1. Introduction
The share of total delivered energy in the building sector is around 20% and the share of total primary energy consumption is around 31% worldwide, which is predicted to increase by an average value of 1.5% annually from 2012 to 2040 [
1,
2]. Hence, due to the relatively high share of energy utilization in this sector, there is a big potential for fossil fuel consumption reduction. This, accordingly, can mitigate the negative environmental and societal impacts of fossil fuel consumption.
One of the alternative solutions to achieve higher efficiency, lower fuel consumption, and a higher share of renewable energy is the concept of decentralized combined cooling, heating, and power generation (CCHP), which is also called polygeneration. Providing power and useful heat in such systems is considered as an alternative or supplementary solution for ever-growing energy-related problems [
3,
4]. In addition, in a polygeneration system, multiple energy sources, including renewable and non-renewable, are used to deliver multiple energy services simultaneously, resulting in lower emissions [
5].
Polygeneration system consists of several numbers of units such as combined heat and power (CHP), boilers, photovoltaic (PV) units, solar heating units, and thermal and electric storage devices. Due to the relatively large number of endogenous and exogenous variables, such as size and type of components, operating strategy, load profile, climate zones, and the availability and price of energy, the design of such systems is complicated [
6,
7,
8]. Using an optimization technique for the correct sizing and defining the correct operating strategies can increase the performance of the system. Therefore, to define the optimal solution, development of the suitable mathematical model is necessary, and hence, modeling and optimization of small-scale polygeneration systems are carried out in several studies.
The important aspects in identifying the optimal design of such systems were the focus of various comprehensive studies [
9,
10]. These aspects include the technologies [
11], performance assessment method, polygeneration supporting mechanisms, energy policies, operating strategies [
12], decision-making variables, optimization techniques, optimization in urban application [
13], and the modeling approach [
4]. These surveys emphasize the necessity of further investigations on multi-criteria assessment and the feasibility study of optimization of complex polygeneration systems, including innovative storage and generation devices and the utilization of multiple fuel sources, including renewable energy [
12].
A polygeneration system for a tourist center in Spain was optimized by Rubio-Maya et al. [
14]. Implementation of the optimal design to supply the energy demand of the building achieved economic and environmental benefits when compared to conventional separate heat and power generation. In the presented case study, a fossil fuel consumption reduction of 18% was reported [
14].
Di Somma et al. used an exergy analysis to identify the optimal design of a polygeneration energy system [
15]. When compared to a conventional separate heat and power generation, 21–36% reduction in primary exergy input for the proposed cases was achieved.
In a study performed by Sanaye et al., a small polygeneration system in a residential building that was located in the south of Iran was investigated [
16]. The results showed energetic and environmental benefits of the polygeneration system. Moreover, for the presented case, due to the high availability of excess heat from the combined heat and power (CHP) units, the thermal chiller that was using this heat for cooling purposes showed advantages over the electric chiller.
Tichi et al. investigated the influences of energy policy on the performance operation of optimal polygeneration systems in Iran [
17]. The price of electricity and the energy market policy in Iran were mentioned as two obstacles for the promotion of such systems. Moreover, it was mentioned that the benefits achieved are highly dependent on the optimal design and operating conditions of the system.
A CCHP system for fulfilling the load demand of an office and a commercial building in Tehran was investigated by Hanafizadeh et al. [
18]. The capacity of the prime movers was optimized regarding economy for three different scenarios and the emission reduction was evaluated for the optimal designs. The optimal solutions resulted in a reduction of electricity purchase from the grid, fuel consumption, and pollutants.
The literature background shows that, despite several studies in this field, more investigations are required in order to overcome current shortcomings. As an example, in several studies, the impacts of outdoor temperature and/or part-load operation on the efficiency and power output of CHP units are not taken into account. Moreover, more research on complex polygeneration systems that consist of several generation and storage units using both renewable and non-renewable energy sources is required. Furthermore, multi-criteria optimization considering the energetic, environmental, and economic performance of a polygeneration system is an area that requires more investigation. Therefore, a method for performance evaluation and the design optimization of complex polygeneration that overcomes some of the shortcomings was proposed by the author [
13,
19]. In the developed model, a particle swarm optimization (PSO) [
20,
21] algorithm was used as the optimization technique. The objective function of the optimization process was to maximize the energetic, economic, and environmental performance of the system relative to a defined reference system.
