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Modelling and Optimal Design of Gas Engine CCHP System in Hospital

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The combined heating and power (CHP) system and the combined cooling, heating and power (CCHP) system have attracted great attention during the last decade. However, many CHP systems don’t perform well in the actual operation. This paper presents a complete hierarchical modeling tool of the gas engine CHP/CCHP system which is built on the software Dymola. Meanwhile, a gas-engine CHP hybrid energy system serving a hospital in Shanghai is studied as a case. To validate the accuracy of newly-built models, the operating data of the CHP part of the system in 2017 is compared with the simulation results, it is found that the minimum error is 2.1%, and the maximum error is 7.0%. Then, the original gas engine CHP hybrid energy system is reconstructed to a gas engine CCHP system. To analyze the feasibility of the optimal design, the conventional energy supply system which was used in the hospital before 2013, the original gas engine CHP hybrid energy system and the optimized gas engine CCHP system are modeled and simulated. From the simulation results, it is found that the primary energy ratio is increased from 72.55% to 133.37%, the payback period of investment is decreased from nearly 11.8 years to 3.9 years, and the CO2 emissions reduction rate is increased from 4.83% to 93.72%. Therefore, the optimization scheme is feasible.
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Modelling and Optimal Design of Gas Engine CCHP System in
Hospital
Qian Zheng Zhang Xuemei* Li Zhiang
School of Mechanical Engineering, Tongji University, China
Abstract
The combined heating and power (CHP) system and the
combined cooling, heating and power (CCHP) system
have attracted great attention during the last decade.
However, many CHP systems don’t perform well in the
actual operation. This paper presents a complete
hierarchical modeling tool of the gas engine CHP/CCHP
system which is built on the software Dymola.
Meanwhile, a gas-engine CHP hybrid energy system
serving a hospital in Shanghai is studied as a case. To
validate the accuracy of newly-built models, the
operating data of the CHP part of the system in 2017 is
compared with the simulation results, it is found that the
minimum error is 2.1%, and the maximum error is 7.0%.
Then, the original gas engine CHP hybrid energy system
is reconstructed to a gas engine CCHP system. To
analyze the feasibility of the optimal design, the
conventional energy supply system which was used in
the hospital before 2013, the original gas engine CHP
hybrid energy system and the optimized gas engine
CCHP system are modeled and simulated. From the
simulation results, it is found that the primary energy
ratio is increased from 72.55% to 133.37%, the payback
period of investment is decreased from nearly 11.8 years
to 3.9 years, and the CO2 emissions reduction rate is
increased from 4.83% to 93.72%. Therefore, the
optimization scheme is feasible.
Keywords: Gas engine, Combined cooling heating and
power, System model, Dynamic simulation, Optimal
design
1 Introduction
Nowadays, the energy crisis and the environmental
impact of fossil fuels have been increasingly serious
globally (Moussawi et al, 2016; Wei et al, 2016; Ameri
et al, 2016; Yousefiet et al, 2017; Zheng et al, 2018;
Jiang et al, 2018). Efficient technology for energy
conservation is urgently needed to ensure energy
supplies and reduce environmental emissions (Jiang et
al, 2018). Combined heating and power (CHP) system
and combined cooling, heating and power (CCHP)
system have received widespread attention due to the
advantage of substantial reliability, energy-saving,
environmental friendliness and cost-saving (Wei et al,
2016; Zheng et al, 2018; Jiang et al, 2018; Das et al,
2018; Afzali et al, 2018; Zhang et al, 2018). CCHP
system is defined as an effective energy system that
generates cooling, heating and power simultaneously,
while CHP system removes cooling from the list ,
mainly through the cascade utilization of energy (Wei et
al, 2016; Kavvadias et al, 2018). In recent years, CHP
and CCHP have been introduced to small-medium scale
places, such as hospitals, hotels, domestic houses and
office buildings (Wei et al, 2016; Zhang et al, 2018;
Santo, 2012; Kavvadias et al, 2010).
CHP has developed rapidly since the first CHP
energy supply technology was introduced in the 1990s.
