h) 2.5 0.025 0.02 0.015 0.01 0.005 0#9Finite Difference Model - Results 16 FDM Cost Optimal Thickness (cm) 00 100 Variations in Optimal Thickness Energy Density and Cost 000 0000 0000 4 000 8 12 16 FDM Energy Density Optimal Thickness ( cm ) 95 FDM Cost Optimal Porosity (%) 90 85 80 80 Variations in Optimal Porosity Energy Density and Cost 85 90 0000 95 000 00 ° 000 0 20 FDM Energy Density Optimal Porosity (%) 20 20 Variations in Optimal Thickness Energy Density and LCOS 16 12 FDM LCOS Optimal Thickness (cm) 100 о 00 00 4 8 12 16 FDM Energy Density Optimal Thickness ( cm ) 100 Variations in Optimal Porosity Energy Density and LCOS FDM LCOS Optimal Porosity (%) 5 90 5 80 80 85 90 000 95 0000 ° ooo o 20 FDM Energy Density Optimal Porosity (%) James et al., 2022 10 20 16 12 FDM LCOS Optimal Thickness (cm) 100 S Variations in Optimal Thickness Cost and LCOS 100 4 8 12 16 FDM Cost Optimal Thickness (cm) Variations in Optimal Porosity Cost and LCOS FDM LCOS Optimal Porosity (%) 95 90 85 20 80 80 85 90 FDM Cost Optimal Porosity (%) 95 100#10Parallel Phase Front Approximation Model Assumes that the melting front of the phase change composite is always parallel to the flow direction of the heat transfer fluid I' th solid thliquid RPCM = Крсм surface • For a given driving temperature difference (cutoff temperature - Transition temperature) the maximum allowable phase change composite layer thickness needed for full charge utilization can be derived assuming a constant phase change composite thermal conductivity • An effective charge utilization can be determined from relating the maximum thermal resistance of the PCM layer to the allowable thickness determined by the cutoff temperature for a given power delivery requirement Target conditions: Qtarget = Crate * Cap Resistance when you reach cutoff: Qtarget Tfluid,cutoff - Tt Rconv + Rcontact + Rcutoff Rcutoff SOC cutoff = 1 - Rmax#11Lumped Mass Approximation Model • Treats the thermal energy storage as an RC-circuit with an effective resistance and capacitance based on material and geometric properties • Defines a time constant capturing the impact of the driving. temperature difference • Relates the time required for a nearly complete discharge (99%) to the time required for discharge determined by the target C-rate % Discharge 63.2% τ Ttransition Ceff Reff Tcutoff 100% Discharge time#12Model Comparisons Optimal TES Heat Exchanger LCOS ( ¢/kWh) Optimal Energy Density (kWh/m³) Optimal TES Heat Exchanger Cost ( $/kWh) 55 25 50 FDM Parametric Optimal 10 5 30 30 35 40 Error Lines ± 5% AAD: 1.6672 45 PPFA Analytical Model Optimal 50 5 FDM Parametric Optimal 40 35 30 25 - 50 20 20 25 FDM Parametric Optimal 20 5 Error Lines ± 5% AAD: 0.15643 30 35 5 40 45 50 55 PPFA Analytical Model Optimal Error Lines ± 5% AAD: 0.24804 15 20 10 PPFA Analytical Model Optimal 25 25 Optimal TES Heat Exchanger LCOS ( ¢/kWh) Optimal Energy Density (kWh/m³) Optimal TES Heat Exchanger Cost ( $/kWh) 55 25 50 50 50 FDM Parametric Optimal 40 ज 30 30 35 40 Error Lines ± 5% AAD: 0.47811 45 FDM Parametric Optimal 45 40 35 30 25 20 50 20 25 Lumped Mass Approximation Optimal 30 Error Lines ± 5% AAD: 1.6923 35 40 45 20 20 FDM Parametric Optimal 1 50 55 5 5 Lumped Mass Approximation Optimal James et al., 2022 10 -06- Error Lines ± 5% AAD: 0.2808 15 20 Lumped Mass Approximation Optimal 25#13Model Comparisons FDM Parametric Optimal FDM Parametric Optimal 35 40 45 50 50 30 ထွက် 30 5 50 Optimal Energy Density (kWh/m³) 30 30 35 40 Error Lines ± 5% AAD: 1.6672 45 PPFA Analytical Model Optimal FDM Parametric Optimal Optimal Thickness ( cm ) Optimal Porosity (%) 20 100 16 12 00 4 50 0 4 FDM Parametric Optimal 95 90 85 Error Lines ± 1 cm AAD: 0.85782 80 16 20 80 85 8 12 PPFA Analytical Model Optimal Optimal Energy