The model aims to optimize a semi self-sustaining polygeneration system, which minimizes the fossil fuel consumption and maximizes the use of renewable energy. Therefore, in the operating strategies, the priority was given to renewable heat and power. Several generation (heating, cooling and electricity) and storage units (heat, cold and electric) that were driven by renewable and non-renewable energy sources were considered in the model. In similar studies, one or more components, which are of interest, are missing, and that has a significant impact on the performance of the polygeneration system. The grid is only used for balancing purposes and increasing the excess power for merely maximizing the profit by playing a role in the day-ahead market is not included in the operating strategies. The details of the optimization tool, energy flow model, and embedded operating strategies are available in the original paper [
13]. In the present study, however, a general outline of the model, the main principle of the optimization model, and the performance evaluation method are described briefly.
The climate zone is one of the parameters that significantly influences the load profile, ambient temperature, availability of the renewable energy sources, such as solar radiation and wind speed, and the operational characteristics of the prime movers [
22]. This consequently can affect the design and performance of polygeneration systems. Hajabdollahi et al. investigated the performance of a polygeneration system to provide the energy demand of hotel building in hot, moderate, and cold climate in Iran [
23]. The objective function was to maximize the annual economic benefits as compared to conventional separate heat and power production system. The size of each component and the operating strategies were optimized. The highest annual benefit was achieved in the hot and moderate climate, and the lowest was achieved in the cold climate. Li et al. proposed an improved method for determining the energy saving ratio of polygeneration systems in various climates using standard regulation in term of energy management through [
24]. The influence of weather on the size of prime movers [
25] and the effect of temperature on the CCHP systems were investigated by Ebrahimi et al. [
26]. The operation of CCHP systems was optimized in terms of energy, economy, and carbon dioxide emission reduction by Cho et al. for different locations with different climate conditions [
27].
When considering the literature, it can be concluded that climate zone is one of the important parameters that can affect the benefits that are achieved by a CCHP system. Hence, to make a correct conclusion about the benefits achieved from a polygeneration system, the effects on the performance should be determined and realized [
24]. However, despite the existing studies, many of the investigated CCHP systems did not include solar heating and power components and storage devices. In addition, due the number of studies are limited and more investigation on the impacts of climate zones on the performance and design of complex polygeneration systems, including solar heating units, solar power installations, and storage devices is required.
Therefore, in this study, in order to examine these effects, the optimal design and performance of complex polygeneration systems for an identical residential building complex that is located in three different climate zones in Iran are investigated. Iran is chosen due to its climate span, but with approximately the same price for electricity and natural gas in the different locations. Moreover, Iran is an attractive case due to the extensive use of fossil fuels for heat and power production presently while it has an abundance solar energy, which can be used in the future energy system.
5. Result and Discussion
The optimization was performed for a polygeneration system with the FEL strategy in Ahvaz, Hamedan, and Tehran. A comparative analysis between the cities was carried out while considering the following terms:
5.1. Component Size
The optimal size of polygeneration systems for each city is shown in
Figure 7. There is no battery, wind turbine, or solar heating panel in any of the optimal solutions due to their high price and the low price of electricity and gas purchase in the energy market in Iran. The size of the PV panels is the maximum value of the search space, since it decreases the CO
2 emission and increases the economy of the system by avoided electricity and fuel purchase costs. The sizes of the electric and thermal chillers are the highest in Ahvaz and the lowest in Hamedan, owing to the high and low cooling demands in these locations, respectively. Consequently, the building in Ahvaz has the largest CHP system since the total power demand highly depends on the size of the electric chiller. In other words, the CHP system cover the power demand (FEL operating strategy) and therefore a larger capacity of CHP system in a city with a higher total power demand (Ahvaz) would be required. The same reasoning holds true in explaining the smaller CHP unit size in Hamedan.