However, it is worth noting that many CHP systems
suffer from the uncertainty of the actual economic
results. Because these CHP systems only provide a
fraction of energy for buildings, such as hot water and a
part of power, while excess energy is still fed by the
conventional energy system. A possible solution is
optimizing the CHP hybrid energy system to a CCHP
system.
During the last decade, many researchers used system
modeling and simulation to optimize the performance
and the design procedure of CHP and CCHP systems.
Wei et al. proposed a multi-objective optimization
model to provide a guiding principle for CCHP system
optimization (Wei et al, 2016). They adopted software
MATLAB and TRNSYS to identify a series of
compromised optimal operation strategies with different
operational parameters using Non-dominated Sorting
Genetic Algorithm-II (NSGA- II). Ameri et al.
described a mixed integer linear programming (MILP)
model to determine the optimal capacity and operation
of seven CCHP systems in eastern Tehran (Iran) (Ameri
et al, 2016). Results showed that compared with
generating heat by boilers and purchasing electricity
from the local grid, the optimal CCHP system was able
to save costs and reduce CO2 emissions. A mixed integer
non-linear programming (MINLP) model was
developed by Zheng et al. to achieve multi-objective
optimization of a smart micro-grid using the modeling
environment GAMS (Zheng et al, 2018). Results
described by four scenarios showed that net present
value, primary energy saving and CO2 emissions were
reduced significantly by installing roof-top PV, ground
source heat pump, natural gas-based CCHP and storage
systems. Espirito Santo proposed a computational
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105
hourly profile simulation methodology (Santo, 2012)
and performed an integrated thermal system simulation
(Santo, 2014) using software COGMCI. An effective
method for the design optimization of CCHP coupled
multi-energy system was developed by Lu et al. (Lu et
al, 2018). They established a correlation model for
configuration and operation optimization based on a bi-
level model construction method, proposed a solution
method, and developed an optimization tool using
MATLAB. Mago et al. optimized CCHP systems for an
office building in Columbus (USA) following the
thermal load (FEL), the electrical loadFTLand a
hybrid electrical-thermal load (HETS) strategies (Mago
et al, 2009). Results showed that HETS was better than
FEL and FTL. Pagliarini et al. studied the feasibility of
integrating an existing natural gas-fired-boiler central
plant in Parma (Italy) into the CCHP system (Pagliarini
et al, 2012). The space heating and cooling loads were
calculated by TRNSYS. The national policies
supporting CHP were found to have a strong influence
on the results. TRNSYS was also used by Rosato et al.
to simulate the performance of a micro-CHP system and
a conventional system (Rosato et al, 2013). Results
showed that the micro-CHP system could significantly
reduce primary energy consumption, carbon dioxide
emissions and operating costs. Hu et al. proposed a
stochastic multi-objective optimization model to
optimize the CCHP operation strategy for different
climate conditions based on operational cost, primary
energy consumption (PEC) and carbon dioxide
emissions (CDE) and added a higher reliability level of
the probability constraint to it (Hu et al, 2014).
Moreover, an incentive model was developed to support
the multi-objective decision analysis. The feasibility of
integrating air-conditioning system and heat storage
tank into the CCHP system was studied by Li et al. (Li
et al, 2014). They formulated the optimal problem as a
nonlinear programming problem using genetic
algorithm (GA). Furthermore, a sensitivity analysis was
conducted to explore the impact of natural gas prices on
system economics. Jannelli et al. developed a 0-1-
dimensional model of a small-size CCHP based on the
integration of a 20 kW diesel engine and a double-effect
water-LiBr absorption chiller on platform AVLBOOST
(Jannelli et al, 2014). The manufacturer's sample data
was used to validate the performance parameters of the
gas engine under different operating conditions and the
average error was found to be less than 5%. Particle
Swarm Optimization (PSO) was used by Hajabdollahi
et al. to optimize the gas engine CCHP system for the
purpose of comparing a new operational strategy named
variable electric cooling ratio (VER) with constant
electric cooling ratio (CER) for different climates
(Hajabdollahi et al, 2015). Piacentino et al. used a
decision tool to optimize the layout, design and strategy
of a CCHP plant simultaneously in the hotel sector
(Piacentino et al, 2015). In addition, two sensitivity
analyses were performed on tax exemption for the fuel
consumed in high-efficiency cogeneration modeand
on the dynamic behavior of the system. Moussawi et al.
