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AHP-based optimal design of a hybrid CCHP system considering economy, energy and emission
Accepted Manuscript Title: GA/AHP-based Optimal Design of a Hybrid CCHP System Considering Economy, Energy and Emission Author: Hossein Yousefi Mohammad Hasan Ghodusi Nejad Younes Noorollahi PII: DOI: Reference:
S0378-7788(16)31929-6 http://dx.doi.org/doi:10.1016/j.enbuild.2016.12.048 ENB 7227
To appear in:
ENB
Received date: Revised date: Accepted date:
6-8-2016 14-12-2016 16-12-2016
Please cite this article as: Hossein Yousefi, Mohammad Hasan Ghodusi Nejad, Younes Noorollahi, GA/AHP-based Optimal Design of a Hybrid CCHP System Considering Economy, Energy and Emission, Energy and Buildings http://dx.doi.org/10.1016/j.enbuild.2016.12.048 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
GA/AHP-based Optimal Design of a Hybrid CCHP System Considering Economy, Energy and Emission Hossein Yousefi*, Mohammad Hasan Ghodusi Nejad, Younes Noorollahi
Faculty of New Sciences and Technologies, University of Tehran, Iran *
[email protected]
1
Highlights A novel hybrid CCHP system is modelled and optimized. PV/T and ICE are coupled to form a hybrid CHP system. Economic, energy saving and environmental objective functions are considered. AHP decision making approach is used to find the best answer.
2
Abstract:
Integration of CCHP systems into large buildings provides considerable advantages, as far as the economy, energy consumption and environment related issues are concerned. In this paper, the integration of a hybrid CCHP system into a commercial building is studied. The hybrid CCHP system consists of a renewable and a non-renewable CHP component, photovoltaic/thermal (PV/T) panels and internal combustion engine (ICE). Three objective functions are considered based on cost saving, energy saving and emission reduction goals where are determined as annual operating cost ratio (AOCR), primary energy saving ratio (PESR) and carbon emission reduction ratio (CERR). Considering each objective function, the GA optimization is applied in three different approaches as a single objective optimization problem to find the best size of the system components. Then, using Analytic Hierarchy Process (AHP), which is one of the most used Multi-Criteria Decision Making (MCDM) procedures, the most profitable answer is determined from the three answers achieved in the optimization process. The results showed a good performance of the system. In PESR optimization, which is selected as the best system size, values of 32.96%, 17.25% and 14.79% was achieved for AOCR, PESR and CERR, respectively. Key words: CCHP system, Cost saving, Primary energy saving, Emission reduction, Optimal sizing, PV/T system
3
1. Introduction The world has faced an increasing demand for energy in different sectors during the recent decade. Additionally, the use of fossil fuels in the past decades, causing intensified emission of Greenhouse Gases (GHG), has led to troubling environmental issues. As a solution to these challenges, utilization of combined cooling, heating and power (CCHP) systems has been proposed. CCHP systems can offer several advantages for both energy consumers and suppliers. A CCHP system can simultaneously meet electricity, cooling and heating needs, providing a large opportunity for energy saving [1], interact with the main grid to participate in demand response and peak shaving programs and ease the integration of renewable energy resources [2]. Also, with reduced primary energy consumption in CCHP systems compared with conventional energy systems, less GHG is emitted to the environment [3]. Energy demand in building sector, as well as other sectors, has grown significantly. Building sector accounts for about 35% of the total energy consumption of the world [4]. On the other hand, buildings have a great potential for energy saving and emission reduction, where it has been predicted that 25% of the CO2 emission reduction in 2030 will come from the building sector [5]. Therefore, integration of CCHP systems, as efficient Distributed Generation (DG) facilities, into buildings, is an undeniable necessity for future energy networks of countries. Development of CCHP systems has drawn a significant attention in researches during recent years. There have been two major approaches in the literature related to optimization of CHP and CCHP systems. Some papers have worked on optimal component sizing of CCHP systems under certain conditions. For instance, Hanafizadeh et al. [6] conducted a study to find the optimal size of CCHP system for commercial and office buildings in Tehran, Iran. Akbari et al. [7] focused on 4
designing a building’s energy system under demand, costs and prices uncertainties by means of robust optimization. Li et al. [8] presented optimization of CCHP system design from three viewpoints for hotels, offices and residential buildings in Dalian (China). Sayyaadi and Abdollahi [9] presented a multi-objective optimization for sizing of a small-scale CCHP system based on three objective functions including the exergetic efficiency, total levelized cost rate of the system product and the cost rate of environmental. On the other hand, some others have studied the optimal operation and energy management of CHP and CCHP systems with regards to predefined constraints. For instance, Rodríguez et al. [10] assessed the performance of hybrid systems composed of solar thermal collectors, photovoltaic panels and natural gas internal combustion engines by the TRNSYS 17 software. Ranjbar et al. [11] presented an evolutionary programming based approach to evaluate the effect of combining fuel cell power plants and wind (a hybrid CHP system) on the operational and performance cost. Liu et al. [12] analyzed the hourly operation of a CCHP system coupled with ground source heat pump under variable loads. Chen et al. [13] studied the performance of a hybrid residential CCHP system based on fuel cell and solar technologies. Several other papers have focused on CCHP systems. Wei et al. [14] proposed a multi-objective optimization model to maximize the energy saving ratio and minimize the energy costs of a micro-CCHP system. Non-dominated Sorting Genetic Algorithm-II was used to identify a series of compromised optimal operation strategies with different operational parameters. Abdollahi and Meratizaman [15] performed a multi-objective optimization to design a small-scale distributed CCHP system. Therefore, three objective functions were considered: exergetic efficiency, total levelized cost rate of the system product and the cost rate of environmental impact. Li et al. [16] analyzed the effect of adding a CCHP system to residential and office buildings. Genetic algorithm was used to solve the 5
problem to save energy, reduce the total cost, and attain excellent environmental performance. Ameri and Besharati [17] proposed a Mixed Integer Linear Programming (MILP) model to determine the optimal capacity and operation of seven CCHP systems in a residential complex in Tehran. The results showed that using an optimal CCHP system integrated with PV panels leads to 40.8% reduction of energy costs and 38.7% reduction of the primary energy consumption. Yang et al. [18] presented a study about district-scale distributed energy resource systems. A MILP model is constructed that can achieve simultaneous optimization of siting, sizing and scheduling. Ascione et al. [19] carried out a multi-objective optimization for energy retrofitting of a hospital to minimize investment cost, primary energy consumption and global cost. It was found from the results that a reduction up to 12.2% in primary energy consumption and up to 24.5% in global cost can be achieved. Ebrahimi and Keshavarz [20] determined the optimum orientation and size of a solar collector for integration with a CCHP system. Maximum rectangle method was applied to find the optimal size of the prime mover. The solar collector specifications were determined in five different climates. Farahnak et al. [21] developed an optimization algorithm to find the best operation point of the Power Generation Unit (PGU) at minimum energy cost. Different sizes of PGU and building were considered and primary energy saving and simple pay-back ratio were evaluated. Braslavsky et al. [22] presented optimal options for distributed energy resource (DER) technologies to reduce greenhouse gas emissions in a large shopping center. As a result, a CCHP system in conjunction with PV, can deliver up to 72% reductions in emissions. Zeng et al. [23] provided a model for coupling of a CCHP system and ground source heat pump (GSHP) based on environment, economy and energy criteria simultaneously and genetic algorithm is used to solve the problem. Guo et al. [24] presented a twostage optimal planning and design method for a CCHP microgrid that 6
simultaneously minimizes the total net present cost and carbon dioxide emission. On the first stage, the optimal design problem was solved using Non-dominated Sorting Genetic Algorithm II (NSGA-II) and on the second stage, a MILP was applied to solve the economic dispatch problem. Sanaye and Sarrafi [25] conducted the energy, exergy and economy optimization of a solar-based CCHP system
containing
conventional
photovoltaic
(PV),
concentrated
photovoltaic/thermal (CPVT), and evacuated tube (ET) collectors. Li et al. [26] modelled a CCHP system to investigate its annual total cost reduction, primary energy saving, and carbon dioxide emission reduction with respect to a reference system under five different operation strategies. Office and residential buildings were selected for the case study of the research. There are some papers in the literature that consider the integration of solar energy into CCHP systems. These all have analyzed the integration of photovoltaic panels and/or solar thermal collectors into conventional CHP components. Application of photovoltaic panels and solar collectors separately, would not lead to a renewable CHP mechanism. In this paper, the use of PV/T panels, as a renewable CHP component, is suggested to be coupled with common CHP components. Hence, not only a novel hybrid CHP system, including both renewable and non-renewable sources of energy, is available, but also a great saving in roof top area utilization for solar panels is achieved and more solar energy could be captured. Therefore, a hybrid CCHP system is proposed and optimal components sizes are determined based on cost, energy consumption and emission considerations. The paper is comprised of six sections. The problem description and the diagram of the system is presented in section 2. Mathematical model is presented in section 3. The optimization approach is described in section 4. Results of the simulation and optimization are presented and discussed in section 5. Finally, the conclusion of the paper is presented in section 6. 7
2. Problem description 2.1. System configuration A conventional CCHP system consists of a power generation unit (PGU), auxiliary boiler and heat-activated cooling component. In this paper, a novel hybrid CCHP system is modelled and optimized. The configuration of the hybrid CCHP system is illustrated in Fig. 1. In this system, an internal combustion engine (ICE) is considered as the main PGU and a renewable combined heat and power device, i.e. photovoltaic/thermal (PV/T) panel, is integrated with the PGU. Both ICE and PV/T provide heat and power for the building. A natural gas-fired boiler and the grid act as heat and power backups, respectively. Combination of absorption chiller (AC) and electric chiller (EC) is considered to provide cooling for the building. To model the partial load operation of the ICE, it is assumed that ICE follows the electrical demand of the building and therefore, it does not have any power transaction with the grid. In contrast, PV/T can feed both load and the gird.