The cold storage appeared in all of the cities with the highest value in Ahvaz because of its high cooling demand. The heat from the CHP unit is used for the heating purpose or in the thermal chiller for cooling purposes. Therefore, there is no heat storage in Hamedan and Tehran, and the size of heat storage in Ahvaz is quite small.
5.2. Performance Analysis
Performance of the systems in terms of energy, environment and economy are shown in
Figure 8. As shown, based on the integrating saving ratio (
ISR), the performance of the polygeneration system is the highest in Ahvaz and the lowest in Hamedan, which are the hottest and coldest cities, respectively. Similarly,
FSR,
CO2ERR, and
ISR are the highest in Ahvaz and the lowest in Hamedan. The
CO2ERR in Ahvaz, Tehran, and Hamedan are around 41%, 34% and 27%, respectively. This shows the significant environmental benefits of the polygeneration systems in all of the cases, especially for a city with a hot climate like Ahvaz. However, the
ATCSR as an economic indicator is relatively low in all of the cases, which is an obstacle in promoting the application of polygeneration systems in Iran. More details about the economy of the system are further discussed in
Section 5.3.
The total heating, cooling, and electricity production by the polygeneration system, the amount of excess heat and the amount of imported and exported electricity from/to the grid for each city are shown in
Figure 9. In Ahvaz, due to the high cooling demand, the size of the CHP to provide the cooling demand of the electric chiller is large, and consequently the amount of heat that is provided by the CHP is relatively high as well. Part of this heat is used for cooling purposes through the thermal chiller. However, not all of the exhaust heat can be used, and therefore, the amount of excess heat in Ahvaz is higher than the other two cities. In Hamedan and Tehran, the amount of available heat from the CHP units is less and the heat can be exploited for heating purposes due to the colder winters. The amount of exported electricity is the highest in Hamedan and the lowest in Ahvaz. This is mainly due to the size of solar installations, which is the same for all of the cities, while the amount of total power demand (including the electricity demand of the electric chiller) is the lowest in Hamedan and the highest in Ahvaz. In addition, during the heating season, which is relatively long in Hamedan, the power demand is comparatively low and the electricity demand is imported from the grid instead of running the CHP at low part-load. Therefore, the amount of imported power is relatively high during the cold season in Hamedan.
As presented in
Figure 8, the value of
CO2ERR for the polygeneration system that is located in Ahvaz is the highest. This value is related to the heating, cooling, and electricity production. However, as shown in
Figure 10, the specific CO
2 emission factor, which is the amount of CO
2 emission per kWh produced electricity by the polygeneration system is the highest (0.24 kg CO
2/kWh
el) in Ahvaz and the lowest (0.1 kg CO
2/kWh
el) in Hamedan. This is mainly due to the high share of power production by the solar PV units in Hamedan, as shown in
Figure 10. The share of power generation by the PV panels is the highest (65%) in Hamedan and the lowest (17%) in Ahvaz. This can be explained by the fact that while the size of solar panel installations is the same in all of the cities, the power demand is the highest in Ahvaz and the lowest in Hamedan.
The share of power self-consumption (the ratio of the consumed power in the building to the total generated power by the polygeneration system) has the highest (98%) and lowest (81%) values in Hamedan and Ahvaz, respectively. Consequently, the share of exported power in Hamedan is the highest, while this value is the lowest in Ahvaz. On the other hand, Ahvaz exhibits the share of power generation by polygeneration systems of 99%, which in comparison to Hamedan (93%) reveals higher self-sufficiency of the system in Ahvaz.
To identify the importance of the thermal chiller and cold storage in the system, the share of cooling production by the thermal chiller and the share of cold storage in cooling demand supply are shown in
Figure 11. As shown, the share of cooling production by the thermal chiller is the highest in Ahvaz (26%) due to the higher amount of exhaust heat from the CHP unit in Ahvaz. The share of cooling-demand supply by the cold storage in Ahvaz and Tehran are around 30%, while in Hamedan it is around 24%. These figures show the significant role of the thermal chiller and cold storage in increasing the performance of the polygeneration system as these devices can exploit the excess heat and power and use them for cooling purposes.
