Heating energy performance and part load ratio characteristics of boiler staging in an office building

Abstract

Commercial buildings account for significant portions of the total building energy in South Korea and thus a variety of research on the boiler operation related to heating energy in office buildings has been carried out thus far. However, most of the researches have been conducted on the boiler itself, i.e., the part load ratio characteristics and the corresponding gas energy consumption patterns are not analyzed in the existing studies. In this study, the part load ratio and the operating characteristics of gas boiler have been analyzed within an office building equipped with the conventional variable air volume system. In addition, the gas consumption among different boiler staging schemes has been comparatively analyzed. As a result, significant portions of total operating hours, heating load and energy consumption has been found to be in a part load ratio range of 0 through 40% and thus energy consumption is significantly affected by boiler efficiency at low part load conditions. This suggests that boiler operation at the part load is an important factor in commercial buildings. In addition, utilizing sequential boiler staging scheme can save a gas usage of about 7%. For annual heating energy saving, applying the sequential control boiler with a 3:7 proportion staging is considered to be the optimal control algorithm for maximum efficiency of boilers.

Keywords

EnergyPlus / boiler / part load ratio / gas consumption / office building / boiler staging

Introduction

In December 2015, international community has adopted the Paris Agreement built upon the convention on the climate changes as a way of reducing green-house gases. Hereupon, South Korea has decided to pursue a goal of reducing emissions of green-house gases by 37% from business-as-usual (BAU) emissions of 850.6 million ton CO₂e until 2030 [¹]. South Korea has been chosen as one of the top 10 countries with the highest level of emissions of green-house gases and using fossil fuel as major energy source in major industries and daily lives in general [²]. Therefore, efforts are required in all fields in society to reduce the level of emissions of green-house gases. In addition, the dependence rate on energy imports in South Korea was estimated to be 96% in 2013, which accounted for the highest proportion among major countries in the world. A high dependence rate on energy imports means that a significant amount of expenses is being used on importation of fossil fuels. Therefore, there is a need to prepare measures on aforementioned issues [³].

In South Korea, many studies have been conducted to reduce the amount of heating energy used in buildings. However, previous studies conducted regarding the HVAC system focused on the enhancement of performance of heat source devices including the condensing boiler, exhaust gas heat collecting method, and installation of feed water pre-heater. In general, there has been an insufficient amount of studies dealing with device capacity and operating characteristics for solving problems including the increase in initial investment expenses, maintenance costs, and space of installation caused by an excessive calculation of heat source devices [⁴]. As for the recent trend of studies in regard of aforementioned issues, first of all, Murray et al. presented degree-days simulation technique coupled with a genetic algorithms optimization procedure to propose optimal retrofit solutions. The results demonstrate the necessity to carry out analysis of a project before retrofit works commence to ensure an optimal approach taken in accordance with the project specific criteria [⁵]. Weissmann et al. analyzed the part load ratio (PLR) index for the central heating system to quantify the effects of identified building and demolition related characteristics on the heating load variability in residential areas in Germany. As a result, the amount of consumed gas ended up decreasing when using the single plant with large capacity, and a conclusion was drawn that the entire PLR of heat source devices was influenced by the heating load to be processed. However, due to insufficient analysis on the efficiency under low part load condition and the amount of consumed gas when operating single plant with large capacity and individual plants, there has been lack of consideration on the judgment on the efficiency of overall period [⁶]. Two of the previous studies used a single plant and focused on the maximum load when calculating the capacity. However, since the criteria of evaluation for efficiency is focused based on PLR under high part load condition, there is a need to assess the efficiency under the low part load condition.

