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Optimal design and performance analysis of solar hybrid CCHP system considering influence of building type and climate condition
Energy 174 (2019) 647e663
Contents lists available at ScienceDirect
Energy journal homepage: www.elsevier.com/locate/energy
Optimal design and performance analysis of solar hybrid CCHP system considering influence of building type and climate condition G. Yang, X.Q. Zhai* Institute of Refrigeration and Cryogenics, Shanghai Jiao Tong University, Shanghai, 200240, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 October 2018 Received in revised form 28 February 2019 Accepted 1 March 2019 Available online 2 March 2019
Incorporating solar energy technologies to the conventional combined cooling, heating and power (CCHP) system has been considered as an effective solution to mitigate the looming energy and environmental challenges. The mathematical model of a conventional CCHP system hybridized with photovoltaic (PV) panels and solar thermal collectors is built in this study. The particle swarm optimization (PSO) algorithm is employed to find the optimal size of key components of the solar hybrid CCHP system. The simulation work of solar hybrid CCHP systems in three building prototypes across seven climate zones is carried out to find appropriate design schemes of these cases. Besides, some guiding principles of the design of the solar hybrid CCHP system in early stage are summarized. The results show that the system under the following thermal load (FTL) strategy is the first choice in most cases of hospitals and hotels, except for the systems of hotels in the cold and very cold zones. On the contrary, the systems in offices perform better in the following electric load (FEL) mode in the majority of climate zones except for the hot-humid zone. Generally, the average optimal integrated performance (S) values of hospitals, hotels and offices can reach 28.95%, 28.20% and 22.69%, respectively. © 2019 Elsevier Ltd. All rights reserved.
Keywords: Combined cooling heating and power Solar energy Hybrid system Particle swarm optimization algorithm Building energy consumption Climate condition
1. Introduction The expanding energy consumptions have led to the aggravation of energy crisis and greenhouse effect. More efficient energy systems and intensive utilization of renewable energy sources are considered as effective solutions and become critical research issues for researchers. Combined cooling, heating and power (CCHP) systems can simultaneously meet electricity, cooling and heating demands based on the energy cascade utilization concept [1]. As an attractive option to overcome energy and environmental challenges, CCHP systems have many merits, such as high efficiency, low emissions and high reliability [2]. Solar energy is one of the foci of renewable energy sources. By virtue of wide availability, inexhaustibility and cleanness, solar energy has huge potential in reducing fossil fuel consumption and greenhouse gas (GHG) emission [3]. Furthermore, hybridizing solar technologies with CCHP systems based on conventional energy sources have come into the spotlight, because the two kinds of technologies can be used in a complementary way [4]. On the one hand, solar
* Corresponding author. E-mail address: [email protected] (X.Q. Zhai). https://doi.org/10.1016/j.energy.2019.03.001 0360-5442/© 2019 Elsevier Ltd. All rights reserved.
technologies reduce fossil fuel consumptions; on the other hand, CCHP technologies enhance the efficiency and reliability. In conclusion, incorporating the solar energy technologies to the CCHP system is an effective solution to mitigate the looming energy and environmental challenges. Different solar technologies have been used in hybrid systems to satisfy different demands of buildings. Asaee et al. [5] evaluated techno-economic impact of retrofitting houses in the Canadian housing stock with PV and BIPV/T systems and predicted the energy savings, GHG emission reductions and tolerable capital costs for regions across Canada. Roselli et al. [6] examined a solar electric driven heat pump serving an office building located in southern Italy and found that the solar energy system is more competitive when there is no battery storage and government incentives will be provided. Roselli et al. [7] analyzed a photovoltaic system satisfying electric, space heating and cooling demand of an office building located in southern Italy and this system showed primary energy saving and equivalent dioxide carbon emission reduction higher than 40% in comparison to the reference conventional system. Ferreira et al. [8] developed a methodology for the thermaleconomic optimization of micro cogeneration units using Stirling engine as prime mover and concentrated solar energy as the heat source and the results showed the system was economically
648
G. Yang, X.Q. Zhai / Energy 174 (2019) 647e663
Nomenclature AGHI ATC ATCSR BCHP CCHP CHP CO2E CO2ERR COP ECR FEL FTL GA GHG IC ICE LHV NOCT OC OEF OEM ORC PEC PESR PGU PSO PV SP SPP ST TES TMY3
annual global horizontal irradiance annual total cost annual total cost saving building cooling, heating and power combined cooling, heating and power combined heating and power CO2 emission CO2 emission reduction ratio coefficient of performance electric cooling ratio following electric load following thermal load genetic algorithm greenhouse gas initial cost internal combustion engine lower heat value nominal operating cell temperature operating cost on-site energy fraction on-site energy matching Organic Rankine Cycle primary energy consumption primary energy saving ratio power generation unit particle swarm optimization photovoltaic separate production simple payback solar thermal thermal energy storage typical meteorological year 3
Symbols A a b C Cap d E F f
area m2 coefficient of the non-linear function coefficient of the non-linear function cooling energy kW$h capacity kW coefficient of the non-linear function electricity kW$h fuel kW$h part load factor
attractive, with a payback period of approximately 10 years. Calise et al. [9] coupled a desiccant-based AHU with a novel CPVT and the system obtained a Primary Energy Saving between 81% and 89%. Generally speaking, the solar energy technologies can be also employed as supplementary sources of electricity or thermal energy when they are integrated with the conventional CCHP system. The available incorporation methods usually include solar thermal (ST) technology and solar photovoltaic (PV) technology. In recent researches, concentrated collectors were used as the heat source to drive Organic Rankine Cycle (ORC) [10,11], combined Brayton and trans-critical CO2 refrigeration cycle [12] and methanol pyrolysis reactions [13]. However, the proposed solar hybrid CCHP systems will be hardly popularized in the near future due to technical complexity and high costs. On the contrary, non-concentrated solar collectors and solar PV panels are more technologically and commercially available, and those two components can be
G H P Q S T
global irradiation kW/m2 heating kW$h price $ thermal energy kW$h integrated performance temperature oC
Greek symbols b solar irradiance coefficient oC1 g temperature coefficient h efficiency m CO2 conversion factor g/kWh w weighting of evaluation criteria Subscripts a ac b c e ec ex f grid h in max mean out NOCT pgu r rated ref rc rh s sh sc t SP ST th waste
ambient absorption chiller boiler cooling electricity electric chiller excess fuel electricity grid heating into maximum value mean value out nominal operating cell temperature power generation unit recovery heat the rated capacity reference recovery heat for space cooling recovery heat for space heating storage energy solar energy used for heating solar energy used for cooling time separate system solar thermal thermal waste heat
integrated into conventional CCHP systems with relatively minor modifications. Consequently, their application has attracted extensive attentions from academics and researchers. Some researchers studied solar hybrid CCHP systems where PV and the conventional prime mover were integrated [14e16]. Meanwhile, some studies utilized the solar thermal collectors to meet heating and cooling demands [17,18]. Wang et al. [19] found that the solar thermal system is more effective than the solar PV when integrated into a CCHP system. Furthermore, Rodríguez et al. [20] and Barbieri et al. [21] analyzed the feasibility of hybrid ST/PV/CHP systems. The contributions of PV and solar thermal panels in a CCHP system to energy saving and CO2 emissions were proved by Anatone et al. [22] and Nosrat et al. [23]. As a kind of distributed energy systems, the performance of the solar hybrid CCHP system is usually determined by the matching degree between the energy demand side and the supply side. In
G. Yang, X.Q. Zhai / Energy 174 (2019) 647e663
practice, the perfect matching is hardly realized, so the back-up energy is necessary. For the energy demand side, features of building energy demands are the intrinsic properties of buildings, which are mainly influenced by the building type and the climatic condition. The effect of building energy demand on the conventional CCHP system has been well studied in recent years. Medrano et al. [24] found that the CCHP system for hospital shows the best performance among systems for four types of commercial building templates. Mago et al. [25] studied the feasibility of cogeneration applications in seven types of buildings in the U.S. and the systems reduced the emission in all the buildings. Farahnak et al. [26] explored mathematical relationships between optimum power generation unit (PGU) capacities and various residential building sizes in Iran. Wang et al. [27] evaluated the performance of CCHP systems for the hotel buildings in five different climate zones and CCHP system in the cold area performed best. Fong and Li [28] investigated the climatic effect on the energy performance of CCHP and it was found that CCHP was favorable to the continental climate with a cold winter and hot summer or the tropical region with a persistent summer. Moreover, Wang et al. [29] analyzed the effect of building categories and climate zones on CCHP performance. Yang et al. [30] found that the effect of building categories and climate zones are much larger than that of operation strategies by using statistical methods. However, the related studies on the solar hybrid CCHP system are rare. Rodríguez et al. [20] studied the optimal designs of hybrid systems in different locations of Spain with diverse climatic characteristics. Pazouki and Haghifam [31] proposed an energy hub based on the solar hybrid CCHP technology and the hub performed better in hot climate areas than cold climate regions. As for the energy supply side, the operation strategy, one of the key factors of energy supply, can realize the management of energy outputs and affect the system performance. Nosrat et al. [32] introduced a dispatch strategy for a hybrid PV-CCHP system to meet the various energy demands. The results showed the PVCCHP had better performance than the PV-CHP system. Brahman and Jadid [33] proposed a novel strategy named Electric Energy Storage for a hybrid CCHP and PV system, which is better than the traditional following electric load (FEL) and following thermal load (FTL) strategies. Fani et al. [34] studied the performance of the CCHP system with solar thermal collectors in four operation strategies, such as FEL, FTL, cooling demand hybrid supply mode and strategy based on an optimization model. Yang and Zhai [35] applied five strategies for a solar hybrid CCHP system based on its output characteristic and analyzed the corresponding performance of the system. The design and optimization processes of solar hybrid CCHP systems are usually defined as a complex multi-dimensional optimization problem with various types of objective functions and non-differentiable constraints. The intelligence optimization algorithms are usually superior from classical methods in finding the optimal or near optimal solution in a reasonable time [36]. In this regard, particle swarm optimization (PSO) algorithm has been employed to solve the optimization problem of solar hybrid CCHP systems. In a novel CCHP system including solar and wind energy resources proposed by Soheyli [37], a co-constrained multi objective PSO algorithm is applied to provide the optimal values of the components and optimal penalty factors. Fani et al. [34] adopted the PSO algorithm to solve the optimization problem of the CCHP system with ST collectors. In a research of a hybrid solar hydrogen system [14], PSO with constriction coefficient was utilized as the optimization algorithm for optimal sizing of the components of each scenario. Additionally, some other researchers used genetic algorithm (GA) to find the optimum system design parameters [38e40].
