Design Optimization of a Small-Scale Polygeneration Energy System in Different Climate Zones in Iran

: Design and performance of polygeneration energy systems are highly inﬂuenced by several variables, including the climate zone, which can affect the load proﬁle as well as the availability of renewable energy sources. To investigate the effects, in this study, the design of a polygeneration system for identical residential buildings that are located in three different climate zones in Iran has been investigated. To perform the study, a model has previously developed by the author is used. The performance of the polygeneration system in terms of energy, economy and environment were compared to each other. The results show signiﬁcant energetic and environmental beneﬁts of the implementation of polygeneration systems in Iran, especially in the building that is located in a hot climate, with a high cooling demand and a low heating demand. Optimal polygeneration system for an identical building has achieved a 27% carbon dioxide emission reduction in the cold climate, while this value is around 41% in the hot climate. However, when considering the price of electricity and gas in the current energy market in Iran, none of the systems are feasible and ﬁnancial support mechanisms or other incentives are required to promote the application of decentralized polygeneration energy systems.


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 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.

Problem Statement
In this section, the characteristics of the proposed method, system configuration, and problem formulation are described briefly. Details of the method, operating strategies, and performance evaluation technique can be found in the method paper [13].
A common approach for the assessment of a polygeneration energy system is to evaluate its performance relative to a reference system as used in previous studies [28][29][30][31][32][33]. In the section below, the reference system and polygeneration system that were used in this study are illustrated and described in detail.

Reference System
The schematic of the reference system is illustrated in Figure 1. In the reference system, the heating is provided by a natural gas driven boiler and the electricity is imported from the utility grid, which is a conventional fossil fuel driven power plant. The cooling demand is provided by an electric chiller. The total power demand is the summation of the building's power demand and the electricity demand of the electric chiller. The equivalent energy of the fuel consumption by the utility grid is calculated by the equations below: where  is the supplied power by the grid, _ is the internal electricity demand and is the electric chiller power demand, and  ƞ is the power plant efficiency, ƞ is the grid distribution line efficiency which consider the losses through the distribution and transmission lines and Δt is the time-step.
The total corresponding energy of the fuel consumption ( ) in the reference system is given as below: where and are the corresponding energy of the fuel utilization in the boiler and the utility grid respectively.

Polygeneration System Configuration
The polygeneration system comprises of power, heating, and cooling units that are accompanied by thermal and electric storage devices. A block diagram of the system is illustrated in Figure 2. The power is provided by solar panels, wind turbines, and a combined heat and power (CHP) unit. The heat can be supplied by the heat recovery unit (HRU) annexed to the CHP, an auxiliary boiler and solar heating (SHU) units. Cooling is supplied by a thermal chiller (Tch) and/or an electric chiller. To exploit the excess heat and power and to overcome the fluctuating nature of renewable sources, thermal and electric storages are added in the configuration. In the default model, the system is connected to the grid; however, it can be easily modified for an off-grid application.
The total equivalent energy of the fuel consumption ( ) is the accumulated fuel energy that is utilized in the boiler ( ), the equivalent energy of the fuel consumption of the imported power from the grid ( ), and the equivalent energy of the fuel consumption in the CHP unit ( ), which can be calculated by the equation below: The corresponding energy of the fuel consumption of the grid import ( , ) can be estimated by the following equation: Figure 1. Block diagram of the reference system [13,19].
The total power demand is the summation of the building's power demand and the electricity demand of the electric chiller. The equivalent energy of the fuel consumption by the utility grid is calculated by the equations below: is the grid distribution line efficiency which consider the losses through the distribution and transmission lines and ∆t is the time-step.
The total corresponding energy of the fuel consumption (F Ref tot ) in the reference system is given as below: where F Ref boiler and F Ref grid are the corresponding energy of the fuel utilization in the boiler and the utility grid respectively.