Among the studies that controlled the number of two or more heat source devices for reducing the energy, Giurca et al. focused on the selection of number and size of boilers required for the heating units of the residential complexes. In this case study they proposed the selection of the number and size of boilers for a heating unit of a residential complex located in Tirgu-Mures, Romania. The conclusion of this study is that the operational safety of the heating unit increases once the number of boilers increases, together with the decrease of thermal energy consumption and the increase of investment costs respectively. Based on their calculations, it is recommended that the heating unit should be equipped with seven boilers, and should be provided with a controller for managing them in cascade [⁷]. Shide and Jia introduced an intelligent and optimal control strategy of energy saving for heat supplying process under four gas boiler group run conditions in a residential district. In addition, to keep boilers run under efficient states with rated load, the tracking time-discrete-control (TDC) and the optimization program of boiler group were further developed. As a result, the boiler group’s optimization program greatly reduced the gas consumption and ensured the thermal supplying at the same time. According to statistics during several years’ run, the natural gas consumption was reduced by 15% than before [⁸]. Wei et al. presented a micro-combined cooling, heating and power (CCHP) system driven by biogas, while adopting the hybrid cooling mode. Moreover, a multi-objective optimization model considering off-design performance of the facilities was proposed to maximize the primary energy savings ratio (PESR) and minimize the energy costs. As a result, the highest level of efficiency and the most economical energy costs were shown when PESRs were 15.03% and 15.06% in winter season, and thus the model was ultimately suggested. However, while the overall energy costs were saved in winter season, the cost-efficiency was degraded from an increase of heating energy due to the use of supplementary boiler. In addition, it was assumed that there was insufficient analysis regarding the supplementary boilers that operated in the time between summer and winter and the annual energy consumption efficiency [⁹].

Among the studies regarding the amount of consumed energy, expense, and partial load ratio while controlling the capacity and number of heat source devices, Lazzarin insisted that to obtain the contributions granted by an act of promotion of the energy savings, the existing traditional boiler must be replaced with a condensing boiler without any further specification. According to this paper, two models of quite different modulating ratios and nominal capacity were considered. As a result, two condensing boilers with a lower capacity than that of the previous boilers and with a modulation factor of 1:9 were suggested as the best alternatives. Suggested boilers showed that the efficiency was 40% higher than the previous boilers while reducing the primary energy by 15% [¹⁰]. The aforementioned studies focused on addressing high load when calculating the capacity of heat source devices and it was found that the efficiency of devices with low load was insufficiently analyzed. On the other hand, Yu et al. analyzed the characteristics of part load in residential buildings and prevented the excessive operation of the system in the low part load condition by controlling the high instantaneous load caused by the time delay and dissatisfaction on pre-set temperature when operating radiant floor heating system. As a result, an approximately 24.7% of energy saving could be achieved depending on the operating efficiency under part load condition. However, due to the difference in schedules of using residential and office buildings, additional controlling method is required to apply them to the office buildings [¹¹]. In addition, Seo and Lee supplemented the aforementioned studies, proceeding the chiller staging control in office buildings. In addition, the amount of energy consumed was analyzed in each interval of part load ratio. When using the sequential distribution method suggested in this study, 10% of the entire amount of energy consumed was saved [¹²]. However, the study conducted by Seo and Lee focused on air-conditioning and insufficiently analyzed the total energy consumed in both air-conditioning and heating source devices. Thus, it is required that the research applying the efficient load distribution method should be conducted by controlling the number of heat source devices depending on the necessity of reducing heating energy consumed in office buildings.

Therefore, this study is a follow-up research of the aforementioned study of Seo and Lee, and it is conducted regarding the control of the number of boilers by partitioning the capacity according to the characteristics of part load ratio and applying various load distribution algorithms for efficient consumption of heating energy used in office buildings. In addition, the capacity of devices is partitioned in three ways after analyzing the annually consumed amount of gas and the annually accumulated operating hours of boilers in the office. Moreover, the study aims to develop a method to increase the efficiency of heating energy by developing combined load distribution algorithm systems using sequential and uniform methods.

Methods

Prior to proceeding this study, the necessity of research has been proved through previous studies. EnergyPlus v6.0 developed by the US Department of Energy was used for this study, and while using the EnergyPlus, the heat balance method recommended by American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) was used for calculating the heating and cooling load. In particular, when calculating the load, the degree of freedom in internal heating elements and systems and plant setups is high, following the user’s scheduled control, and detailed analysis is available [¹³]. In addition, it has a big advantage to simulate the systematic connection between each component by executing zone, surface, air heat balance, system and plant modeling which are the main components of building energy simulation integrally step by step at each time. Figure 1 shows the flowchart of the study. There are five cases simulated in this study, which are classified depending on the boiler capacity and controlling method. In addition, the optimal boiler controlling method has been suggested through the analysis of representative days and annual data.