conducted a simulation study using TRNSYS software
for diesel engine-driven CCHP systems used to provide
electricity, space heating, space cooling and sanitary hot
water (SHW) to a typical residential family house in
Beirut (Moussawi et al, 2015). Wang et al. simulated
and evaluated four different gas-engine CCHP systems
applied for a remote island using TRNSYS, and the
results showed that the one adopting the double-effect
absorption chiller and the gas-fired boiler was the best
option (Wang et al, 2016). Based on the environment,
economy and energy criteria simultaneously, Zeng et al.
optimized the CCHPGSHP coupling system model by
GA and demonstrated the practicality of the
optimization model by case analysis (Zeng et al, 2016).
Mat Isa et al. developed a CHP system consisting of
grid-connected photovoltaic (PV), fuel cell and battery,
and performed the techno-economic analysis of the
proposed system using hybrid optimization model for
electric renewable simulation (HOMER) software in
order to assess the feasibility of applying the system for
a hospital building in Malaysia (Isa et al, 2016). Calise
et al. developed a detailed dynamic simulation model of
the CCHP system using TRNSYS and evaluated three
different system operating strategies, namely: Thermal
Load Tracking mode (TLT), Maximum Power Thermal
Load Tracking mode (MPTLT) and Electricity Load
Tracking mode (ELT) (Calise et al, 2017). Yang et al.
proposed a gas turbine-driven CCHP system combining
solar thermal energy and compressed air energy storage
(S-CAES) and developed system off-design models. In
comparison with the corresponding optimized CCHP
system without S-CAES, the system with S-CAES
performed better (Yang et al, 2017).
However, few models are built with hierarchical architecture,
so models and sub-models lack reusability and are difficult
to debug separately. In this study, the gas engine
CHP/CCHP system is modeled and dynamically simulated
in Dymola software (“DYMOLA Systems Engineering,
Multi-Engineering Modeling and Simulation based on
Modelica and FMI.) using Modelica language. Based on
the results of dynamic simulation, the feasibility of
optimization from the gas engine CHP hybrid energy
system to the gas engine CCHP system will be discussed.
The main contributions in this research are summarized as
follows: (1) Simulation models of important equipment and
the whole system of CHP hybrid energy system and CCHP
system are built and validated. (2) The original gas engine
CHP hybrid energy system is optimized to a gas engine
CCHP system for a case study. (3) The feasibility of
optimization is analyzed by comparing the performance of
two systems.
This paper is organized as follows: Section 2
describes the case for optimization, presents the
modeling approach and validation results for simulation
models of the important equipment in CHP and CCHP
systems. Section 3 gives the load calculation results and
Modelling and optimal design of gas engine CCHP system in hospital
106 Proceedings of Asian Modelica Conference 2022
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system optimal design for the case building. Section 4
presents the performance evaluation method used in this
study. Section 5 analyzes and compares the performance
of the original gas engine CHP hybrid energy system
and the gas engine CCHP system. The conclusions are
drawn in the last section.
2 Methodology
2.1 Case study
The case building, Ruijin Hospital North, is located
in Jiading District, Shanghai, China, with a floor area of
72000 m2. A conventional energy supply system was
applied to this hospital before 2013. By replacing the hot
water boilers with a 334kW gas engine and two heat
exchangers, the conventional system was optimized to a
gas engine CHP system in October 2013. The energy
supply layouts of the conventional system and the gas
engine CHP system are shown in Figure 1 and Figure 2,
respectively, and the main parameters of system
components are shown in Table 1.
Ruijin Hospital North has stable electrical load and
hot-water heating load, and the former is much higher
than the latter. Due to the current policy that power
generated by self-provided units can only be grid-
connected but not exported to the grid, the gas engine
CHP system applied to this hospital operates in the
"power determined by heat" energy supply mode, i.e.
only the demand for sanitary hot water is considered to
be necessarily met by the CHP unit, based on which the
corresponding generated power output is connected to
the hospital power supply system.
