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Simulation and evaluation of a biomass gasification-based combined cooling, heating, and power system integrated with an organic Rankine cycle
Accepted Manuscript Simulation and evaluation of a biomass gasification-based combined cooling, heating, and power system integrated with an organic Rankine cycle
C.Y. Li, T. Deethayat, J.Y. Wu, T. Kiatsiriroat, R.Z. Wang PII:
S0360-5442(18)31052-1
DOI:
10.1016/j.energy.2018.05.206
Reference:
EGY 13041
To appear in:
Energy
Received Date:
12 January 2018
Accepted Date:
31 May 2018
Please cite this article as: C.Y. Li, T. Deethayat, J.Y. Wu, T. Kiatsiriroat, R.Z. Wang, Simulation and evaluation of a biomass gasification-based combined cooling, heating, and power system integrated with an organic Rankine cycle, Energy (2018), doi: 10.1016/j.energy.2018.05.206
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ACCEPTED MANUSCRIPT 1
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Simulation and evaluation of a biomass gasificationbased combined cooling, heating, and power system integrated with an organic Rankine cycle
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C.Y. Lia, T. Deethayatb, J.Y. Wua,*, T. Kiatsiriroatb, and R.Z. Wanga
2 3
6
a Institute
of Refrigeration and Cryogenics, Shanghai Jiao Tong Univerasity, Dongchuan Road
7 8
800, Shanghai 200240, China b Department
of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang
9
Mai 50200, Thailand
10
11 12
*
Corresponding author. Tel.: +86 21 34206776; fax: +86 21 34206814
E-mail address: [email protected] (Jingyi Wu)
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Abstract: A biomass gasification-based combined cooling, heating, and power system
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integrated with an organic Rankine cycle is investigated. The energetic, economic, and
15
environmental performances of the system in cooling mode and heating mode are analyzed.
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Based on the electrical and thermal demands of a large office building in Shanghai, the
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monthly and annual operation of the system is simulated and its performance is evaluated.
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Results show that the system has a better performance in its heating mode than in its cooling
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mode. Meanwhile, the advantage of this system over the system without the ORC module
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decreases with the increase of the thermal demand. The annual primary energy saving ratio,
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cost saving ratio, and CO2 emission reduction ratio are 20.7%, 11.1%, and 43.7%,
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respectively. The increments of the above criteria owing to installation of the ORC module
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are 3.2%, 7.1%, and 1.4%, respectively. Sensitivity analysis shows that the equivalence ratio
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of biomass gasification has a relatively greater influence of the system on all aspects, which
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can increase the above criteria by up to 6.6%, 6.3%, and 3.6%, respectively. In addition, the
26
parameters of the biomass feedstock and the public grid on different aspects affect the
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system performance on the corresponding aspect significantly.
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Keywords: biomass gasification; combined cooling, heating, and power system; organic
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Rankine cycle; simulation and evaluation
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Nomenclature a, b
coefficients in chemical formula of biomass (-)
ai, bi, ci
fitting coefficients (-)
C
capacity of the equipment (kW)
CE
CO2 emission (kg)
CERR
CO2 emission reduction ratio (-)
COP
coefficient of performance (-)
cost
unit price (Yuan/kW or Yuan/kWh)
COST
cost (Yuan)
cp
specific heat capacity at constant pressure (kJ∙mol-1∙K-1)
CSR
cost saving ratio (-)
E
electric power (kW)
En
energy output per unit time(kW)
ER
equivalence ratio (-)
F
fuel energy consumption rate (kW)
h
specific enthalpy (kJ/kg)
H0 f
heat of formation (kJ/mol)
Hvap
latent heat of vaporization (kJ/kg)
I
interest rate (-)
K
equilibrium constant (-)
l
service life (year)
LHV
lower heating value (kJ/mol)
m
molar flow rate of air per mol biomass (mol/mol)
mi
molar fraction of element (-)
ṁ
mass flow rate (kg/s)
n
molar generation rate per mol biomass (mol/mol)
N
number (-)
PEC
primary energy consumption (kWh)
PESR
primary energy saving ratio (-)
Q
thermal power (kW)
R
capital recovery factor (-)
SD
standard deviation (-)
T
temperature (K)
W
work (kWh)
w
molar water content per mol biomass (mol/mol)
Y
value of data (unit of the represented data)
x
proportion of waste heat for thermal demand (-)
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xi
molar fraction (-)
Subscripts amb
ambient
b
boiler
bio
biomass or biomass feedstock
c
cooling
cap
capital
cchp
combined cooling, heating, and power system
cg
cold gas
conv
conventional system
d
demand
el
electrical or electricity
exp
experiment
g
public grid
gen
generator of the organic Rankine cycle
h
heating
hr
hour
max
maximum
min
minimum
model
model prediction
ng
natural gas
op
operation
orc
organic Rankine cycle
p
pump of the organic Rankine cycle
pgu
power generation unit
r
reduction zone of the gasifier
rec
waste heat recovery
release
release
s
syngas
sac
single-effect absorption chiller
t
turbine of the organic Rankine cycle
Greek symbols η
efficiency (-)
μ
CO2 emission factor (kg/kWh)
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Abbreviations
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AC
air conditioner
CSWD
Chinese Standard Weather Data
FEL
following the electric load
G
generator
GSHP
ground source heat pump
HX
heat exchanger
ICE
internal combustion engine
LPG
liquefied petroleum gas
NG
natural gas
ORC
organic Rankine cycle
PGU
power generation unit
SAC
single-effect absorption chiller
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1. Introduction
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A combined cooling, heating, and power (CCHP) system, in which the waste heat of the
35
prime mover is recovered for cooling and heating purposes, provides an efficient way to
36
achieve energy saving, economic saving, and greenhouse gas emission reduction [1].
37
However, due to the issues of energy crisis and environment pollutions, an even higher
38
advantage can be obtained by feeding the system with renewable energy. Biomass is an
39
attractive alternative to fossil fuels of the CCHP system owing to its abundance [2], wide
40
distribution [3], and CO2 neutrality [4]. Among the energy conversion technologies of
41
biomass, gasification is a self-sufficient thermo‒chemical process that can be used to produce
42
clean gaseous fuel [5]. Owing to the optimum utilization of available biomass feedstock,
43
since both hydrogen and carbon contribute to the heating value to a great extent, biomass
44
gasification has higher energy conversion efficiency than combustion and pyrolysis [6].
45
Furthermore, the gasification product, called syngas or producer gas, is applicable to various
46
prime movers of CCHP systems, such as solid oxide fuel cells [7], gas turbines [8], internal
47
combustion engines (ICEs) [9], and Stirling engines [10]. Therefore, biomass gasification-
48
based CCHP systems have attracted increasing attention.
49
Studies in biomass gasification-based CCHP systems were conducted on multiple
50
aspects. Puig-Arnavat et al. [11] modeled and evaluated different configurations of CCHP
51
systems based on biomass gasification. The systems of all the studied configurations could
52
be considered as “high efficiency systems” owing to relatively high overall efficiency,
53
electrical equivalent efficiency, primary energy saving, and exergy efficiency, with the
54
values of 53.2%−64.2%, 34.1%−42.7%, 7.8%−8.9%, and 18.9%−21.8%, respectively. Wang
55
et al. [12] investigated the effect of key parameters on the performance of a CCHP system
56
integrated with a biomass gasifier. The gasifier could provide syngas with a lower heating
57
value (LHV) of 5.665 MJ/m3 and the gasification efficiency is up to 77.45% when it was
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operated at the optimized condition. Meanwhile, for the entire CCHP system, it could have
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lower CO2 and SO2 emissions with total energy efficiency and syngas yield of 75.43% and
60
2.476 m3/kg, respectively.
61
Besides changing the system itself, combined with other types of energy recourses is
62
another approach to improve the system performance. With the co-firing technology proven
63
applicable and more efficient for different types of prime movers of CCHP systems, such as
64
ICEs [13], micro turbines [14], steam turbines [15], and gas turbines [16], addition of fossil
65
fuel into syngas becomes a simple and direct way to improve the performance of biomass
66
gasification-based CCHP systems. Wang et al. [17] studied a CCHP system based on co-
67
firing of syngas and natural gas (NG). With the mixture ratio of NG to syngas increased from
68
0 to 1, the energy and exergy efficiency of the system could be improved from 70.0% to
69
79.5% and from 21.9% to 35.6%, respectively. Li et al. [18] evaluated and compared the
70
performance of a CCHP system when liquefied petroleum gas (LPG) was added into different
71
biomass-derived fuel gases. It was found that the CCHP system fueled by syngas was the
72
most attractive to improve system performance by adding LPG. The primary energy ratio,
73
exergy efficiency, and energy saving ratio could be improved by up to 7.4%, 5.1%, and
74
17.8%, respectively.