8
Figure 1. CCHP system schematic
Technical characteristics of the system components are provided in Table 1. The data used for PV/T panels, including PV/T reference electrical efficiency, temperature coefficient and heat loss coefficient, is derived from FOTOTHERM Cs panels with a nominal power of 250W [27]. The ICE electrical efficiency coefficients are used to determine the electrical efficiency of the ICE in partial load performance. The average efficiency of Iran grid is considered which is about 32%. Table 1. Technical characteristics of the system components
PV/T reference electrical efficiency (%)
15.5
Temperature coefficient (% / °C)
0.43
2
Heat loss coefficient (W/m .K)
9.12 9
DC/AC inverter efficiency (%) ICE electrical efficiency coefficients (%) Heat recovery system efficiency (%)
90 a= 0.1 b= 0.4 c= -0.2 80
Boiler efficiency (%)
80
Grid efficiency (%)
32
COP of absorption chiller (Whth/Whth)
0.7
COP of electric chiller (Whth/Whel)
3
3. CCHP system model The mathematical model of the CCHP system was implemented in MATLAB. The goal is to find the optimal capacity of the system components with respect to predefined objective functions. The decision variables are nominal capacity of the ICE and AC (in kW) as well as the number of PV/T panels. The nominal capacity of the boiler and EC (in kW) are dependent variables and are determined based on decision variables. Table 2 presents the considered range for main variables. Table 2. Range of main variables of the problem
PICE ,nom (kW)
100-500
Q AC ,nom (kW)
100-900 0-250
N PVT (#)
3.1. Constraints The total energy demand of the building is in the form of heat and electricity. Electricity is needed to meet the electrical load and the power fed to the EC, while
10
heat demand is for building heating and input heat of the AC. Therefore, it can be written as: Pdem (t ) PL (t )
Q EC (t ) COPEC
(1)
Q AC (t ) COPAC
(2)
Qdem (t ) Q H (t )
As stated previously, the ICE operates in electrical load following mode. Therefore, in some time steps, it may operate in partial load and so that the electrical efficiency would be reduced. Partial load ratio is defined to calculate the ratio of load to the nominal capacity: PLR (t )
Pdem (t ) 1 PICE ,nom
(3)
The ICE efficiency is a function of partial load ratio [16]: ICE (t ) a b .PLR (t ) c .PLR 2 (t )
(4)
Too low PLR values result in low electric efficiency. Hence, an on/off threshold should be defined too prohibit the ICE to work in very low load ratios. This threshold is set as 25% and therefore, the generated power by the ICE is calculated as follows: 0 0 PLR 25% PICE (t ) Pdem (t ) 25% PLR 100% P PLR 100% ICE , nom
(5)
Then, the fuel consumption of the ICE can be found: Q f (t )
PICE (t )
11
ICE
(6)
The recovered heat is a function of the ICE and heat recovery system efficiencies. So, the heat is found by the following equation: Q ICE (t ) HRS 1 ICE (t ) Qf (t )
(7)
Power generation of solar panel is a function of its effective area. The area of each PV/T panel, as in the company catalog, is about 1.6m2. Hence, total area of panels is found: APVT 1.6N PVT
(8)
The generated power by the PV/T panels can be calculated as follows: PPVT (t ) APVT DC / ACPVT (t )G PoA (t )
(9)
In PV/T panels, the circulating water acts as coolant and can reduce the cell temperature, so that the reduction of efficiency can somewhat be avoided. But, in this paper, this positive effect is not considered and therefore, it is assumed that an increase in cell temperature will cause an efficiency drop. The cell efficiency in each time step is calculated as: PVT (t ) ref 1 Tcell (t ) T ref
(10)
where, T ref is the reference temperature equal to 25°C. Different relations have been proposed for the calculation of the cell temperature. In order to do this, equation (11) is used which is an empirical relation for poly-crystalline silicon cells [28]: Tc (t ) 30 0.0175 G PoA (t ) 300 1.14 T a (t ) 25
(11)
The heat generated by PV/T panels can be determined as [28,29]: Q PVT (t ) APVT G PoA (t )( ) 1 PVT (t ) U L Tcell (t ) T a (t )
where, ( ) is considered 0.9 as in [30]. 12
(12)
Both AC and EC should provide cooling for the building: Q EC (t ) Q AC (t ) QC (t )
(13)
But it is considered that the AC is prior to the EC in cooling provision. It means, in cases that the cooling load is less than the nominal power of the AC, the EC will not operate: Q AC (t ) QC (t )
(14)
Q EC (t ) 0
(15)
For the time steps that the cooling load exceeds the AC nominal power, the deficiency is met by the EC: Q AC (t ) Q AC ,nom
(16)
Q EC (t ) QC (t ) Q AC (t )
(17)
After the whole year simulation, the nominal power of the EC can be found: Q EC ,nom max Q EC
(18)
The recovered heat of CHP components, i.e. ICE and PV/T, and the boiler heat have to meet the heat demand of the building stated in equation (2): QICE (t ) Q PVT (t ) Q B (t ) Qdem (t )
(19)
If the generated heat by the ICE and PV/T is sufficient to meet the heat demand, then the boiler will not operate. In this case, the surplus heat of the ICE, if it exists, is exhausted. When the ICE and PV/T heat is less than the demand, the boiler will cover the deficiency. Therefore, after a whole year simulation, the nominal capacity of the boiler can be found as follows:
13
Q B,nom max Q B
(20)
The fuel consumed by the boiler is determined as: Q f ,B (t )
Q B (t )
B
(21)
Then, the total fuel consumed by the CCHP system can be found: Qf ,Total Q f Q f ,B
(22)
The total electricity demand of the building stated in equation (1), is met by the ICE, PV/T and grid: PICE (t ) PPVT load (t ) Pgrid (t ) Pdem (t )
(23)
Since the system operates in following electrical load mode, all power generated by the ICE is fed to the load. In cases that the ICE power is not enough to meet the load, PV/T power will provide the deficiency. If both ICE and PV/T are not able to meet the load, electricity will be imported from the grid. Besides, excess power of the PV/T, if it exists, will be sold to the grid: PPVT (t ) PPVT load (t ) PPVT grid (t )
(24)
3.2. Objective functions Three different objective functions related to economy, energy saving and emissions are defined for optimization. All objective functions are defined as a comparison with the reference system which includes a boiler, an EC and the grid. Each objective function is optimized separately in a single objective optimization approach and the results are compared with each other.