649
The literature review proves the feasibility of the solar hybrid CCHP system. However, in previous studies, the effects of building types and climate conditions were not comprehensively analyzed and the instructional design parameters of the solar hybrid CCHP system were lacking. Therefore, in this study, the CCHP system hybridized with both the PV and ST systems is modeled and the PSO algorithm is applied to find the optimum values of design parameters. Besides, the simulations of solar hybrid CCHP systems of three different buildings in seven climate zones in the U.S. are carried out. Based on the simulation results, the energy output characteristics and operation performance of solar hybrid CCHP systems are investigated. At last, the instructional design schemes in all the situations are summarized to guide the design of the solar hybrid system in early stage. With regard to the following sections of this paper, Section 2 describes the energy demands of the reference buildings. In Section 3, the model of the hybrid CCHP system and the PSO optimization algorithm are introduced. In Section 4, the optimal design results and the performance of solar hybrid CCHP systems in different buildings under different climate conditions are analyzed. And then, the instructional design schemes of solar hybrid systems are determined. Finally, some significant conclusions are summarized. 2. Energy demands descriptions 2.1. Weather conditions The Building America gives a clear definition of climate zones in the U.S. based on heating degree days, average temperatures, and precipitation [41]. The zones are hot-humid, hot-dry, mixed-dry, mixed-humid, marine, cold, very cold, and subarctic. This analysis excludes the subarctic zone where the buildings have little cooling demands. As shown in Table 1, seven cities are selected as the representatives of these climates [42]. In addition, the typical meteorological year 3 (TMY3) [43] conditions of the representative cities are adopted in the study. The basic weather parameters, such as annual global horizontal irradiance (AGHI) and annual average ambient temperature (Ta,mean), are presented in Fig. 1. Generally speaking, the AGHI is high in the low-latitude and dry regions; meanwhile the average ambient temperature decreases as the latitude increases. 2.2. Building energy demands The U.S. Department of Energy (DOE) Building Technologies program has developed 16 reference energy models for the most common commercial buildings in the U.S.. In this paper, the models of hospitals, hotels and offices are employed to assess the suitability of the solar hybrid CCHP systems. The reference building models are described in detail in the report published by the National Renewable Energy Laboratory [42] and available as EnergyPlus input files [44]. Energy demands of the three reference buildings in Table 1 Representative location of each typical climate. Climate zone
Representative City
Coordinates
Hot-humid Hot-Dry Mixed-Humid Mixed-Dry Marine Cold Very Cold
Miami Phoenix Atlanta Albuquerque San Francisco Chicago Duluth
25.65 N, 33.45 N, 33.63 N, 35.04 N, 37.62 N, 41.78 N, 46.83 N,
80.43 W 111.98 W 84.43 W 106.62 W 122.40 W 87.75 W 92.22 W
650
G. Yang, X.Q. Zhai / Energy 174 (2019) 647e663
variation range and many statistical outliers. Fig. 3 presents the changing characteristics of energy demands in different seasons and climate zones. Generally, the shifts in climate and seasons have obvious influence on the heating and cooling demands rather than the electric demands. With the increase in the latitude, the heating demands grow while the cooling demands reduce. However, the distribution of cooling demands does not obey the simple linear trend well, because cooling demands are relatively low in the mixed-dry climate and relatively large in the cold climate. In addition, the hospital is most energy intense among all the building types.
3. System modelling and optimization method 3.1. System modelling
Fig. 1. Annual global horizontal irradiance and annual average ambient temperature of each climate zone.
seven representative climate locations were simulated by using EnergyPlus simulation software; besides, the data of energy demands are available at the online Database on the OpenEI platform [45]. Fig. 2 illustrates the boxplots of hourly energy demands of the three building templates based on building energy demands data of 365 days in seven climate zones. Obviously, each reference building has its own characteristics of energy demands: 1) for hospitals, the cooling and heating demands are stable all day long; the electric demands are higher at daytime while lower at night; 2) for hotels, the heating and electric demands change synchronously and peak at morning and evening, but the cooling demands keep large from 7:00 to 22:00; 3) for offices, the energy demands are high only during working time; besides, they are very fluctuant due to large
Based on the three reference buildings, the solar hybrid CCHP system is developed to meet the building energy demands. The energy demands of a building are mainly divided into three parts: 1) electric demands, E; 2) cooling demands for space cooling, Qc; 3) heating demands for space heating and domestic hot water, Qh. Fig. 4 shows the energy flow of a solar hybrid CCHP system. In this study, the PGU is an internal combustion engine (ICE). The PGU consumes the natural gas and produce the electricity. Besides, PV transfers sunlight to electricity in the daytime. The system is connected to the grid, so the shortage of electricity can be compensated and the excess electricity can be sent back to the grid. Then, the recovered heat from PGU is mixed with the heat from ST collectors and the total heat is used to provide heating and drive the absorption chiller. Meanwhile, the storage tank is employed to adjust the heat output. In addition, the auxiliary boiler and the electric chiller are used as back-up devices to provide additional heating and cooling, respectively. The model of the hybrid CCHP system is realized in Matlab software. The energy flows of this system are presented and analyzed as follows. Besides, the details of the
Fig. 2. Variation trend of hourly energy demands of hospitals, hotels and offices for a day based on boxplots of building energy demand data in seven climate zones within a whole year.
G. Yang, X.Q. Zhai / Energy 174 (2019) 647e663
651
Fig. 3. Annual normalized energy demands of hospitals, hotels and offices in different climate zones.
Fig. 4. Energy flows of the hybrid CCHP system with thermal storage tank, PV and solar thermal collector.
parameters for the PGU and absorption chiller are presented in Appendix A [29,46]. The electrical energy balance is expressed as:
Egrid;in ¼ E þ Eec Epgu EPV Egrid;out ¼ Epgu þ EPV E Eec
Epgu þ EPV E þ Eec Epgu þ EPV > E þ Eec
h i EPV ¼ APV GhPV ¼ APV Ghref hinverter 1 b Tcell Tref þ gLogG (3)
(1)
where Egrid,in is the electricity from the grid; Egrid,out is the excess electricity sold back to the grid; Epgu and EPV are the electricity produced by the PGU and PV panels, respectively; Eec is the electricity demand of the electric chiller. The generated electricity from the PGU can be estimated as:
Epgu ¼ Fpgu hpgu;e
[48]:
(2)
where Fpgu is fuel consumption of the PGU, hpgu,e is the PGU generation efficiency. The generated electricity from PV can be calculated by the following equation developed by Evans [47] and Fesharaki et al.