Polygeneration System Configuration
The polygeneration system comprises of power, heating, and cooling units that are accompanied by thermal and electric storage devices. A block diagram of the system is illustrated in Figure 2. The power is provided by solar panels, wind turbines, and a combined heat and power (CHP) unit. The heat can be supplied by the heat recovery unit (HRU) annexed to the CHP, an auxiliary boiler and solar heating (SHU) units. Cooling is supplied by a thermal chiller (Tch) and/or an electric chiller. To exploit the excess heat and power and to overcome the fluctuating nature of renewable sources, thermal and electric storages are added in the configuration. In the default model, the system is connected to the grid; however, it can be easily modified for an off-grid application.
The total equivalent energy of the fuel consumption (F Poly tot ) is the accumulated fuel energy that is utilized in the boiler (F  The corresponding energy of the fuel consumption of the grid import (P Poly g,imp ) can be estimated by the following equation: (5) where η grid el is the efficiency of the power plant, η grid dl is the grid efficiency that considers the losses in the distribution and transmission losses and ∆t is the time-step of the simulation.
where ƞ is the efficiency of the power plant, ƞ is the grid efficiency that considers the losses in the distribution and transmission losses and Δt is the time-step of the simulation. It should be mentioned that, since a high degree of self-sustainability in term of power is the main intention of the optimization model, generating excess power on purpose and participating in the day-ahead market is out of the scope of this work. In other words, maximizing profit by selling the electricity to the grid is not an immediate goal in the optimization process.

Operating Strategy Description
There are three operating strategies incorporated in the model namely, following electric load (FEL), modified base load (MBL), and following thermal load (FTL). In the FEL strategy, the CHP provides the electricity demand, which follows the electric demand at each time-step, resulting in low excess power from the CHP. In the MBL, the CHP is operated at its rating power and provides a base load as far as the demand is above the base load. However, if the demand falls below the base load, the CHP follows the power demand and operates similarly to the FEL. In the FTL strategy, the CHP units supply the total heating demand and the boiler operates for covering more heating demand. In this case study, being self-sustained in terms of power is assumed to be an important issue, and therefore, the FEL is chosen as the operating strategy.
The electricity demand is firstly provided by solar or wind energy, and then through discharging the storages and next by the CHP units. If there is any excess or deficit power, it will be exported or imported to/from the grid. The boiler will be operated if there is any heat deficit. In the case of excess heat, it will be stored in the storage. Additional features of the operating strategies can be acquired from the method paper [13].

Performance Evaluation
To investigate the benefits of polygeneration systems, their performance relative to the reference system regarding energy, economy, and environment should be evaluated. Annualized Total Cost Saving Ratio (ATCSR), Fuel Saving Ratio (FSR), and CO2 Emission Reduction Ratio (CO2ERR) are the three criteria that are used for energetic, economic, and environmental evaluation (3-E analysis) of It should be mentioned that, since a high degree of self-sustainability in term of power is the main intention of the optimization model, generating excess power on purpose and participating in the day-ahead market is out of the scope of this work. In other words, maximizing profit by selling the electricity to the grid is not an immediate goal in the optimization process.

Operating Strategy Description
There are three operating strategies incorporated in the model namely, following electric load (FEL), modified base load (MBL), and following thermal load (FTL). In the FEL strategy, the CHP provides the electricity demand, which follows the electric demand at each time-step, resulting in low excess power from the CHP. In the MBL, the CHP is operated at its rating power and provides a base load as far as the demand is above the base load. However, if the demand falls below the base load, the CHP follows the power demand and operates similarly to the FEL. In the FTL strategy, the CHP units supply the total heating demand and the boiler operates for covering more heating demand. In this case study, being self-sustained in terms of power is assumed to be an important issue, and therefore, the FEL is chosen as the operating strategy.
The electricity demand is firstly provided by solar or wind energy, and then through discharging the storages and next by the CHP units. If there is any excess or deficit power, it will be exported or imported to/from the grid. The boiler will be operated if there is any heat deficit. In the case of excess heat, it will be stored in the storage. Additional features of the operating strategies can be acquired from the method paper [13].

Performance Evaluation
To investigate the benefits of polygeneration systems, their performance relative to the reference system regarding energy, economy, and environment should be evaluated. Annualized Total Cost Saving Ratio (ATCSR), Fuel Saving Ratio (FSR), and CO 2 Emission Reduction Ratio (CO2ERR) are the three criteria that are used for energetic, economic, and environmental evaluation (3-E analysis) of the polygeneration system as frequently used in the similar analysis [28][29][30][31][32][33]. In this section, the performance evaluation method is explained in brief and more details are available in the original work [13].
The CO 2 Emission Reduction Ratio (CO2ERR) is estimated, as below: Poly tot CO 2 Ref tot (6) where CO 2 Poly tot and CO 2 Ref tot are the CO 2 emissions of the polygeneration and reference system, respectively.
Fuel Saving Ratio (FSR) is calculated by the following equation: where F Poly tot and F Ref tot are the equivalent energy of the fuel utilization in the polygeneration system and reference system, respectively.
The Annualized Total Cost Saving Ratio (ATCSR) is given, as follows: where ATC Ref tot and ATC Poly tot are the annualized total cost of the reference and the polygeneration system through its lifetime.
Payback Period (PBP) and Internal Rate of Return (IRR) are the other metrics that are used in the economic evaluation of the polygeneration system.