14
The first objective function is Annual Operating Cost Ratio (AOCR) and is defined as: AOC CCHP AOCR 1 AOC ref
100
(25)
where, AOC CCHP and AOC ref are annual operating cost of the CCHP and reference system, respectively. AOC comprises operating and maintenance costs of system components and utility power and natural gas charges. For the CCHP system, the revenue of exporting PV/T power to the grid will be subtracted from the total costs. Therefore, AOC CCHP and AOC ref can be written as: 8760 8760 8760 AOC CCHP C O &M ,i E i (t ) Pgrid (t ) C grid NG (t ) C ng i t 1 t 1 t 1
8760 PPVT grid (t ) C PVT t 1 8760 8760 8760 AOC ref C O &M ,i E i (t ) Pgrid ,ref (t ) C grid NG ref (t ) C ng i t 1 t 1 t 1
(26)
(27)
where, NG ref is the natural gas consumption of the boiler in reference mode to meet the heating load and Pgrid ,ref is the total electricity consumption in reference mode to meet both electrical load and power needed for the EC to provide the cooling load. The O&M costs of the system are presented in Table 3. For the CCHP system, O&M costs of all five components are calculated, while for the reference system, the costs of ICE, PV/T and AC are excluded. Time-of-use electricity tariff, natural gas charge and PV/T feed-in-tariff are also presented in Table 3. Table 3. Economic details of the system
Operating and maintenance costs ($/kWh) 15
Internal combustion engine
0.02
PV/T panel
0.01
Boiler
0.008
Absorption chiller
0.01
Electric chiller
0.008
Time-of-use electricity tariff ($/kWh) Hours: 1-7, 24
0.023
Hours: 8-19
0.047
Hours: 20-23
0.094
Natural gas charge ($/m3)
0.043
PV/T feed-in-tariff ($/kWh)
0.26
The second objective function is defined to assess the energy consumption of the CCHP system in comparison with the reference system. Primary Energy Saving Ratio (PESR) is defined as: PEC CCHP PESR 1 PEC ref
100
(28)
where, PEC CCHP and PEC ref are primary energy consumption of the building in CCHP and reference mode, respectively and are calculated as follows: 8760 8760
PEC CCHP Q f ,Total (t )
P t 1
t 1
8760
PEC ref
PL (t ) t 1
grid
grid
t 1
16
B
(29)
grid
8760
Q H (t )
(t )
8760
Q t 1
C
(t )
COPEC
(30)
The third objective function of the optimization is Carbon Emission Reduction Ratio (CERR) and is defined to compare the Carbon emissions of the CCHP system with the reference system. CERR is written as: CE CERR 1 CCHP CE ref
100
(31)
where, CE CCHP and CE ref are Carbon emissions of CCHP and reference system, respectively and are determined as follows: 8760
8760
t 1
t 1
CE CCHP EFgrid Pgrid (t ) EFng Q f ,Total (t ) 8760 Q (t ) CE ref EFgrid PL (t ) C COPEC t 1
EFgrid
8760 Q H (t ) EF ng t 1 B
(32)
(33)
and EFng are the emission factors of grid electricity and natural gas
consumption, respectively, and can be found on Table 4. Table 4. Carbon emission factors [31-32]
Grid electricity EF (kg/kWh)
0.598
Natural gas EF (kg/kWh)
0.202
4. Optimization approach The model is implemented in MATLAB and optimized using genetic algorithm (GA). The details of the algorithm are presented in Table 5. All three objective functions are optimized separately in three single objective GA approaches, while in each optimization procedure, the two other values are calculated too in order to select the best answer. For instance, while optimizing AOCR, PESR and CERR are 17
also calculated for all individuals of the population. As AOCR is optimized, the answer in the population with maximum value of PESR and CERR is selected. This will be repeated for PESR and CERR optimizations. Therefore, three different answers, each with different values of AOCR, PESR and CERR, are obtained. Finally, using Analytical Hierarchy Process (AHP), the best answer is selected [33]. The AHP process is illustrated in Fig. 2. Table 5. Genetic algorithm characteristics
Population size
50
Number of generations
100
Crossover percentage (%)
80
Mutation percentage (%)
30
Selection method
Roulette Wheel
Selection pressure
8
Figure 2. AHP process for best answer selection
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5. Results and discussion 5.1. Case study The model was applied to a case study which is a four story commercial building located near Tehran, Iran’s capital city. The useful area of the building is almost 4000 m2 and the official working hours are 9 to 13 and 15 to 23. The height of the building is approximately 12 m, and therefore, the surface area to volume ratio is calculated about 0.306 1/m. In addition, sufficient rooftop area is available to easily install up to 250 solar panels. The electrical, cooling and heating load of the building for a typical winter and summer day is illustrated in Fig. 3. As Iran enjoys a good potential of solar energy utilization, solar panels are good alternatives to be integrated into the buildings. Iran, located in the world’s Sun Belt, has an annual average of sun radiation about 20–30 MJ/m2 that is even higher in the central regions [34]. The solar radiation data, extracted from the nearest meteorological station to the case study building, show that the region enjoys an average 3000 sunny hours per year, which is a great potential. The meteorological data including solar irradiation and ambient temperature is extracted from a meteorological file in TRNSYS software. Fig. 4 illustrates the Plane-of-Array irradiation.
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Typical Winter
Typical Summer
Electrical Load (kW)
300 250 200 150 100 50 0
Hour
Figure 3.a. A typical daily electrical load profile of the building
Thermal Load (kW)
Typical Winter Heating
Typical Summer Cooling
1000 900 800 700 600 500 400 300 200 100 0
Hour
Figure 3.b. A typical heating and cooling load profile of the building
20
Typical Winter
Typical Summer
PoA Irradiation (kW/m2)
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
Hour
Figure 4. Plane of array solar irradiation
5.2. Optimization results As stated previously, three answers are obtained as a result of three optimization approaches. Details of the answers including component sizes and objective functions values are presented in Table 6. Table 6. Details of three answers of the optimization
AOCR opt. PESR opt. CERR opt.
PICE ,nom
N PV /T
Q B ,nom
Q AC .nom
Q EC ,nom
AOCR
PESR
CERR
(kW)
(#)
(kW)
(kW)
(kW)
(%)
(%)
(%)
330
250
322
501
388
57.19
6.89
2.64
104
250
164
215
674
32.96
17.25
14.79
100
250
166
206
683
31.76
17.23
14.85
From Table 6, it can be seen that in all three answers, the number of PV/T panels obtained is the maximum allowable value, i.e. 250. In CERR optimization, ICE capacity is determined as its minimum allowable value, while in PESR 21
optimization, it is near the minimum. Another point is that in PESR and CERR optimization, the results are approximately alike and slight differences in the capacities can be seen. First answer shows a great performance in AOCR value, while in PESR and CERR is not suitable. Second and third answers appear good in the values of PESR and CERR while having a considerable performance in AOCR. 5.2.1. Decision making The task is to find the most acceptable and profitable answer via a decision making process. In order to do this, AHP is used. From Fig. 2, there are three choices while having three criteria. The first step is to determine the priority of criteria and their weights. It is assumed that all three criteria have same importance and therefore, their weights are considered equal. Hence, the weight matrix of criteria is:
W criteria
1 3 1 3 1 3
(34)
The next step is developing the comparison matrices for the answers based on each criterion. Regarding the values in Table 6, the comparison matrices are written as:
Z AOCR
5 5 1 1 1 8 1 8 1 1 9 1 9 1 5 1 2 ; Z PESR 8 1 2 ; Z CERR 9 1 1 2 1 5 1 2 1 8 1 2 1 9 2 1
(35)
Finally, determining the weight local priority, and using W criteria , the overall priority is calculated. The final result of AHP is depicted in Fig. 5. The hierarchy consistency ratio (CR) is calculated as 2.56% which is acceptable.
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AOCR
PESR
CERR
0.45 0.4
0.3745 0.3529
Overall Weight
0.35 0.3
0.2725
0.25 0.2 0.15 0.1 0.05 0 Ans. 1
Ans. 2
Ans. 3
Figure 5. AHP overall weights for the three answer of optimization
From the Fig. 5, it is clear that answer 2 is the most profitable answer, due to its overall weight. It means that answer 2 is the best combination for the CCHP system components capacities and will provide 32.96%, 17.25% and 14.79% cost saving, energy saving and emission reduction, respectively, which is the best achieved outcome of the CCHP system. In the following, the detailed performance of answer 2 is presented. 5.2.2. System performance The performance of the CCHP system consists of different aspects. Providing power, heating and cooling should be analyzed and the performance of the components is studied. The system provides power for the building by the ICE, PV/T and grid. Fig. 6 illustrates the breakdown of the electrical demand, including both electrical load on Fig. 3.a and the power needed to run the EC, provided by different sources. In Fig. 6.a, since there is no need for cooling in a winter day, the electrical load profile and the demand are alike. It can be seen that in this profile, most of the 23
demand is met by the ICE and in peak hours, the grid power is entered. A very small portion of the load in just three hours of the day is provided by the PV/T. In Fig. 6.b, it can be seen that there is a difference between the demand and electrical load profiles due to the power needed for the EC. For the initial hours of the day, i.e. 1 to 9, the AC capacity (215 kW) is sufficient to meet the cooling load showed on Fig. 3.b, while by increasing the cooling demand in following hours, the EC have to provide the deficiency. Here, the ICE operates in full load or near full load in almost all hours of the day, but a considerable portion of power is imported from the grid in peak load hours. With increased solar radiation, the share of the PV/T has also increased compared with the winter day. On the whole, in the winter day, about 84.37% of the power demand is provided by the ICE and almost the rest by the grid (the share solar panels is only 0.24%). For the summer day this is totally different. About 52.7% of the demand is met by the grid, while the share of ICE and PV/T is 43% and 4.3%, respectively.
Electrical Demand (kW)
ICE
PVT
Grid
Electrical Load
200 180 160 140 120 100 80 60 40 20 0
Hour
Figure 6.a. Electrical demand (winter day)
24
ICE
PVT
Grid
Electrical Load
Electrical Demand (kW)
600 500 400 300 200 100 0
Hour
Figure 6.b. Electrical demand (summer day)
The heating load of the building in winter day is provided by the ICE and PV/T as well as the boiler. Fig. 7 illustrates the share of each component in heating provision. The ICE and PV/T are participated in providing heating in all hours of the day, while the boiler is entered in peak load hours. As the dispatch strategy is set on electrical load following, the exhausted heat from ICE is noticeable. Although this can be improved by adding the system a thermal storage tank or changing the dispatch strategy to thermal load following, this is out of the scope of this paper. Totally, about 71% of the heat demand is provided by the ICE. The next is about 16% PV/T heat generation, while the share of the boiler is approximately 13%.