where APV is the total area of PV module; href is the reference module efficiency at the reference temperature Tref and at a solar irradiance G on the module equal to 1 kW/m2; hiverter is the efficiency of the inverter and is set as 98% [49]. Tcell is the PV cell temperature under the environmental conditions; b and g are the solar irradiance and temperature coefficients for the PV module. In this paper, Tref, href, g, and b are set as 25 C, 12.5%, 0.12 and 0.0045 C-1, respectively [47,48]. Tc can be estimated as:
Tcell ¼ Ta þ
TNOCT 20 G 800
(4)
where Ta is the ambient temperature; TNOCT is the nominal
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operating cell temperature (NOCT), which is defined as the cell temperature measured under open-circuit when the ambient temperature is 20 C, the irradiance is 800 W/m2 and the wind speed is 1 m/s. TNOCT is usually around 45 C [48]. The electricity used by the chiller is calculated as:
8 Qtþ1;s ¼ Qt;s þ Vt;s;in Qt;s;in Vt;s;out Qt;s;out hs ; t2½0; 8760Þ > >
Contents lists available at ScienceDirect
Energy journal homepage: www.elsevier.com/locate/energy
Optimal design and performance analysis of solar hybrid CCHP system considering influence of building type and climate condition G. Yang, X.Q. Zhai* Institute of Refrigeration and Cryogenics, Shanghai Jiao Tong University, Shanghai, 200240, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 15 October 2018 Received in revised form 28 February 2019 Accepted 1 March 2019 Available online 2 March 2019
Incorporating solar energy technologies to the conventional combined cooling, heating and power (CCHP) system has been considered as an effective solution to mitigate the looming energy and environmental challenges. The mathematical model of a conventional CCHP system hybridized with photovoltaic (PV) panels and solar thermal collectors is built in this study. The particle swarm optimization (PSO) algorithm is employed to find the optimal size of key components of the solar hybrid CCHP system. The simulation work of solar hybrid CCHP systems in three building prototypes across seven climate zones is carried out to find appropriate design schemes of these cases. Besides, some guiding principles of the design of the solar hybrid CCHP system in early stage are summarized. The results show that the system under the following thermal load (FTL) strategy is the first choice in most cases of hospitals and hotels, except for the systems of hotels in the cold and very cold zones. On the contrary, the systems in offices perform better in the following electric load (FEL) mode in the majority of climate zones except for the hot-humid zone. Generally, the average optimal integrated performance (S) values of hospitals, hotels and offices can reach 28.95%, 28.20% and 22.69%, respectively. © 2019 Elsevier Ltd. All rights reserved.
Keywords: Combined cooling heating and power Solar energy Hybrid system Particle swarm optimization algorithm Building energy consumption Climate condition
1. Introduction The expanding energy consumptions have led to the aggravation of energy crisis and greenhouse effect. More efficient energy systems and intensive utilization of renewable energy sources are considered as effective solutions and become critical research issues for researchers. Combined cooling, heating and power (CCHP) systems can simultaneously meet electricity, cooling and heating demands based on the energy cascade utilization concept [1]. As an attractive option to overcome energy and environmental challenges, CCHP systems have many merits, such as high efficiency, low emissions and high reliability [2]. Solar energy is one of the foci of renewable energy sources. By virtue of wide availability, inexhaustibility and cleanness, solar energy has huge potential in reducing fossil fuel consumption and greenhouse gas (GHG) emission [3]. Furthermore, hybridizing solar technologies with CCHP systems based on conventional energy sources have come into the spotlight, because the two kinds of technologies can be used in a complementary way [4]. On the one hand, solar
* Corresponding author. E-mail address: [email protected] (X.Q. Zhai). https://doi.org/10.1016/j.energy.2019.03.001 0360-5442/© 2019 Elsevier Ltd. All rights reserved.
technologies reduce fossil fuel consumptions; on the other hand, CCHP technologies enhance the efficiency and reliability. In conclusion, incorporating the solar energy technologies to the CCHP system is an effective solution to mitigate the looming energy and environmental challenges. Different solar technologies have been used in hybrid systems to satisfy different demands of buildings. Asaee et al. [5] evaluated techno-economic impact of retrofitting houses in the Canadian housing stock with PV and BIPV/T systems and predicted the energy savings, GHG emission reductions and tolerable capital costs for regions across Canada. Roselli et al. [6] examined a solar electric driven heat pump serving an office building located in southern Italy and found that the solar energy system is more competitive when there is no battery storage and government incentives will be provided. Roselli et al. [7] analyzed a photovoltaic system satisfying electric, space heating and cooling demand of an office building located in southern Italy and this system showed primary energy saving and equivalent dioxide carbon emission reduction higher than 40% in comparison to the reference conventional system. Ferreira et al. [8] developed a methodology for the thermaleconomic optimization of micro cogeneration units using Stirling engine as prime mover and concentrated solar energy as the heat source and the results showed the system was economically
648
G. Yang, X.Q. Zhai / Energy 174 (2019) 647e663
Nomenclature AGHI ATC ATCSR BCHP CCHP CHP CO2E CO2ERR COP ECR FEL FTL GA GHG IC ICE LHV NOCT OC OEF OEM ORC PEC PESR PGU PSO PV SP SPP ST TES TMY3
annual global horizontal irradiance annual total cost annual total cost saving building cooling, heating and power combined cooling, heating and power combined heating and power CO2 emission CO2 emission reduction ratio coefficient of performance electric cooling ratio following electric load following thermal load genetic algorithm greenhouse gas initial cost internal combustion engine lower heat value nominal operating cell temperature operating cost on-site energy fraction on-site energy matching Organic Rankine Cycle primary energy consumption primary energy saving ratio power generation unit particle swarm optimization photovoltaic separate production simple payback solar thermal thermal energy storage typical meteorological year 3
Symbols A a b C Cap d E F f
area m2 coefficient of the non-linear function coefficient of the non-linear function cooling energy kW$h capacity kW coefficient of the non-linear function electricity kW$h fuel kW$h part load factor
attractive, with a payback period of approximately 10 years. Calise et al. [9] coupled a desiccant-based AHU with a novel CPVT and the system obtained a Primary Energy Saving between 81% and 89%. Generally speaking, the solar energy technologies can be also employed as supplementary sources of electricity or thermal energy when they are integrated with the conventional CCHP system. The available incorporation methods usually include solar thermal (ST) technology and solar photovoltaic (PV) technology. In recent researches, concentrated collectors were used as the heat source to drive Organic Rankine Cycle (ORC) [10,11], combined Brayton and trans-critical CO2 refrigeration cycle [12] and methanol pyrolysis reactions [13]. However, the proposed solar hybrid CCHP systems will be hardly popularized in the near future due to technical complexity and high costs. On the contrary, non-concentrated solar collectors and solar PV panels are more technologically and commercially available, and those two components can be
G H P Q S T
global irradiation kW/m2 heating kW$h price $ thermal energy kW$h integrated performance temperature oC
Greek symbols b solar irradiance coefficient oC1 g temperature coefficient h efficiency m CO2 conversion factor g/kWh w weighting of evaluation criteria Subscripts a ac b c e ec ex f grid h in max mean out NOCT pgu r rated ref rc rh s sh sc t SP ST th waste
ambient absorption chiller boiler cooling electricity electric chiller excess fuel electricity grid heating into maximum value mean value out nominal operating cell temperature power generation unit recovery heat the rated capacity reference recovery heat for space cooling recovery heat for space heating storage energy solar energy used for heating solar energy used for cooling time separate system solar thermal thermal waste heat
integrated into conventional CCHP systems with relatively minor modifications. Consequently, their application has attracted extensive attentions from academics and researchers. Some researchers studied solar hybrid CCHP systems where PV and the conventional prime mover were integrated [14e16]. Meanwhile, some studies utilized the solar thermal collectors to meet heating and cooling demands [17,18]. Wang et al. [19] found that the solar thermal system is more effective than the solar PV when integrated into a CCHP system. Furthermore, Rodríguez et al. [20] and Barbieri et al. [21] analyzed the feasibility of hybrid ST/PV/CHP systems. The contributions of PV and solar thermal panels in a CCHP system to energy saving and CO2 emissions were proved by Anatone et al. [22] and Nosrat et al. [23]. As a kind of distributed energy systems, the performance of the solar hybrid CCHP system is usually determined by the matching degree between the energy demand side and the supply side. In
G. Yang, X.Q. Zhai / Energy 174 (2019) 647e663
practice, the perfect matching is hardly realized, so the back-up energy is necessary. For the energy demand side, features of building energy demands are the intrinsic properties of buildings, which are mainly influenced by the building type and the climatic condition. The effect of building energy demand on the conventional CCHP system has been well studied in recent years. Medrano et al. [24] found that the CCHP system for hospital shows the best performance among systems for four types of commercial building templates. Mago et al. [25] studied the feasibility of cogeneration applications in seven types of buildings in the U.S. and the systems reduced the emission in all the buildings. Farahnak et al. [26] explored mathematical relationships between optimum power generation unit (PGU) capacities and various residential building sizes in Iran. Wang et al. [27] evaluated the performance of CCHP systems for the hotel buildings in five different climate zones and CCHP system in the cold area performed best. Fong and Li [28] investigated the climatic effect on the energy performance of CCHP and it was found that CCHP was favorable to the continental climate with a cold winter and hot summer or the tropical region with a persistent summer. Moreover, Wang et al. [29] analyzed the effect of building categories and climate zones on CCHP performance. Yang et al. [30] found that the effect of building categories and climate zones are much larger than that of operation strategies by using statistical methods. However, the related studies on the solar hybrid CCHP system are rare. Rodríguez et al. [20] studied the optimal designs of hybrid systems in different locations of Spain with diverse climatic characteristics. Pazouki and Haghifam [31] proposed an energy hub based on the solar hybrid CCHP technology and the hub performed better in hot climate areas than cold climate regions. As for the energy supply side, the operation strategy, one of the key factors of energy supply, can realize the management of energy outputs and affect the system performance. Nosrat et al. [32] introduced a dispatch strategy for a hybrid PV-CCHP system to meet the various energy demands. The results showed the PVCCHP had better performance than the PV-CHP system. Brahman and Jadid [33] proposed a novel strategy named Electric Energy Storage for a hybrid CCHP and PV system, which is better than the traditional following electric load (FEL) and following thermal load (FTL) strategies. Fani et al. [34] studied the performance of the CCHP system with solar thermal collectors in four operation strategies, such as FEL, FTL, cooling demand hybrid supply mode and strategy based on an optimization model. Yang and Zhai [35] applied five strategies for a solar hybrid CCHP system based on its output characteristic and analyzed the corresponding performance of the system. The design and optimization processes of solar hybrid CCHP systems are usually defined as a complex multi-dimensional optimization problem with various types of objective functions and non-differentiable constraints. The intelligence optimization algorithms are usually superior from classical methods in finding the optimal or near optimal solution in a reasonable time [36]. In this regard, particle swarm optimization (PSO) algorithm has been employed to solve the optimization problem of solar hybrid CCHP systems. In a novel CCHP system including solar and wind energy resources proposed by Soheyli [37], a co-constrained multi objective PSO algorithm is applied to provide the optimal values of the components and optimal penalty factors. Fani et al. [34] adopted the PSO algorithm to solve the optimization problem of the CCHP system with ST collectors. In a research of a hybrid solar hydrogen system [14], PSO with constriction coefficient was utilized as the optimization algorithm for optimal sizing of the components of each scenario. Additionally, some other researchers used genetic algorithm (GA) to find the optimum system design parameters [38e40].