Simulation methodology

Selection of simulation tool

EnergyPlus v6.0 developed by the US Department of Energy was used as a theoretical analysis tool to conduct this study. EnergyPlus is the program combining the advantage of DOE-2 in system analysis and the advantage of BLAST in load analysis [¹⁴]. In addition, the building cooling and heating load analysis was based on the heat balance method recommended by the ASHRAE. The credibility of this program was verified by developing simulation tools as per the ASHRAE 140 guidelines which were the representative dynamic simulation protocol. In addition, the computations were based on the fully integrated solution of zone, air and surface balances, system and plant [¹⁵]. Finally, detailed information on the assumptions, detailed algorithm and validation of EnergyPlus models related to boiler calculations can also be found in Ref. [¹⁵].

Simulation model

This study proceeded the analysis after dividing the representative periods into intermediary seasons (April, May, September, and October) when air-conditioning and heating devices are used together and the winter season (November, December, January, February, and March) when only heating are provided. The weather data in the area of Incheon, South Korea, provided by EnergyPlus 6.0 were used, and 17th April and 23rd January were chosen as the representative days since they show the average temperature and load patterns among the representative periods of intermediary seasons and winter seasons for the research. As for the heating schedule on representative days, operating hours were chosen as 6 a.m. to 7 p.m. on weekdays in consideration of internal heating factors in office buildings [¹⁶]. The VAV (variable air volume) system was used, and boiler capacity was chosen to be 290000 W, which was the capacity that could handle the maximum heating load required on the relevant building. As for internal heating and schedule value, 9.3 m²/person was set on occupants based on ASHRAE 90.1, 2004, and 9.1 W/m² was set for the illumination. In addition, 14.4 W/m² was entered for electronic devices [¹⁷]. Figure 2 demonstrates the internal heating schedule used in this study, and Table 1 lists the simulation condition. Figure 3 is a simulation model that is a large-sized office building used in the previous study of Refs. [¹²,¹⁶]. This model was made according to ASHRAE 90.1, 2004, and is a prototype model provided by EnergyPlus [¹⁷]. The size of building includes the width of 48 m, length of 73 m, and height of 14 m, and the window area accounts for 45%.

Load distribution algorithm

Figure 4 presents the load distribution algorithm. As shown in the figure, EnergyPlus v6.0 suggests three types of load distribution algorithm including optimal, sequential, and uniform [¹²]. There is a need to enter minimum part load ratio, maximum part load ratio, and optimum part load ratio values in Table 2 in priority when establishing the boiler model in EnergyPlus. The “optimal” algorithm can determine the priority in the system to distribute the load, and it can also designate the maximum PLR of the operating device. In addition, the boiler operating in priority recognizes the entered optimum part load ratio value as an upper limit, and the remaining load that exceeds them is recognized by the device of the next priority. The “sequential” algorithm, like the optimal algorithm, first distributes the load to the system with top priority. However, the difference between the two algorithms is that the “sequential” algorithm recognizes the maximum part load ratio as the operating limit. Lastly, as for the uniform algorithm, all the installed heat source devices operate in the case of load regardless of the priority, and the load is identically processed. However, according to the part load ratio input values of the boilers applied in the study as tabulated in Table 2, there is no difference in entered values between optimum part load ratio and maximum part load ratio, which are entered variables of optimal control algorithms as stated above. Thus, the optimal control algorithm and the sequential control algorithm are identically operated. Therefore, as what is pursued by the analysis of the load distribution control algorithm according to the fact that the changes made in input values of part load ratio is different from the suggestion of the optimal load distribution control algorithm in staging control which is the goal of this study, the analysis on the optimal control algorithm is excluded in the research among the three types of control algorithms [¹²]. To provide more detailed explanation of the aforementioned three types of algorithms, the following cases of circumstances were used. First of all, the load processed amount according to the load distribution algorithm in the case of 120 kW heating load in the buildings that required up to 200 kW of heating load is shown in Fig. 5. Assuming that two boilers in the capacity of 100 kW were installed in the buildings, first of all, the uniform algorithm distributes the same load to the device. Therefore, it processes 60 kW of load in each boiler at the same time. Then, once “sequential” algorithm is applied, Boiler_1 operates first according to the priority. In addition, once the maximum part load ratio is set at 1, Boiler_1 processes 100 kW of the load which is the highest level that can be treated, and the remaining load is processed in Boiler_2. The optimum algorithm recognizes the optimum part load ratio as the optimal value and also as the limit as explained above. Once the optimum part load ratio in Boiler_1 is set at 0.9, it processes 90 kW of the load, and the remaining load is processed in Boiler_2.