25
ICE
PVT
Boiler
400
Heating Load (kW)
350 300 250 200 150 100 50 0
Hour
Figure 7. The heating load profile provided by different sources
The cooling task of the building is done by the AC and EC. The breakdown of the cooling load in a summer day, is shown in Fig. 8. In hours 1-7 and 24, EC is turned off and AC provides cooling for the building. On the other hand, in hours 8-23, although the AC is operating in full load mode, its capacity is not sufficient for the cooling load and the EC covers the rest of the demand.
Cooling Load (kW)
AC
EC
1000 900 800 700 600 500 400 300 200 100 0
Hour
Figure 8. The cooling load profile provided by different sources
26
Another important factor is the power transactions of the CCHP system and the grid. As stated previously, the ICE does not send power to the grid and the only power exporting component is PV/T. Fig. 9 illustrates the power transactions in the typical winter and summer day. In the winter day, almost all of the generated solar power is sold to the grid and only in hours 8, 9 and 17 solar power is sent to the load. In contrast, in the summer day, although the generated solar power is a lot more than in the winter day, most of it is sent to the load and a small portion is sold to the grid. This is due the model strategy, where the self-sufficiency of the building is considered more important than exporting power to the grid; hence, with increased building demand, the sold power to the grid decreases. Totally in a whole year, about 549202 kWh electricity is bought from the grid while about 22206 kWh is sold.
Grid Import
Solar export
Total Solar
Electrical Power (kW)
100 80 60 40 20 0 -20 -40
Hour
Figure 9.a. Power transaction of the system and the grid (winter day)
27
Grid Import
Solar export
Total Solar
Electrical Power (kW)
400 350 300 250 200 150 100 50 0 -50
Hour
Figure 9.b. Power transaction of the system and the grid (summer day)
Finally, the partial load performance of the ICE should be analyzed, as low values of PLR lead to low efficiency. For all operating hours of the ICE in a year, PLR values are extracted. Fig. 10 illustrates the relative frequency of the PLR for full and partial load operation intervals. For about 58% of the operating hours, the ICE operates in full load which is a considerable share. Besides, in a cumulative look, about 78% of the year, the PLR is more than 80% that means a good ICE performance.
28
Relative Frequency (%)
70 60 50 40 30 20 10 0 100
[90,100)
[80,90)
[70,80)
[60,70)
[50,60)
[40,50)
[0,40)
PLR interval (%)
Figure 10. Relative frequency of PLR value
6. Conclusion Modelling and optimization of the size and performance of CHP and CCHP systems, have attracted lots of attention in recent years. In this paper, a novel hybrid CCHP system based on renewable and non-renewable energy sources is modelled and optimized based on economy, energy and emission objective functions for a commercial building. In three different approaches, genetic algorithm is conducted to find three different answers. Then, through a multicriteria decision making approach (AHP), the most valuable answer is selected and its energy performance is analyzed. In AOCR optimization, more than 57% annual operating cost saving is obtained, while the energy saving and emission reduction was not considerable. In PESR and CERR optimizations, the results were approximately alike. Although the value of cost saving was reduced, a significant growth in energy saving and emission reduction was achieved. A notable point is that in all three answers, the number of PV/T panels was found as its maximum allowable value, which asserts the importance of solar panels in the performance and structure of the CCHP system. 29
Applying AHP, the PESR optimization answer gained a score of 0.3745 which is the highest weight among answers. So the performance of this answer was analyzed. In a typical winter day, most of the electricity demand of the building was met by the ICE and only on peak hours of the night, power was imported from the grid. On the other hand, in a typical summer, due to the increase in power demand because of the EC, the share of grid electricity was increased. For heating demand in a winter day, most of the load was served by the ICE and PV/T and only 13% of the load was provided by the boiler. The cooling load provision in a summer day, was approximately shared equally between the AC and EC, where AC provided about 46% of the cooling and EC met the rest. On the whole, concluded from the research results, the advantages of the CCHP system utilization are clear. It not only can bring significant economic profits, but also can ease the energy savings and CO2 emission reductions. Therefore, CCHP systems are one of the best solutions for the energy crisis that the world has faced.
30
Nomenclature Acronyms CCHP Combined Cooling, Heating and Power CHP Combined Heat and Power AOCR Annual Operating Cost Ratio PESR Primary Energy Saving Ratio CERR Carbon Emission Reduction Ratio PLR Partial Load Ratio AHP Analytical Hierarchy Process MCDM Multi Criteria Decision Making PGU Power Generation Unit GA Genetic Algorithm Symbols Electrical power (kW) P Thermal power (kW) Q Efficiency (%) Time step (h) t Area of solar panels (m2) A Number of solar panels N Solar irradiance (kW/m2) G Temperature coefficient (power) (%/°C) Temperature (°C) T Heat loss coefficient (kW/m2.K) UL Coefficient of performance COP Cost ($) C Partial load ratio (%) PLR Annual operating cost ($) AOC Generated energy (kWh) E Annual natural gas consumption (m3) NG CE Carbon emission (kg) Emission factor (kg/kWh) EF W Weight matrix of AHP criteria Weight matrix of AHP alternatives Z
31
Subscripts ICE PVT B AC EC nom f e HRS PoA ref cell a C H L dem grid DC / AC o &m
i ng
PVT load PVT grid criteria CCHP
Internal combustion engine Photovoltaic/thermal Boiler Absorption Chiller Electric Chiller Nominal capacity Fuel Electrical Heat recovery system Plane of Array Reference value Solar cell Ambient Cooling load Heating load Electrical load Demand Grid DC/AC inverter Operation and maintenance cost Index of components Natural gas PV/T power to load PV/T power to grid AHP criteria CCHP system
References 1. Tichi, S. G., M. M. Ardehali, and M. E. Nazari. "Examination of energy price policies in Iran for optimal configuration of CHP and CCHP systems based on particle swarm optimization algorithm." Energy Policy 38, no. 10 (2010): 6240-6250. 2. Gu, Wei, Zhi Wu, Rui Bo, Wei Liu, Gan Zhou, Wu Chen, and Zaijun Wu. "Modeling, planning and optimal energy management of combined cooling, heating and power microgrid: A review." International Journal of Electrical Power & Energy Systems 54 (2014): 26-37. 3. Liu, Mingxi, Yang Shi, and Fang Fang. "Optimal power flow and PGU capacity of CCHP systems using a matrix modeling approach." Applied energy 102 (2013): 794-802. 4. Wang, Jiangjiang, Ying Yang, Tianzhi Mao, Jun Sui, and Hongguang Jin. "Life cycle assessment (LCA) optimization of solar-assisted hybrid CCHP system." Applied Energy 146 (2015): 38-52. 5. Brandoni, Caterina, and Massimiliano Renzi. "Optimal sizing of hybrid solar micro-CHP systems for the household sector." Applied Thermal Engineering75 (2015): 896-907. 6. Hanafizadeh, Pedram, Javad Eshraghi, Pouria Ahmadi, and Amirmohammad Sattari. "Evaluation and sizing of a CCHP system for a commercial and office buildings." Journal of Building Engineering 5 (2016): 67-78. 7. Akbari, Kaveh, Mohammad Mahdi Nasiri, Fariborz Jolai, and Seyed Farid Ghaderi. "Optimal investment and unit sizing of distributed energy systems under uncertainty: A robust optimization approach." Energy and Buildings 85 (2014): 275-286. 8. Li, Miao, Hailin Mu, Nan Li, and Baoyu Ma. "Optimal design and operation strategy for integrated evaluation of CCHP (combined cooling heating and power) system." Energy 99 (2016): 202-220. 9. Abdollahi, Gholamhossein, and Hoseyn Sayyaadi. "Application of the multi-objective optimization and risk analysis for the sizing of a residential small-scale CCHP system." Energy and Buildings 60 (2013): 330-344. 10. Rodríguez, Laura Romero, José Manuel Salmerón Lissén, José Sánchez Ramos, Enrique Ángel Rodríguez Jara, and Servando Álvarez Domínguez. "Analysis of the economic feasibility and reduction of a building’s energy consumption and emissions when integrating hybrid solar thermal/PV/micro-CHP systems." Applied Energy 165 (2016): 828-838.