5.3. Economic Evaluation
The cost breakdown and the share of each type of cost for the polygeneration systems are shown in
Figure 12. The capital cost is the highest in Ahvaz and the lowest in Hamedan, which is mainly related to the size of the CHP system. The fuel cost is the highest in Ahvaz and the lowest in Tehran. The fuel consumption in Ahvaz is mainly related to the fuel consumption in the CHP system. The fuel consumption in Hamedan is mainly due to the high heating demand and high amount of imported power from the grid. The costs/benefits of electricity purchased/sold from/to the grid are proportional to the amount of imported and exported electricity.
The share of the capital cost in Ahvaz, Tehran, and Hamedan are around 56%, 59% and 54%, respectively, and the differences between these values are not significant. However, the share of fuel cost is the highest in Hamedan (40%) and the lowest in Ahvaz (26%). Despite the highest share of PV power generation in Hamedan, the share of fuel cost in a cold city, such as Hamedan, is significantly larger than a hot city like Ahvaz, which is mainly due to the higher heating demand in Hamedan.
The payback period (PBP),
ATCSR, and levelized cost of electricity (LCOE) are the metrics used in this study. To calculate the LCOE, the electricity is assumed as the main product and the produced cooling by the thermal chiller and the produced heat by the CHP as by-products, which can bring income to the system. To assign a price for the produced heat by the CHP, the production cost of the same amount of heat by a boiler is substituted. For cooling that is produced by the thermal chiller, the cost is calculated based on the electricity consumption of an electric chiller as if the cooling was produced by an electric chiller. The LCOE is a metric that is frequently used in the feasibility evaluation of power systems. However, because of the simultaneous production of electricity and useful heat in a polygeneration system, this value can be misleading and therefore it should be used along with the other metrics. As presented in
Table 8, the levelized cost of electricity is the highest in Hamedan and the lowest in Ahvaz. The payback periods are relatively long for all of the cities and the implementation of the polygeneration system in Iran is not economically feasible with the current energy market. LCOE of the polygeneration system in all of the cases is higher than the price of electricity in the energy market in Iran (0.06 USD/kWh
el). This, along with the high payback period (more than 19 years), shows that the polygeneration system is not an economically viable choice in Iran.
The results show that the performance of the polygeneration system in the identified case study is higher in the building that is located in a hot city, such as Ahvaz, when compared to the building located in a cold city such as Hamedan. A higher share of power production by PV units has a positive impact on the environmental performance of a polygeneration system. Using the exhaust gas of the CHP system for cooling purposes has a significant effect on the performance of the system. Moreover, the results show the significant role of the thermal chiller and cold storage in improving the performance of the polygeneration system.
These results can be used as guidance in the planning stage of polygeneration system implementation. However, due to the large number of variables that are involved, these results cannot be generalized to other cases and every project should be investigated individually.
6. Conclusions
In this study, the performance of polygeneration systems for an identical building that is located in three cities in Iran with cold, moderate and hot climates has been investigated. In all of the cities, the application of the polygeneration systems shows significant environmental and energetic benefits relative to the reference system. One important finding is the importance of thermal chiller and cold storage in increasing the performance of the polygeneration systems. Effective exploitation of excess heat and power for cooling production increases the performance of the polygeneration system.
The comparative analysis shows that the polygeneration system in Ahvaz with a higher cooling demand is superior regarding energy, environment and economy. The CO2 emission reduction potential in a building that is located in Ahvaz is around 41%, while this value in Hamedan is around 27%. However, the economic indicators, such as low (even negative) ATCSR and high payback periods, are not promising, which can be a significant obstacle for promoting the application of the polygeneration systems. As a general conclusion, regardless of the energetic and environmental benefits of the polygeneration system in the case studies, with the presented electricity tariff in the residential building in Iran, the implementation of a polygeneration energy system is not economically feasible. Various financial mechanisms, such as feed-in-tariffs, tax reduction, and subsidies, can promote the application of these systems.