75
Although the addition of fossil fuel can help to improve the energy performance, it was
76
also pointed out that the fossil energy consumption and environment pollution would increase
77
[17], which would weaken the advantage of biomass-based systems. To overcome this
78
drawback, hybrid energy systems, in which multiple renewable energy resources were
79
integrated, were investigated. Wang and Yang [19] proposed a CCHP system integrated with
80
a biomass gasifier and a solar evacuated collector. The entire system could achieve the
81
primary energy ratio and exergy efficiency of 57.9% and 16.1%. More importantly, the
82
carbon emission reduction ratio could reach as high as 95.7%. Besides solar energy, ground
83
source heat pump (GSHP), which can be used for spacing cooling [20] and heating [21],
84
provides another option of integration. Li et al. [22] conducted research on a combined
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heating and power system coupling biomass partial gasification with GSHP. The coupling
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with GSHP made a better use of flue gas at mid-low temperature and increased the coefficient
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of performance (COP) of the GSHP. The overall efficiency and energy efficiency of the
88
system were 72.12% and 40.13%, respectively. In addition to theoretical analysis, Esen and
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Yuksel [23] conducted experiments on a heating system using various renewable energy
90
sources, including biogas, solar energy, and GSHP. The system was satisfactorily operated
91
without any serious defects for heating greenhouses. Jradi and Riffat [24] tested performance
92
of a hybrid solar‒biomass ORC-based micro-CCHP system under different operation
93
conditions. The system was observed to be reliable with a smooth and quiet operation. The
94
overall efficiency of the system could be up to 83.08%.
95
However, for a system that supplies multiple types of energy, if the analysis only focused
96
on the system itself, then it should be considered as an analysis of an ideal condition where
97
all outputs of the system are fully used. In practice, on the contrary, the mismatch between
98
the output and the demand occurs constantly, which leads to the generation of excess energy
99
and requirement for auxiliary energy from time to time [25]. Therefore, demands are a
100
significant factor that affects the performance of the system. According to the study by Wang
101
et al. [26] on a typical biomass gasification-based CCHP system when supplying energy to a
102
hotel building, the energy efficiency of the system had the highest value of 37% during
103
summer and the lowest value of only 16% in transitional seasons. Some other studies, such
104
as Yang et al.’s research on a biomass gasification-based CCHP system [27], Li et al.’s
105
research on a solar/autothermal hybrid gasification CCHP system [28], and Palomba et al.’s
106
research on a gasification‒SOFC based tri-generation system [29], also indicated the
107
importance of energy demands to the performance of the CCHP systems based on biomass
108
gasification. The studies on optimization of biomass gasification-based CCHP systems in
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consideration of energy demands are rare, but there are many researches on methods to
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weaken the effect of mismatch between outputs and demands on the CCHP system driven by
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fossil fuels. In general, to deal with the energy shortage situation, supplementing energy, such
112
as NG and electricity, is a common treatment. For the excess energy, the possible choices
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can be divided into two types. One is to optimize the operation strategies of the CCHP system
114
in order to avoid excess energy as much as possible. Some researchers optimized existing
115
operation strategies using different methods including dynamic programming [30], multi-
116
objective decision-making methods [31], and some other novel optimization methods [32];
117
others came up with novel strategies, such as the minimum distance operation strategy [25],
118
and the compromised electric–thermal load strategy [33]. The other way is to make full use
119
of the excess energy. If the thermal demand were taken as priority, there would be excess
120
electricity. The common approach is to sell it back to public grid, which is strictly limited by
121
the local policy and thus not available for many places [34]. Similarly, excess thermal energy
122
would be produced if the system were operated in the mode with electrical demand taken as
123
priority. To make full use of the excess thermal energy, researchers have focused on two
124
approaches: one is to store it in thermal storage units
125
with it. The first approach is direct but passive, as the system can only store and release
126
thermal energy when conditions permit [35]; whereas the second one is indirect but flexible,
127
as it can dynamically adjust the electrical and thermal output of the system to match user
128
demands.
and the other is to generate electricity
129
With development and maturity of organic Rankine cycle (ORC) technology on utilizing
130
waste heat from CCHP system’s prime movers, including the gas turbine [36], the NG engine
131
[37], and the marine diesel engine [38], studies about the effect of ORC integration with
132
CCHP systems were carried out. Fang et al. [35] evaluated a CCHP system integrated ORC
133
and electric chiller using simulated demands of representative days in different seasons. It
134
was found that comparing with the CCHP system without ORC and electric chiller, the
135
primary energy consumption, CO2 emission, and total cost could all be reduced in the
136
representative days owing to favorable match between outputs and demands. Hajabdollahi
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[39] introduced a method to determine the optimum prime mover of a CCHP−ORC system
138
and to find its benefit at different load demands. Results showed that the highest annual
139
benefit was obtained when there was the most heating load demand. Comparatively, the gas
140
turbine was more profitable with lower electrical and higher heating demands, while the
141
diesel engine was more advantageous when both electrical and heating demands were high.
142
Wu et al. [40] proposed and evaluated CCHP−ORC systems based on natural gas, solar
143
energy, and a biomass boiler. Results indicated that the ORC unit had higher potential to
144
reduce CO2 emission than annual total cost. The solar−ORC system and the biomass−ORC
145
system had the best economic and environmental performance, respectively. Meanwhile, it
146
was also observed that the variation of different energy demands had different effects on the
147
three CCHP−ORC systems.
148
According to the literature review above, it can be found that the CCHP system based
149
on biomass gasification suffers from the issue of mismatch between energy outputs and
150
demands. The ORC integration provides an option for this system to solve this problem by
151
enabling the original system to recover excess waste heat, which can be deduced from Refs.
152
[35, 39, 40]. However, there is no research on the biomass gasification-based CCHP system
153
integrated with an ORC module. On the other hand, as indicated in Ref. [40], the effect of
154
ORC integration varies with the energy resources and configurations of the system. As for
155
the system based on biomass gasification, for instance, the parameters of gasification, the
156
cost factors involving the gasifier and biomass feedstock, and the environmental benefit from
157
usage of biomass, have a great effect on the energy, economic, and environmental
158
performances of the system, respectively. Therefore, the conclusions of existed studies
159
cannot provide effective reference quantitatively for ORC integration with a biomass
160
gasification-based CCHP system. In addition, as a result of relatively low LHV of syngas, the
161
efficiency of the prime mover of the system is low [41], and the proportion of the waste heat
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is high accordingly, which makes the investigation on making full use of excess waste heat
163
more necessary.
164
For the above reasons, in this study, a biomass gasification-based CCHP system
165
integrated with an ORC is evaluated. In this system, other than being the prime mover as in
166
many biomass systems, the ORC module is powered by excess waste heat of the prime mover
167
as a complement component to obtain higher energy conversion ratio. The main objective of
168
the present work is to analyze the energetic, economic, and environmental performance of
169
the proposed system considering characteristics of both biomass and CCHP systems. The
170
specific objectives are:
171
To evaluate hourly performance of the system under different demand conditions and to
172
analyze the advantages and disadvantages of the system over the conventional system.
173
To evaluate monthly and annual performance of the system based on operation simulation.
174
To compare performance of the proposed system and the system without the ORC module,
175
and find out under what seasonal conditions the proposed system performs better than the
176
system without the ORC module.
177
To analyze the effect of variations of key parameters on the performance of the system.
178
In order to achieve the objectives above, the paper is organized as follows. First, the
179
configuration and operation strategy of the system is introduced. Second, the key components
180
of the system are modeled and the criteria on energetic, economic, and environmental aspects
181
are presented. Third, the performances of the system in cooling and heating modes are
182
analyzed respectively. Fourth, the monthly and annual performances of the system are
183
evaluated based on operation simulations, and the sensitivity analysis of the annual
184
performance is carried out. Finally, the main conclusions are presented.
185
2. System description
186
2.1. Energy demands of the building
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In this study, a hypothetic office building with a cover area of 55584 m2, located in
188
Shanghai, is selected as a case for analysis and evaluation. Shanghai is a city with a typical
189
subtropical monsoon climate. According to the database of Chinese Standard Weather Data
190
(CSWD) [42], the annual average temperature is 15.6°C, and the temperature range of the
191
whole year is from −4.5°C to 36.8°C [43]. The monthly relative humidity ranges from 67%
192
to 83%. In summary, Shanghai has a hot summer, a not too cold winter and mild transient
193
seasons. The humidity is high all year round. The hourly temperature and relative humidity
194
[43] are shown in Fig. 1. In this building, the energy demands compromise electricity supply,
195
cooling energy for space cooling, and heating energy for space heating and domestic hot
196
water. Electricity supply and domestic hot water are required in each season. Space heating
197
and cooling are required mainly in winter and summer, respectively, and also in a little time
198
in transient seasons. The hourly energy demands of the building are obtained from the
199
simulation software EnergyPlus V8.8.0 [44] using meteorological data from CSWD. The
200
simulation results are shown in Fig. 2. It can be found out that generally the heating load in
201
winter is much smaller than the cooling load in summer. Besides the weather factors, the
202
operation pattern of the building also has an impact on this result. In an office building, the
203
operation time is mainly during the daytime, which has higher temperature among the hours
204
in a day. As a result, the cooling requirement in summer gets higher and the heating demand
205
in winter gets lower. The peak electrical, cooling, and heating demands are 1515.3 kW,
206
2695.5 kW and 1484.2 kW, respectively.
207
2.2. The biomass gasification-based CCHP system integrated with an ORC
208
As shown in Fig. 3, the CCHP system consists of a main system and an auxiliary system.