To ensure the stability of power generation and waste
heat output, and to prolong the service life of the
equipment, there are only two states of the gas engine
generator set in actual operation: rated state (100% load)
and shutdown (0% load). In consideration of the
resulting mismatch between the stable sanitary hot water
input on the supply side and the ever-changing hot-water
heating load on the demand side, a heat storage tank was
installed to control the start and stop of the gas engine
according to its water level. The gas engine will be
started when the water level drops to 0.2 meters and shut
down when the water level rises to 4.8 meters.
Natural gas
Hot water
boiler 1 Hot water
boiler 2 Heating
boiler 2
Heating
boiler 1
Water tank
Building
Hot-water
heating load Cooling
load Heating
load
Electric
chiller 1 Electric
chiller 2
Local grid
Natural
gas flow Hot
water flow
Cooling
flow Electricity
flow
Heating
flow
Figure 1. Energy supply layout of conventional system
Natural gas
Heat
Exchanger 1 334kW
Gas engine Heating
boiler 2
Heating
boiler 1
Water tank
Building
Hot-water
heating load Cooling
load Heating
load
Electric
chiller 1 Electric
chiller 2
Local grid
Electric
load
Natural
gas flow
Cylinder
jacket cooling
water flow
Heating
flow
Hot
water flow Cooling
flow Electricity
flow
Figure 2. Energy supply layout of gas engine CHP system
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Table 1 Main parameters of system components
Affiliated
System
Component
Parameter
Value
Both
Heating
boiler 1&2
Rated heat
supply/kW
2000
Efficiency/%
90
Hot water
tank
Volume/m3
50
Electric
chiller 1&2
Rated cooling
capacity/kW
4515
Rated COP
6.22
Convent-
ional
system
Hot water
boiler 1&2
Rated heat
supply/kW
200
Efficiency/%
90
Gas
engine
CHP
system
334kW gas
engine
Model
Schmitt
334
Rated
electrical
power
generation/kW
334
Rated jacket
water waste
heat/kW
485
Heat
exchanger 1
Flow form
Counter
flow
Quantity
2
Nominal heat
transfer
coefficient/
W/(m2*K)
5.476
Heat transfer
area/m2
15
2.2 Simulation model
Simulation models of individual devices and overall
systems are established in Dymola software by
employing Modelica language. Dymola software
supports hierarchical model composition, libraries of
truly reusable components, connectors and composite
acausal connections. The modeling method of this study
is to build the complete hierarchical simulation model of
systems based on the connection of equipment models
(gas engine generator set model, LiBr absorption chiller
set model, plate type heat exchanger model, electric
chiller set model, hot water tank model and gas boiler
model) and system control models used to control on-
off conditions, operating hours and operating strategies.
Since Modelica Standard Library includes most of the
required equipment models, it is only necessary to build
the additional gas engine model and LiBr absorption
chiller model.
2.2.1 Gas engine
This study mainly focuses on the system's overall
performance. Since the gas engine is just one component
of the whole system, its performance parameters, such
as electrical power, total heat recovery, exhaust gas heat,
coolant heat, mixture heat, fuel input, natural gas
consumption, electrical efficiency, thermal efficiency
and total efficiency, rather than internal structural
parameters, should be mainly concerned. The
performance documentation provided by manufacturers
contains specific performance parameters for different
gas engine models at 50% load, 75% load and 100%
load, based on which the performance parameters of gas
engines operating in the range of 50% load to 100% load
can be calculated by interpolation method. Therefore,
the gas engine performance parameter model consists of
two parts: a performance parameter sheet model used to
store the datasheet of different samples and an
interpolation model used to read the datasheet and
output the corresponding performance parameters
according to the input parameter (electricity demand).
Up to now, this performance parameter sheet model
library has stored datasheets of more than 20 samples of
different brands such as Mannheim, Caterpillar and
Schmitt.
2.2.2 LiBr absorption chiller
There are many types of LiBr absorption chiller, among
which single-effect hot water type and double-effect
flue gas type are used in the present study. The internal
structure of the whole LiBr absorption chiller model is
shown in Figure 3. The LiBr absorption chiller model
also consists of a performance parameter sheet model
and an interpolation model.