Simulation cases

In this study, the annual heating load data in the office buildings were analyzed, and in consideration of the amount of heating energy needed, the boiler staging control was proceeded with a total of two boilers based on the Case_1 boiler with a capacity of 290 kW. First of all, the capacity of one 290 kW boiler in the base model was partitioned into 5:5, 3:7, and 7:3. In the previous studies regarding the control of the number of boilers in office buildings, the partition of capacity in 3:7 was found to show the highest level of efficiency among the partitioned capacity of 5:5 and 3:7, etc [¹⁸]. However, the capacity partition of 5:5, 3:7, and 7:3 were considered to compare the efficiency as well as the amount of consumed energy from the load distribution algorithm to proceed the research. Boiler_2 in each case was partitioned in the capacity after excluding the capacity of Boiler_1 among the entire capacities. The characteristics of each case are shown in Table 3.

Boiler performance curve

This study used the boiler efficiency cubic performance curve of EnergyPlus in order to produce a better simulation of the quantitative boiler performance [¹⁴]. The efficiency of the performance curve tends to change depending on PLR, and PLR shows the load operated in comparison with the entire boiler capacity during the operation of the boiler. Equation (1) is the aforementioned boiler efficiency cubic curve. The boiler considered in this study is the conventional gas-fired boiler whose performance curve is illustrated in Fig. 6. Table 4 shows the coefficient of the applied performance formula and a total of four entered coefficients of a, b, c, and d have been calculated through a series of regression procedures based on the actual catalog data from Company A in the US, which is represented in Fig. 6.

(1)\mathrm{B}\mathrm{o}\mathrm{i}\mathrm{l}\mathrm{e}\mathrm{r}\mathrm{E}\mathrm{f}\mathrm{f}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{y}\mathrm{C}\mathrm{u}\mathrm{r}\mathrm{v}\mathrm{e}\mathrm{O}\mathrm{u}\mathrm{t}\mathrm{p}\mathrm{u}\mathrm{t}=a+b·\mathrm{P}\mathrm{L}\mathrm{R}+c·{\mathrm{P}\mathrm{L}\mathrm{R}}^2+d·{\mathrm{P}\mathrm{L}\mathrm{R}}^3,

where PLR= Boiler part-load ratio.

Results analysis

Variations of boiler load and outdoor temperature

To identify the load patterns in each time prior to the analysis of the annual amount of consumed energy, representative days were chosen to analyze the heating load. Figure 7 depicts the hourly heating load of AHU heating coil and outdoor air temperature in representative days. As mentioned above, representative days were 17th April and 23rd January. The outdoor air temperature in the representative day of the intermediary seasons ranged from 6°C to 16°C, and the one in the winter season ranged from -4.5°C to 0.5°C. In addition, the load pattern of the representative day in the intermediary seasons ranged from 0 to 88 kW, and the load pattern of the representative day in the winter season ranged from 30 to 287 kW. As shown in Fig. 2, the internal heat generation is reduced by 5% in human bodies, 10% in illumination, and 20% in electronic devices after 8 p.m. Indoor temperature is the lowest during night times when the system is not operated and the outdoor temperature is the lowest, while the heating load is the highest at 6 a.m. when the boiler starts operating to keep up with the pre-set temperature.

