Optimization of Borehole Thermal Energy Storage …...goal was to find an optimal buffer tank...
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Optimization of Borehole Thermal Energy Storage System Design using Comprehensive Coupled Simulation Models 1,2 3 1,2 1,2 1 1,2 Bastian Welsch , Wolfram Rühaak , Daniel O. Schulte , Julian Formhals , Kristian Bär , Ingo Sass 1 2 3 Technische Universität Darmstadt - Institute of Applied Geosciences - Department of Geothermal Science & Technology | Darmstadt Graduate School of Excellence Energy Science and Engineering | Federal Institute for Geosciences and Natural Resources, Hannover Large-scale borehole thermal energy storage (BTES) is a promising technology in the development of sustainable, renewable and low-emission district heat- ing concepts (BÄR ET AL. 2015, WELSCH ET AL. 2016). Such systems consist of sev- eral components and assemblies like the borehole heat exchangers (BHE), other heat sources (e.g. solarthermics, combined heat and power plants, indus- trial waste heat), distribution networks, diurnal buffer storages and heating installations. The complexity of these systems necessitates numerical simula- tions in the design and planning phase. Generally, the subsurface components are simulated separately from the above ground components of the district heating system. However, as fluid and heat are exchanged, the subsystems interact with each other and thereby mutually affect their performances. For a proper design of the overall system, it is thus imperative to take into account these interdependencies. Bastian Welsch Technische Universität Darmstadt Department of Geothermal Science & Technology Schnittspahnstrasse 9 D-64287 Darmstadt [email protected] 1. Introduction We want to thank for the financial support by the DFG in the framework of the Excellence Initiative, Darmstadt Graduate School of Excellence Energy Science and Engineering (GSC 1070). BÄR, K., RÜHAAK, W., WELSCH, B., SCHULTE, D.O., HOMUTH, S., SASS, I. (2015): Seasonal High Temperature Heat Storage with Medium Deep Borehole Heat Exchangers. Energy Procedia 76: 351-360. FORSYTHE G.E., MALCOLM M.A., MOLER C.B. (1977): Com- puter methods for mathematical computations. Prentice- Hall: Englewood Cliffs, NJ WELSCH, B., RÜHAAK, W., SCHULTE, D.O., BÄR, K., SASS, I. (2016): Characteristics of Medium Deep Borehole Ther- mal Energy Storage. Submitted to International Journal of Energy Research. 100 200 300 400 500 600 700 Buffer storage volume [m³] 24 24.5 25 25.5 26 26.5 27 27.5 28 28.5 Trend Line Iterations 100 200 300 400 500 600 700 Buffer storage volume [m³] 20 21 22 23 24 25 26 27 Trend Line Iterations Optimum Heat from solar collectors to buffer storage [MWh] Heat from buffer storage to BTES [MWh] a) b) Contact Acknowledgement References Figure 3: Setup of the simulation model for the proof of concept. The developed coupling procedure allows for a co-simulation of all sys- tem components, considering the interaction of the different subsys- tems. First simulation results show a high level of detail. Furthermore, the concept is suited for the mathematical optimization of the components and the opera- tional parameters. Consequently, an adjustment of the system under economic and ecologic consider- ations can be ensured and a more precise prognosis of the system's performance can be realized. This allows for an optimized sys- tem layout, which helps to cut costs for the installation and to maximize the ecological benefit of such renewable heating systems. 2. Coupling 4. Proof of concept Figure 2: Optimization concept. 6. Optimization results 7. Conclusion Figure 5: Heat amounts that are transferred (a) from the solar collectors to the buffer storage and (b) from the buffer storage to the BTES for the respective iter- ation of the buffer volume.. 5. Simulation results Figure 4: Behavior of the trial coupling system during the first simulation day. 0 20 40 60 80 100 Temperature buffer storage [°C] 0 200 400 600 800 1000 Solar insolation [W/m²] 0 20 40 60 80 100 Fluid temperature BTES [°C] 0 5 10 15 20 25 30 Cumulative heat stored in BTES [Mwh] at top at bottom BHE return BHE supply 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 Time of day charging of buffer tank charging of borehole storage Figure 1: Coupling concept for the simulations of the subsystems. Based on a TCP/IP communication we have developed an interface for the cou- pling of a simulation package for heating, ventilation and air conditioning For the optimization, the MATLAB function fminbnd is used, which is based on Brent's algorithm as described in FORSYTHE ET AL. (1977). It combines a golden-section search and a parabolic inter- polation for finding a mini- mum on a fixed interval. (HVAC) installations (MATLAB/Simulink) with a finite element software for the mod- eling of the heat flow in the subsurface (FE- FLOW) and the under- ground in-stallations. 3. Optimization 1 1 Solar institute Jülich The fictive system for the proof of concept consists of a large solar array that charges a buffer storage, which in turn charges a BTES. The optimization goal was to find an optimal buffer tank volume, for which the amount of stored heat in the BTES gets largest during three consecutive days. The optimization algorithm finds a maximum in the amount of heat trans- ferred from the buffer to the BTES at a buffer volume of about 210 m³ after 11 iterations.