209
The main system comprises a biomass gasifier, an ICE, a generator, an ORC module, a
210
single-effect absorption chiller (SAC), a fan coil and some heat exchangers. In this subsystem,
211
biomass is converted into fuel gas, called syngas, in the gasifier. The ICE and generator
212
constitute the power generation unit (PGU), which is fueled by the syngas. During the power
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generation process, waste heat is produced in the jacket water and exhaust gas. The waste
214
heat is recovered through hot water in exchangers in series. Then the hot water is used as a
215
heat source of the ORC module to generate more power, of the SAC for space cooling, of the
216
fan coils for space heating and of the domestic hot water. When the main system fails to
217
provide sufficient energy for the building, the auxiliary system will activate. The electricity
218
is supplemented from the grid, and the cooling energy is supplied by an electric air
219
conditioner (AC), which also consumes the electricity from the grid. Heating load for space
220
heating and domestic hot water is supplemented by a boiler fueled by NG. It is noted that the
221
conventional system, used as the base case, shares the same configuration as the auxiliary
222
system.
223
2.3. The operation strategy of the system
224
Generally, the system is operated in following the electrical load (FEL) mode with some
225
modifications, which means that the system satisfies the demand of electricity prior to
226
thermal energy. The operation status of the system is mainly determined by electrical
227
demand. Unlike common CCHP systems, in this system, if there is excess waste heat, instead
228
of being released, it is converted into electricity through the ORC module. Specifically, the
229
dimensionless energy demand area is divided into five areas according to the relation between
230
heat and power, as shown in Fig. 4. AB is the output curve of a typical PGU from ASHRAE
231
Handbook [45]. In the idle operation condition of the PGU, part or all of the waste heat can
232
be used for power generation through the ORC module, which forms the curve of AD.
233
Similarly, each operation point of the PGU becomes a curve through distributing waste heat
234
for electrical and thermal usage. Ultimately, the output curve of the PGU turns into an output
235
area, with BC as the right boundary, which is the output curve of the PGU in full-load
236
condition. BF and BG are two rays, as the boundaries of area that both electrical and thermal
237
demands exceed the maximum of corresponding output of the PGU. Thus, the whole demand
238
area is divided by the boundaries of this output area of the PGU and the rays of BF and BG.
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239
The characteristics of each area are shown in Table 1, and the detailed operation strategy of
240
the system in different areas is as follows.
241
(1) When the demand point is located in Area 1, both thermal and electrical demands are
242
small. The PGU runs in the idle condition. The electrical demand is satisfied with ORC, the
243
thermal demand is satisfied with the waste heat, and the remaining waste heat is released to
244
the environment, as expressed in Eqs. (1) and (2): Eorc Ed ,
(1)
Qrelease Q pgu,min Eorc / ηorc Qd ,
(2)
245
where Eorc is the power generation by ORC. Ed and Qd are the electrical and thermal demand,
246
respectively. Qd is the sum of waste heat demand for both heating load and cooling load, as
247
expressed in Eq. (3). Qrelease is the excess waste heat of PGU which is released to the
248
environment. Qpgu,min is the recoverable waste heat of the PGU in the idle condition. ηorc is
249
the efficiency of ORC. Qd
Qh, d Q c, d , ηh, rec COPsac
(3)
250
where Qh,d, and Qc,d are demands of heating and cooling, respectively. ηh,rec is the efficiency
251
of waste heat recovery for heating, and is assumed as 0.8 [46]. COPsac is the COP of the
252
SAC, and is assumed as 0.7 [47].
253 254
(2) Area 2 is the output area of the PGU. In this area, the system operates at the matching point between demand and output, which satisfies the following relations:
E pgu Eorc Ed ,
(4)
x Q pgu Qd ,
(5)
Eorc (1 x) Q pgu ηorc ,
(6)
255
where x is the proportion of waste heat for thermal demand. Epgu and Qpgu are electrical output
256
and recoverable waste heat of the PGU, respectively.
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257
(3) In Area 3, the main system is capable of satisfying thermal demand by operating the
258
PGU in full-load condition. However, in case that the thermal demand is satisfied, even if all
259
the remaining waste heat is used for ORC, there is not enough power for electrical demand.
260
Due to the priority of electrical demand, the PGU is operated in full-load mode to generated
261
power as much as possible. Meantime, the excess waste heat is also used for power generation
262
with ORC. The insufficient part of electrical demand is supplemented from the grid. The
263
relevant relations are expressed in Eqs. (7−9):
E pgu,max Eorc E g , el Ed ,
(7)
x Q pgu,max Qd ,
(8)
Eorc (1 x) Q pgu,max ηorc ,
(9)
264
where Eg,el is the electricity from the grid as supplement. Epgu.max and Qpgu.max are the electrical
265
and thermal output of the PGU as full-load condition.
266
(4) Area 4 represents the demand situation that the electrical demand is less than the
267
maximum power output of the PGU but the thermal output is insufficient when matching
268
electrical output with demand. In this case, the short part of thermal energy is supplemented
269
from the auxiliary system. Due to the higher efficiency to generate heating energy than
270
cooling energy, the heating demand takes the priority over cooling demand. Because the
271
thermal energy is insufficient, the ORC module is off. The relevant relations are expressed
272
as Eqs. (10−15).
273
If Q pgu
E pgu Ed ,
(10)
Eorc 0 .
(11)
Q pgu ηh,rec Fng ηb Qh,d ,
(12)
Qh, d , then ηh, rec
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E g,c COPel Qc ,d ,
(13)
274
where Fng is the energy consumption of NG. ηb is the efficiency of the auxiliary boiler. Eg,c
275
and COPel are the grid electricity consumption and the COP of electric AC. The value of
276
COPel is assumed as 3.5.
277
278 279 280
If
Qh, d Q Q Q pgu h, d c , d , then ηh, rec ηh, rec COPsac Q pgu , h ηh, rec Qh, d ,
(14)
(Q pgu Q pgu ,h ) COPsac E g ,c COPel Qc ,d ,
(15)
where Qpgu,h is the waste heat of the PGU used for heating. (5) In Area 5, both electrical and thermal demands are more than output of the PGU in full-load condition. In this case, the PGU operates in full-load condition: E pgu E pgu , max ,
(16)
Q pgu Q pgu ,max .
(17)
281
Because there is no excess waste heat, the ORC module is also off. Both electricity and
282
thermal energy are required from the auxiliary system. The electricity from the grid as
283
supplement is calculated by Eq. (18), and the energy consumption of the auxiliary system for
284
thermal demand is calculated by Eqs. (12−15).
E pgu E g , el Ed . 285 286
(18)
For all the above cases, the cooling and heating demands satisfied by recovering waste heat of the PGU (Qc,pgu and Qh,pgu, respectively) can be calculated as Qc , pgu Qc , d E g , c COPel ,
(19)
Qh, pgu Qh,d Fng ηboiler .
(20)
287
In this way, except for minor operating points in Area 1, there is no excess energy for all
288
the other operating points in other areas, which can further improve the energy utilization
289
rate of the whole system. Meantime, in the area below the curve A-B-G, because a part of
ACCEPTED MANUSCRIPT 17 of 55
290
the electrical demand is satisfied with the ORC module, the biomass consumption is reduced.
291
Based on the operation strategy above, a MATLAB code has been developed to simulate the
292
operation of the system with the energy demand data obtained in Section 2.1. The flow chart
293
of the code is shown in Fig. 5. Besides the energy demand of the building and necessary
294
parameters of the operation parameters, the following models of the components are also
295
required: (1) the model of the PGU, to calculate power and thermal output when heating
296
value of syngas input (Qs) changes; (2) the model of biomass gasifier, to reveal the relation
297
between Qs production and various gasification parameters, including heating value of the
298
biomass feedstock (Qs), molar fraction of water (w) and dry-basis mass fraction of the
299
elements (mi) of the biomass feedstock, and equivalence ratio (ER); (3) the model of the ORC
300
model, to obtain efficiency of ORC at different operational temperatures (T). The above-
301
mentioned models are introduced in the next section in detail. Ultimately, through the
302
operation simulation, the detailed input, output and process parameters of the whole system
303
can be obtained.
304
To analyze the effect of addition of the ORC module on this system, the operation of a
305
CCHP system without ORC is also simulated. Except that there is no ORC module in this
306
comparative system, it has an identical layout to the other parts of the proposed system. The
307
comparative system is operated with standard FEL strategy, which can be referred to Ref.
308
[46].
309
3. Mathematical models
310
The methodology of this study is outlined in Fig. 6. It consists of demand simulation,
311
operation simulation, and evaluation. The input and output data of each process can be found
312
in Fig. 5. The simulations of demand and operation have been introduced in the previous
313
section. The criteria can be calculated with simulation results and characteristic parameters
314
of corresponding energy sources. The simulation models and evaluation criteria are
315
introduced in this section.