649
The literature review proves the feasibility of the solar hybrid CCHP system. However, in previous studies, the effects of building types and climate conditions were not comprehensively analyzed and the instructional design parameters of the solar hybrid CCHP system were lacking. Therefore, in this study, the CCHP system hybridized with both the PV and ST systems is modeled and the PSO algorithm is applied to find the optimum values of design parameters. Besides, the simulations of solar hybrid CCHP systems of three different buildings in seven climate zones in the U.S. are carried out. Based on the simulation results, the energy output characteristics and operation performance of solar hybrid CCHP systems are investigated. At last, the instructional design schemes in all the situations are summarized to guide the design of the solar hybrid system in early stage. With regard to the following sections of this paper, Section 2 describes the energy demands of the reference buildings. In Section 3, the model of the hybrid CCHP system and the PSO optimization algorithm are introduced. In Section 4, the optimal design results and the performance of solar hybrid CCHP systems in different buildings under different climate conditions are analyzed. And then, the instructional design schemes of solar hybrid systems are determined. Finally, some significant conclusions are summarized. 2. Energy demands descriptions 2.1. Weather conditions The Building America gives a clear definition of climate zones in the U.S. based on heating degree days, average temperatures, and precipitation [41]. The zones are hot-humid, hot-dry, mixed-dry, mixed-humid, marine, cold, very cold, and subarctic. This analysis excludes the subarctic zone where the buildings have little cooling demands. As shown in Table 1, seven cities are selected as the representatives of these climates [42]. In addition, the typical meteorological year 3 (TMY3) [43] conditions of the representative cities are adopted in the study. The basic weather parameters, such as annual global horizontal irradiance (AGHI) and annual average ambient temperature (Ta,mean), are presented in Fig. 1. Generally speaking, the AGHI is high in the low-latitude and dry regions; meanwhile the average ambient temperature decreases as the latitude increases. 2.2. Building energy demands The U.S. Department of Energy (DOE) Building Technologies program has developed 16 reference energy models for the most common commercial buildings in the U.S.. In this paper, the models of hospitals, hotels and offices are employed to assess the suitability of the solar hybrid CCHP systems. The reference building models are described in detail in the report published by the National Renewable Energy Laboratory [42] and available as EnergyPlus input files [44]. Energy demands of the three reference buildings in Table 1 Representative location of each typical climate. Climate zone
Representative City
Coordinates
Hot-humid Hot-Dry Mixed-Humid Mixed-Dry Marine Cold Very Cold
Miami Phoenix Atlanta Albuquerque San Francisco Chicago Duluth
25.65 N, 33.45 N, 33.63 N, 35.04 N, 37.62 N, 41.78 N, 46.83 N,
80.43 W 111.98 W 84.43 W 106.62 W 122.40 W 87.75 W 92.22 W
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variation range and many statistical outliers. Fig. 3 presents the changing characteristics of energy demands in different seasons and climate zones. Generally, the shifts in climate and seasons have obvious influence on the heating and cooling demands rather than the electric demands. With the increase in the latitude, the heating demands grow while the cooling demands reduce. However, the distribution of cooling demands does not obey the simple linear trend well, because cooling demands are relatively low in the mixed-dry climate and relatively large in the cold climate. In addition, the hospital is most energy intense among all the building types.
3. System modelling and optimization method 3.1. System modelling
Fig. 1. Annual global horizontal irradiance and annual average ambient temperature of each climate zone.
seven representative climate locations were simulated by using EnergyPlus simulation software; besides, the data of energy demands are available at the online Database on the OpenEI platform [45]. Fig. 2 illustrates the boxplots of hourly energy demands of the three building templates based on building energy demands data of 365 days in seven climate zones. Obviously, each reference building has its own characteristics of energy demands: 1) for hospitals, the cooling and heating demands are stable all day long; the electric demands are higher at daytime while lower at night; 2) for hotels, the heating and electric demands change synchronously and peak at morning and evening, but the cooling demands keep large from 7:00 to 22:00; 3) for offices, the energy demands are high only during working time; besides, they are very fluctuant due to large
Based on the three reference buildings, the solar hybrid CCHP system is developed to meet the building energy demands. The energy demands of a building are mainly divided into three parts: 1) electric demands, E; 2) cooling demands for space cooling, Qc; 3) heating demands for space heating and domestic hot water, Qh. Fig. 4 shows the energy flow of a solar hybrid CCHP system. In this study, the PGU is an internal combustion engine (ICE). The PGU consumes the natural gas and produce the electricity. Besides, PV transfers sunlight to electricity in the daytime. The system is connected to the grid, so the shortage of electricity can be compensated and the excess electricity can be sent back to the grid. Then, the recovered heat from PGU is mixed with the heat from ST collectors and the total heat is used to provide heating and drive the absorption chiller. Meanwhile, the storage tank is employed to adjust the heat output. In addition, the auxiliary boiler and the electric chiller are used as back-up devices to provide additional heating and cooling, respectively. The model of the hybrid CCHP system is realized in Matlab software. The energy flows of this system are presented and analyzed as follows. Besides, the details of the
Fig. 2. Variation trend of hourly energy demands of hospitals, hotels and offices for a day based on boxplots of building energy demand data in seven climate zones within a whole year.
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Fig. 3. Annual normalized energy demands of hospitals, hotels and offices in different climate zones.
Fig. 4. Energy flows of the hybrid CCHP system with thermal storage tank, PV and solar thermal collector.
parameters for the PGU and absorption chiller are presented in Appendix A [29,46]. The electrical energy balance is expressed as:
Egrid;in ¼ E þ Eec Epgu EPV Egrid;out ¼ Epgu þ EPV E Eec
Epgu þ EPV E þ Eec Epgu þ EPV > E þ Eec
h i EPV ¼ APV GhPV ¼ APV Ghref hinverter 1 b Tcell Tref þ gLogG (3)
(1)
where Egrid,in is the electricity from the grid; Egrid,out is the excess electricity sold back to the grid; Epgu and EPV are the electricity produced by the PGU and PV panels, respectively; Eec is the electricity demand of the electric chiller. The generated electricity from the PGU can be estimated as:
Epgu ¼ Fpgu hpgu;e
[48]:
(2)
where Fpgu is fuel consumption of the PGU, hpgu,e is the PGU generation efficiency. The generated electricity from PV can be calculated by the following equation developed by Evans [47] and Fesharaki et al.