Part load ratio variations

Figure 8 exhibits the PLR patterns in representative days in the intermediary seasons and winter season in Case_1 which indicates that one boiler is in charge of all the heating loads without controlling the number of boilers, and hence shows the identical patterns with the heating load in representative days explained in Fig. 5. As explained above, the highest level of PLR is shown between 6 a.m. and 7 a.m. when the heating system starts operating, and the heating load decreases due to an increase of outdoor air temperature and internal heating sources. Therefore, PLR decreases as well. The outdoor air temperature at 7 p.m. is similar to the one at 6 p.m. However, the indoor heating load increases due to a decrease in internal heating load, making PLR increase as well. PLR patterns are identical in the intermediary seasons and winter season. However, due to the difference in heating load values, the PLR in the representative day of the winter season tends to be higher than that in the representative day of the intermediary seasons.

Figure 9 shows the PLR in the representative day of the intermediary seasons in Cases_2 and 3, while Fig. 10 shows the PLR in the representative day of the intermediary seasons in Cases_4 and 5. Cases_2, 3, 4, and 5 use two boilers, but there is a difference in terms of boiler capacity and load distribution algorithm. As for Case_2, two boilers with the identical capacity were controlled by using the uniform algorithm. As explained above, the uniform algorithm evenly deals with the heating load as the system operates at the same time once the indoor heating load occurs. Therefore, as for Case_2 where two boilers with the same capacity were installed, two boilers showed the same PLR. As for Case_3 and 4, the capacity of the first boiler was 30% of the entire boiler capacity, and that of the second boiler was 70% of the entire boiler capacity. However, there was a difference in terms of load distribution algorithm. As for Case_3, the identical uniform algorithm was used with the one in Case_2, and the two boilers tended to share the same amount of load. However, due to the difference in capacity, Boiler_1 with a relatively low capacity tends to show a higher PLR than that of Boiler_2.

On the other hand, Case_4 uses the sequential algorithm. As explained above, according to the sequential algorithm, Boiler_2 deals with the remaining heating load once the PLR of Boiler_1 reaches 100%. However, during the representative day of the intermediary seasons, Boiler_1 is able to deal with all the heating loads. Thus, Boiler_2 does not operate and the PLR is shown as 0%. As opposed to Cases_3 and 4, Case_5 indicates that the capacity of Boiler_1 is 70% of the entire boiler capacity, and the capacity of Boiler_2 is 30% of the entire boiler capacity, and the sequential algorithm is applied in the same manner with Case_4. Since the capacity of Boiler_1 is greater in Case_5 than in Case_4, the PLR of Boiler_1 in Case_5 is lower than that of Boiler_1 in Case_4. As for Boiler_2, Boiler_1 handles all the heating loads in the same manner with Case_4. Therefore, the PLR is 0%, and, hence, Boiler_2 does not operate.

Figures 11 and 12 show the patterns of PLR by time during the representative day of winter season. As for Case_2, all the systems operate in the same capacity and time period. Therefore, two boilers indicate the same PLR in the same manner as seen during the representative day of the intermediary seasons. However, as the heating load is higher than that during the representative day of the intermediary seasons, they show a relatively high PLR. Case_3 also show similar patterns with those seen during the representative day of the intermediary seasons. However, as Boiler_1 is unable to process all the heating loads after reaching 100% of the PLR at 6 a.m., Boiler_2 deals with the remaining heating load. Thus, there is a relatively low difference in the PLR between Boiler_1 and Boiler_2 compared to other time periods. While Cases_4 and 5 use the same load distribution methods, the capacity of Boiler_1 and Boiler_2 are different. The overall pattern is very similar, but there is a difference in the pattern of Boiler_2 because of the difference in the capacity of Boiler_1. Boiler_1 in Case_4 with the capacity that is 30% of the entire capacity shows 100% of PLR at 6 a.m. to 8 a.m., and 7 p.m., making Boiler_2 operate and handle the remaining load. However, as for Case_5, Boiler_1 with a larger capacity deals with most of the loads, and Boiler_2 operates only at 6 a.m. when the heating load is the highest. Hereupon, it was confirmed that different PLR values were shown due to the difference in the capacity of boilers and load distribution algorithm. This is expected to directly influence the gas consumption for the heating in buildings.