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11. Ranjbar, Mohammad Reza, and Mohsen Mohammadian. "Economic analysis of hybrid system consists of fuel cell and wind based CHP system for supplying grid-parallel residential load." Energy and Buildings 68 (2014): 476-487. 12. Liu, Wei, Guanyi Chen, Beibei Yan, Zhihua Zhou, Haowei Du, and Jian Zuo. "Hourly operation strategy of a CCHP system with GSHP and thermal energy storage (TES) under variable loads: a case study." Energy and Buildings 93 (2015): 143-153. 13. Chen, Xi, Guangcai Gong, Zhongmin Wan, Caizhi Zhang, and Zhengkai Tu. "Performance study of a dual power source residential CCHP system based on PEMFC and PTSC." Energy Conversion and Management 119 (2016): 163-176. 14. Wei, Dajun, Alian Chen, Bo Sun, and Chenghui Zhang. "Multi-objective optimal operation and energy coupling analysis of combined cooling and heating system." Energy 98 (2016): 296-307. 15. Abdollahi, Gh, and M. Meratizaman. "Multi-objective approach in thermoenvironomic optimization of a small-scale distributed CCHP system with risk analysis." Energy and Buildings 43, no. 11 (2011): 3144-3153. 16. Li, Longxi, Hailin Mu, Weijun Gao, and Miao Li. "Optimization and analysis of CCHP system based on energy loads coupling of residential and office buildings." Applied Energy 136 (2014): 206-216. 17. Ameri, Mohammad, and Zahed Besharati. "Optimal design and operation of district heating and cooling networks with CCHP systems in a residential complex." Energy and Buildings 110 (2016): 135148. 18. Yang, Yun, Shijie Zhang, and Yunhan Xiao. "An MILP (mixed integer linear programming) model for optimal design of district-scale distributed energy resource systems." Energy 90 (2015): 1901-1915. 19. Ascione, Fabrizio, Nicola Bianco, Claudio De Stasio, Gerardo Maria Mauro, and Giuseppe Peter Vanoli. "Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: A new approach to assess cost-optimality." Applied Energy 174 (2016): 37-68. 20. Ebrahimi, Masood, and Ali Keshavarz. "Designing an optimal solar collector (orientation, type and size) for a hybrid-CCHP system in different climates."Energy and Buildings 108 (2015): 10-22. 21. Farahnak, Mehdi, Mahmood Farzaneh-Gord, Mahdi Deymi-Dashtebayaz, and Farshad Dashti. "Optimal sizing of power generation unit capacity in ICE-driven CCHP systems for various residential building sizes." Applied Energy158 (2015): 203-219.
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22. Braslavsky, Julio H., Josh R. Wall, and Luke J. Reedman. "Optimal distributed energy resources and the cost of reduced greenhouse gas emissions in a large retail shopping centre." Applied Energy 155 (2015): 120-130. 23. Zeng, Rong, Hongqiang Li, Runhua Jiang, Lifang Liu, and Guoqiang Zhang. "A novel multi-objective optimization method for CCHP–GSHP coupling systems." Energy and Buildings 112 (2016): 149-158. 24. Guo, Li, Wenjian Liu, Jiejin Cai, Bowen Hong, and Chengshan Wang. "A two-stage optimal planning and design method for combined cooling, heat and power microgrid system." Energy Conversion and Management 74 (2013): 433-445. 25. Sanaye, Sepehr, and Ahmadreza Sarrafi. "Optimization of combined cooling, heating and power generation by a solar system." Renewable Energy 80 (2015): 699-712. 26. Li, Longxi, Hailin Mu, Nan Li, and Miao Li. "Analysis of the integrated performance and redundant energy of CCHP systems under different operation strategies." Energy and Buildings 99 (2015): 231-242. 27. http://www.fototherm.com/en/fototherm-cs-series.html [Accessed February 16, 2016] 28. Kalogirou, Soteris A. Solar energy engineering: processes and systems. Academic Press, 2013. 29. Florschuetz, L. W. "Extension of the Hottel-Whillier model to the analysis of combined photovoltaic/thermal flat plate collectors." Solar energy 22, no. 4 (1979): 361-366. 30. Duffie, John A., and William A. Beckman. Solar engineering of thermal processes. Vol. 3. New York: Wiley, 2013. 31. http://www.eia.gov/oiaf/1605/emission_factors.html [Accessed February 16, 2016] 32. "2006 IPCC guidelines for national greenhouse gas inventories.", Intergovernmental Panel On Climate Change, ch. 2, 2006. 33. Tzeng, Gwo-Hshiung, and Jih-Jeng Huang. Multiple attribute decision making: methods and applications. Ch. 2. CRC press, 2011. 34. Najafi, G., B. Ghobadian, R. Mamat, T. Yusaf, and W. H. Azmi. "Solar energy in Iran: Current state and outlook." Renewable and Sustainable Energy Reviews 49 (2015): 931-942.
34
S0378-7788(16)31929-6 http://dx.doi.org/doi:10.1016/j.enbuild.2016.12.048 ENB 7227
To appear in:
ENB
Received date: Revised date: Accepted date:
6-8-2016 14-12-2016 16-12-2016
Please cite this article as: Hossein Yousefi, Mohammad Hasan Ghodusi Nejad, Younes Noorollahi, GA/AHP-based Optimal Design of a Hybrid CCHP System Considering Economy, Energy and Emission, Energy and Buildings http://dx.doi.org/10.1016/j.enbuild.2016.12.048 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
GA/AHP-based Optimal Design of a Hybrid CCHP System Considering Economy, Energy and Emission Hossein Yousefi*, Mohammad Hasan Ghodusi Nejad, Younes Noorollahi
Faculty of New Sciences and Technologies, University of Tehran, Iran *
[email protected]
1
Highlights A novel hybrid CCHP system is modelled and optimized. PV/T and ICE are coupled to form a hybrid CHP system. Economic, energy saving and environmental objective functions are considered. AHP decision making approach is used to find the best answer.
2
Abstract:
Integration of CCHP systems into large buildings provides considerable advantages, as far as the economy, energy consumption and environment related issues are concerned. In this paper, the integration of a hybrid CCHP system into a commercial building is studied. The hybrid CCHP system consists of a renewable and a non-renewable CHP component, photovoltaic/thermal (PV/T) panels and internal combustion engine (ICE). Three objective functions are considered based on cost saving, energy saving and emission reduction goals where are determined as annual operating cost ratio (AOCR), primary energy saving ratio (PESR) and carbon emission reduction ratio (CERR). Considering each objective function, the GA optimization is applied in three different approaches as a single objective optimization problem to find the best size of the system components. Then, using Analytic Hierarchy Process (AHP), which is one of the most used Multi-Criteria Decision Making (MCDM) procedures, the most profitable answer is determined from the three answers achieved in the optimization process. The results showed a good performance of the system. In PESR optimization, which is selected as the best system size, values of 32.96%, 17.25% and 14.79% was achieved for AOCR, PESR and CERR, respectively. Key words: CCHP system, Cost saving, Primary energy saving, Emission reduction, Optimal sizing, PV/T system
3
1. Introduction The world has faced an increasing demand for energy in different sectors during the recent decade. Additionally, the use of fossil fuels in the past decades, causing intensified emission of Greenhouse Gases (GHG), has led to troubling environmental issues. As a solution to these challenges, utilization of combined cooling, heating and power (CCHP) systems has been proposed. CCHP systems can offer several advantages for both energy consumers and suppliers. A CCHP system can simultaneously meet electricity, cooling and heating needs, providing a large opportunity for energy saving [1], interact with the main grid to participate in demand response and peak shaving programs and ease the integration of renewable energy resources [2]. Also, with reduced primary energy consumption in CCHP systems compared with conventional energy systems, less GHG is emitted to the environment [3]. Energy demand in building sector, as well as other sectors, has grown significantly. Building sector accounts for about 35% of the total energy consumption of the world [4]. On the other hand, buildings have a great potential for energy saving and emission reduction, where it has been predicted that 25% of the CO2 emission reduction in 2030 will come from the building sector [5]. Therefore, integration of CCHP systems, as efficient Distributed Generation (DG) facilities, into buildings, is an undeniable necessity for future energy networks of countries. Development of CCHP systems has drawn a significant attention in researches during recent years. There have been two major approaches in the literature related to optimization of CHP and CCHP systems. Some papers have worked on optimal component sizing of CCHP systems under certain conditions. For instance, Hanafizadeh et al. [6] conducted a study to find the optimal size of CCHP system for commercial and office buildings in Tehran, Iran. Akbari et al. [7] focused on 4
designing a building’s energy system under demand, costs and prices uncertainties by means of robust optimization. Li et al. [8] presented optimization of CCHP system design from three viewpoints for hotels, offices and residential buildings in Dalian (China). Sayyaadi and Abdollahi [9] presented a multi-objective optimization for sizing of a small-scale CCHP system based on three objective functions including the exergetic efficiency, total levelized cost rate of the system product and the cost rate of environmental. On the other hand, some others have studied the optimal operation and energy management of CHP and CCHP systems with regards to predefined constraints. For instance, Rodríguez et al. [10] assessed the performance of hybrid systems composed of solar thermal collectors, photovoltaic panels and natural gas internal combustion engines by the TRNSYS 17 software. Ranjbar et al. [11] presented an evolutionary programming based approach to evaluate the effect of combining fuel cell power plants and wind (a hybrid CHP system) on the operational and performance cost. Liu et al. [12] analyzed the hourly operation of a CCHP system coupled with ground source heat pump under variable loads. Chen et al. [13] studied the performance of a hybrid residential CCHP system based on fuel cell and solar technologies. Several other papers have focused on CCHP systems. Wei et al. [14] proposed a multi-objective optimization model to maximize the energy saving ratio and minimize the energy costs of a micro-CCHP system. Non-dominated Sorting Genetic Algorithm-II was used to identify a series of compromised optimal operation strategies with different operational parameters. Abdollahi and Meratizaman [15] performed a multi-objective optimization to design a small-scale distributed CCHP system. Therefore, three objective functions were considered: exergetic efficiency, total levelized cost rate of the system product and the cost rate of environmental impact. Li et al. [16] analyzed the effect of adding a CCHP system to residential and office buildings. Genetic algorithm was used to solve the 5
