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1208 A. Hesaraki et al. / Renewable and Sustainable Energy Reviews 43 (2015) 1199–1213 the simplest for pre-design to the most advanced one for detailed design. In the more advanced programs the simulation results are more reliable as the higher number of data inputs increases the accuracy of the model [117]. CONFLOW as a simple and fast simulation program is suitable to be used for the pre-design phase in well configuration [113]. CONFLOW is able to calculate only 2D hydraulic and thermal processes without energy transport. Three models of AST, TRNAST and TWOW are used to predict both energy and entropy of ATES. The AST simulation program is used to model the heat conduction and convection in the porous medium of the ATES system. Never- theless, AST is able to model the thermal behavior and a flow field of a single well. To model two wells, TWOW and TRNAST simula- tion programs, which are based on the AST program, can be used. The TWOW model allows for thermo-hydraulic interaction between the two wells. However, TRNAST is appropriate for thermally and hydraulically independent wells with no interaction and is applicable within the simulation environment of TRNSYS. A more advanced program to be used for detailed design is 3D model FEFLOW. In addition to the hydraulic and thermal field, FEFLOW is able to calculate solute transport in porous media under saturated and unsaturated conditions for two wells. Also, chemical reactions and degradation mechanisms are considered in FEFLOW. This program is highly time consuming, however. Kranz and Bartels [118] used TRNAST and FEFLOW to model ATES-HP used in the German Parliament Building [119]. The aim of simulation was to enhance the storage efficiency in terms of the energy recovery factor. The simplified model of TRNAST was verified with a detail model of FEFLOW. 7. Discussion Table 4 gives a summary of past projects regarding different seasonal thermal energy storage systems in combination with a Table 4 Past projects showing a range of combinations of seasonal thermal energy storage with heat pumps. heat pump. As can be seen, the mean COP of most of these storage systems are in the vicinity of 4. The review showed that the applications of a heat pump with duct thermal energy storage are wider compared to other systems, as many references are assigned to DTES-HP. The reason could be due to lower stored temperature in DTES than other systems and a need for a heat pump as auxiliary heating system. In addition, investigation of past projects indicated that in ATES-HP the solar collector was not mainly used for charging the energy storage but was used for providing DHW. Therefore, as shown in Table 4 most examples of ATES-HP lacked a large collector area. Almost all ATES-HP listed in Table 4 were used for both heating and cooling in large applications. Hence, in large buildings with both heating and cooling demand this type of storage system is recommended. The COP of a heat pump and solar fraction improved with increasing storage volume and solar collector area. Based on energy conservation given by Eq. (2) COP, SF, collector area and storage volume are related. There are many studies, e.g. [120,45,57,62,121,122], that have conducted sensitivity analyses to investigate which factor has the most influence on efficiency of the system. Increasing storage capacity would cause higher tem- perature during winter time and lower temperature during summer time [57,121,122] in the storage. This would be favourable for the COP of a heat pump during the heating season. Therefore, increasing storage volume improved the COP of the heat pump by reducing the compressor work [123]. However, when the storage volume was large enough, then the effect of volume on COP of the heat pump became negligible [124]. In addition, increasing storage volume would also affect the solar fraction [125]. By increasing the storage volume, the tem- perature in storage would not fluctuate very much. This would cause an approximately constant temperature in the storage system over the year. This favoured collector efficiency [62] due to there being no sudden jump in inlet temperature to the collector. However, this correspondence between storage volume Energy demand (GJ) Total heating area (m2) type of the building 100, Single house 7,000, 55 Houses 525 Dwelling þ 3,500 m2 6,900, 92 Houses 24,800, 300 Apartments 12,000, School, sport center 1,375, Institute building 500, Detached house 40,000, Houses and school 7,410, 52 Detached houses 15,000, School building Collector area (m2) 20 2,875 13,000 1,050 2,900 1,600 211 50 7,300 573 1,500 400 180 Storage volume (m3) 300 10,000 55,000 3,000 5,700 4,500 1,050 5,100 37,500 88,000 85,000 TES Mean COP, temp. heating/cooling (1C) Saving, SF (%) Application Heating Heating, DHW Heating, DHW Heating, DHW Heating, DHW Heating Heating, DHW Heating, cooling Heating, DHW Heating Heating Refs. [57] [64,65] [66] [65,128] [67,68,129] [13,130] [131,65] [71] [83,13,74] [108] [22,65,73] [22] [22] HWTS-HP Gaziantep, 44 Turkey Lambohov, 3,000 Sweden Södertuna, 23,000 Sweden Herlev, 4,520 Denmark Munich, 8,280 Germany WGPS-HP Eggenstein, 3,276 Germany Stuttgart, 349 Germany DTES-HP Harbin, China 144 Crailsheim, 14,760 Germany DLSC, Canada 2,328 Sunclay, 4,000 Sweden Kranebitten, 4,400 Austria ISPRA. Italy 280 14–40 5–6 83 5–70 4.4 37 15–65 2.3 66 10–85 35 30–95 1.7 47 10–80 37 10–50 460 3–8 4/21 88 20–85 4.9 50 10–16 6.2 78 7–15 64 60,000 6–10 53 Heating, DHW 2,250 5–60 80 Heating, DHWPDF Image | Renewable and Sustainable Energy Reviews 43
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