Density (kWh/m³) Optimal Thickness ( cm ) 20 35 Error Lines ± 5% AAD: 0.47811 40 45 Lumped Mass Approximation Optimal FDM Parametric Optimal 16 12 FDM Parametric Optimal Error Lines ±2.5% porosity AAD: 0.71524 90 95 100 PPFA Analytical Model Optimal Optimal Porosity (%) 100 95 90 00 подобос 4 85 Error Lines ± 1 cm AAD: 0.49879 Error Lines ± 2.5% porosity AAD: 1.925 0 50 0 8 4 12 16 Lumped Mass Approximation Optimal 80 20 80 85 90 Lumped Mass Approximation Optimal 95 100 James et al., 2022#14Model Comparisons FDM Parametric Optimal FDM Parametric Optimal 55 55 Optimal TES Heat Exchanger Cost ($/kWh) 50 50 45 35 30 25 25 20 20 25 55 50 FDM Parametric Optimal 20 20 16 12 00 Optimal Thickness ( cm ) 100 Optimal Porosity (%) FDM Parametric Optimal 85 90 95 $6 S Error Lines ± 5% AAD: 0.15643 Error Lines ± 1 cm AAD: 0.2187 0 80 30 35 40 45 50 55 0 4 8 80 12 16 85 20 PPFA Analytical Model Optimal PPFA Analytical Model Optimal Optimal TES Heat Exchanger Cost ($/kWh) 45 40 35 30 25 20 20 25 30 Error Lines ± 5% AAD: 1.6923 35 40 45 8 FDM Parametric Optimal Error Lines ±2.5% porosity AAD: 0.93202 90 95 100 PPFA Analytical Model Optimal Optimal Thickness ( cm ) Optimal Porosity ( % ) 100 20 16 12 FDM Parametric Optimal 85 85 90 95 Error Lines ± 1 cm AAD: 0.42371 80 80 50 55 4 8 12 Lumped Mass Approximation Optimal 16 20 Lumped Mass Approximation Optimal James et al., 2022 ooo oooodo 0 00 0000000000 00 0 0 0000 ° 00 85 Error Lines ±2.5% porosity AAD: 3.7321 90 95 Lumped Mass Approximation Optimal 100#15Model Comparisons FDM Parametric Optimal FDM Parametric Optimal 35 40 45 50 50 30 ထွက် 30 5 50 Optimal Energy Density (kWh/m³) 30 30 35 40 Error Lines ± 5% AAD: 1.6672 45 PPFA Analytical Model Optimal FDM Parametric Optimal Optimal Thickness ( cm ) Optimal Porosity (%) 20 100 16 12 00 4 50 0 4 FDM Parametric Optimal 95 90 85 Error Lines ± 1 cm AAD: 0.85782 80 16 20 80 85 8 12 PPFA Analytical Model Optimal Optimal Energy Density (kWh/m³) Optimal Thickness ( cm ) 20 35 Error Lines ± 5% AAD: 0.47811 40 45 Lumped Mass Approximation Optimal FDM Parametric Optimal 16 12 FDM Parametric Optimal Error Lines ±2.5% porosity AAD: 0.71524 90 95 100 PPFA Analytical Model Optimal Optimal Porosity (%) 100 95 90 00 подобос 4 85 Error Lines ± 1 cm AAD: 0.49879 Error Lines ± 2.5% porosity AAD: 1.925 0 50 0 8 4 12 16 Lumped Mass Approximation Optimal 80 20 80 85 90 Lumped Mass Approximation Optimal 95 100 James et al., 2022#16Conclusions ● As the C-Rate decreases and the driving temperature difference increases, the porosities and thicknesses needed to maximize energy density, minimize cost, or minimize LCOS will both increase · Higher C-Rates and lower driving temperature differences necessitate thinner PCC layer thicknesses and lower porosity conductivity additives for optimizing thermal storage devices For the assumed material cost estimates, minimizing LCOS or energy-specific capital costs requires thicker PCC layers with less conductivity enhancing material compared to those needed to maximize the effective energy density Simplified models that incorporate elements of the Ragone framework were presented which can aid in accelerating the evaluation of thermal energy storage heat exchanger designs#17Acknowledgments • Allison Mahvi - University of Wisconsin Madison / NREL • Jason Woods - NREL#18Bibliography Woods et al., Rate capability and Ragone plots for phase change thermal energy storage. Nat Energy 6, 295–302 (2021) James et al., Optimizing phase change composite thermal energy storage using the thermal Ragone framework. J. Energy Storage 56, 105875 (2022)#19Questions Nelson James [email protected] This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Building Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes. NREL/PR-5500-84951