ACCEPTED MANUSCRIPT 18 of 55
316
3.1. Model of the biomass gasifier [48]
317
The equilibrium model developed by Shama and Sheth, called “3-reaction model”, is
318
adopted to simulate gasification process in this study. The detailed basic assumptions of this
319
model can be referred to Ref. [48]. This model comprises elemental molar balances,
320
thermodynamic balance and equilibrium balances. The former two are based on the global
321
gasification reaction, which can be written as CH a O b wH 2 O m(O 2 3.76 N 2 ) ns ( x1H 2 x2 CO x3CO 2 x4 H 2 O x5CH 4 x6 N 2 ) nc C , (21)
322
where CHaOb is the chemical formula of biomass feedstock based on a single atom of carbon,
323
which can be calculated with weight percentage of the element C, H and O; m is the molar
324
flow rate of air per mol of biomass feedstock. 3.76 represents the approximate molar ratio of
325
N2 to O2 of air. ns and nc are production of syngas and char, respectively, in mol per mol
326
CHaOb. x1 to x6 are the molar fraction of the corresponding gaseous compositions of syngas,
327
respectively. Therefore, the elemental molar balance for C, H, O and N can be written as
1 ns ( x2 x3 x5 ) nc ,
(22)
2 w a ns (2 x1 2 x4 4 x5 ) ,
(23)
w b 2m ns ( x2 2 x3 x4 ) ,
(24)
2 3.76 m ns 2 x6 .
(25)
328
The gasification is assumed adiabatic, which means the sum of enthalpy heat of
329
formation of all compositions at the initial state is equal to that at equilibrium state.
330
Thermodynamic balance can be expressed as H 0f ,bio w( H 0f ,H 2O(l) H vap ) m( H 0f ,O2 3.76 H 0f , N 2 ) ΔQ n g
x H i
0 f ,i
nc H 0f ,C
Tr
Tamb
(ns
x c
i p ,i
nc c p ,C ) , (26)
331
where H0 f,bio is the heat of formation of biomass feedstock; H0 f,H2o(l) is the heat of
332
formation of liquid water; Hvap is the vaporization heat of water. It is noted that the heat of
333
formation is defined as the change of enthalpy during formation from its constituent elements.
334
Therefore, all elements, such as O2, N2, H2 and C in this case, have a heat of formation of
ACCEPTED MANUSCRIPT 19 of 55
335
zero, as there is no change of enthalpy involved in their formation. The H0 f of compounds
336
involved in this article can be found in Table 2. ∆Q is the heat input (such as by increasing
337
the temperature of the inlet air or the biomass feedstock) to the gasifier. cp,i is the specific
338
heat capacity of component i. Tamb and Tr are temperature of the ambient and the reduction
339
zone of the gasifier, respectively. Tamb is also considered as the temperature of biomass
340
feedstock and air input to the system. The polynomial fittings of cp,i are adopted, as expressed
341
in Eq. (27). The polynomial coefficients are obtained from Ref. [49], as listed in Table 3. c p (a0 a1 T a2 T 2 a3 T 3 a4 T 4 ) 103 .
342 343
(27)
The equilibrium balances are based on three reversible reactions of the gasification process: C 2H 2 CH 4 ,
(28)
CO H 2 O CO 2 H 2 ,
(29)
CH 4 H 2 O CO 3H 2 .
(30)
344
In the equilibrium state, the molar fractions of gaseous species of the reversible reactions
345
are bounded by equilibrium constant (K). The relations between xi and K of the above-
346
mentioned three reactions are given by Eqs. (31−33) in turn: K1
x5 x12
,
(31)
K2
x1 x3 x2 x4
,
(32)
K3
x13 x2 . x5 x4
(33)
347
The equilibrium constant is a function of reaction temperature T, as expressed in Eq.
348
(34). The coefficients of the functions of the three reactions are listed in Table 4. In this model,
349
Tr is considered as the reaction temperature of the three related reversible reactions.
ln K b1 T 1 b2 ln T b3 T b4 T 2 b5 T 2 b6 .
(34)
ACCEPTED MANUSCRIPT 20 of 55
350
The molar fractions of gaseous species are bounded by Dalton’s law of partial pressure: 6
x
i
1.
(35)
i 1
351
Thus, after obtaining the parameters of biomass feedstock such as a, b and w, and the
352
ambient temperature, there are ten variables (Tr, m, x1~x6, ns, and nc) and nine equations.
353
Therefore, the gasification temperature and syngas production per mol of biomass feedstock
354
can be calculated after determining the air input. The flow chart of gasification process
355
simulation can be referred to Ref. [48]. Generally, ER, defined as the ratio of the actual air
356
input to the air required for complete combustion, is used to measure the air input. The
357
relation between ER and m is expressed as ER
358 359
m . 1 0.25 a 0.5 b
(36)
The gasification is evaluated by cold gas efficiency (ηcg), which is defined as the lower heating value ratio of syngas to biomass feedstock, as expressed in Eq. (37).
ηcg
( x1 LHV1 x2 LHV2 x5 LHV5 ) ns . LHVbio
(37)
360
The model is validated with the experimental data reported in Ref. [50]. The validation
361
results are shown in Fig. 7. The standard deviation (SD) [48], which is defined as Eq. (38), is
362
used to assess the error of each data group.
SD
[(Yexp Ymodel ) / Yexp ]2 i 1 N 1 N
1/ 2
.
(38)
363
where Yexp and Ymodel are the values from the experiment and the model, respectively. N is the
364
number of data. It can be observed that the model agrees well with the experiment results
365
when ER changes. The predictions for molar fractions of CO and H2, which are the main
366
components of the syngas, are accurate. The SDS of molar fractions of CO2 and CH4 are
367
relatively larger, but the ranges of them are still within 10%. Moreover, as noncombustible
ACCEPTED MANUSCRIPT 21 of 55
368
and minor component respectively, they have little effect on the prediction of ηcg. As a result,
369
ηcg has a satisfying SD with the value of 1.26%.
370
Eventually, the biomass energy input to the system can be estimated by
Qbio Qs / ηcg .
(39)
371 372
In this study, it is assumed that the gasifier is operated with a fixed ER of 0.35. The input
373
rate of air and biomass feedstock can be adjusted to stabilize operation of the gasifier on the
374
fixed ER and to satisfy syngas requirement of PGU. The influence of the changing of the set
375
value of ER is discussed in the sensitivity analysis section. Meanwhile, in Shanghai, the
376
ambient temperature ranges from about 270 K to 310 K. According to the simulation results
377
and summary in Ref. [51], Tr ranges from 973 K to 1373 K. Therefore, the ambient
378
temperature is assumed as a constant value of 290 K and the error caused by this is considered
379
negligible. The properties of the biomass feedstock [50] adopted in this article and the
380
simulation results of syngas at nominal condition are listed in Table 5.
381
3.2. Model of the organic Rankine cycle
382
According to the results from Ref. [52], R245fa is a low temperature working fluid and
383
commercially available for waste heat recovery. It also has comparatively high molecular
384
weight, which can make the system compact due to reducing the mass flow rate of working
385
fluid. Besides, as a hydrofluorocarbon, it has zero value of ozone depletion potential.
386
Therefore, R245fa is selected as the working fluid of the ORC module in this article.
387
Thermodynamic model [52] is widely used in simulating the performance of the ORC
388
module. As shown in Fig. 8(a), the ORC module is composed of an evaporator, a turbine with
389
a generator, a condenser, and a pump. Four processes are included in the ideal ORC:
390
isentropic compression (1→2), isobaric heating (2→3), isentropic expansion (3→4) and
391
isobaric heat rejection (4→1), as can be seen in Fig. 8(b). Due to the energy loss in the pump
ACCEPTED MANUSCRIPT 22 of 55
392
and turbine, the process of 3→4 and 1→2 become non-isentropic. As a result, the actual cycle
393
is 1→2a→3→4a→1. The work output of the ORC module (Worc) is the difference between
394
work output of turbine (Wt) and the work consumption of the pump (Wp):
Worc Wt W p . 395
(40)
Wt and Wp can be calculated with Eqs. (41) and (42): orc ( h3 h4 ) , Wt ηt m
Wp
(41)
m orc (h2 h1 ) , ηp
(42)
396
where ηt and ηp are the efficiencies of the turbine and pump, respectively, and are both
397
assumed as 0.8 [40]. hi is the enthalpy of the working fluid at state point i, which is obtained
398
from REFPROP 9.1 [53] developed by the National Institute of Standards and Technology
399
of the United States. ṁorc is the mass flow rate of the ORC fluid, which is determined by
m orc 400
Qorc , h3 h2
(43)
where Qorc is heat obtained from waste heat of the PGU in the evaporator.
401
According to the equations above and considering the efficiency of the electric generator
402
(ηgen), the total efficiency of the ORC module can be written as Eq. (44). The value of ηgen is
403
assumed as 0.85 [35].
ηorc
404
[(h3 h4 ) ηt (h2 h1 ) η p1 ] ηgen h3 h2
.
(44)
3.3. Model of the power generation unit
405
The performance of the PGU is related to its size. Therefore, the size of the PGU is
406
determined first. As mentioned in the previous section, the operation strategy of the system
407
is actually a modified FEL, so the size of the PGU is determined by the maximum power
408
output of the system. The maximum rectangle method is adopted to choose the size of the
409
PGU [54]. The maximum area XY of the cumulative electricity demand curve in Fig. 9,
ACCEPTED MANUSCRIPT 23 of 55
410
determines the size of the PGU, and Y, with the value of 843.24 kW, is the maximum power
411
output of the system. In this system, electricity is generated by PGU and ORC module.
412
Therefore, the energy output of the PGU should satisfy following equation: E pgu , max Q pgu , max ηorc 843.24 kW .