Figure 3. Internal structure of the LiBr absorption chiller
model
1. Performance parameter sheet model
The performance parameter sheet model stores the
datasheet of main rated parameters of LiBr absorption
chiller samples, such as cooling capacity, heat
consumption, heat source temperature, inlet and outlet
temperature of cooling water, inlet and outlet
Modelling and optimal design of gas engine CCHP system in hospital
108 Proceedings of Asian Modelica Conference 2022
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temperature of chilled water, COP, etc. Unlike the stable
performance parameters of gas engines at rated
operating conditions, the most important performance
parameter of LiBr absorption chiller, coefficient of
performance (COP), is influenced by several factors and
is therefore given as the COP curve by manufacturers.
For ease of reading, the COP curve is converted into 4
data tables: chilled water temperature correction table,
cooling water temperature correction table, heat source
temperature correction table and cooling capacity
correction table. Each table contains two columns,
where the first column is the independent parameter
(chilled water temperature, cooling water temperature,
heat source temperature, cooling capacity) and the
second column is the corresponding COP.
2. Interpolation model
The input parameter of the interpolation model is the
cooling load, and the output parameters are the actual
cooling capacity, the heat exchange capacity of the
generator and the heat removed by the cooling water.
The specific calculation process is as follows.
The actual COP of LiBr absorption chiller can be
calculated by the following formula, which is provided
in the LiBr absorption chiller technical manual.
(1)
Since the cooling capacity datasheet needs to be read
on the basis of partial load rate, it’s necessary to convert
the cooling load into partial load rate.
The cooling load ( ) can be calculated as follows:
(2)
where, is the flow rate of chilling water, kg/s; is
the specific heat of chilled water, kJ/(kg·°C); and
respectively represent the inlet temperature and set
temperature of chilling water, °C .
The maximum load rate and the minimum
temperature limit of the heat source must be taken into
consideration when calculating the partial load rate
(PLR) of LiBr absorption chiller. PLR is between the
minimum and maximum load rate, and is 0 when the
heat source temperature is lower than its minimum.
Then, the actual heat consumption ( ) of LiBr
absorption chiller can be calculated as follows:
(3)
where, represents the actual cooling capacity
read by the performance parameter sheet model, kW.
The heat removed by the cooling water includes the
heat released by the absorber and the condenser, and the
heat absorbed by the LiBr absorption chiller includes the
heat absorbed by the evaporator and the generator.
Neglecting the heat dissipation of pump, the energy
balance equation can be expressed as:
(4)
where, represents the heat removed by the
cooling water, kW.
2.3 Validation
2.3.1 Actual operation data
Monthly operation data of the case CHP unit in 2017 is
investigated, as shown in Table 2, to validate the
accuracy of models.
Table 2. Operation data of the case CHP unit in 2017
Month
Boot
hour
(h)
Power
generation
(kWh)
Natural gas
consumption
(Nm3)
Thermo-
electric
ratio
1
402
134,165
35,303
1.19
2
357
119,135
35,937
1.04
3
336
112,212
33,010
1.18
4
249
83,103
25,112
1.24
5
181
60,318
17,456
1.20
6
163
54,572
15,717
1.02
7
96
32,129
9,319
1.40
8
121
40,397
11,716
1.42
9
126
42,159
12,253
1.26
10
156
52,028
15,071
1.14
11
174
57,975
16,851
1.13
12
221
73,692
20,925
1.20
Total
2,580
861,885
248,670
1.09
2.3.2 Model validation
As shown in Figure 4 and Figure 5, discrepancies
between the simulation results and operation data are
less than 10% for both system power generation and
natural gas consumption, and thus, the model accuracy
is validated. Therefore, the subsequent results of system
operation characteristics and optimization are expected
to be of sufficient accuracy. An error or robust design
analysis would be required to predict expected accuracy
of additional model predictions.
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Figure 4. Model validation of system power generation
Figure 5. Model validation of natural gas consumption
3 System optimization
3.1 Load calculation of the case building
To optimize the design of system and further simulate it,
its necessary to calculate the hourly cooling load,
heating load and hot water load of the case building.
