1. Introduction
According to the reports of the International Energy Agency (IEA) and Energy Information Administration (EIA) in 2015 [
1,
2], the world’s total energy consumption has been increasing from 1971 to 2013 and buildings consume more than 30% of the global energy. These trends have highlighted the social needs of energy savings and environment protection and many researchers have developed high-efficiency technologies for buildings. Among them, the heat storage system is an effective way of improving the efficiency of heating and cooling systems [
3].
A heat storage system solves the temporal, quantitative, and qualitative gaps that may appear between supply and demand of thermal energy, and helps improve efficiency by supplying advanced and centralized energy to the load-side. In particular, it is useful for renewable heat sources, such as solar energy systems, which produce different amounts of heat energy depending on the weather conditions and has a huge time gap between production and consumption [
4]. In addition, district heating systems also need to store the thermal energy produced to help cope with the demand.
In buildings, the use of a heat storage system is more effective for load leveling of the heat source if the building load is concentrated. In particular, it has a huge effect when the building is under peak load at a particular time because it can reduce the capacity of the heat source systems significantly. On the other hand, it is difficult to reduce the capacity of buildings, such as hotels and hospitals, because it is important to have a reliable air conditioning system. Instead, these can make the heat source system idle through the use of a heat storage tank as a buffer station. In addition, it can save operation costs with a heat storage system by utilizing the electricity tariff effectively, which is called “nighttime electricity service” offered from the Korea Electric Power Corporation [
5]. The building can take advantage of the low rates if a building operates systems to store heat and power during the night for the next day load. Furthermore, according to the tariff of electricity offered from the Korea Electric Power Corporation, the electricity price is classified according to the load time (light, medium, heavy) and season.
On the other hand, enormous design variables are generated to use energy efficiently by applying not only a heat storage system, but also many design elements, such as renewable heat sources, complex system combinations, and multiple unit application. Accordingly, the design process becomes increasingly more complex to optimize. Even if considering only a heat storage system, the initial construction costs would be increased by the storage tank and control system. Moreover, heat losses occur by the difference between the heat storage and heat rejection time highlighting the need for an optimal design and strategy for energy efficient and economical operation.
Many studies of the heat storage systems on the heat storage materials and storage methods have been carried out, such as ice storage system, thermally activated building system, and using phase change material (PCM) [
6,
7,
8]. In addition, several studies on the economic and efficient design method have been carried out in recent years. Yu et al. [
9] examined the design capacity of a heat storage system through cases and derived the best combination considering the system performance and energy consumption. As for optimization using an algorithm, Sun et al. [
10] reviewed the optimal operation method of a heat storage system, particularly for peak load shifting. According to the manuscript, the method can be configured according to the type of thermal storage system, such as the thermally activated building system or using PCM. Despite that, most studies focused on the day for optimization; hence, it is necessary to consider a more extended period of time for the design days. Ikeda and Ooka [
11] examined the optimal operation method of an energy storage system and suggested the economic optimal operation in accordance with the rates. In the United Kingdom, a study of the optimal design and operation of the system combined with a storage tank was conducted for district heating by considering three standard electricity tariffs. On the other hand, there is a limit on the decision of the system capacities at a constant value [
12]. Shirazi et al. [
13] optimized the ice thermal energy storage system considering the compressor ratio and temperature as the design parameter and the optimal solution could improve both the exergetic efficiency and total costs. Wu et al. [
14] evaluated an optimal system combination, including a thermal storage system considering the size of each system, operation schedule and pipelines to establish an energy network. They considered several system combinations for a case study but the design parameters of each system were limited. Unlike previous studies performed on the optimization of a thermal storage system, it was insufficient according to the decision of the system capacity in the design process. For more optimization in design and operation, it is necessary to develop an optimal design method including capacity decision.
In this study, to propose the optimal design method of the heat source system including a thermal storage tank, the conventional design process of a thermal storage system was considered and an optimal method was developed utilizing a genetic algorithm. The optimization process of this study integrates a variety of input data, such as weather conditions, building, heat sources, system efficiency, economy, and operational conditions. In this paper, an optimization model is constructed based on the iSIGHT (Dassault Systèms Simulia Corp., Providence, RI, USA) tool to validate the proposed method and the optimization results are developed according to the representative load patterns of the daytime, nighttime, and 24 h to evaluate the usefulness of the method. In addition, a feasibility study was carried out with the conventional designs.
4. Optimization Results and Discussion
4.1. Optimum Design Solutions Analysis
In this paper, 30,000 individuals in each case were examined to obtain the best designs, which are summarized in
Table 8. According to the results, the optimal designs were drawn differently depending on the load pattern and objective function.
Figure 11 presents the optimal design solutions.
First of all, the results of Cases 1 and 2 showed the largest difference according to the objectives, even in the same daytime load. In Case 1 of minimizing LCC, the optimal solution was derived to design the heat pump as 79% of the peak load and the heat storage tank with 12% of the daily total load. On the other hand, in Case 2 of minimizing the energy cost, the optimal solution was to design 40% of the peak load as the capacity of the heat pump and heat storage tank with 54% of the daily total load. Compared to Case 1, it was determined that Case 2 had a larger capacity of the heat storage tank, to store heat energy sufficiently using the relatively low cost of late-night electricity. In terms of the costs, the energy cost in Case 2 was approximately 156,000 KRW/year smaller but the initial cost in Case 2 was double so Case 1 cut 4,400,000 KRW at the LCC compared to Case 2.
In Cases 3 and 4, the results were the same. The heat pump was planned to 98% of the peak load and the heat storage tank was designed as 1% of the daily total load. As a result, the heat pump was designed to operate directly to the building load rather than act as a storage operation using the low cost of midnight electricity effectively.