Transcript of Optimization of Borehole Thermal Energy Storage …...goal was to find an optimal buffer tank...
Optimization of Borehole Thermal Energy Storage System Design using Comprehensive Coupled Simulation Models
1,2 3 1,2 1,2 1 1,2Bastian Welsch , Wolfram Rühaak , Daniel O. Schulte , Julian Formhals , Kristian Bär , Ingo Sass1 2 3Technische Universität Darmstadt - Institute of Applied Geosciences - Department of Geothermal Science & Technology | Darmstadt Graduate School of Excellence Energy Science and Engineering | Federal Institute for Geosciences and Natural Resources, Hannover
Large-scale borehole thermal energy storage (BTES) is a promising technology in the development of sustainable, renewable and low-emission district heat-ing concepts (BÄR ET AL. 2015, WELSCH ET AL. 2016). Such systems consist of sev-eral components and assemblies like the borehole heat exchangers (BHE), other heat sources (e.g. solarthermics, combined heat and power plants, indus-trial waste heat), distribution networks, diurnal buffer storages and heating installations. The complexity of these systems necessitates numerical simula-tions in the design and planning phase. Generally, the subsurface components are simulated separately from the above ground components of the district heating system. However, as fluid and heat are exchanged, the subsystems interact with each other and thereby mutually affect their performances. For a proper design of the overall system, it is thus imperative to take into account these interdependencies.
Bastian Welsch
Technische Universität Darmstadt
Department of Geothermal Science & Technology
Schnittspahnstrasse 9
D-64287 Darmstadt
1. Introduction
We want to thank for the financial support by the DFG in the framework of the Excellence Initiative, Darmstadt Graduate School of Excellence Energy Science and Engineering (GSC 1070).
BÄR, K., RÜHAAK, W., WELSCH, B., SCHULTE, D.O., HOMUTH, S., SASS, I. (2015): Seasonal High Temperature Heat Storage with Medium Deep Borehole Heat Exchangers. Energy Procedia 76: 351-360.
FORSYTHE G.E., MALCOLM M.A., MOLER C.B. (1977): Com-puter methods for mathematical computations. Prentice-Hall: Englewood Cliffs, NJ
WELSCH, B., RÜHAAK, W., SCHULTE, D.O., BÄR, K., SASS, I. (2016): Characteristics of Medium Deep Borehole Ther-mal Energy Storage. Submitted to International Journal of Energy Research.
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Buffer storage volume [m³]
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Buffer storage volume [m³]
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BT
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Wh
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a) b)
Contact
Acknowledgement
References
Figure 3: Setup of the simulation model for the proof of concept.
The developed coupling procedure allows for a co-simulation of all sys-tem components, considering the interaction of the different subsys-tems. First simulation results show a high level of detail.Furthermore, the concept is suited for the mathematical optimization of the components and the opera-tional parameters. Consequently, an adjustment of the system under economic and ecologic consider-ations can be ensured and a more precise prognosis of the system's performance can be realized.This allows for an optimized sys-tem layout, which helps to cut costs for the installation and to maximize the ecological benefit of such renewable heating systems.
2. Coupling
4. Proof of concept
Figure 2: Optimization concept.
6. Optimization results
7. Conclusion
Figure 5: Heat amounts that are transferred (a) from the solar collectors to the buffer storage and (b) from the buffer storage to the BTES for the respective iter-ation of the buffer volume..
5. Simulation results
Figure 4: Behavior of the trial coupling system during the first simulation day.
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Temperature buffer storage [°C]
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Solar insolation [W/m²]
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Fluid temperature
BTES [°C]
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Cumulative heatstored in BTES
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at top
at bottom
BHE return
BHE supply
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Time of day
charging of buffer tank
charging of borehole storage
Figure 1: Coupling concept for the simulations of the subsystems.
Based on a TCP/IP communication we have developed an interface for the cou-pling of a simulation package for heating, ventilation and air conditioning
For the optimization, the MATLAB function fminbnd is used, which is based on Brent's algorithm as described in FORSYTHE ET AL. (1977). It combines a golden-section search and a parabolic inter-polation for finding a mini-mum on a fixed interval.
(HVAC) installations (MATLAB/Simulink) with a finite element software for the mod-eling of the heat flow in the subsurface (FE-FLOW) and the under-ground in-stallations.
3. Optimization
1
1Solar institute Jülich
The fictive system for the proof of concept consists of a large solar array that charges a buffer storage, which in turn charges a BTES. The optimization goal was to find an optimal buffer tank volume, for which the amount of stored heat in the BTES gets largest during three consecutive days.
The optimization algorithm finds a maximum in the amount of heat trans-ferred from the buffer to the BTES at a buffer volume of about 210 m³ after 11 iterations.