problem to save energy, reduce the total cost, and attain excellent environmental performance. Ameri and Besharati [17] proposed a Mixed Integer Linear Programming (MILP) model to determine the optimal capacity and operation of seven CCHP systems in a residential complex in Tehran. The results showed that using an optimal CCHP system integrated with PV panels leads to 40.8% reduction of energy costs and 38.7% reduction of the primary energy consumption. Yang et al. [18] presented a study about district-scale distributed energy resource systems. A MILP model is constructed that can achieve simultaneous optimization of siting, sizing and scheduling. Ascione et al. [19] carried out a multi-objective optimization for energy retrofitting of a hospital to minimize investment cost, primary energy consumption and global cost. It was found from the results that a reduction up to 12.2% in primary energy consumption and up to 24.5% in global cost can be achieved. Ebrahimi and Keshavarz [20] determined the optimum orientation and size of a solar collector for integration with a CCHP system. Maximum rectangle method was applied to find the optimal size of the prime mover. The solar collector specifications were determined in five different climates. Farahnak et al. [21] developed an optimization algorithm to find the best operation point of the Power Generation Unit (PGU) at minimum energy cost. Different sizes of PGU and building were considered and primary energy saving and simple pay-back ratio were evaluated. Braslavsky et al. [22] presented optimal options for distributed energy resource (DER) technologies to reduce greenhouse gas emissions in a large shopping center. As a result, a CCHP system in conjunction with PV, can deliver up to 72% reductions in emissions. Zeng et al. [23] provided a model for coupling of a CCHP system and ground source heat pump (GSHP) based on environment, economy and energy criteria simultaneously and genetic algorithm is used to solve the problem. Guo et al. [24] presented a twostage optimal planning and design method for a CCHP microgrid that 6
simultaneously minimizes the total net present cost and carbon dioxide emission. On the first stage, the optimal design problem was solved using Non-dominated Sorting Genetic Algorithm II (NSGA-II) and on the second stage, a MILP was applied to solve the economic dispatch problem. Sanaye and Sarrafi [25] conducted the energy, exergy and economy optimization of a solar-based CCHP system
containing
conventional
photovoltaic
(PV),
concentrated
photovoltaic/thermal (CPVT), and evacuated tube (ET) collectors. Li et al. [26] modelled a CCHP system to investigate its annual total cost reduction, primary energy saving, and carbon dioxide emission reduction with respect to a reference system under five different operation strategies. Office and residential buildings were selected for the case study of the research. There are some papers in the literature that consider the integration of solar energy into CCHP systems. These all have analyzed the integration of photovoltaic panels and/or solar thermal collectors into conventional CHP components. Application of photovoltaic panels and solar collectors separately, would not lead to a renewable CHP mechanism. In this paper, the use of PV/T panels, as a renewable CHP component, is suggested to be coupled with common CHP components. Hence, not only a novel hybrid CHP system, including both renewable and non-renewable sources of energy, is available, but also a great saving in roof top area utilization for solar panels is achieved and more solar energy could be captured. Therefore, a hybrid CCHP system is proposed and optimal components sizes are determined based on cost, energy consumption and emission considerations. The paper is comprised of six sections. The problem description and the diagram of the system is presented in section 2. Mathematical model is presented in section 3. The optimization approach is described in section 4. Results of the simulation and optimization are presented and discussed in section 5. Finally, the conclusion of the paper is presented in section 6. 7
2. Problem description 2.1. System configuration A conventional CCHP system consists of a power generation unit (PGU), auxiliary boiler and heat-activated cooling component. In this paper, a novel hybrid CCHP system is modelled and optimized. The configuration of the hybrid CCHP system is illustrated in Fig. 1. In this system, an internal combustion engine (ICE) is considered as the main PGU and a renewable combined heat and power device, i.e. photovoltaic/thermal (PV/T) panel, is integrated with the PGU. Both ICE and PV/T provide heat and power for the building. A natural gas-fired boiler and the grid act as heat and power backups, respectively. Combination of absorption chiller (AC) and electric chiller (EC) is considered to provide cooling for the building. To model the partial load operation of the ICE, it is assumed that ICE follows the electrical demand of the building and therefore, it does not have any power transaction with the grid. In contrast, PV/T can feed both load and the gird.
8
Figure 1. CCHP system schematic
Technical characteristics of the system components are provided in Table 1. The data used for PV/T panels, including PV/T reference electrical efficiency, temperature coefficient and heat loss coefficient, is derived from FOTOTHERM Cs panels with a nominal power of 250W [27]. The ICE electrical efficiency coefficients are used to determine the electrical efficiency of the ICE in partial load performance. The average efficiency of Iran grid is considered which is about 32%. Table 1. Technical characteristics of the system components
PV/T reference electrical efficiency (%)
15.5
Temperature coefficient (% / °C)
0.43
2
Heat loss coefficient (W/m .K)
9.12 9
DC/AC inverter efficiency (%) ICE electrical efficiency coefficients (%) Heat recovery system efficiency (%)
90 a= 0.1 b= 0.4 c= -0.2 80
Boiler efficiency (%)
80
Grid efficiency (%)
32
COP of absorption chiller (Whth/Whth)
0.7
COP of electric chiller (Whth/Whel)
3
3. CCHP system model The mathematical model of the CCHP system was implemented in MATLAB. The goal is to find the optimal capacity of the system components with respect to predefined objective functions. The decision variables are nominal capacity of the ICE and AC (in kW) as well as the number of PV/T panels. The nominal capacity of the boiler and EC (in kW) are dependent variables and are determined based on decision variables. Table 2 presents the considered range for main variables. Table 2. Range of main variables of the problem
PICE ,nom (kW)
100-500
Q AC ,nom (kW)
100-900 0-250
N PVT (#)
3.1. Constraints The total energy demand of the building is in the form of heat and electricity. Electricity is needed to meet the electrical load and the power fed to the EC, while
10
heat demand is for building heating and input heat of the AC. Therefore, it can be written as: Pdem (t ) PL (t )
Q EC (t ) COPEC
(1)
Q AC (t ) COPAC
(2)
Qdem (t ) Q H (t )
As stated previously, the ICE operates in electrical load following mode. Therefore, in some time steps, it may operate in partial load and so that the electrical efficiency would be reduced. Partial load ratio is defined to calculate the ratio of load to the nominal capacity: PLR (t )
Pdem (t ) 1 PICE ,nom
(3)
The ICE efficiency is a function of partial load ratio [16]: ICE (t ) a b .PLR (t ) c .PLR 2 (t )
(4)
Too low PLR values result in low electric efficiency. Hence, an on/off threshold should be defined too prohibit the ICE to work in very low load ratios. This threshold is set as 25% and therefore, the generated power by the ICE is calculated as follows: 0 0 PLR 25% PICE (t ) Pdem (t ) 25% PLR 100% P PLR 100% ICE , nom
(5)
Then, the fuel consumption of the ICE can be found: Q f (t )
PICE (t )
11
ICE
(6)
The recovered heat is a function of the ICE and heat recovery system efficiencies. So, the heat is found by the following equation: Q ICE (t ) HRS 1 ICE (t ) Qf (t )
(7)
Power generation of solar panel is a function of its effective area. The area of each PV/T panel, as in the company catalog, is about 1.6m2. Hence, total area of panels is found: APVT 1.6N PVT
(8)
The generated power by the PV/T panels can be calculated as follows: PPVT (t ) APVT DC / ACPVT (t )G PoA (t )
(9)
In PV/T panels, the circulating water acts as coolant and can reduce the cell temperature, so that the reduction of efficiency can somewhat be avoided. But, in this paper, this positive effect is not considered and therefore, it is assumed that an increase in cell temperature will cause an efficiency drop. The cell efficiency in each time step is calculated as: PVT (t ) ref 1 Tcell (t ) T ref
(10)
where, T ref is the reference temperature equal to 25°C. Different relations have been proposed for the calculation of the cell temperature. In order to do this, equation (11) is used which is an empirical relation for poly-crystalline silicon cells [28]: Tc (t ) 30 0.0175 G PoA (t ) 300 1.14 T a (t ) 25
(11)
The heat generated by PV/T panels can be determined as [28,29]: Q PVT (t ) APVT G PoA (t )( ) 1 PVT (t ) U L Tcell (t ) T a (t )
where, ( ) is considered 0.9 as in [30]. 12
(12)
Both AC and EC should provide cooling for the building: Q EC (t ) Q AC (t ) QC (t )
(13)
But it is considered that the AC is prior to the EC in cooling provision. It means, in cases that the cooling load is less than the nominal power of the AC, the EC will not operate: Q AC (t ) QC (t )
(14)
Q EC (t ) 0
(15)
For the time steps that the cooling load exceeds the AC nominal power, the deficiency is met by the EC: Q AC (t ) Q AC ,nom
(16)
Q EC (t ) QC (t ) Q AC (t )
(17)
After the whole year simulation, the nominal power of the EC can be found: Q EC ,nom max Q EC
(18)
The recovered heat of CHP components, i.e. ICE and PV/T, and the boiler heat have to meet the heat demand of the building stated in equation (2): QICE (t ) Q PVT (t ) Q B (t ) Qdem (t )
(19)
If the generated heat by the ICE and PV/T is sufficient to meet the heat demand, then the boiler will not operate. In this case, the surplus heat of the ICE, if it exists, is exhausted. When the ICE and PV/T heat is less than the demand, the boiler will cover the deficiency. Therefore, after a whole year simulation, the nominal capacity of the boiler can be found as follows:
13
Q B,nom max Q B
(20)
The fuel consumed by the boiler is determined as: Q f ,B (t )
Q B (t )
B
(21)
Then, the total fuel consumed by the CCHP system can be found: Qf ,Total Q f Q f ,B
(22)
The total electricity demand of the building stated in equation (1), is met by the ICE, PV/T and grid: PICE (t ) PPVT load (t ) Pgrid (t ) Pdem (t )
(23)
Since the system operates in following electrical load mode, all power generated by the ICE is fed to the load. In cases that the ICE power is not enough to meet the load, PV/T power will provide the deficiency. If both ICE and PV/T are not able to meet the load, electricity will be imported from the grid. Besides, excess power of the PV/T, if it exists, will be sold to the grid: PPVT (t ) PPVT load (t ) PPVT grid (t )
(24)
3.2. Objective functions Three different objective functions related to economy, energy saving and emissions are defined for optimization. All objective functions are defined as a comparison with the reference system which includes a boiler, an EC and the grid. Each objective function is optimized separately in a single objective optimization approach and the results are compared with each other.