In Cases 5 and 6, the optimal design solutions were derived similarly. In this load pattern for 24 h, there is a slight difference between the peak load and the average load so it is more efficient to operate the heat pump directly. Accordingly, it was determined that the capacity of the heat pump is similar to the peak load. Regarding the costs, in Case 6, the energy cost was approximately 9000 KRW/year smaller.
Overall, the optimal design solutions were derived differently depending on the purpose and the objective function of the building and its characteristics stood out in the daytime load. Moreover, the ratio of each cost was different with the load patterns. In this regard, the design solution that meets the design objectives of the building can be deduced.
4.2. Review of Operation Planning
The proposed design method is a way to consider the operation together based on the designed load profile. In this section, the operation aspects of the optimal designs were determined.
Figure 12 presents the operation planning according to the load pattern and objective function. In this paper, a representative 2 days of the design days is shown. The graph shows the hourly energy rate. The histogram of the upper along the
X-axis represents the amount of energy corresponding to the building load with the heat pump and the heat storage tank. The lower part along the
X-axis means the heat storage energy rates. The line graph represents the energy remaining in the storage tank.
First, as shown in
Figure 12, Case 1 operates the heat pump directly to the building load. On the other hand, Case 2 stores the heat energy during the night by utilizing the available storage time and discharges a significant amount of heat during the day, which is to minimize the energy cost by utilizing mostly low-cost electricity. Cases 3 and 4 mainly operate the heat pump directly on the building load during the air-conditioning time when the energy cost is relatively low. Cases 5 and 6 indicate that the period to store heat energy is insufficient so the heat pump mainly operates directly.
The proposed method makes it possible to examine the remaining energy of the heat storage tank and check the operation aspects as to whether the system works efficiently. According to the outcome, each system works properly to meet the building load, and the required heat energy is stored so the system is determined to be designed effectively without being excessive.
4.3. Feasibility Study
In this section, a feasibility study was conducted to verify the validity compared to the conventional designs. In this study, Case 1 was selected as a representative design, which is to minimize the
LCC in the daytime load. To compare with the conventional designs, the following three cases were set. In Case 1-1, the heat pump is operated without a heat storage tank. Case 1-2 was followed by the conventional thermal system design process and the capacity of the heat pump and required heat storage tank were calculated when the air-conditioning and heat storage operation time were 9 h and 10 h, respectively. Case 1-3 was applied to real-working practice; the heat pump capacity was set to 50% of the peak load and the heat storage tank was designed to store the amount of energy by driving the heat pump for 10-h storage operation.
Figure 13 and
Table 9 lists the estimated initial investment costs, annual energy costs and
LCC for each design.
As shown in
Table 8, all the designs show a difference in each part of the cost. First, comparing with the case to operate the only heat pump without a heat storage tank, the optimal solution could reduce 215,000 KRW of the initial investment cost and 768,000 KRW in the
LCC. Through this, it was determined that it can be more efficient to combine a heat storage tank. In addition, the annual energy costs when the case with the conventional design process was 90,000 KRW cheaper than the optimum but the initial costs required an additional 2,000,000 KRW so it is inefficient in terms of the
LCC. In the case of real-working practical design, it was the most excessive design having more than 10 million KRW as the initial investment cost and the energy cost and
LCC were higher than other cases as well.
In addition, the conventional designs should consider the safety factor including heat losses from the heat storage tank. If not, it would not be satisfied with the load, as shown in
Figure 14. In this regard, the proposed method is a useful way in that the optimal design solution is derived after checking the operation planning of the designs.
Therefore, the design solutions proposed in this study are eligible for the optimal heat storage system compared to any other conventional designs. Furthermore, real-working practice is required to improve the designs to prevent oversizing, resulting in higher investment cost.
4.4. Optimal Solutions with a Multi Objective Approach
In previous sections, the designs differed according to the objective functions. In particular, in the daytime load pattern, the difference between the designs was significant so that it is essential to approach the optimal design solutions using a multi-objective genetic algorithm. Therefore, in this section, an analysis with a multi-objective genetic approach was conducted. To solve the multi-objective problem, the Pareto analysis method was accompanied to derive multiple optimal solutions.
Figure 15 presents the Pareto front result of the daytime load pattern.
The optimal design solutions are distributed from one objective function to another objective function. This analysis approach helps consumers make an efficient choice considering their economic conditions.
5. Conclusions
In this study, we proposed a design method of heat source system including heat storage tank utilizing a genetic algorithm and derived optimum design solutions according to three load patterns and different objective functions. The main results of this paper are as follows:
This paper proposed a design method of a heat source system including heat storage tank utilizing a genetic algorithm and derived optimal design solutions according to three load patterns and different objective functions. The main results of this paper were as follows:
According to the load patterns and objective functions, a range of solutions were derived to meet the design purposes and the costs were different irregularly. When the load occurs during the day, the solutions showed the largest differences with the objective functions.
By checking the operation planning, the proposed method could consider the efficient operation, oversized-design, heat losses, safety factor, and energy remaining in the heat storage tank.
The proposed method could make the most efficient design in terms of the initial investment cost and LCC compared to the conventional heat storage designs, as well as the system using only the heat source system. Moreover, it was confirmed that it is necessary to improve the method in the real working-process, which led to energy and economic consumption by oversizing the system.
Since an oversized design operates inefficiently, it was confirmed that a thermal storage system is required for optimal design.
In the future, the optimal design guidelines will be developed regarding the scale, purpose, and region of building for economic and energy efficiency. In addition, this study will continue to utilize the proposed method in a more efficient manner, especially when using a renewable energy system, which has more benefits for introducing a heat storage system to solve the high initial investment cost and the intermittent energy production.