The specific flow direction and flow distribution of
the hot water are of no importance in modeling, hence
only the hot water load is considered. During the
simulation, the monthly hot water load is distributed to
the hourly load according to the load factor method
(Shan, 1989). The hourly hot water load is shown in
Figure 6.
Figure 6. Hourly hot water load of the case building
The cooling load and heating load are calculated by
the software HDY-SMAD (HDY-SMAD, HVAC
Load Calculation and Analysis Software.”). The hourly
cooling and heating load are shown in Figure 7.
According to the calculation results, the maximum
hourly cooling load and heating load are 9102.63 kW
and 3723.11 kW, respectively.
Figure 7. Hourly cooling and heating load of the building
3.2 Optimal design
To improve the comprehensive performance and
economy of CHP unit, the original gas engine CHP
system is optimized to a gas engine CCHP system. The
CCHP part of optimized CCHP system will provide all
the heating load and hot water load of the hospital, along
with most of the cooling load and electric load. The
remaining part of the cooling load is provided by the
0.0
4.0×104
8.0×104
1.2×105
1.6×105
0.0 4.0×1048.0×1041.2×1051.6×105
Operation data (kWh)
Simulation results (kWh)
+10%
-10%
0
1×104
2×104
3×104
4×104
01×1042×1043×1044×104
Operation data (m3)
Simulation results (m3)
+10%
-10%
0
50
100
150
200
250
300
350
1
549
1097
1645
2193
2741
3289
3837
4385
4933
5481
6029
6577
7125
7673
8221
Load(kWh)
Time(h)
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1
627
1253
1879
2505
3131
3757
4383
5009
5635
6261
6887
7513
8139
Load(kWh)
Time(h)
cooling
heating
Modelling and optimal design of gas engine CCHP system in hospital
110 Proceedings of Asian Modelica Conference 2022
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electric chiller and excess electric load is fed with power
purchased from the local grid.
Fuel gas
flow
Natural gas
Heat
exchanger 1
340kW
Gas
engine
Heat
exchanger 2
4300kW
Gas
engine
Water tank
Building
Hot-water
heating load Cooling
load Heating
load
Electric
chiller
Local grid
Electric
load
Absorption
chiller 1
Absorption
chiller 2
Heating
flow
Natural
gas flow
Cylinder
jacket cooling
water flow
Hot
water flow Cooling
flow Electricity
flow
Figure 8. Energy supply layout of gas engine CCHP
system
Figure 9. Model schematic of gas engine CCHP system
The energy supply layout and model schematic of
optimized CCHP system is shown in Figure 8 and
Figure 9, respectively. A 4300kW gas engine and four
heat exchangers are used to replace heating water boilers,
while two absorption chillers are used to replace an
electric chiller. Main parameters of new system
components are shown in the Table 3. The system
process is as follows. The 334kW gas engine supplies
all the sanitary hot water. In summer, two absorption
chillers are preferred to meet the cooling load of the
hospital, and the shortage is met by the electric
refrigeration unit. The flue gas generated by the
operation of the 4300kW gas engine is passed into the
double-effect flue gas type absorption chiller for cooling,
and the jacket water (rated at 98 °C) generated by it and
the remaining jacket water of the 334kW gas engine are
accessed to the single-effect hot water type absorption
chiller. In winter, the flue gas of the 4300kW gas engine
is introduced into the flue gas-hot water plate heat
exchanger for heating. The jacket water (rated at 98 °C)
generated by it and the remaining jacket water of the
334kW gas engine are also used for heating.
The system follows the hybrid operating mode. The
334kW gas engine is operated at full load in winter and
summer. It is started and stopped according to the water
level in the transition season. The 4300kW gas engine is
only operated in winter and summer and it is controlled
by return water temperature.