where APV is the total area of PV module; href is the reference module efficiency at the reference temperature Tref and at a solar irradiance G on the module equal to 1 kW/m2; hiverter is the efficiency of the inverter and is set as 98% [49]. Tcell is the PV cell temperature under the environmental conditions; b and g are the solar irradiance and temperature coefficients for the PV module. In this paper, Tref, href, g, and b are set as 25 C, 12.5%, 0.12 and 0.0045 C-1, respectively [47,48]. Tc can be estimated as:
Tcell ¼ Ta þ
TNOCT 20 G 800
(4)
where Ta is the ambient temperature; TNOCT is the nominal
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operating cell temperature (NOCT), which is defined as the cell temperature measured under open-circuit when the ambient temperature is 20 C, the irradiance is 800 W/m2 and the wind speed is 1 m/s. TNOCT is usually around 45 C [48]. The electricity used by the chiller is calculated as:
8 Qtþ1;s ¼ Qt;s þ Vt;s;in Qt;s;in Vt;s;out Qt;s;out hs ; t2½0; 8760Þ > >
> : max Qt;s;out ¼ hs Qt;s (14)
Eec
Qec ¼ COPec
(5)
where Qec is the cooling produced by the electric chiller; COPec is the coefficient of performance. of the electric chiller. The cooling balance is estimated as:
Qc ¼ Qec þ Qac
(6)
hs ¼
where Qac is the cooling from the absorption chiller; Qac can be calculated as:
Qac ¼ Qrc COPac
(7)
where Qrc is the heat supplied to the absorption chiller; COPac is the coefficient of performance of the absorption chiller. The heating balance is calculated as:
Qh ¼ Qrh þ Qb
(8)
where Qrh and Qb are the heat from the PGU and the boiler, respectively. Qb can be calculated as:
Qb ¼ Fb hb
(9)
where Fb is fuel consumed by the boiler; hb is the boiler efficiency. The utilization of the recovered heat (Qr) and the heat produced by the solar thermal collector (Qsolar) is expressed as:
Qr þ Qsolar Vs;in Qs;in þ Vs;out Qs;out ¼ Qrc þ Qrh þ Qw;ex
(10)
where Qw,ex is the excess heat; Qs,in and Qs,out are the input and output heat of the heat storage tank respectively. Vs,in and Vs,out are the operation variable of the storage tank, which are 0 or 1. When Vs,in is equal to 1, it means that the recovered heat is stored in the storage tank. Otherwise, it means that the recovered heat is released from the storage tank. The heat from the solar thermal system is estimated with the following equation based on the European Standard EN 12975-2 [50]:
h 2 i Qsolar ¼ Asolar Ghsolar ¼ Asolar G a0 a1 T * a2 T *
(11)
where a0 is the optical efficiency, a1 and a2 are correction factors. Herein, a0, a1, and a2 are 0.65, 0.7 W/m2 oC and 0.0028 W/m2 oC2, respectively [21]. T* is calculated as:
T* ¼
ðTout þTin Þ 2
Ta
G
Qs;out Qs;in
(15)
where Qs,in is the thermal energy obtained by the thermal tank; Qs,out is the energy recovered by the thermal tank. Besides, some important assumptions are shown as follows: (1) The whole system is assumed to be totally reliable. (2) The tiled angles of PV panels and solar thermal collectors are equal to the local latitude. (3) The heat storage tank is in an ideal state. The temperature distribution in the tank is uniform, without considering the temperature stratification. The thermal water tank works at 85 C. (4) The internal combustion engine is a stable heat source. The outlet water temperature of heat recovery heat for the internal combustion engine is 85 C and the inlet temperature is 75 C. (5) The solar thermal system is an active type solar water heating system, the output temperature is set as 85 C and the inlet temperature is 75 C. If the temperature of working fluid in solar loop does not reach higher than the set temperature, heat transfer will not occur and the working fluid will return to solar collectors to achieve more heat and temperature.” (6) In order to carry out the multiple parameter optimization of the complex solar hybrid CCHP system with a reasonable speed. The models of auxiliary equipment (electric chiller and gas boiler) is based on a simplified hypothesis. The technical parameters (COP and boiler efficiency) are assumed to be constant. (7) The excess electricity is sent to the grid, but the energy and economic effects of the excess part is not included in the analysis of the hybrid CCHP system. Although the income of excess electricity always has positive influence on the economic performance, the matching between the CCHP system energy output and the building energy demand will decrease because of excess electricity. So the income of excess electricity is not considered in the optimization process to get the system design schemes with good matching performance.
3.2. System evaluation
(12)
The heat recovered from the PGU can be estimated as:
Qr ¼ Fpgu ,hpgu;r
where Qt,s, Qt,s,in and Qt,s,out are the hourly storage heat, input heat and output heat of the thermal storage tank, respectively; Qs,max is the rated capacity of the storage tank; max(Qt,s,in) and max (Qt,s,out) represent the maximum input heat and output heat of the storage tank; hs is the thermal efficiency of the storage tank and is defined as,
(13)
where hpgu,r is the PGU heat recovery efficiency. The energy flows of the thermal storage tank are expressed as:
3.2.1. Performance criterion The annual performance of the solar hybrid CCHP system are assessed by three basic indexes, including primary energy saving ratio (PESR), annual total cost saving ratio (ATCSR) and CO2 emission reduction ratio (CO2ERR). The brief introduction is shown as follows. The details of the parameters for evaluation criteria are presented in [51].
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PESR is presented as the ratio of the saving energy of the CCHP system in comparison to the separate production (SP) system to the energy consumption of SP system, which is expressed as:
PECSP PECCCHP PESR ¼ PECSP
(16)
where PECSP is the primary energy consumptions (PEC) of SP system, PECCCHP is the PEC of CCHP system. CO2ERR is formulated as the ratio of the reducing CO2 emission of the CCHP system to the CO2 emission of SP system:
CO2 ERR ¼
CO2 ESP CO2 ECCHP CO2 ESP
(17)
where CO2ESP and CO2ECCHP are the amounts of CO2 emission (CO2E) from an SP system and a CCHP system, respectively. ATCSR is defined as the ratio of the reducing annual total cost of the CCHP system to the annual total cost of SP system:
ATCSR ¼
ATCSP ATCCCHP ATCSP
(18)
where ATCsp and ATCCCHP are the annual total cost (ATC) of an SP system and a CCHP system, respectively.
ATCSP ¼ R,ICSP þ OCSP
Table 3 Price of the facilities in the solar hybrid CCHP system. Facility
Unit
Price
PGU [34] Heating coil [51] Boiler [56] Absorption chiller [56] Electric chiller [57] Storage tank [56] PV system [49] Solar thermal System [58]
$/kW $/kW $ $ $/kW $/kW $/kW $/m2
1350 30 205*Capb^0.87 540*Capac^0.872 350 33 2130 200
SPP ¼
ICCCHP ICSP OCSP OCCCHP
(22)
where, ICCCHP and ICSP are the initial costs of CCHP system and SP system, respectively. OCCCHP and OCSP are the annual operation cost of CCHP system and SP system respectively. Then the SPP calculated here is the payback period of the incremental cost of the CCHP system over the SP system. 3.2.2. Matching criterion On-site energy matching (OEM) [59] and on-site energy fraction (OEF) [59] are used to assess the matching performance between the system and the building. The OEF is defined as the ratio of onsite generated energy to the building energy demand. They can be calculated as:
(19)
ATCCCHP ¼ R,CCCHP þ OCCCHP
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OEFe ¼
Epgu þ EPV Egrid;out Eec;CCHP E
(23)
OEFh ¼
Qr;h þ Qsolar;h Qh
(24)
(20)
where R is the capital recovery factor; ICsp and ICCCHP are the initial cost of an SP system and a CCHP system; OCsp and OCCCHP are the operating cost of an SP system and a CCHP system. In this paper, the building types and climates are the two main factors considered in our research and the geographical influence is neglected. The average prices and representative technical parameters are used for evaluation and analysis in this paper. The details of parameter employed for evaluation and analysis are shown in Table 2. And the prices of the facilities in CCHP system are showed Table 3. S is defined as the integrated performance of the CCHP system, and it can be expressed as,
S ¼ u1 PESR þ u2 CO2 ERR þ u3 ATCSR
(21)
where the u1, u2 and u3 are the respective weights of PESR, CO2ERR and ATCSR., which are set to [1/3,1/3, 1/3]. The simple payback period (SPP) can be calculated as
Table 2 Main parameters in the simulation and evaluation processes. Variable
Symbol
Unit
Value
COP of water-cooled electric central chiller [44] Thermal efficiency of the storage tank [46] Boiler efficiency [52] Grid generation efficiency [29] Grid transmission efficiency [29] CO2 conversion factor of electricity from grid [51] CO2 conversion factor of natural gas [51] Low heat value of natural gas [53] Price of natural gas [54] Price of electricity [55]
COPe
e % % % % g/kWh g/kWh kWh/m3 $/m3 $/kWh
5.5 95.6 90.0 35.0 92.2 923 220 10.56 0.256 0.1028
hs hb he hgrid me mf
LHV Pf2 Pe,on
8 Eec;CCHP $COPec þ Qac > > OEFc ¼ > > > Qc > > > < Eec;CCHP $COPec OEFc;ec ¼ > Qc > > > > > Q > > : OEFc;ac ¼ ac Qc
(25)
where Eec,CCHP is the electricity produced by the system for electricity chiller; The contribution of solar system is defined as the ratio of energy generated by the solar system to the building energy demand. The electric, heating and cooling contribution of the solar system can be calculated as:
OEFse ¼
EPV EPV;grid;out Eec;PV E
(26)
OEFsh ¼
Qsolar;h Qh
(27)
8 Eec;PV ,COPec þ Qac;PV > > OEFsc ¼ > > > Qc > > > < Eec;PV ,COPec OEFsc;ec ¼ > Qc > > > > > Q > ac;PV > : OEFsc;ac ¼ Qc
(28)
The OEM is defined as the ratio of on-site generated energy used
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for the demand satisfaction to total on-site generated energy. They can be defined as:
Epgu þ EPV Egrid;out OEMe ¼ Epgu þ EPV
(29)
8 Qr þ Qsolar Qh;waste > > > OEMth ¼ > Qrh þ Qsolar > > > > < Qac OEMth;c ¼ > Q þ Qsolar > rh > > > > > Qh > : OEMth;h ¼ Qrh þ Qsolar
(30)
where Qh,waste is the waste heat energy.