(45)
413
The output characteristics of the PGU with different sizes are based on the manufacture’s
414
database of GE Jenbacher modules especially adapted to work with syngas. According to
415
comparing output characteristics of PGU in database and Eq. (44), the J312 GS, with
416
maximum electrical output of 765 kW by itself and maximum electrical output of 80 kW by
417
ORC is selected as the PGU of this system, which is close to the restriction of Eq. (44). J316
418
GS, with maximum electrical output of 850 kW is selected as the PGU of the comparative
419
system. The part-load performance, including electrical output and recoverable waste heat
420
from jacket water and exhaust gas with respect to syngas heating value input, is obtained
421
from Ref. [11]. They are all fitted into linear relations, as expressed in Eq. (46), and the
422
correlation results are list in Table. 6.
En pgu c0 c1 Qs , 423
where Enpgu stands for energy of electricity or waste heat.
424
3.4. Evaluation criteria
(46)
425
The advantage or disadvantage of a CCHP system is usually reflected by comparing with
426
the conventional system. In this article, the evaluation includes energetic, economic, and
427
environmental aspects. The related criteria are as follows.
428
3.4.1. Primary energy saving ratio (PESR)
429 430
Primary energy saving ratio is defined as the ratio of primary energy saved by the CCHP system to that consumed by the conventional system, which is expressed as
ACCEPTED MANUSCRIPT 24 of 55
PECconv PECcchp
PESR
(47)
,
PECconv
431
where PECconv and PECcchp are primary energy consumption of the conventional system and
432
the CCHP system in a period of time, respectively. They can be calculated as
PECconv
((E
PECcchp
(Q
d
Qc / COPel ) / ηg Qh / ηb )
,
(48)
( E g ,el E g ,c ) / ηg Fng )
433
(49) , where ηg is the energy conversion efficiency of the grid regarding the energy loss during
434
power generation and transmission. The value of ηg is 0.323 in China [46].
435
3.4.2. Cost saving ratio (CSR)
bio
436
The cost of the system includes the capital cost and operation cost, where capital cost is
437
the sum of all equipment in the system considering capital recovery, and operation cost
438
includes cost of all types of energy resources, as expressed in Eqs. (50−52):
COST COSTcap COSTop COSTcap R
COSTop
(cost
bio
N hr 365 24
cost
(50)
,
cap ,i
Ci ,
Qbio costel E g costng Fng ) ,
(51) (52)
439
where COST, COSTcap and COSTop are total cost of the system, capital cost, and operation
440
cost, respectively. R is the capital recovery factor, which can be calculated with Eq. (53).
441
Conventionally, R is an annualized factor. Therefore, Nhr/(365∙24) is added to calculate the
442
cost of any period of time such as a month or an hour, where Nhr is the number of hours in
443
this period of time. costcap,i and Ci are the unit cost and the capacity of equipment i,
444
respectively. The unit prices of the equipment are listed in Table 7. costbio, costel, and costng
445
are the unit price of biomass, electricity from the public grid and NG, which are listed in
446
Table 8.
ACCEPTED MANUSCRIPT 25 of 55
R
I (1 I )l (1 I )l 1 ,
(53)
447
where I is the interest rate, which is 6.55% in China. n is the service life of the equipment, in
448
year. In this article, the service lives of all equipment are assumed as 20 years.
449 450
The cost of the conventional system can also be calculated with Eqs. (50−52), with some changes on the calculation of electricity and NG consumption in Eq. (52):
COSTop ,conv 451
(COST
el
( Ed Qc / COPel ) / ηg COSTng Qh / ηb )
(54)
Thus, the cost saving ratio (CSR) can be calculated as CSR
452
.
COSTconv COSTcchp COSTconv
.
(55)
3.4.3. CO2 emission reduction ratio (CERR)
453
The issue of global warming is one of the world’s greatest concerns. As was previously
454
mentioned, this system has great potential in reducing CO2 due to utilization of biomass and
455
recovery of waste heat. Therefore, CO2 emission of the system is evaluated in this article.
456
Similarly, CO2 emission reduction ratio is adopted for the evaluation: CERR
CEconv CEcchp CEconv
,
(56)
457
where CEcchp and CEconv are the CO2 emission of the conventional system and the CCHP
458
system, respectively, which are estimated as
CEconv CEcchp
(μ
(μ
( Ed Qc / COPel ) / ηg μng Qh / ηb ) ,
(57)
Qbio μel ( E g ,el E g ,c ) / ηg μng Fng ) ,
(58)
el
bio
459
where μel, μng and μbio are the CO2 conversion factors of the electricity from the grid, NG and
460
biomass feedstock, respectively, whose values are listed in Table 9. It should be noted that,
461
owing to the neutrality of biomass in relation to global warming, the CO2 emission of biomass
462
feedstock is estimated with the effect of production and transportation of it [59].
ACCEPTED MANUSCRIPT 26 of 55
463
4. Results and discussion
464
4.1. Performance analysis of the system
465
In this section, the performance of the system along with the change of electrical and
466
thermal demands is analyzed. The performance of the system in cooling and heating mode is
467
studied respectively. It is noted that although domestic hot water is required all year round,
468
in the most time of summer, the cooling demand is much more than this part of heating
469
demand. Therefore, the system is considered to be operated in cooling mode in summer.
470
4.1.1. Comparison among the criteria
471
The PESR, CSR and CERR of the system in cooling and heating mode are shown in Figs.
472
10 and 11, respectively. Among the three criteria, CERR has the highest value because of the
473
neutrality of biomass in relation to global warming. The CO2 emission of biomass is only
474
derived from production and transportation, which results in much lower CO2 conversion
475
factor of biomass than other energy resources. Therefore, the value of CERR is high in the
476
whole map. CSR has the lowest value, and is the only criteria with negative values because
477
the capital cost of the system is very high. Meanwhile, as the cost of the biomass has no
478
significant advantage over other energy resources, the difference of capital cost is even more
479
difficult to offset.
480
4.1.2. The variations of criteria with respect to demands
481
It can be observed that each of the three criteria has a maximum value as a center and
482
the value of the criterion decreases from the center to the border. The features of decrease of
483
the three criteria along with energy demands are different. Generally, PESR and CSR
484
decrease more sharply as energy demands decrease, whereas CERR decreases more evidently
485
as energy demands increase. This reflects that the advantages of the system on different
486
aspects result from different factors. The saving of primary energy and cost is mainly because
ACCEPTED MANUSCRIPT 27 of 55
487
of the waste heat utilization. Therefore, with low energy demand, due to the low efficiency
488
of the PGU and high capital cost of the system, it is difficult to save primary energy and cost.
489
As the increase of energy demands, the efficiency of the PGU is higher; this results in higher
490
efficiency of the main system. Meanwhile, the capital cost is easier to offset with the increase
491
of the energy saving, owing to utilization of the waste heat. In this occasion, the effect of
492
supplementary energy from the auxiliary system is weak. On the contrary, the CO2 reduction
493
is mainly because of the replacement of fossil fuel with biomass. When energy demands are
494
low, even if the efficiency of the system is low, the system demand can be satisfied with
495
biomass alone. Because the fossil fuel is fully replaced by biomass, the CO2 reduction ratio
496
is still high. However, when energy demands exceed the output capacity of the main system,
497
electricity and NG consumption from the auxiliary system make CO2 emission increase
498
greatly with the growth of energy demands. Therefore, CERR decreases slowly on the lower
499
direction of the preferable zone, and decreases sharply on the upper right direction. To
500
summarize, it is more important to improve waste heat utilization efficiency for the better
501
performance of the system on energy and economic aspects, while it is more effective to
502
utilize biomass as energy recourse to improve environmental performance of the system.
503
4.1.3. Comparison between cooling and heating modes
504
Comparatively, the system performs better in heating mode than in cooling mode in
505
consideration of all three criteria. Because for the conventional system, the cooling demand
506
and heating demand are satisfied by electric AC and gas furnace, respectively. The COP of
507
SAC is lower than the efficiency of waste heat recovery of heating. On the other hand, in the
508
conventional system, the cooling energy is generated by electric AC, whose COP is 3.5. The
509
efficiency of the public grid is 0.323, and therefore, the efficiency of generating cooling
510
energy is greater than 1. However, owing to the heat loss in the boiler, the efficiency of
511
heating energy generation in the conventional system is only 0.8. To sum up, in the CCHP
ACCEPTED MANUSCRIPT 28 of 55
512
system, the waste heat is more efficiently used in heating mode, whereas in the conventional
513
system, the efficiency of cooling energy generation is higher. As a result, the effect of the
514
waste heat recovery is more apparent for heating than for cooling. Energy price is another
515
factor for the difference of the economic performance of the system between heating mode
516
and cooling mode. According to the corresponding ways to generate energy, energy
517
generation efficiency and energy source price, the prices of the cooling and heating energy
518
per unit are 0.267 Yuan/kWh and 0.481 Yuan/kWh, respectively, in the conventional system.
519
That is to say, the conventional system has lower efficiency and much higher cost when
520
generating heating energy than cooling energy. Therefore, economic advantage of CCHP
521
system in heating mode over cooling mode is the greatest among the three aspects. However,
522
according to the calculation with corresponding efficiencies and CO2 conversion factors, the
523
CO2 emission rates of generating cooling energy and heating energy are 0.264 kg/kWh and
524
0.275 kg/kWh, respectively, which are very close. Meanwhile, as analyzed previously, the
525
replace ratio of fossil fuel with biomass is the key to reduce CO2 emission. Therefore, the
526
gap of CERR between the cooling mode and the heating mode is the narrowest, and the
527
descending trends from the areas with maximum values are similar. In addition, it can be
528
observed that the areas with better performance, which are colored red and yellow, are larger
529
in heating mode than in cooling mode. Therefore, it is indicated that the system also has
530
higher possibility to have better performance in heating mode than in cooling mode.