Gas consumption in representative days

The gas consumption in representative days was analyzed based on the previously analyzed PLR. The previously analyzed PLR is the value that was calculated by dividing the heating load in the system by the system capacity. The pattern of gas consumption seen during the representative days is the same as that of the aforementioned PLR. Therefore, the analysis of gas consumption pattern in each time during representative days was not included in this study. Instead, a comparative analysis was conducted on the entire gas consumption used for the entire representative day. The gas consumption is closely related to the PLR and the efficiency of boilers. The matters related to the efficiency of boilers are presented in the analysis of the annual gas consumption in Section 4.4, and the relationship between the PLR and the gas consumption is stated in Section 4.3. Tables 5 and 6 represent the results of the gas consumption of Boilers_1 and 2 and the entire gas consumption during the representative days of the intermediary seasons and winter season. Case_4 indicated the least gas consumption, and the gas consumption was recorded in an ascending order of Case_5, Case_3, Case_1, and Case_2. Cases_1 and 2 have the same amount of gas consumption. In Case_1, only one boiler was used, thus there was no gas consumption in Boiler_2. As explained above, two identical boilers operated at the same time in Case_2. Therefore, there was a difference in the values between the intermediary seasons and the winter season, but the amount of gas consumption used in each boiler was identical. When comparing Case_2 with Case_1, the heating load applied to each boiler in Case_2 was only half of that in Case_1. However, as the system capacity was reduced to half, the PLR turned out to be identical in the end. Thus, Cases_1 and 2 showed the same PLR and the same amount of consumed gas, and the only difference was in the number of boilers. In Case_3, when the heating load is required in the uniform algorithm, two boilers operate at the same time. As explained above, since the capacity of Boiler_2 was greater than that of Boiler_1 in Case_3, it was found that the PLR of Boiler_2 was significantly lower than that of Boiler_1 when loads were identically applied. According to the characteristics of boilers, as the PLR decreases, the efficiency of boilers also decreases. Through the lower PLR in Boiler_2, it is assumed that more gas has been consumed. However, the difference of the amount of gas consumed between Cases_3 and 1 was very small. The capacity of the boilers entered in Case_4 was the same as that in Case_3, but the load distribution algorithm was different. As for the sequential algorithm entered in Case_4, only Boiler_1 operates most of the time except for certain hours when there is much heating load as Boiler_2 operates while the PLR of Boiler_1 reaches 100%. Therefore, a higher level of PLR is maintained in Case_4 than in Case_3. Hereupon, the amount of gas consumed seems to be less. As for Case_5, the capacity of Boiler_1 is greater than that of Boiler_2. Therefore, Boiler_1 deals with all the heating loads in the intermediary seasons when there is less heating load. Hereupon, the amount of gas consumed in Boiler_2 is 0. In the winter season, only Boiler_1 with a large capacity operates most of the time, and Boiler_2 operates only during certain hours when the heating load is much required. Therefore, among the cases except for Case_1 when there was only one boiler, the amount of gas consumed in Boiler_1 in Case_5 seemed to be the lowest.

Detailed analysis on annual data

Figure 6 shows the boiler efficiency curve in each PLR section of the boiler. As explained above, the following curve is the efficiency curve made according to the actual survey data by Company A in the US. All the boilers used in the relevant study have the identical efficiency curve. As the PLR increases, the efficiency of boilers increases as well. Once the PLR reaches 100%, up to 84% of the boiler efficiency is achieved. In this study, the low efficiency section was assumed as PLR of 0% to 40% where the efficiency was less than 70%, and the high efficiency section was assumed from PLR of 40% to 100% where the efficiency was 70% or higher. Therefore, analysis was carried out by dividing the sections into two for precise analysis: PLR of less than 40% and PLR of 40% or higher.