Table 3. Main parameters of gas engine and heat
exchanger
Component
Parameter
Value
4300kW gas
engine
Model
Mannheim 4300
Rated electrical power
generation
4300 kW
Flue gas waste heat
2304 kW
Rated jacket water
waste heat
1379 kW
Heat
exchanger 2
Flow form
Counter flow
Quantity
4
Nominal heat transfer
coefficient
2.728
W/(m2K)
Heat transfer area
25 m2
Absorption
chiller 1
Model
BROAD BE300
Type
Double-effect
smoke type
Rated cooling capacity
3489 kW
Absorption
chiller 2
Model
BROAD BDH200
Type
Single-effect hot
water type
Rated cooling capacity
2046 kW
4 Economy, energy efficiency,
environment (3E)
In order to evaluate the comprehensive performance of
gas engine system and gas engine CCHP system, the 3E
analysis (Gao et al, 2022) is conducted in this section.
4.1 Energy efficiency analysis
Primary energy ratio ( ) is adopted to evaluate the
energy efficiency of system. Primary energy ratio is
defined as the ratio between the output energy and the
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input primary energy of the system, and can be
expressed as follows:
(5)
where, and respectively represent the system
electrical power generation and electrical power use
from local grid, kJ. and respectively represent
the system heat supply and cooling capacity, kJ.
represents the system fuel consumption, Nm3.
represents the low calorific value of fuel, kJ.
represents the product of electrical power generation
efficiency and transmission efficiency of local grid, %.
4.2 Economy analysis
Economic analysis is based on two indicators: annual
operating cost and payback period of investment.
System annual operating cost ( ) is the sum of
annual fuel cost ( ), annual electrical power cost ( )
and system maintenance cost ( ), and can be
expressed as follows:
(6)
For reconstruction, system payback period of
investment ( ) is the recovery period of system's
incremental investment ( ), and can be expressed as
follows:
(7)
where, and respectively represent the
annual operating cost of conventional system and
reconstructed system.
4.3 Environment analysis
CO2 emissions per unit capacity ( ) and CO2
emissions reduction rate ( ) are important indicators
for environment analysis, which can be calculated as
follows:
(8)
(9)
(10)
Where, represents the system CO2 emissions, Nm3/s.
and respectively represent the CO2 emission
coefficient of local grid and natural gas. and
respectively represent the CO2 emissions per unit
capacity of conventional system and reconstructed
system, Nm3/kJ. (In engineering calculation and trade
settlement, China stipulates that the volume at a pressure
of 101.325 KPa and a temperature of 293.15 K is
defined as a standard cubic meter, expressed in Nm3. For
the convenience of analysis and calculation, this unit is
used uniformly in the following.)
5 Results and discussion
5.1 Simulation results
The simulation results of system electricity generation,
natural gas consumption and electricity consumption are
obtained by dynamic simulation with Dymola software,
as shown in Table 4.
Table 4. Simulation results
Conventional
energy supply
system
Original gas
engine CHP
hybrid energy
system
Optimized gas
engine CCHP
system
Cooling load
(kWh)
14,001,281.7
Heating load
(kWh)
5,566,748.1
Hot water
load (kWh)
818,322.0
Power
generation
(kWh)
0
865,709.6
15,358,726.5
Natural gas
consumption
(Nm3)
169.0
184.4
846.1
Power
consumption
(kWh)
17,958,094.1
17,092,384.5
1,113,431.5
5.2 Analysis results of 3E
5.2.1 Energy efficiency analysis
According to the test of the hospitals energy station, the
average low calorific value of natural gas is 34.308
MJ/Nm3. Moreover, product of electrical power
generation efficiency and transmission efficiency of
local grid is assumed to be 40% in this study. Then, the
primary energy ratios can be obtained by Eq. (5), and
the results are shown in Figure 10.
Modelling and optimal design of gas engine CCHP system in hospital
112 Proceedings of Asian Modelica Conference 2022
November 24-25, 2022, Tokyo, Japan
DOI
10.3384/ecp193105
Figure 10. Primary energy ratio
5.2.2 Economy analysis
In Shanghai, the price of natural gas is 2.45 yuan/m³ for
distributed energy systems and is 3.82 yuan/m³ for gas
boilers. Besides, the electricity price is 0.641 yuan/kWh
for non-resident users. For conventional energy supply
system, original gas engine CHP hybrid energy system
and optimized gas engine CCHP system, the average
system maintenance cost is 0.024 yuan/kWh, 0.12
yuan/kWh, 0.15yuan/kWh, respectively.