531
4.2. Evaluation of the system based on operation simulation
532
In this section, the system is evaluated based on operation simulation and is compared
533
with the system without the ORC module.
534
4.2.1. Monthly performance on the energetic aspect
535
As shown in Fig. 12, the PESR of the system with and without the ORC module ranges
536
from 18.7% to 24.2% and from 11.8% to 20.0%, respectively. The system with ORC module
ACCEPTED MANUSCRIPT 29 of 55
537
has better performance from January to May and from October to December, and has a
538
performance that is not as good as in the rest months. Besides the efficiency factor as analyzed
539
before, the demand factor is also important. As shown in Fig. 2, the cooling demand of the
540
system is relatively high. In cooling mode, the system is operated in thermally overloaded
541
condition for many hours. For the system with the ORC module, in these hours, there is no
542
excess waste heat for the ORC module. Thus, the energy performance cannot be improved
543
by the ORC module. On the contrary, because of the smaller capacity of the PGU, more
544
electricity is required from the grid for the electrical and cooling demands, which leads to a
545
decrease of primary energy that can be saved. Although, as can be observed from Fig. 2, there
546
are also many hours with a low cooling demand, in which there is excess waste heat for ORC
547
to improve the energy performance of the system, the value of monthly PESR still decreases.
548
Therefore, the energy performance of the system with the ORC module is not as good as than
549
that of the system without the ORC module in summer. However, in winter and transient
550
seasons, the circumstance is the opposite. Owing to the low thermal demand, there is much
551
excess waste heat. The system with the ORC module performs better by generating electricity
552
with the excess waste heat. Therefore, it can be concluded that the energy performance of the
553
system is improved by the ORC module when there is much excess waste heat, and is
554
deteriorated when the system is thermally overloaded. By using ORC module, the energy
555
performance is improved by 6.1%−8.1% in the months with relatively low thermal demand,
556
and the variations of PESR are from −1.2% to 0.8% in the months with relatively high thermal
557
demand. Therefore, it can be concluded that the addition of ORC module can satisfactorily
558
solve the problem of recovering excess waste heat and thus, improve the energy performance
559
of the system.
560
4.2.2. Monthly performance on the economic aspect
ACCEPTED MANUSCRIPT 30 of 55
561
As shown in Fig. 13, the CSR ranges from 8.4% to 17.7% and from −3.2% to 8.6% for
562
the system with and without the ORC module, respectively. The variation trend of the CSR
563
in each system is similar to that of the PESR. Because of the purchase of grid electricity for
564
cooling demand during thermally overloaded hours, the CSR is lower from May to September
565
than in other months, ranging from 9.6% to 10.3%. Unlike the PESR, the CSR can be
566
improved in every month by adding the ORC module, with the variation ranging from 3.1%
567
to 11.8%. As previously mentioned, even in the months with high cooling demand, there are
568
many hours with low cooling demand. In these hours, owing to the power generation of the
569
ORC, the consumption of biomass feedstock decreases, thus, the operation cost of the system
570
is reduced. Meanwhile, in the thermally overloaded hours, the operation cost increases as the
571
increase of cooling demand. However, as can be observed from Fig. 10, the value of the CSR
572
decreases little with the increase of the cooling demand. Thus, the saved operation cost in the
573
hours with low thermal load exceeds the incremental cost in thermally overloaded hours,
574
which leads to increase of CSR in the month with more thermal demand. From the analysis
575
above and results shown in Fig. 13, it can be deduced that, the increase of the grid electricity
576
for cooling affects less significantly on the economic performance of the system. It is more
577
important to reduce biomass consumption by making use of the waste heat. For this reason,
578
the increment of the CSR by installation of the ORC is the largest among the three criteria.
579
The smallest and largest increment is in August and November respectively. In addition, after
580
installing the ORC module, the economic benefit can be achieved in every month.
581
4.2.3. Monthly performance on the environmental aspect
582
The CERRs of the system with and without the ORC module and their difference are
583
shown in Fig. 14. As discussed previously, the CO2 reduction of the CCHP system based on
584
biomass gasification is mostly due to the replacement of fossil fuel with biomass. Because
585
both systems have high ratios of the replacement, they both own relatively high monthly
ACCEPTED MANUSCRIPT 31 of 55
586
CERR, ranging from 35.5% to 45.8% and from 36.8% to 41.7%, respectively, for the system
587
with and without the ORC module. In the months with high thermal demand, under the dual
588
influence of lower efficiency and lower ratio of replacement of fossil fuel with biomass, the
589
decrease of the CERR due to the installation of the ORC module is higher than that of the
590
PESR, ranging from 0.5% to 1.3%. Furthermore, for the same reason, the effect of thermal-
591
demand overload is more noticeable on the environmental performance than on energetic and
592
economic performance for both systems. In the system with the ORC module, the decrease
593
of the CERR due to the overload of thermal demand is the sharpest, with the difference of
594
10.3% between the maximum and minimum value. Meanwhile, the CERRs of the system
595
without the ORC module in the thermally overloaded month are much lower than the other
596
months except November. In the months with low thermal demand, there is excess waste heat
597
for ORC, thus the environmental performance is improved. However, because the ratio of
598
replacement of fossil fuel with biomass in the CCHP system is relatively high, the
599
improvement by the ORC module is limited. As a result, the increment of CERR in these
600
months is the smallest among the three criteria, ranging from 4.0% to 5.8%.
601
4.2.4. Summary of monthly performance and analysis of annual performance
602
In summary, from May to September, the system is operated in cooling mode, which is
603
at a disadvantage on all the three aspects compared with heating mode. More importantly,
604
the system is in thermally overloaded condition in these months, which leads to even poorer
605
performances in the system with ORC, and little increase or even decrease of the criteria by
606
installing the ORC module. However, in the other months, owing to the utilization of excess
607
waste heat by ORC, the system has greater improvement in terms of all the three aspects. The
608
improvement is different due to the different influence factors of each aspect. The economic
609
performance is influenced by efficiency and economic factors simultaneously. The improve
610
the CSR, the key is to reduce biomass consumption. The ORC module has a positive effect
ACCEPTED MANUSCRIPT 32 of 55
611
on CSR in every month. As such, the installation of ORC module has the most significant
612
improvement of the annual CSR, which is improved from 4.0% to 11.1%, as shown in Fig.
613
15. Nevertheless, due to the capital cost to be set off, the annual CSR has lowest value among
614
all the three criteria. Owing to the much lower CO2 conversion factor of biomass than that of
615
fossil fuel and relatively high ratio of replacement of fossil fuel with biomass, the value of
616
the annual CERR is much higher than PESR and CSR. However, because of relatively high
617
decrease in overload condition and low increase in low-thermal-demand condition of the
618
CERR, the installation of the ORC module can only help improve the annual CERR from
619
42.3% to 43.7%, which is the least increment among the three criteria. The annual PESR can
620
be improved from 17.4% to 20.6%. It is only influenced by the efficiency factor. However,
621
similar to CERR, the most effective way to improve PESR is also to reduce the energy
622
consumption from the auxiliary system. By installing the ORC module, the decrease of PESR
623
in overload condition is mostly due to the reduction of the PGU capacity, and the increase in
624
the months with low thermal demand is only due to the utilization of excess waste heat. As a
625
result, the increment of annual PESR is between the annual CSR and CERR.
626
Although the results are obtained only from the operation simulation in Shanghai, the
627
results can also be expanded. From the seasonal comparison between the system with and
628
without the ORC module, the feasibility to integrate an ORC module with biomass
629
gasification-based CCHP system in different climate regions can be deduced. Generally, the
630
effectiveness of integration decreases with the increase of thermal demands. The integration
631
is relatively effective in the regions with a wild climatic condition as there is plenty of excess
632
waste heat. Meanwhile, as analyzed previously, the system has better performance and larger
633
advantage to integrate an ORC module in heating mode than in cooling mode. Therefore, for
634
the same amount of thermal demand, the system in hot zones is preferable to add an ORC
635
module than in cold zones.
636
4.2.5. Result comparison with references
ACCEPTED MANUSCRIPT 33 of 55
637
The comparison among results from this study and those from some references are
638
presented in Table 10. The system is compared with CCHP‒ORC systems of other types
639
before and after integrating the ORC module. Herein, in all these studies, the ORC module
640
is a supplementary device. The comparison comprises annual performance, highest/lowest
641
performance on a single aspect, and most/least beneficial to integrate an ORC module in
642
consideration of variation of each criterion. The numbers of study and the corresponding
643
system type are:
644
(1) This study. A CCHP‒ORC system based on biomass gasification.
645
(2) Ref. [57]. A CCHP‒ORC system fueled by natural gas and coupled with GSHP.
646
(3) Ref. [58]. A combined heating and power‒ORC system fueled by natural gas.
647
(4) Ref. [35]. A CCHP‒ORC system fueled by natural gas.