3.4. Optimization method As shown in Fig. 5, the particle swarm optimization (PSO) algorithm is adopted to optimize the design parameters of the hybrid CCHP system. The optimization objective is to maximize the integrated performance (S) in this paper. Table 4 lists design parameters and their range of variations. To make sure the global optimum can be found, the simulation tests have been carried out and the ranges of variables are selected. The simulation and optimization work is carried out in Matlab software. Based on the optimization design program, the optimum design parameters of solar hybrid CCHP systems for three reference building templates in seven climate zones are determined. 4. Discussion and results 4.1. System configuration
3.3. Operation strategy The operation strategy plays an important role to determine the operation point of the hybrid CCHP system. The hybrid CCHP system is usually managed by either the FEL strategy or the FTL strategy. In this study, the conventional operation strategies are combined with a thermal storage strategy proposed by Zheng et al. [51]. The modified FEL and FTL strategies have been introduced in the previous studies of CCHP systems [20,34]. In FEL strategy, the PGU will try to meet the electric demands in priority. The thermal energy tank is used to store the excess heat and makes up for the shortage of heat. The remaining energy-supply gap of heating and cooling will be filled by the boiler and the electric chiller, respectively. In the FTL strategy, given the state of thermal storage, the PGU will run according to the cooling and heating demands. The thermal energy tank is actively used to store and release the heat and the grid is employed as the back-up power.
4.1.1. Selection of core CCHP equipment As building function features and climate conditions can influence the energy demands, the design of the solar hybrid CCHP system is different among different building types and climate
Table 4 Range of design parameters. Variables
Unit
Range
PGU capacity (Cappgu) Absorption chiller capacity (Capac) Thermal storage capacity (CapTES) Area of PV (APV) Area of ST (AST)
kW kW kW m2 m2
[0, [0, [0, [0, [0,
10 Max(E)] 10 Max (Qc)] 100 Max (Qh)] Max(A)]a Max(A)]a
a The maximum roof area of hospital, hotel and office are 3739, 1478 and 3563 m2 [44].
Fig. 5. Optimization method based on particle swarm optimization.
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zones. Herein, the ratio of PGU capacity and mean electric demand (Cappgu/Emean), the ratio of absorption chiller capacity and mean cooling demand (Capac/Cmean) and the ratio of absorption chiller capacity and PGU capacity (Capac/Cappgu) are introduced to measure the PGU size, the absorption chiller size and the relative size of the PGU and the absorption chiller, respectively. When the system runs in the FEL mode, the electric parameter will play a key role in the design process. Thus the Cappgu/Emean and the Capac/Cappgu can generally describe the characteristics of system configurations. Fig. 6 presents the three normalized design parameters of hybrid CCHP systems under the FEL strategy. In most cases, the Cappgu/ Emean ranges from 1.25 to 1.55. For hospitals, the Cappgu/Emean gradually becomes small from the hot-humid zone to the very cold zone. As shown in Fig. 3, the electric demands are similar in these climate zones. Namely, the installed PGU gets small as the climate turns cold. The Capac/Cappgu values are around 2.23 in the warmest four zones, but their values drop in the remaining zones and reach as low as 0.52 in the marine zone. In cold regions, more heat from the solar hybrid CCHP system is used to satisfy the heating demands, thus the absorption chiller becomes small. For systems of hotels, the Cappgu/Emean is 1.46 in the humid-hot climate and it slightly grows to 1.54 with the climate cooling. However, the Capac/
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Cappgu gradually declines from the hot-humid zone to the very cold zone, ranging from 0.80 to 1.72. While the cooling and heating demands of hotel buildings change smoothly with the climate varying, the Capac/Cmean stays around 0.46 in each zone. For systems in offices, the configuration parameters are fluctuant among these climate zones. The Cappgu/Emean and the Capac/Cappgu are largest in the humid-hot zone. The cooling demands of the office in the humid-hot zone are large while the heating demands are very small. Thus, both the PGU and the absorption chiller are necessary to be large enough to satisfy the cooling demands. In other zones, the Cappgu/Emean and the Capac/Cappgu fluctuate in a small range. The average values of Cappgu/Emean and Capac/Cappgu are 1.44 and 1.16, respectively. Fig. 7 displays main parameters of CCHP systems in the FTL mode. When the system runs according to the FTL mode, the Capac/ Cmean and the Capac/Cappgu are given priority in the analysis. As both hospitals and hotels have steady and regular cooling and heating demands, their configuration parameters are similar. The Capac/Cmean values of systems in hospitals and hotels are roughly fixed at 0.22 in all the climates; meanwhile, the Capac/Cappgu gradually decreases from the humid-hot zone to the very cold climate zone. However, there are some differences between
Fig. 6. Normalized capacities of prime generation movers and absorption chillers for CCHP systems in different building types and climate zones under the FEL strategy.
Fig. 7. Normalized capacities of prime generation movers and absorption chillers for CCHP systems in different building types and climate zones under the FTL strategy.
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hospitals and hotels. On the one hand, the Capac/Cappgu values of systems for hospitals in the humid-hot zone and the very cold climate zone are 0.73 and 0.63, respectively. On the other hand, because of the lower ratio of cooling and electricity, the Capac/ Cappgu values of systems in hotels are smaller than those of systems in hospitals, ranging from 0.26 to 0.62. Moreover, compared with hospitals and hotels, the cooling and heating demands of offices are more sensitive to the weather variation, and the cooling demands are inversely proportional to the heating demands. So all the generated heat is almost used by the large absorption chiller for cooling when the weather is hot. The Capac/Cmean of systems in offices becomes larger as the weather gets cold, ranging from 0.76 to 3.27. And they are all larger than those of systems in hotels and hospitals. However, the Capac/Cappgu values are relatively steady when the climate changes. The Capac/Cappgu oscillates around 1.43. Namely, the absolute size of the absorption chiller does not change much with the climate variation, and the absorption chiller can well meet peak load in cold regions.
reaches 83%. However, when systems in hotels run according to the FEL mode, ST systems occupy nearly all the available area. Because the electric demands of hotels are relatively low during the daytime, large PV systems will reduce the load rate of the PGU instead of the installed capacity. Offices have large electric demands only during work time and the ratio of cooling/heating and electricity is relatively low. In consequence, when the systems for offices runs under the FEL strategy, PV systems are preferred in response to similar concerns of cases of hospitals in cold areas. Fig. 9 shows that PV systems are preferred in most cases when the solar hybrid CCHP system follows the FTL strategy. Whereas, the office building in the hot-humid zone is an exception, where the 78% of total area is dominated by the ST systems. As its cooling demands are huge and steady in the daytime and positively correlated with the solar radiation, the ST system is very helpful to the cooling production in the humid-hot zone. In addition, for hotel buildings, only PV systems are installed in the cold and very cold zones, and their occupancy rates are 45% and 31%, respectively. The small installed PV system is employed to avoid the excess electricity due to low electric and cooling demands in the daytime.
4.1.2. Selection of solar system In this paper, the PV system and the ST system are employed as the auxiliary systems for the energy production. Generally speaking, the PV system is used to supplement the electricity and drive the electric chiller, while the ST system is used to provide extra heat. Therefore, when the system runs under the FEL strategy, the ST system is usually the first choice. In contrast, when the FTL mode is adopted, the PV system is used in priority. As shown in Fig. 8, in the FEL mode, all the cases follow the above-mentioned principle in the rough, except some cases in office buildings. Hospitals have steady cooling and heating demands, so ST systems in the FEL mode nearly occupy all the available area in the warmest four zones at low latitudes. While the latitude escalates and the climate becomes cold, the performance of ST systems gets worse. Therefore, in the coldest three regions, large PV systems are used in hospitals to satisfy the peak electric load in the daytime. The PV systems can also reduce the installed capacity of the PGU properly. As a result, the maximum percentage of installed PV area
In the FEL mode, the system meets the electric demands in priority, thus the satisfaction of cooling and heating demands is hard to meet. As shown in Fig. 10 and Fig. 11, the values of OEFe and OEMe are about 100% in all the cases, so almost all the electric demands are satisfied by the system with little waste of electricity under the FEL strategy. The OEFh values are all above 80% and the OEFc values are all below 60%, which means that the cooling demands are not well satisfied because the generated heat cannot cover both the heating and cooling demands of the buildings. As the climate gets cold, the OEMth,h increases and the OEMth,c decreases. It means that more generated heat is used for heating as the climate becomes colder. In general, both the building type and the climate condition have influence on the values of OEF and OEM. For systems
Fig. 8. Installed area of PV and ST of the hybrid CCHP system in the FEL mode.