Figure 13 demonstrates the amount of gas consumed in the low efficiency section where PLR is 0 to 40%. According to Fig. 13, Case_4 has the lowest gas consumption of 30.8 MWh in the low efficiency section of boilers, and Cases_1 and 2 both have the highest gas consumption of 120.8 MWh. The reason for Cases_1 and 2 to have the identical values was that, as explained in Section 4.3, one boiler was divided into two boilers, which led the load to be handled in one boiler to reduce to half. However, the capacity of boilers was reduced to half as well, and thus there was no change in the PLR. In addition, as they had the same boiler efficiency curves, the amount of gas consumed was found to be the same. As for Case_3, Boiler_1 which constituted 30% of the entire capacity and Boiler_2 which had 70% of the entire capacity operated at the same time. As the two boilers operated at the same time even when a low heating load was required and Boiler_2 which constituted 70% of the entire capacity maintained a low PLR, it seemed that the amount of gas consumed was found to be high in Case_3 in the low efficiency section. As for Case_5, Boiler_2 does not operate until the PLR of Boiler_1 which constitutes 70% of the entire capacity reaches 100% according to the characteristics of the load distribution algorithm. However, since the intermediary seasons require a lower heating load than the winter season, Boiler_1 with a larger capacity has a lower PLR. Therefore, Case_5 has less amount of gas consumed compared to Cases_1, 2, and 3. As for Case_4 which had the least amount of gas consumed, Boiler_1 constituted 30% of the entire boiler capacity, thus a high level of PLR is maintained in the winter season when a high heating load is required, and a relatively high PLR is maintained compared to other cases due to the low capacity in the intermediate seasons when a low heating load is required. This shows that Case_4 has the least amount of gas consumed in the end. Table 7 shows the operating hours of each boiler, which directly influences the amount of gas consumed as shown in Fig. 13. As explained above, Cases_1 and 2 have different capacities of boilers but have an identical PLR. Therefore, their operating hours are the same as well. As for Case_3, two boilers operate at the same time in the same manner as Case_2. However, as the capacity of Boiler_2 is greater in Case_3 than that in Case_2, the PLR is maintained lower than that of Boiler_1, even if the heating loads applied are identical. In addition, when distributing the load, the remaining load is processed by Boiler_2 if there is a load that cannot be handled by Boiler_1 with a smaller capacity. Therefore, the operating hours of Boiler_2 tend to be relatively longer than those in Cases_1 and 2. On the other hand, as Boiler_1 in Case_3 has a small capacity than other boilers, the heating load is processed in the relatively high PLR section even if the same heating load is dealt with. Thus, less operating hours are shown compared to Cases_1 and 2 in the section of 0 to 40% part load. As for Case_4 which consumed the least amount of gas in the low efficiency section, the operating hours were found to be the shortest as well. First of all, according to the characteristics of the load distribution algorithm, once the PLR of Boiler_1 reaches 100%, Boiler_2 operates. However, since the capacity of Boiler_1 is low, a higher PLR is shown in the intermediary seasons when a low heating load is required, and even a higher PLR is shown in the winter season when a high heating load is required. Therefore, the operating hours of Boiler_1 in the low efficiency section are the shortest among those in all cases. Boiler_2 has less operating hours than other cases. However, they are still longer than those in Case_5. This is due to the difference in the capacity of Boiler_1. Boiler_1 in Case_5 has a larger boiler capacity. Boiler_1 is able to handle the heating load occurring during most of the intermediary seasons and in the winter season at day times. Therefore, there are longer operating hours of Boiler_1, but the operating hours of Boiler_2 are the least among the entire cases. The low efficiency section is where the boiler shows a low efficiency, and as the operating hours are shorter and the amount of gas consumed is less in the section, the efficiency of the operation of boilers turns out to be higher.

Figure 14 illustrates the amount of consumed gas in the high efficiency section where PLR is 40% or higher. According to Fig. 14, Cases_1 and 2 had the lowest amount of gas consumed (approximately 34.4 MWh) in the high efficiency section of the boiler, and Case_4 had the highest amount of gas consumed (approximately 93.7 MWh). The reason for such results can be interpreted through Table 8. As for Cases_1 and 2, boilers operate for 1710 h on an annual basis in Table 7, which indicates the low efficiency section. On the other hand, they only operate for 152 h in the high efficiency section. As the capacity of boilers is designed to handle the maximum heating load, the PLRs of boilers are low due to the excessive capacity during most of the period except for several special days. Therefore, it seems that as the operating hours in the high efficiency section are too short, the amount of gas consumed is also low. As for Case_3, longer boiler operating hours are shown compared to Cases_1 and 2 in the high efficiency section. Even if the load distribution algorithm is used in the same manner as Case_2, the capacity of Boiler_1 is as low as 30% of the entire capacity. Therefore, a high PLR is continuously maintained during the intermediary seasons and in the winter season at day times when the heating load is low. Therefore, they have a higher amount of gas consumed compared to Cases_1 and 2. Case_4 indicates the highest amount of gas consumed among cases analyzed in this study due to the capacity of boilers and interaction of the applied load distribution algorithm. According to the characteristics of the sequential algorithm, Boiler_2 operates once the PLR of the boiler assigned in priority reaches 100%. However, Case_4 has the longest operating hours in the high efficiency section compared to other cases due to the low capacity of Boiler_1. Case_5 uses the identical load distribution algorithm as Case_4. However, Boiler_1 with the highest priority constitutes 70% of the entire boiler capacity. Thus, there are longer operating hours in the low efficiency section compared to the high efficiency section where the PLR is 40% or higher.