Optimization Problem Formulation
In this part, a general overview of the objective function and the optimization method is presented. Details of the optimization problem formulation and the operating strategies are provided in the previous work [13,19].

Optimization Model Overview
As shown in Figure 3, the optimization tool has three main sections: the optimizer; the performance analysis; and, the energy flow model. A Particle Swarm Optimization (PSO), which is a population-based algorithm, is used as the optimization technique. The PSO was introduced by Kennedy and Eberhart [20], and it is shown to be a cost-effective method for identifying high-quality solutions [34,35]. The operating mechanism of the PSO algorithm is explained in the method paper [13], and more details about the PSO algorithm can be found in the original work by Kennedy and Eberhart [20].

Objective Function
The objective function is formulated in order to maximize the three metrics that are mentioned in Section 2.3: FSR; ATCSR; and, CO2ERR. The decision variables are the size of the components and the goal of the optimization process is to maximize an integrated saving ratio (ISR) that includes all of the mentioned metrics and is given as follows: where , and are the weighting factors. The objective function is formulated as a minimization problem, as given below: In this study, the economic weighting factor is 0.5 and the environmental and energetic weighting factors are 0.25 due to their interdependency. However, a further optimized design can be investigated by considering other combinations of energy, economy, and environmental criteria through a minor alteration of the objective function by considering the other distribution of the weighting factors.

Application to the Case Study
To investigate the impact of climate zones on the optimal design of a polygeneration system and its performance, the energy system of an identical residential building that is located in three climate zones in Iran is optimized for each location. Since the building's specification and the determination of its load demand characteristics are beyond the scope of this study, the data from another study that was conducted by Ehyaei et al. [36] is used.

Case Study Description
The load demand of a hypothetical 10-story residential building with four apartments in each floor and each apartment with an average floor area of 200 m 2 was investigated by Ehyaei et al. [36]. Ehyaei and Bahadori, determined the load demand of the identical residential building that was located in three cities with different climate specifications in Iran [37]: Ahvaz in south of Iran in a desert climate with very hot summer and mild, short winters; Tehran in the north of Iran in a cold semi-arid climate and moderate winters; and, Hamedan in north-west of Iran in a cold climate with very cold winters, as shown in Figure 4. The TRNSYS database is used to identify the weather data,

Objective Function
The objective function is formulated in order to maximize the three metrics that are mentioned in Section 2.3: FSR; ATCSR; and, CO2ERR. The decision variables are the size of the components and the goal of the optimization process is to maximize an integrated saving ratio (ISR) that includes all of the mentioned metrics and is given as follows: where w 1 , w 2 and w 3 are the weighting factors.
The objective function is formulated as a minimization problem, as given below: In this study, the economic weighting factor w 3 is 0.5 and the environmental and energetic weighting factors are 0.25 due to their interdependency. However, a further optimized design can be investigated by considering other combinations of energy, economy, and environmental criteria through a minor alteration of the objective function by considering the other distribution of the weighting factors.

Application to the Case Study
To investigate the impact of climate zones on the optimal design of a polygeneration system and its performance, the energy system of an identical residential building that is located in three climate zones in Iran is optimized for each location. Since the building's specification and the determination of its load demand characteristics are beyond the scope of this study, the data from another study that was conducted by Ehyaei et al. [36] is used.