648
As can be observed from Table 10, among the four systems, the ORC integration makes
649
the least beneficial for the system in this study. One reason is that some energy is lost in the
650
process of biomass gasification, which cannot be recovered by the ORC module. On the other
651
hand, during the selection of the ORC fluid in this study, environment and commercial
652
availability are also taken into consideration. As a result, the efficiency of the ORC module
653
is lower than that in other studies. It is indicated that to improve the performance of this
654
system, to recover heat from gasification is one of the approaches. Overall, except for the
655
CCHP‒ORC system assisted by GSHP, the addition of the ORC module makes the most
656
benefit on economic aspect. For the performance of the entire system, the biomass
657
gasification based CCHP‒ORC system has higher performance on the environmental aspect,
658
but lower performance on the energetic and economic aspects. Due to the energy loss during
659
biomass gasification, as mentioned previously, and the low efficiency of the PGU caused by
660
low heating value of the syngas, the efficiency of the system is lower than the other ones.
661
This also affects the economic performance of the system. However, the main reason of lower
662
economic performance is the high capital cost of the additional components, the gasification
ACCEPTED MANUSCRIPT 34 of 55
663
device of the system. The same situation can also be observed in the CCHP‒ORC system
664
assisted by GSHP, which can save more on the operation cost (monthly CSR) but less on the
665
total cost (annual CSR) due to the expense on GSHP. Actually, high initial investment is a
666
common problem for renewable energies. Meanwhile, because the relatively high expense
667
on the treatment of biomass feedstock, the cost of the biomass feedstock has no distinct
668
advantage. For the above reasons, the overall economic performance of the investigated
669
system is worse than the CCHP‒ORC system driven by fossil fuels. Comparatively, the major
670
advantage of the investigated system lies in its environmental performance, owing to the
671
large difference of CO2 emission characteristics between biomass and fossil fuels. In terms
672
of operation mode, it can be found that all the systems perform better in heating mode, such
673
as in cold months or places, than in cooling mode, which reflects the advantage of the system
674
in cooling mode, as analyzed previously. The most benefits are obtained mainly in spring,
675
autumn, and places with mild climate, while the least amounts of benefits appear mainly in
676
summer, winter, and cold places. This agrees with the relative findings in previous sections.
677
In summary, to make more benefit, the ORC module should be integrated with the system
678
where there is less thermal demand, which agrees with the conclusion in this study.
679
4.3. Sensitivity analysis
680
In previous simulation and evaluation, some key parameters were assumed as constant.
681
Therefore, a sensitivity analysis is carried out to assess the effect of variation of the relevant
682
parameters on performances of the system. This can also help to find out the improvement
683
potential of the system by changing the parameters. The parameters involved in the sensitivity
684
analysis and the ranges of their possible values are listed in Table 11. It is noted that, to
685
reduce the number of involved parameters, the change of performance of the ORC module is
686
represented by the change of the working fluid. The change of other parameters of the ORC
687
module, including efficiencies of the pump, turbine and generator, are considered equivalent
688
to that of the working fluid, for they all influence ηorc ultimately. The efficiencies of the ORC
ACCEPTED MANUSCRIPT 35 of 55
689
module when using working fluids from Ref. [43], which are proven available for low-grade
690
waste heat, are calculated. According to the results, R134a and R11, with the ηorc values of
691
7.58% and 8.77%, respectively, are the least and most efficient working fluids. Therefore,
692
their values are considered as the range of the ηorc.
693
The results of the sensitivity analysis for PESR, CSR, and CERR are shown in Figs.
694
16−18, respectively. Generally, with the variations of various parameters, the advantage of
695
the system on energetic and economic aspects may be close to non-existent. However, owing
696
to the quite low CO2 conversion factor of biomass, the influence of the parameter variations
697
on CERR is relatively small. In the worst case, the value of CERR can still be as high as
698
38.0%, which means the integration would be environmentally effective in any circumstance
699
that one of the parameters was changed. Specifically, it can be observed that among the first
700
six parameters, which are relevant to all the three criteria, the gasification parameter, ER, has
701
the most noticeable effect on the performances of the system. If the ER were set as 0.25, the
702
PESR, CSR, and CERR could be improved by 6.6%, 6.3% and 3.6%, respectively. This is
703
because biomass is the major energy source of the system; therefore, reducing the
704
consumption of the biomass feedstock can effectively improve the performance of the system
705
on all aspects. Without considering some technical problems such as relatively low reaction
706
speed and high tendency to agglomerate, operating the gasifier under lower ER has great
707
potential to make comprehensive improvement on the system performance. Besides the
708
efficiency factor, the cost and CO2 emission factor, COSTf and μf respectively, also have large
709
effect on the corresponding criterion. Due to the change of COSTf and μf, the variations of
710
CSR and CERR range from −7.5% to 7.5% and −4.4% to 4.3%, respectively. The parameters
711
of the grid can also have great influence on the performance of the system. With the increase
712
of the grid efficiency, the value of PESR can be reduced to as low as 6.3%. Meanwhile, the
713
cost saving ratio would be only 1.5% if the cost of electricity were at the minimal value.
714
Owing to the much lower CO2 conversion factor of biomass than the other energy resources,
ACCEPTED MANUSCRIPT 36 of 55
715
the minimum CERR is still as high as 42.2% when the minimum value of μf is adopted. In
716
other words, in terms of energetic and economic aspects, the integration would not be
717
effective for the system located in places with high grid efficiency. It is noted that the
718
efficiency of the ORC module, with a relatively low value and a narrow variation range, can
719
barely influence the performance of the system on any aspect. The relevant variations are
720
within ±0.3% for all three criteria. This means, in terms of the existing ORC fluids that are
721
mature and commercially available, any change should not be considered as an improvement
722
the performance of the entire system. The parameters relevant to thermal energy, such as
723
COPs of chillers and some other efficiencies, only affect on the waste heat utilization. The
724
influence of their variations on the performance of the system is also very limited. In
725
summary, efficiency of the grid, cost of the grid electricity and biomass feedstock, and
726
emission factor of biomass feedstock have noticeable effect on energetic, economic, and
727
environmental aspects, respectively. Meanwhile, all three criteria are sensitivity to the
728
variation of equivalence ratio of biomass gasification.
729
5. Conclusions
730
In this study, a biomass gasification-based combined cooling, heating, and power system
731
integrated with organic Rankine cycle is simulated and evaluated. The simulation is based on
732
a large office building located in Shanghai and the evaluation is composed of energetic,
733
economic, and environmental aspects. The main conclusions that can be drawn are as follows:
734
(1) Generally, this system can achieve benefits on energetic, economic and
735
environmental aspects. The primary energy saving ratio, cost saving ratio, and CO2
736
emission reduction ratio are 20.6%, 11.1%, and 43.7%, respectively. Comparing with
737
the system without the ORC module, the increments of the above criteria are 3.2%,
738
7.1%, and 1.4%, respectively.
ACCEPTED MANUSCRIPT 37 of 55
739
(2) Because of different energy conversion efficiencies of generating heating and
740
cooling energy in this system and the conventional system, the performance of this
741
system is better in heating mode than in cooling mode on all the three aspects.
742
(3) Thermal demand is a decisive factor for the performance of the system. In the month
743
that the system is operated in thermally overloaded condition, it performs poorly;
744
meanwhile, the effect of installation of ORC module is slightly positive or even
745
negative. When the thermal load is low, the system has good performance as well as
746
obvious improvement owing to the ORC module. Therefore, the climate regions in
747
preferable order to install an ORC module are mild-climate regions > cold regions>
748
hot regions.
749
(4) Sensitivity analysis shows that the equivalence ratio of gasification has large effect
750
on the performances of the system on all the three aspects. The increment of PESR,
751
CSR, and CERR with the decrease of the ER can be up to 6.6%, 6.3%, and 3.6%,
752
respectively. The parameters of the grid and biomass feedstock can also affect the
753
corresponding criteria significantly. The efficiencies of the components of the system
754
have little influence on the performance of the system.
755
Acknowledgments: This work is supported by the Key Project of the National Science
756
Foundation of China for international academic exchanges under the contract No.
757
51561145012. The support from the Sino-Thai Cooperation Research Project under National
758
Research Council of Thailand is also appreciated.
759
References
760
[1] Li M, Mu H, Li N, Ma B. Optimal design and operation strategy for integrated evaluation
761
of CCHP (combined cooling heating and power) system. Energy 2016, 99, 202−220.
762
[2] De Meyer A, Snoeck M, Cattrysse D, Van Orshoven J. A reference data model to support
763
biomass supply chain modelling and optimisation. Environ. Modell. Softw. 2016, 83, 1‒11.
ACCEPTED MANUSCRIPT 38 of 55
764
[3] Chen WH, Peng J, Bi, XT. A state-of-the-art review of biomass torrefaction,
765
densification and applications. Renew. Sustain. Energy Rev. 2015, 44, 847‒866.
766
[4] Mikulčić H, Von Berg E, Vujanović M, Duić N. Numerical study of co-firing pulverized
767
coal and biomass inside a cement calciner. Waste Manage. Res. 2014, 32(7), 661−669.
768
[5] Shabbar S, Janajreh I. Thermodynamic equilibrium analysis of coal gasification using
769
Gibbs energy minimization method. Energy Convers. Manag. 2013, 65, 755−763.