14
The first objective function is Annual Operating Cost Ratio (AOCR) and is defined as: AOC CCHP AOCR 1 AOC ref
100
(25)
where, AOC CCHP and AOC ref are annual operating cost of the CCHP and reference system, respectively. AOC comprises operating and maintenance costs of system components and utility power and natural gas charges. For the CCHP system, the revenue of exporting PV/T power to the grid will be subtracted from the total costs. Therefore, AOC CCHP and AOC ref can be written as: 8760 8760 8760 AOC CCHP C O &M ,i E i (t ) Pgrid (t ) C grid NG (t ) C ng i t 1 t 1 t 1
8760 PPVT grid (t ) C PVT t 1 8760 8760 8760 AOC ref C O &M ,i E i (t ) Pgrid ,ref (t ) C grid NG ref (t ) C ng i t 1 t 1 t 1
(26)
(27)
where, NG ref is the natural gas consumption of the boiler in reference mode to meet the heating load and Pgrid ,ref is the total electricity consumption in reference mode to meet both electrical load and power needed for the EC to provide the cooling load. The O&M costs of the system are presented in Table 3. For the CCHP system, O&M costs of all five components are calculated, while for the reference system, the costs of ICE, PV/T and AC are excluded. Time-of-use electricity tariff, natural gas charge and PV/T feed-in-tariff are also presented in Table 3. Table 3. Economic details of the system
Operating and maintenance costs ($/kWh) 15
Internal combustion engine
0.02
PV/T panel
0.01
Boiler
0.008
Absorption chiller
0.01
Electric chiller
0.008
Time-of-use electricity tariff ($/kWh) Hours: 1-7, 24
0.023
Hours: 8-19
0.047
Hours: 20-23
0.094
Natural gas charge ($/m3)
0.043
PV/T feed-in-tariff ($/kWh)
0.26
The second objective function is defined to assess the energy consumption of the CCHP system in comparison with the reference system. Primary Energy Saving Ratio (PESR) is defined as: PEC CCHP PESR 1 PEC ref
100
(28)
where, PEC CCHP and PEC ref are primary energy consumption of the building in CCHP and reference mode, respectively and are calculated as follows: 8760 8760
PEC CCHP Q f ,Total (t )
P t 1
t 1
8760
PEC ref
PL (t ) t 1
grid
grid
t 1
16
B
(29)
grid
8760
Q H (t )
(t )
8760
Q t 1
C
(t )
COPEC
(30)
The third objective function of the optimization is Carbon Emission Reduction Ratio (CERR) and is defined to compare the Carbon emissions of the CCHP system with the reference system. CERR is written as: CE CERR 1 CCHP CE ref
100
(31)
where, CE CCHP and CE ref are Carbon emissions of CCHP and reference system, respectively and are determined as follows: 8760
8760
t 1
t 1
CE CCHP EFgrid Pgrid (t ) EFng Q f ,Total (t ) 8760 Q (t ) CE ref EFgrid PL (t ) C COPEC t 1
EFgrid
8760 Q H (t ) EF ng t 1 B
(32)
(33)
and EFng are the emission factors of grid electricity and natural gas
consumption, respectively, and can be found on Table 4. Table 4. Carbon emission factors [31-32]
Grid electricity EF (kg/kWh)
0.598
Natural gas EF (kg/kWh)
0.202
4. Optimization approach The model is implemented in MATLAB and optimized using genetic algorithm (GA). The details of the algorithm are presented in Table 5. All three objective functions are optimized separately in three single objective GA approaches, while in each optimization procedure, the two other values are calculated too in order to select the best answer. For instance, while optimizing AOCR, PESR and CERR are 17
also calculated for all individuals of the population. As AOCR is optimized, the answer in the population with maximum value of PESR and CERR is selected. This will be repeated for PESR and CERR optimizations. Therefore, three different answers, each with different values of AOCR, PESR and CERR, are obtained. Finally, using Analytical Hierarchy Process (AHP), the best answer is selected [33]. The AHP process is illustrated in Fig. 2. Table 5. Genetic algorithm characteristics
Population size
50
Number of generations
100
Crossover percentage (%)
80
Mutation percentage (%)
30
Selection method
Roulette Wheel
Selection pressure
8
Figure 2. AHP process for best answer selection
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5. Results and discussion 5.1. Case study The model was applied to a case study which is a four story commercial building located near Tehran, Iran’s capital city. The useful area of the building is almost 4000 m2 and the official working hours are 9 to 13 and 15 to 23. The height of the building is approximately 12 m, and therefore, the surface area to volume ratio is calculated about 0.306 1/m. In addition, sufficient rooftop area is available to easily install up to 250 solar panels. The electrical, cooling and heating load of the building for a typical winter and summer day is illustrated in Fig. 3. As Iran enjoys a good potential of solar energy utilization, solar panels are good alternatives to be integrated into the buildings. Iran, located in the world’s Sun Belt, has an annual average of sun radiation about 20–30 MJ/m2 that is even higher in the central regions [34]. The solar radiation data, extracted from the nearest meteorological station to the case study building, show that the region enjoys an average 3000 sunny hours per year, which is a great potential. The meteorological data including solar irradiation and ambient temperature is extracted from a meteorological file in TRNSYS software. Fig. 4 illustrates the Plane-of-Array irradiation.
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Typical Winter
Typical Summer
Electrical Load (kW)
300 250 200 150 100 50 0
Hour
Figure 3.a. A typical daily electrical load profile of the building
Thermal Load (kW)
Typical Winter Heating
Typical Summer Cooling
1000 900 800 700 600 500 400 300 200 100 0
Hour
Figure 3.b. A typical heating and cooling load profile of the building
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Typical Winter
Typical Summer
PoA Irradiation (kW/m2)
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
Hour
Figure 4. Plane of array solar irradiation
5.2. Optimization results As stated previously, three answers are obtained as a result of three optimization approaches. Details of the answers including component sizes and objective functions values are presented in Table 6. Table 6. Details of three answers of the optimization
AOCR opt. PESR opt. CERR opt.
PICE ,nom
N PV /T
Q B ,nom
Q AC .nom
Q EC ,nom
AOCR
PESR
CERR
(kW)
(#)
(kW)
(kW)
(kW)
(%)
(%)
(%)
330
250
322
501
388
57.19
6.89
2.64
104
250
164
215
674
32.96
17.25
14.79
100
250
166
206
683
31.76
17.23
14.85
From Table 6, it can be seen that in all three answers, the number of PV/T panels obtained is the maximum allowable value, i.e. 250. In CERR optimization, ICE capacity is determined as its minimum allowable value, while in PESR 21
optimization, it is near the minimum. Another point is that in PESR and CERR optimization, the results are approximately alike and slight differences in the capacities can be seen. First answer shows a great performance in AOCR value, while in PESR and CERR is not suitable. Second and third answers appear good in the values of PESR and CERR while having a considerable performance in AOCR. 5.2.1. Decision making The task is to find the most acceptable and profitable answer via a decision making process. In order to do this, AHP is used. From Fig. 2, there are three choices while having three criteria. The first step is to determine the priority of criteria and their weights. It is assumed that all three criteria have same importance and therefore, their weights are considered equal. Hence, the weight matrix of criteria is:
W criteria
1 3 1 3 1 3
(34)
The next step is developing the comparison matrices for the answers based on each criterion. Regarding the values in Table 6, the comparison matrices are written as:
Z AOCR
5 5 1 1 1 8 1 8 1 1 9 1 9 1 5 1 2 ; Z PESR 8 1 2 ; Z CERR 9 1 1 2 1 5 1 2 1 8 1 2 1 9 2 1
(35)
Finally, determining the weight local priority, and using W criteria , the overall priority is calculated. The final result of AHP is depicted in Fig. 5. The hierarchy consistency ratio (CR) is calculated as 2.56% which is acceptable.