Fig. 9. Installed area of PV and ST of the hybrid CCHP system in the FTL mode.
4.2. Matching performance of systems
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Fig. 10. OEF values of electricity, heating and cooling in the FEL mode.
Fig. 11. OEM values of electric and thermal energy in the FEL mode.
in hospitals, the OEFh and the OEMth,h of systems in the warmest four zones are lower than those of systems in the remaining zones, but the distributions of OEFc and OEMth,c are reversed. Additionally, the capacities of absorption chillers drop in the coldest three zones. In short, with the climate cooling, the heating satisfaction substantially improves in expense of the systems' ability to produce cooling. For systems in hotels, the OEFh values are above 95% and the OEFc ranges from 35% to 47% in the warmest five zones. However, both the OEFh and OEFc become lower in the cold zone and the very cold zone. Although the OEMth,h gradually grows from the humid-hot zone to the very cold zone, the heat produced by the system can't well afford the growing heating demands. For systems of offices, the OEFh reaches about 95% in all the climate because the absolute heating demands are very low. In comparison, the OEFc is relatively lower and more fluctuate as the climate changes. Although the cooling demands of offices are the largest in the hothumid climate, the system achieves a second largest OEFc of 51% among systems in all the climate zones. It is because the absorption chiller is very large and almost all the generated heat is used for cooling. From the hot-dry zone to the marine zone, the OEFc grows from 36% to 63% while the OEMth decreases from 90% to 56%. In
these zones, the cooling demands decrease dramatically but the heating demands grow slightly; besides, fluctuations of the cooling and heating demands increase. As a result, the systems spend most heat on satisfying cooling but the heat cannot be fully used. In the cold and very cold zones, as the growing heating demands consume more generated heat, the OEFc becomes lower. When the system runs according to the FTL strategy, the system will try to meet the cooling and heating demands in priority. In addition, the corresponding electricity is used to satisfy the electric demands and drive the electric chiller. Fig. 12 and Fig. 13 presents the values of OEF and OEM under the FTL strategy. Generally speaking, systems in the FTL mode achieve better performance on the supply of cooling and heating than those in the FEL mode; besides, the electric demands can be fully satisfied in all the cases but offices cases. For hospitals, systems can satisfy the electric demands in all the zones because their OEF values are close to 100%. However, the OEMe ranges only from 62% to 75% and the excess electricity is produced because of the large heating to electricity ratios of hospitals. The OEFh is steady and almost 100%, but the OEFc gradually decreases from 80% to 68% as the climate gets cold. Although the excess electricity is produced, the matching between
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Fig. 12. OEF values of electricity, heating and cooling in the FTL mode.
Fig. 13. OEM values of electric and thermal energy in the FTL mode.
systems and hospital buildings is fairly good. For hotels, the electric demands can be well met but the surplus electricity is an issue, which is similar to the cases of hospitals. The heating demands cannot be fully satisfied in the cold and very cold zone, where the mean OEFh descends to 89%. Although the OEFc also changes with climate, their low values were found in mild climates, including mixed-humid, mixed-dry and marine. It is because less electricity is produced by the system to drive the electric chillers and their OEFc,ec values are just around 43%. For systems of offices, the OEFe values are above 88% in the warmest three zones, but they are below 80% in other four zones. In contrast, the OEMe grows with the climate cooling, ranging from 82% to 93%. When the climate gets colder, the sum of cooling and heating becomes lower. As a result, the mismatch between the corresponding electric production and the electric demands become more significant in the coldest four zones at high latitudes. In addition, systems can satisfy 96% of the heating demands and 66% of the cooling demands averagely. Because the cooing demands are relatively low and out of sync with heating demands, the OEFc,ac values of systems in offices are larger than those of systems in hospitals and hotels, reaching 49% averagely.
4.3. Contribution of solar systems Fig. 14 shows the electricity, heating and cooling contribution of ST and PV systems in the FEL mode. The solar systems make no contribution to the electricity supply in some cases for hospital and hotel buildings, because PV systems are not adopted in these cases. For the buildings with PV systems, the electric contribution percentage of solar system ranges from 3.23% to 16.73%. OEFsh values generally decrease with the latitude growing. As ST systems occupy all the available installation area of hotels, they make greater contribution to heating supply in hotels than in other buildings. The corresponding OEFsh ranges from 18.17% to 24.25%. On the contrary, ST systems for offices have smallest OEFsh with an average value of 6.38%. In a case under the FEL strategy, the ST system always occupies much more installation area than the PV system. As a result, the majority of contribution to cooling supply is achieved by the absorption chiller driven by the thermal energy from the ST system. Generally, the distribution of OEFsc is similar with that of OEFsh and its values mainly ranges from 4.66% to 11.54%. The solar system can satisfy 18.78% of total cooling demands for office in the hot-humid climate, owing to good solar energy resources and large scale of ST system.
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Fig. 14. The electric, heating and cooling contribution of solar systems in the FEL mode.
Fig. 15. The electric, heating and cooling contribution of solar systems in the FTL mode.
The electricity, heating and cooling contribution of ST and PV systems in the FTL mode is described in Fig. 15. The OEFse mainly ranges from 9.63% to 16.45%. The systems achieve good OEFse in the hot-dry and mixed dry climates due to dry climate and rich solar resources. As the PV systems are relatively small in some cases of hotel and office buildings, they have a OEFse lower than 5%. For most cases in the FTL mode, the ST system in are relatively smaller than the PV system. In consequence, the OEFsh values are below 5% in the majority cases. Besides, the cooling contribution usually benefits from electric chiller driven by electric energy from the PV system. The mean OEFsc value of solar systems for hotels are 8.87%; besides, it is better than that of solar systems for hospitals and offices. It should be highlighted that the solar system for office in the hot-humid climate realizes largest contribution to cooling and heating supply among all the cases. The corresponding OEFsh and OEFsc can reach 15.56% and 15.36%, respectively.
4.4. System evaluation and decision-making Fig. 16 and Fig. 17 describe the PESR, ATCSR, CO2ERR and S values of systems in the three kinds of buildings across seven climate zones. Apparently, for hospitals, the systems in the FTL mode have better integrated performance; meanwhile, the advantages of other three performance indicators can also be observed, with the average PESR, ATCSR and CO2ERR being 39.34%, 8.74% and 48.75%, respectively. For systems in hotels, the distribution of S shows a variation in the climates. Those in the FTL mode have better integrated performance from the hot-humid zone to the marine zone. Besides, the systems in the FTL mode have distinct advantages in PESR, ATCSR and CO2ERR in the hot-humid and marine zones. In the cold and very cold zones, the systems in the FEL mode have higher S due to their advantages in energy saving. For systems in offices, the system performance under two operation strategies are similar
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Fig. 16. PESR, ATCSR and CO2ERR of solar hybrid CCHP systems for hospitals, hotels and offices in seven climate zones.
Fig. 17. The integrated performance (S) of solar hybrid CCHP systems for hospitals, hotels and offices in seven climate zones.
Fig. 18. The payback period of solar hybrid CCHP systems for hospitals, hotels and offices in seven climate zones.
when the climate changes from the hot-humid zone to the mixedhumid zone, as their PESR, ATCSR and CO2ERR are similar. In other climates, the FEL strategy seems to be the first choice due to the
obvious superiority in corresponding performance indicators including PESR, ATCSR and CO2ERR. As shown in Fig. 18, the simple payback periods of all cases are
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given to evaluate the feasibility of an investment of solar hybrid system. Systems for hospitals usually have shortest SPP among three building types. The SPP values systems for hospitals are all lower than 5 years in the FTL mode. However, systems for offices has the worst economic performance. Besides, the systems in the FTL mode have shorter payback period than those in the FEL mode. In the FTL mode, the changes of climate have significant influence on the SPP of systems for offices and hotels. As the climates becomes cold, the SPP of systems for offices increases from to 6.75 to 9.47. On the contrary, the SPP of systems for offices decreases 5.54 yearse4.47 years. Based on the integrated performance, the optimal design schemes of systems in hospitals, hotels and office across seven climate zones are shown in Fig. 17. On the whole, hospitals are the most suitable for the application of solar hybrid CCHP systems among the three buildings types. When the optimal strategy and the design scheme are adopted, the average S values of the optimal systems for hospitals, hotels and offices can achieve 28.95%, 28.20% and 22.69%, respectively. The detail information of each optimal system is listed in Appendix B.