Tables 9 and 10 show the annual operating hours and the amount of gas consumed in every PLR section of each case during the operating period. The operating hours are indicated as ‘h’, and the amount of gas consumed is expressed as MWh. In the section where PLR is 0 to 40% and where there is a low efficiency according to the characteristics of the performance curve, Case_1 has consumed approximately 78% of gas compared to the annual amount of gas consumed. In the same section, Case_2 has consumed approximately 78% of gas, followed by approximately 75% in Case_3, approximately 25% in Case_4, and approximately 64% in Case_5. According to the results of the comparison of the amount of gas consumed in every PLR section, Case_4 had the least amount of gas consumed. The reason for this is that the capacity of Boiler_1 was low, but it is also assumed that the role of the load distribution algorithm is heavily influential. As mentioned above, according to the characteristics of the sequential algorithm, Boiler_2 operates once the PLR of Boiler_1 assigned in priority reaches 100%. However, due to the low capacity of Boiler_1, it is assumed that there are longer operating hours in the high efficiency section compared to other cases. This is the number of hours that are six times higher than those in Cases_1 and 2 and three times higher than those in Case_5, except for Case_4. Therefore, it has the highest amount of gas consumed in the high efficiency section compared to other cases. The amount of gas consumed in Case_4 in the high efficiency section is 93.7 MWh which is less than 120.87 MWh consumed in Cases_1 and 2 in the low efficiency section. However, there was a huge difference on heat load that was processed due to the difference in boiler capacity. This indicates that Case_4 has the least amount of gas consumed, as load is more efficiently processed even if the amount of load processed is identical to that in other cases.

Conclusions

This study has modeled the large-sized office buildings using EnergyPlus while applying the boiler staging control and load distribution algorithm to precisely analyze the annual operating performance of boilers according to the part load ratio characteristics. Hereupon, the research has been conducted to achieve the goal of suggesting the optimal boiler staging ratio and heat load distribution algorithm in large-sized office buildings. The conclusion has been drawn in this study as follows:

According to the results of the analysis of the annual operating performance based on part load ratio characteristics of boilers in each case, Case_1 as a base model was found to consume approximately 78% of the annual gas in the low PLR range from 0 to 40%. When two boilers were operated at the same time to uniformly distribute the heating load, Cases_2 and 3 were found to consume approximately 78% and 75% of the annual energy, respectively, in the same PLR range. In addition, when two boilers were sequentially operated according to their own priority scheme, Cases_4 and 5 were found to consume approximately 25% and 64% of annual gas, respectively, in the same PLR range.

According to the results of the analysis of gas energy consumption in the intermediary seasons and winter season, Case_4 was found to consume the lowest amount of gas both in the intermediary seasons and winter season due to the combined effects of PLR and the corresponding boiler efficiency.

According to the results of the comparison on annual gas consumption, it was found that the annual gas consumptions in Cases_2 and 3 that used the uniform algorithm were estimated to be identical or lower than that in Case_1 due to the characteristics of the load distribution scheme. On the other hand, Case_4 could achieve the energy saving by 27% and 19% in the intermediary seasons and winter season, respectively. In addition, Case_4 saved approximately 20% of total annual gas in comparison to Case_1.

In conclusion, applying the boiler sequential control algorithm by dividing the capacity of boilers in the ratio of 3:7 turned out to be the optimal boiler staging scheme for the maximized efficiency of boiler operations.

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