1.1. Research Background
With the emergence of worldwide environmental problems such as global warming, goals have been set to reduce the use of fossil fuels and greenhouse gases (GHGs); major countries have implemented various policies to achieve these goals. China set a target to achieve a 35% proportion of renewable energy by 2030 through its Renewable Energy 13·5 Plan, and it is supporting projects for the development of renewable energy systems, such as wind power generation and photovoltaics [
1]. Japan set its target proportion of renewable energy power to 22–24% by 2030, and it is planning to promote renewable energy as the main source of power for achieving this goal. The economic efficiency of renewable energy is planned by innovating technologies of renewable energy systems and reducing the cost of renewable energy; to achieve this goal, financing issues are being resolved and permission for occupation is being extended by up to 30 years to secure business stability [
2]. Germany has established and promoted their Energiewende policy, informed by their Energy Concept 2010, as the basis for future energy policies. Energy Concept 2010 suggests a stepwise approach to achieving their energy and climate change goals by 2050, and it places emphasis on reducing GHG emissions by 80–95% compared to those in 1990 [
3]. South Korea has a goal to increase the proportion of renewable energy generation to 20% by 2030, through its Renewable Energy “3020” Implementation Plan (
Figure 1), and it is planning to increase the use of renewable energy, such as solar energy and wind power, to more than 95% in new facilities. To this end, it is promoting architecture incorporating renewable energy through the zero-energy building (ZEB) certification system and improving site restrictions and other systems that hinder business profitability [
4].
The Korea Energy Agency identified the building sector as the largest energy consumer among all consumers, and one that exhibits a continuous increase in energy consumption. It expects energy consumption to increase by approximately 50% by 2050 if energy efficiency is not improved and, thus, ZEBs are needed to contribute to energy saving and a reduction in GHG emissions [
5]. Through the 2019 Global Status Report for Buildings and Construction, the Global Alliance for Buildings and Construction identified buildings and the construction sector as the main targets for the reduction in GHG emissions because they use 36% of the energy and represent 39% of gas emissions [
6] (as shown in
Figure 2).
Renewable energy applied to ZEBs includes solar energy, solar heat, and geothermal energy. Geothermal energy systems, which use the constant temperature of the ground, transfer the high indoor temperature to the ground in summer and respond to cooling/heating and domestic hot water loads by absorbing heat from the ground in winter. These systems have attracted attention as systems with high efficiency and high performance because they can be used regardless of climatic conditions and time, and they are more stable than air as heat sources or heat sinks.
Geothermal energy systems, however, require a high initial investment cost compared to other renewable energy sources. For this reason, their distribution has not been promoted in the same way as other energy sources. In addition, it is difficult to relate the load pattern to the use of the building, the partial load operation characteristics of the heat pump, and the performance characteristics of the length of the ground heat exchanger. As a result, the initial investment cost may increase due to the overdesigned capacity, and it is necessary to improve the efficiency of geothermal energy systems and to optimally design the capacity according to the load pattern.
Several studies were conducted to improve the efficiency of geothermal energy systems. Bae et al. [
7] conducted a thermal response test (TRT) by installing four types of ground heat exchangers (GHEXs) in one place. Heat-exchange performance was analyzed for high-density polyethylene (HDPE), HDPE-nano, spiral fin, and coaxial types. They revealed that the thermal resistance of the borehole can be an important element in TRT, but the influence of the increase in the thermal conductivity of the pipe material itself is not large. Park et al. [
8] conducted analysis on the optimal design of the length of the ground heat exchanger according to the entering water temperature (EWT) of the heat pump using optimization simulation software. Kim et al. [
9] analyzed the effects of design elements, such as the geometry and length of the ground heat exchanger and the capacity of the heat storage tank, on the system performance for actual buildings. Kim et al. [
10] also determined the optimal design for the length of the ground heat exchanger, considering EWT for the analysis of economic efficiency. They found that the system efficiency and the borehole length increased as EWT decreased during cooling, and they analyzed its consequence.
Optimization algorithms were also used in other studies; for example, an optimization algorithm was used to set the characteristics of the passive elements of a building (e.g., direction and area ratio) as design variables and obtain values that lead to minimum energy consumption [
11]. Moon et al. [
12] conducted research on the optimal design of the capacity of a geothermal energy system for office buildings. They conducted research on cost optimization by setting the sum of the initial investment cost and the operating cost over 20 years as the objective function. The capacities of the ground heat exchanger, heat pump, and heat storage tank were set as design variables.
Various studies were conducted to improve the efficiency of geothermal systems, but studies on capacity design, considering both the efficiency and the cost of the system, are not sufficient. In addition, design methods that consider the load pattern of the building along with efficiency and cost when designing the capacity of a geothermal system have not been established. Therefore, research is required to derive the optimal design values according to the load pattern of the building, considering the aforementioned conditions and using an optimization algorithm.
In this study, modeling was performed according to the load patterns of buildings, and a geothermal system was constructed. In addition, the design elements (ground heat exchanger, heat pump, and heat storage tank) of the geothermal system were optimized. During the optimal design of the system capacity, an analysis was conducted on the influence of the load pattern of each building; the subsequent performance and economic efficiency of the geothermal system were analyzed.
1.2. Research Method
In this study, the optimal design results according to the load pattern were analyzed. The effect of each load pattern on the capacities of the design elements (ground heat exchanger, heat pump, and heat storage tank) of the geothermal system was analyzed.