661
(4) For systems in hospitals and hotels, the FTL strategy is concluded to be the first choice in nearly all the situations except for the hotels in the cold zone very cold zones. In comparison, the systems in offices perform better in the FEL mode in the majority of climate zones except for the hothumid zone. Generally, the average optimal values of S for systems in hospitals, hotels and offices are 28.95%, 28.20% and 22.69%, respectively. Acknowledgement The study has been supported by the National Key R&D Program of China (Grant No. 2017YFC0704200). Appendix A. Parameters of energy flow model and evaluation criteria The generation efficiency of PGU is presented as:
hpgu;e ¼ a1 f 3pgu þ b1 f 2pgu þ c1 fpgu þ d1 5. Conclusion In this paper, the solar hybrid CCHP system is modeled and the PSO optimization algorithm is adopted to study design parameters. The simulation and the optimization of hybrid CCHP systems are carried out in three building types across seven climate zones. According to the simulation results, the performance of solar hybrid CCHP systems is discussed and the recommended design schemes are summarized. Some significant results are concluded as follow: (1) In the FEL mode, the Cappgu/Emean and the Capac/Emean can describe the system configurations. Most values of Cappgu/ Emean range from 1.25 to 1.55. The average Capac/Cappgu of systems in hospitals dives from 2.23 to 0.52 at the mixed-dry zone. In comparison, the Capac/Cappgu in hotels and offices slightly slides as the climate gets cold. In the FTL mode, the Capac/Cmean and the Capac/Cappgu are key parameters in the system design phase. The Capac/Cmean of systems in hospitals and hotels is roughly fixed at 0.22, while the Capac/Cappgu gradually decreases with the climate cooling. For systems in offices, the Capac/Cmean ranges from 0.76 to 3.27 and the Capac/Cappgu oscillates around 1.43. (2) The energy output characteristics and the system performance are distinctive in different buildings under different climate conditions. In the FEL mode, all the values of OEFe and OEMe are near 100%, but the values of OEFh and OEFc are relatively low. The OEFh values are above 80% and most OEFc values are below 60%. In the FTL mode, although the values of OEFe can achieve 100%, the OEMe just ranges from 62% to 75%. In most cases, both the OEFh and OEFc are able to be over 86% and 57%, respectively. (3) In the FEL mode, compared to PV systems, ST systems make a better contribution to the satisfaction of heating and cooling. ST systems for hotels achieve greatest OEFsh, ranging from 18.17% to 24.25%. On the contrary, ST systems for offices have smallest OEFsh. The distribution of OEFsc is similar with that of OEFsh, ranging from 4.66% to 11.54%. In the FEL mode, PV systems are more useful than ST systems. The OEFse mainly ranges from 9.63% to 16.45%. The mean OEFsc value of solar systems for hotels can reach 8.87%.
(A.1)
where fpgu is part load factor of the PGU; a1,b1,c1,d1 are the coefficient of the third order polynomial, which are shown in Table A1 [46]. The minimum of fpgu is 0.2.The part load factor is calculated as:
fpgu ¼
Epgu Epgu;rated
(A.2)
where Epgu is the generated electricity from the PGU; Epgu,rated is the rated electricity output of PGU. The PGU heat recovery efficiency is calculated as:
hpgu;r ¼ a2 f 3pgu þ b2 f 2pgu þ c2 fpgu þ d2
(A.3)
where a2,b2,c2,d2 are the coefficient of the non-linear function, which are shown in Table A1 [46]. The coefficient of performance of the absorption chiller is estimated as:
COPac ¼
0 a3 f 2ac þ b3 fac þ c3
fac < 0:2 fac 0:2
(A.4)
where fac is the part load factor of absorption chiller, the coefficients a3,b3,c3 are shown in Table A1 [29,46]. Table A1 Coefficients of polynomial equations i
ai
bi
ci
di
1 2 3
0.4679 0.4845 0.6181
1.1705 1.0934 0.8669
0.998 0.8379 0.4724
0.002 0.7221 e
Appendix B. Optimal design parameters of hybrid CCHP systems The optimum design parameters of solar hybrid CCHP systems are listed in Table B1, Table B2 and Table B3.
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Table B1 Optimum configurations of solar hybrid CCHP systems of hospitals in seven climate zones
Hot-humid Hot-Dry Mixed-Humid Mixed-Dry Marine Cold Very Cold
Mode
Cappgu/Emean
Capac/Cmean
Capac/Cappgu
Captes/Cappgu
Capec/Cappgu
Capb/Cappgu
Apv/Atotal
Ast/Atotal
FEL FTL FEL FTL FEL FTL FEL FTL FEL FTL FEL FTL FEL FTL
1.48 1.52 1.47 1.43 1.46 1.46 1.42 1.18 1.26 1.32 1.39 1.37 1.30 1.21
0.62 0.21 0.74 0.21 0.66 0.20 0.98 0.26 0.12 0.18 0.22 0.22 0.19 0.26
2.23 0.73 2.33 0.70 2.19 0.67 2.18 0.68 0.41 0.58 0.66 0.67 0.42 0.63
0.71 0.03 0.74 0.03 0.70 0.03 0.69 0.24 3.69 0.02 6.71 0.14 3.28 0.73
4.33 3.76 3.80 3.43 3.99 3.42 2.81 2.95 3.62 3.27 4.22 3.86 1.56 3.77
0.47 0.25 0.36 0.15 0.41 0.30 0.48 0.27 0.71 0.44 0.82 0.52 1.01 0.72
0% 100% 0% 100% 0% 100% 13% 100% 85% 100% 47% 100% 77% 100%
100% 0% 100% 0% 100% 0% 87% 0% 15% 0% 52% 0% 18% 0%
Table B2 Optimum configurations of solar hybrid CCHP systems of hotels in seven climate zones
Hot-humid Hot-Dry Mixed-Humid Mixed-Dry Marine Cold Very Cold
Mode
Cappgu/Emean
Capac/Cmean
Capac/Cappgu
Captes/Cappgu
Capec/Cappgu
Capb/Cappgu
Apv/Atotal
Ast/Atotal
FEL FTL FEL FTL FEL FTL FEL FTL FEL FTL FEL FTL FEL FTL
1.46 1.65 1.48 1.45 1.49 1.51 1.49 1.55 1.48 1.52 1.51 1.63 1.54 1.73
0.41 0.25 0.50 0.26 0.43 0.22 0.59 0.22 0.50 0.18 0.44 0.22 0.37 0.21
1.07 0.58 1.16 0.62 0.91 0.45 0.96 0.35 0.86 0.31 0.80 0.38 0.52 0.26
11.53 0.07 12.73 0.09 11.28 0.12 12.76 0.09 12.21 0.38 10.34 0.10 10.28 0.10
2.89 2.45 2.64 2.80 2.78 2.77 1.99 1.94 1.46 2.07 2.80 2.53 2.32 2.06
0.74 0.09 0.68 0.24 1.00 1.22 1.16 0.98 1.15 0.40 2.38 2.19 2.86 2.39
0% 85% 0% 86% 0% 90% 0% 86% 0% 91% 0% 45% 0% 31%
100% 15% 100% 14% 100% 10% 100% 14% 100% 9% 100% 0% 100% 0%
Table B3 Optimum configurations of solar hybrid CCHP systems of offices in seven climate zones
Hot-humid Hot-Dry Mixed-Humid Mixed-Dry Marine Cold Very Cold
Mode
Cappgu/Emean
Capac/Cmean
Capac/Cappgu
Captes/Cappgu
Capec/Cappgu
Capb/Cappgu
Apv/Atotal
Ast/Atotal
FEL FTL FEL FTL FEL FTL FEL FTL FEL FTL FEL FTL FEL FTL
1.77 1.64 1.39 1.18 1.53 1.50 1.32 1.15 1.40 1.28 1.52 1.41 1.49 1.44
1.06 0.94 0.70 0.76 1.02 1.09 1.64 1.94 2.02 2.08 1.55 1.68 2.61 3.27
1.61 1.53 1.12 1.44 1.18 1.29 1.08 1.47 1.35 1.52 1.10 1.28 1.13 1.46
3.82 5.19 3.94 7.90 3.91 6.66 4.34 7.88 5.15 8.94 3.89 6.94 4.02 8.99
2.60 2.27 3.03 3.30 2.41 2.06 2.25 2.01 2.15 1.89 2.72 2.49 2.43 2.09
0.01 0.01 0.01 0.01 1.71 1.25 0.04 0.05 0.23 0.21 2.22 1.85 3.20 3.11
0% 22% 89% 96% 73% 100% 100% 100% 86% 100% 86% 100% 74% 100%
100% 78% 11% 4% 27% 0% 0% 0% 14% 0% 14% 0% 26% 0%
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