Through investigation and calculation, the
incremental investment of original gas engine CHP
hybrid energy system and optimized gas engine CCHP
system is 4 million yuan and 27.58 million yuan
respectively. Based on Eq. (6)-(7), the payback period
of investment of three systems are shown in Table 5.
Table 5. Payback period of investment calculation
Conventional
energy supply
system
Original gas
engine CHP
hybrid energy
system
Optimized
gas engine
CCHP
system
Incremental
investment
(yuan)
-
4,000,000
27,580,000
Annual net
operating cost
(yuan)
16,567,599
16,227,434
9,581,721
Payback period
of investment
(year)
-
11.8
3.9
5.2.3 Environment analysis
According to the survey, the CO2 emission coefficient
of natural gas source in Shanghai is 1.04Nm3/Nm3
natural gas and the national average CO2 emission
coefficient of electrical power generation is 0.412
Nm3/(kWh). Based on these, Eq. (8)-(10) are used to
calculate total CO2 emission, CO2 emissions per unit
capacity and CO2 emissions reduction rate of these
systems. Calculation results are shown in Figure 11 and
Figure 12.
Figure 11. Total CO2 emission
Figure 12. CO2 emissions per unit capacity and CO2
emissions reduction rate
5.3 Feasibility analysis
Through the above evaluation calculation, it is easy to
find that, compared with the original gas engine CHP
hybrid energy system, the optimized gas engine CCHP
system is improved a lot. Firstly, the optimized system
solves the economic problem efficiently. The payback
period of investment is decreased from 11.8 years to 3.9
years. Meanwhile, the energetic and environmental
performance of the system are also optimized. Primary
energy ratio is increased by 83.83% and CO2 emissions
reduction rate is increased by 93.40%. Therefore, it can
be concluded that reconstructing a CHP hybrid energy
system to a CCHP system has a high feasibility.
6 Conclusions
In this paper, a complete hierarchical modeling method
of the gas engine CHP/CCHP system was presented.
And the models of gas engine generator set, heat
exchanger and LiBr absorption chiller were then built
based on theoretical analysis, mathematical equation
and the performance curve of equipment. Then we
validated the accuracy of the presented model by taking
the gas engine CHP system of a hospital in Shanghai as
68.75% 72.55%
133.37%
0.00%
20.00%
40.00%
60.00%
80.00%
100.00%
120.00%
140.00%
160.00%
Conventional
energy supply
system
Original gas
engine CHP
hybrid energy
system
Optimized gas
engine CCHP
system
7398910.5 7042254.2
459613.7
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
8000000
Conventional
energy
supply
system
Original gas
engine CHP
hybrid
energy
system
Optimized
gas engine
CCHP
system
(Nm3)
0.207 0.197
0.013
0
4.83%
93.72%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0
0.05
0.1
0.15
0.2
0.25
Conventional
energy supply
system
Optimized gas
engine CCHP
system
(Nm3/kJ)
Session C: Thermal and power system (3), Mechanics system
DOI
10.3384/ecp193105
Proceedings of Asian Modelica Conference 2022
November 24-25, 2022, Tokyo, Japan
113
an example. By modeling and simulating the system, we
found that the minimum error between the calculated
results of the model and the operational data of this
system in 2017 is 2.1% and the maximum error is 7.0%.
The results meet the requirement that the simulation
deviation is less than 10%, which validates the accuracy
of the model.
Furthermore, we reconstructed the original gas engine
CHP hybrid energy system to a gas engine CCHP
system in our model. Then the conventional energy
supply system used in the hospital before 2013, the
original gas-engine CHP hybrid energy system and the
optimized gas engine CCHP system were modeled and
simulated to analyze the feasibility of this optimization.
The simulation results show that the gas engine CCHP
system can increase the primary energy ratio from 72.55%
to 133.37%, shorten the payback period from nearly
11.8 years to 3.9 years and increase the CO2 reduction
rate from 4.83% to 93.72%, which validates the
feasibility of this optimization.
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