Figure 3 shows the flow chart of this study. First, building modeling was performed for each load pattern to calculate the loads of the target buildings. The items considered during the modeling of the target building were as follows: the thermal transmittance of the external wall, window area ratio, and floor height were considered as the external elements of the building, according to the use of each building, and the air-conditioning operating period, internal heat (occupants, lighting, and equipment load), and cooling/heating set-point temperature were considered as internal elements. When the loads of the target buildings were calculated using these values, the geothermal system, on the basis of the maximum load, was constructed by comparing and analyzing the values of cooling and heating loads. Optimal design was performed for the constructed geothermal system using optimization software. GSHP closed-loop ground arrays are designed for much longer lifetimes than 20 years, but the operation period was arbitrarily set at 20 years for economic analysis. The system operation period was set to 20 years, and the subsequent results were compared and analyzed. Particle swarm optimization (PSO) was selected as the optimization algorithm on the basis of a previous study [
12].
In this study, a hospital, a school, and an apartment building were set as target buildings. To analyze the energy demand of each building, the location of the target building and the thermal transmittance of the building’s external wall were entered. The buildings were located in Busan, in the southern part of South Korea, which generally has a mild oceanic climate, with an average annual temperature of approximately 14 °C.
The thermal transmittance of the southern region, according to the Energy Saving Design Standards of Building [
13] provided by the Ministry of Land, Infrastructure, and Transport of South Korea, was applied to the external walls of both residential and nonresidential buildings.
Table 1 shows the thermal transmittance according to building use.
1.2.1. Hospital
To analyze the energy demand for a ward in the hospital building, modeling was performed; as shown in
Figure 4, the size of the unit-type hospital room was set to
[
14]. The building had five stories and its total floor area was 2112 m
2.
The internal conditions of the hospital building and the schedules for occupancy and air conditioning were set on the basis of the prototype of hospital buildings provided by the United States (US) Department of Energy [
15].
Figure 5 shows the occupancy and internal conditions of the hospital building. Air conditioning was operated 24 h a day. Occupancy, lighting, and equipment loads were applied differently for weekdays, Saturdays, and Sundays. The internal conditions applied to the prototype were prepared based on the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) Standard 90.1 [
16] and ASHRAE Standard 62.1 [
17], and the internal conditions of the hospital building are shown in
Table 2. The set-point temperatures for cooling and heating were set to 26 and 20 °C, respectively. The cooling and heating periods were set to May–September and November–February by referring to the average temperature in Busan, South Korea.
The cooling/heating loads of the hospital building were analyzed using dynamic analysis simulation software. The simulation time interval was set to 5 min, and the simulation was performed for system operation over a one-year period.
Figure 6 shows the daily cooling/heating load patterns of the hospital building. In the case of the ward, the cooling load was found to be relatively higher than the heating load due to the internal heat generated by the patients and equipment. The maximum load during the cooling operation was approximately 44.4 kW on 20 September, and the maximum load during the heating operation was approximately 36.9 kW on 31 January. In this study, the system was designed on the basis of the maximum cooling/heating loads. Therefore, the capacity of the geothermal system of the hospital building was designed to respond to the maximum cooling load of 44.4 kW.
1.2.2. School
To analyze the energy demand of the school building, modeling was performed as shown in
Figure 7. The building had four stories with corridors, and the classroom was designed to be
[
18].
As with the hospital, the internal conditions of the school building and the schedules for occupancy and air conditioning were set on the basis of the prototype of buildings provided by the US Department of Energy [
15].
Figure 8 shows the occupancy and internal conditions of the school building. Due to the nature of the school building, occupancy and air conditioning were not considered on weekends.
Table 3 shows the internal conditions applied to the school building. As the building was located in Busan, the same cooling/heating set-point temperatures and air-conditioning operating periods as the hospital building were applied.
When the cooling/heating loads of the school building were analyzed, the daily maximum cooling and heating loads were found to be similar.
Figure 9 shows the annual cooling and heating load patterns of the school building. The maximum load during the cooling operation was approximately 95.7 kW on 22 July, and the maximum load during the heating operation was approximately 92.5 kW on 12 February. On the basis of the daily cooling/heating loads, the capacity of the geothermal system of the school building was designed to respond to the maximum cooling load.
1.2.3. Apartment
Once again, with the apartment building, modeling was performed on the basis of the prototype provided by the US Department of Energy [
16], as shown in
Figure 10.
The internal conditions of the apartment building and the schedules for occupancy and air conditioning were also set on the basis of the prototype of buildings provided by the US Department of Energy [
15].
Figure 11 shows the occupancy and internal conditions of the apartment building. It was assumed that one or more persons were present for 24 h. In the case of the apartment building, the schedules were set to all day without the division of weekdays and weekends.
Table 4 shows the internal conditions applied to the apartment building.
Figure 12 shows the daily cooling/heating load patterns of the apartment building. The heating load was found to be higher than the cooling load. It appears that load patterns different from those of the hospital were observed because the values of the internal conditions (lighting and equipment loads) were smaller than those of the hospital, even though the 24 h air-conditioning operating schedules were the same. The maximum load during the cooling operation was approximately 28.4 kW on 3 September, and the maximum load during the heating operation was approximately 76.5 kW on 10 January. On the basis of the daily cooling/heating loads, the capacity of the geothermal system of the apartment building was designed to respond to the maximum heating load.