Case Study Description
The load demand of a hypothetical 10-story residential building with four apartments in each floor and each apartment with an average floor area of 200 m 2 was investigated by Ehyaei et al. [36]. Ehyaei and Bahadori, determined the load demand of the identical residential building that was located in three cities with different climate specifications in Iran [37]: Ahvaz in south of Iran in a desert climate with very hot summer and mild, short winters; Tehran in the north of Iran in a cold semi-arid climate and moderate winters; and, Hamedan in north-west of Iran in a cold climate with very cold winters, as shown in Figure 4. The TRNSYS database is used to identify the weather data, including the solar radiation, temperature, and wind speed with an hourly time-step [38]. The average daily solar radiation in Ahvaz, Tehran and Hamedan are 5.4, 4.7 and 4.6 kWh/m 2 /day, respectively. The ambient temperature and wind speed for each city are presented in Table 1. including the solar radiation, temperature, and wind speed with an hourly time-step [38]. The average daily solar radiation in Ahvaz, Tehran and Hamedan are 5.4, 4.7 and 4.6 kWh/m 2 /day, respectively. The ambient temperature and wind speed for each city are presented in Table 1.  A comprehensive investigation regarding the thermal and electrical demand of a residential building in Tehran, Iran was carried out by Ehyaei et al. [36]. Then, the study was performed for the same building as if it was located in Hamedan and Ahvaz. The average domestic hot water load demand is shown in Figure 5, however, the profile changes on a monthly basis. The power demand of the building excluding the power demand of the electric chiller is also shown in Figure 5. These two load profiles are assumed to be the same in all of the buildings. The heating and cooling load profiles of the building for the representative months in each city are shown in Figure 6. More information about the building specifications and its load demand can be found in the original work [36].  A comprehensive investigation regarding the thermal and electrical demand of a residential building in Tehran, Iran was carried out by Ehyaei et al. [36]. Then, the study was performed for the same building as if it was located in Hamedan and Ahvaz. The average domestic hot water load demand is shown in Figure 5, however, the profile changes on a monthly basis. The power demand of the building excluding the power demand of the electric chiller is also shown in Figure 5. These two load profiles are assumed to be the same in all of the buildings. The heating and cooling load profiles of the building for the representative months in each city are shown in Figure 6. More information about the building specifications and its load demand can be found in the original work [36].  In the present study, the model was simulated for one-year operation running with an hourly time-step (8760 h). The peak power, heating, and cooling demand of the building and the aggregated  In the present study, the model was simulated for one-year operation running with an hourly time-step (8760 h). The peak power, heating, and cooling demand of the building and the aggregated Energies 2018, 11,1115 In the present study, the model was simulated for one-year operation running with an hourly time-step (8760 h). The peak power, heating, and cooling demand of the building and the aggregated electricity, heating, and cooling demand during a one-year operation are shown in Table 2. For the economic evaluation, the interest rate and inflation rate of 13% and 10% are assumed, respectively, and the lifetime of the project is considered to be 20 years. The efficiency of the power plant (gas turbine based) and the efficiency of the grid in the reference system are assumed to be 35% and 88%, respectively, corresponding to the low efficiency of the power plants as well as the grid transmission lines in Iran [39,40]. Table 2. Peak demand and annual aggregated power, heating, and cooling demand of the building [36].

Demand
Type City

Ahvaz Hamedan Tehran
Peak demand (kW) All of the operating strategies that are embedded in the system have advantages and drawbacks, and the choice of operating strategy is related to the project requirement and specification [19]. However, since self-sustainability in terms of power and independency from the grid is assumed to be necessary in the defined case, the FEL operating strategy is assigned.
Technical specification of the components, the related costs, the electricity and gas tariff, and the grid emission factors that are used in the case study are shown in Tables 3-6. Due to the limited available space for solar PV and solar heating panels and thermal storages, the sizes of these components are limited in the search space, as shown in Table 7.

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:

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.
Energies 2017, 10, x FOR PEER REVIEW 12 of 19 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.

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 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.

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.
Energies 2017, 10, x FOR PEER REVIEW 13 of 19 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. (a) (b) (c) Figure 9. Yearly production (a); excess heat (b) and imported/exported power (c).
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 CO2 emission factor, which is the amount of CO2 emission per kWh produced electricity by the polygeneration system is the highest (0.24 kg CO2/kWhel) in Ahvaz and the lowest (0.1 kg CO2/kWhel) 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. 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. (a) (b) (c) Figure 9. Yearly production (a); excess heat (b) and imported/exported power (c).
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 CO2 emission factor, which is the amount of CO2 emission per kWh produced electricity by the polygeneration system is the highest (0.24 kg CO2/kWhel) in Ahvaz and the lowest (0.1 kg CO2/kWhel) 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. 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. 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.
(a) (b) Figure 11. Share of cooling production by the thermal chiller (Tch) (a) and the share of cold storage in cooling demand supply (b).

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 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. 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.
(a) (b) Figure 11. Share of cooling production by the thermal chiller (Tch) (a) and the share of cold storage in cooling demand supply (b).

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

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.
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/kWhel). 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. 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.

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 CO 2 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.
Author Contributions: S.G.S. conceived and designed the model; A.M. and V.M. contributed jointly by supervising the overall work and the overall structure of the paper; S.G.S. wrote the paper.