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AOCR
PESR
CERR
0.45 0.4
0.3745 0.3529
Overall Weight
0.35 0.3
0.2725
0.25 0.2 0.15 0.1 0.05 0 Ans. 1
Ans. 2
Ans. 3
Figure 5. AHP overall weights for the three answer of optimization
From the Fig. 5, it is clear that answer 2 is the most profitable answer, due to its overall weight. It means that answer 2 is the best combination for the CCHP system components capacities and will provide 32.96%, 17.25% and 14.79% cost saving, energy saving and emission reduction, respectively, which is the best achieved outcome of the CCHP system. In the following, the detailed performance of answer 2 is presented. 5.2.2. System performance The performance of the CCHP system consists of different aspects. Providing power, heating and cooling should be analyzed and the performance of the components is studied. The system provides power for the building by the ICE, PV/T and grid. Fig. 6 illustrates the breakdown of the electrical demand, including both electrical load on Fig. 3.a and the power needed to run the EC, provided by different sources. In Fig. 6.a, since there is no need for cooling in a winter day, the electrical load profile and the demand are alike. It can be seen that in this profile, most of the 23
demand is met by the ICE and in peak hours, the grid power is entered. A very small portion of the load in just three hours of the day is provided by the PV/T. In Fig. 6.b, it can be seen that there is a difference between the demand and electrical load profiles due to the power needed for the EC. For the initial hours of the day, i.e. 1 to 9, the AC capacity (215 kW) is sufficient to meet the cooling load showed on Fig. 3.b, while by increasing the cooling demand in following hours, the EC have to provide the deficiency. Here, the ICE operates in full load or near full load in almost all hours of the day, but a considerable portion of power is imported from the grid in peak load hours. With increased solar radiation, the share of the PV/T has also increased compared with the winter day. On the whole, in the winter day, about 84.37% of the power demand is provided by the ICE and almost the rest by the grid (the share solar panels is only 0.24%). For the summer day this is totally different. About 52.7% of the demand is met by the grid, while the share of ICE and PV/T is 43% and 4.3%, respectively.
Electrical Demand (kW)
ICE
PVT
Grid
Electrical Load
200 180 160 140 120 100 80 60 40 20 0
Hour
Figure 6.a. Electrical demand (winter day)
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ICE
PVT
Grid
Electrical Load
Electrical Demand (kW)
600 500 400 300 200 100 0
Hour
Figure 6.b. Electrical demand (summer day)
The heating load of the building in winter day is provided by the ICE and PV/T as well as the boiler. Fig. 7 illustrates the share of each component in heating provision. The ICE and PV/T are participated in providing heating in all hours of the day, while the boiler is entered in peak load hours. As the dispatch strategy is set on electrical load following, the exhausted heat from ICE is noticeable. Although this can be improved by adding the system a thermal storage tank or changing the dispatch strategy to thermal load following, this is out of the scope of this paper. Totally, about 71% of the heat demand is provided by the ICE. The next is about 16% PV/T heat generation, while the share of the boiler is approximately 13%.
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ICE
PVT
Boiler
400
Heating Load (kW)
350 300 250 200 150 100 50 0
Hour
Figure 7. The heating load profile provided by different sources
The cooling task of the building is done by the AC and EC. The breakdown of the cooling load in a summer day, is shown in Fig. 8. In hours 1-7 and 24, EC is turned off and AC provides cooling for the building. On the other hand, in hours 8-23, although the AC is operating in full load mode, its capacity is not sufficient for the cooling load and the EC covers the rest of the demand.
Cooling Load (kW)
AC
EC
1000 900 800 700 600 500 400 300 200 100 0
Hour
Figure 8. The cooling load profile provided by different sources
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Another important factor is the power transactions of the CCHP system and the grid. As stated previously, the ICE does not send power to the grid and the only power exporting component is PV/T. Fig. 9 illustrates the power transactions in the typical winter and summer day. In the winter day, almost all of the generated solar power is sold to the grid and only in hours 8, 9 and 17 solar power is sent to the load. In contrast, in the summer day, although the generated solar power is a lot more than in the winter day, most of it is sent to the load and a small portion is sold to the grid. This is due the model strategy, where the self-sufficiency of the building is considered more important than exporting power to the grid; hence, with increased building demand, the sold power to the grid decreases. Totally in a whole year, about 549202 kWh electricity is bought from the grid while about 22206 kWh is sold.
Grid Import
Solar export
Total Solar
Electrical Power (kW)
100 80 60 40 20 0 -20 -40
Hour
Figure 9.a. Power transaction of the system and the grid (winter day)
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Grid Import
Solar export
Total Solar
Electrical Power (kW)
400 350 300 250 200 150 100 50 0 -50
Hour
Figure 9.b. Power transaction of the system and the grid (summer day)
Finally, the partial load performance of the ICE should be analyzed, as low values of PLR lead to low efficiency. For all operating hours of the ICE in a year, PLR values are extracted. Fig. 10 illustrates the relative frequency of the PLR for full and partial load operation intervals. For about 58% of the operating hours, the ICE operates in full load which is a considerable share. Besides, in a cumulative look, about 78% of the year, the PLR is more than 80% that means a good ICE performance.
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Relative Frequency (%)
70 60 50 40 30 20 10 0 100
[90,100)
[80,90)
[70,80)
[60,70)
[50,60)
[40,50)
[0,40)
PLR interval (%)
Figure 10. Relative frequency of PLR value
6. Conclusion Modelling and optimization of the size and performance of CHP and CCHP systems, have attracted lots of attention in recent years. In this paper, a novel hybrid CCHP system based on renewable and non-renewable energy sources is modelled and optimized based on economy, energy and emission objective functions for a commercial building. In three different approaches, genetic algorithm is conducted to find three different answers. Then, through a multicriteria decision making approach (AHP), the most valuable answer is selected and its energy performance is analyzed. In AOCR optimization, more than 57% annual operating cost saving is obtained, while the energy saving and emission reduction was not considerable. In PESR and CERR optimizations, the results were approximately alike. Although the value of cost saving was reduced, a significant growth in energy saving and emission reduction was achieved. A notable point is that in all three answers, the number of PV/T panels was found as its maximum allowable value, which asserts the importance of solar panels in the performance and structure of the CCHP system. 29
Applying AHP, the PESR optimization answer gained a score of 0.3745 which is the highest weight among answers. So the performance of this answer was analyzed. In a typical winter day, most of the electricity demand of the building was met by the ICE and only on peak hours of the night, power was imported from the grid. On the other hand, in a typical summer, due to the increase in power demand because of the EC, the share of grid electricity was increased. For heating demand in a winter day, most of the load was served by the ICE and PV/T and only 13% of the load was provided by the boiler. The cooling load provision in a summer day, was approximately shared equally between the AC and EC, where AC provided about 46% of the cooling and EC met the rest. On the whole, concluded from the research results, the advantages of the CCHP system utilization are clear. It not only can bring significant economic profits, but also can ease the energy savings and CO2 emission reductions. Therefore, CCHP systems are one of the best solutions for the energy crisis that the world has faced.
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Nomenclature Acronyms CCHP Combined Cooling, Heating and Power CHP Combined Heat and Power AOCR Annual Operating Cost Ratio PESR Primary Energy Saving Ratio CERR Carbon Emission Reduction Ratio PLR Partial Load Ratio AHP Analytical Hierarchy Process MCDM Multi Criteria Decision Making PGU Power Generation Unit GA Genetic Algorithm Symbols Electrical power (kW) P Thermal power (kW) Q Efficiency (%) Time step (h) t Area of solar panels (m2) A Number of solar panels N Solar irradiance (kW/m2) G Temperature coefficient (power) (%/°C) Temperature (°C) T Heat loss coefficient (kW/m2.K) UL Coefficient of performance COP Cost ($) C Partial load ratio (%) PLR Annual operating cost ($) AOC Generated energy (kWh) E Annual natural gas consumption (m3) NG CE Carbon emission (kg) Emission factor (kg/kWh) EF W Weight matrix of AHP criteria Weight matrix of AHP alternatives Z
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Subscripts ICE PVT B AC EC nom f e HRS PoA ref cell a C H L dem grid DC / AC o &m
i ng
PVT load PVT grid criteria CCHP
Internal combustion engine Photovoltaic/thermal Boiler Absorption Chiller Electric Chiller Nominal capacity Fuel Electrical Heat recovery system Plane of Array Reference value Solar cell Ambient Cooling load Heating load Electrical load Demand Grid DC/AC inverter Operation and maintenance cost Index of components Natural gas PV/T power to load PV/T power to grid AHP criteria CCHP system
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