770
[6] Sansaniwal SK, Pal K, Rosen MA, Tyagi S K. Recent advances in the development of
771
biomass gasification technology: A comprehensive review. Renew. Sustain. Energy Rev.
772
2017, 72, 363−384.
773
[7] Minutillo M, Perna A, Jannelli E, Cigolotti V, Nam SW, Yoon SP, Kwon BW. Coupling
774
of Biomass Gasification and SOFC–Gas Turbine Hybrid System for Small Scale
775
Cogeneration Applications. Energy Procedia 2017, 105, 730−737.
776
[8] Lv X, Gu C, Liu X, Weng Y. Effect of gasified biomass fuel on load characteristics of
777
an intermediate-temperature solid oxide fuel cell and gas turbine hybrid system. Int. J.
778
Hydrog. Energy 2016, 41(22), 9563−9576.
779
[9] Elsner W, Wysocki M, Niegodajew P, Borecki R. Experimental and economic study of
780
small-scale CHP installation equipped with downdraft gasifier and internal combustion
781
engine. Appl. Energy 2017, 202, 213−227.
782
[10] Skorek-Osikowska A, Kotowicz J, Uchman W. Thermodynamic assessment of the
783
operation of a self-sufficient, biomass based district heating system integrated with a Stirling
784
engine and biomass gasification. Energy 2017, 141, 1764−1778.
785
[11] Puig-Arnavat M, Bruno JC, Coronas A. Modeling of trigeneration configurations based
786
on biomass gasification and comparison of performance. Appl. Energy 2014, 114, 845−856.
787
[12] Wang H, Yan J, Dong L. Simulation and economic evaluation of biomass gasification
788
with sets for heating, cooling and power production. Renew. Energy 2016, 99, 360−368.
ACCEPTED MANUSCRIPT 39 of 55
789
[13]
Barik D, Murugan S. Investigation on combustion performance and emission
790
characteristics of a DI (direct injection) diesel engine fueled with biogas–diesel in dual fuel
791
mode. Energy 2014, 72, 760-771.
792
[14] Pantaleo AM, Camporeale S, Shah N. Natural gas–biomass dual fuelled microturbines:
793
Comparison of operating strategies in the Italian residential sector. Appl. Therm. Eng. 2014,
794
71(2), 686−696.
795
[15]
796
cycles through primary steam reforming of the natural gas. Energy 2007, 32(4), 371-377.
797
[16] Pihl E, Heyne S, Thunman H, Johnsson F. Highly efficient electricity generation from
798
biomass by integration and hybridization with combined cycle gas turbine (CCGT) plants for
799
natural gas. Energy 2010, 35(10), 4042−4052.
800
[17] Wang J, Mao T, Sui J, Jin H. Modeling and performance analysis of CCHP (combined
801
cooling, heating and power) system based on co-firing of natural gas and biomass gasification
802
gas. Energy 2015, 93, 801−815.
803
[18] Li CY, Wu JY, Zheng CY, Wang, RZ. Effect of LPG addition on a CCHP system based
804
on different biomass-derived gases in cooling and power mode. Appl. Therm. Eng. 2017,
805
115, 315−325.
806
[19] Wang J, Yang Y. Energy, exergy and environmental analysis of a hybrid combined
807
cooling heating and power system utilizing biomass and solar energy. Energy Convers.
808
Manage. 2016, 124, 566−577.
809
[20] Esen H, Inalli M, Esen M. A techno-economic comparison of ground-coupled and air-
810
coupled heat pump system for space cooling. Build. Environ. 2007, 42(5), 1955-1965.
811
[21] Esen H, Inalli M, Esen M. Technoeconomic appraisal of a ground source heat pump
812
system for a heating season in eastern Turkey. Energy Convers. Manage. 2006, 47(9-10),
813
1281-1297.
De Ruyck J, Delattin F, Bram S. Co-utilization of biomass and natural gas in combined
ACCEPTED MANUSCRIPT 40 of 55
814
[22]Li H, Zhang X, Liu L, Wang S, Zhang G. Proposal and research on a combined heating
815
and power system integrating biomass partial gasification with ground source heat pump.
816
Energy Convers. Manage. 2017, 145, 158−168.
817
[23] Esen M, Yuksel T. Experimental evaluation of using various renewable energy sources
818
for heating a greenhouse. Energy Build. 2013, 65, 340‒351.
819
[24] Jradi M, Riffat S. Modelling and testing of a hybrid solar‒biomass ORC-based micro-
820
CHP system. Int. J. Energy Res. 2014, 38(8), 1039‒1052.
821
[25]Zheng CY, Wu JY, Zhai XQ. A novel operation strategy for CCHP systems based on
822
minimum distance. Appl. Energy 2014; 128; 325−335.
823
[26] Wang JJ, Yang K, Xu ZL, Fu C. Energy and exergy analyses of an integrated CCHP
824
system with biomass air gasification. Appl. Energy 2015, 142, 317−327.
825
[27] Yang K, Zhu N, Yuan T. Analysis of optimum scale of biomass gasification combined
826
cooling heating and power (CCHP) system based on life cycle assessment (LCA). Procedia
827
Eng. 2017, 205, 145−152.
828
[28] Li X, Shen Y, Kan X, Hardiman TK, Dai Y, Wang CH. Thermodynamic assessment of
829
a solar/autothermal hybrid gasification CCHP system with an indirectly radiative
830
reactor. Energy 2017, 142, 201−214.
831
[29] Palomba V, Prestipino M, Galvagno A. Tri-generation for industrial applications:
832
Development of a simulation model for a gasification-SOFC based system. Int. J. Hydrog.
833
Energy 2017, 42(46), 27866−27883.
834
[30] Facci AL, Andreassi L, Ubertini S. Optimization of CHCP (combined heat power and
835
cooling) systems operation strategy using dynamic programming. Energy 2014, 66,
836
387−400.
837
[31] Wu JY, Wang J L, Li S. Multi-objective optimal operation strategy study of micro-
838
CCHP system. Energy 2012, 48(1), 472−483.
ACCEPTED MANUSCRIPT 41 of 55
839
[32] Teng X, Wang X, Chen Y, Shi W. A simple method to determine the optimal gas turbine
840
capacity and operating strategy in building cooling, heating and power system. Energy Build.
841
2014, 80, 623−630.
842
[33] Han G, You S, Ye T, Sun P, Zhang H. Analysis of combined cooling, heating, and power
843
systems under a compromised electric–thermal load strategy. Energy Build. 2014, 84,
844
586−594.
845
[34] Zheng CY, Wu JY, Zhai XQ, Wang RZ. Impacts of feed-in tariff policies on design and
846
performance of CCHP system in different climate zones. Appl. Energy 2016, 175, 168−179.
847 848
[35] Fang F, Wei L, Liu J, Zhang J, Hou G. Complementary configuration and operation of
849
a CCHP‒ORC system. Energy 2012, 46(1), 211−220.
850
[36] Mohammadi A, Kasaeian A, Pourfayaz F, Ahmadi MH. Thermodynamic analysis of a
851
combined gas turbine, ORC cycle and absorption refrigeration for a CCHP system. Appl.
852
Therm. Eng. 2017, 111, 397−406.
853
[37] Seyedkavoosi S, Javan S, Kota K. Exergy-based optimization of an organic Rankine
854
cycle (ORC) for waste heat recovery from an internal combustion engine (ICE). Appl.
855
Therm. Eng. 2017, 126, 447−457.
856
[38] Song J, Song Y, Gu CW. Thermodynamic analysis and performance optimization of an
857
Organic Rankine Cycle (ORC) waste heat recovery system for marine diesel engines. Energy
858
2015, 82, 976−985.
859
[39] Hajabdollahi H. Investigating the effects of load demands on selection of optimum
860
CCHP‒ORC plant. Appl. Therm. Eng. 2015, 87, 547−558.
861
[40] Wu Q, Ren H, Gao W, Weng P, Ren J. Design and operation optimization of organic
862
Rankine cycle coupled trigeneration systems. Energy 2017, 142, 666−677.
ACCEPTED MANUSCRIPT 42 of 55
863
[41] Raman P, Ram NK. Performance analysis of an internal combustion engine operated on
864
producer gas, in comparison with the performance of the natural gas and diesel
865
engines. Energy 2013, 63, 317−333.
866
[42] US Department of Energy. Introduction to weather data sources. https://www.energyp
867
lus.net/weather/sources#CSWD. 2017.
868
[43] US Department of Energy. Weather data by location, Shanghai, Chinese Standard
869
Weather Data (CSWD). https://www.energyplus.net/weather-location/asia_wmo_region_2/C
870
HN//CHN_Shanghai.Shanghai.583620_CSWD. 2017.
871
[44]US Department of Energy. Energy plus. http://www.energyplus.gov. 2017.
872
[45]ASHRAE, 2012 ASHRAE Handbook -- HVAC Systems and Equipment (I-P), Har/Cdr
873
ed.; ASHRAE: Atlanta, the United States, 2012.
874
[46]Wang JJ, Jing YY, Zhang CF, Zhai ZJ. Performance comparison of combined cooling
875
heating and power system in different operation modes. Appl. Energy 2011, 88(12),
876
4621−4631.
877
[47] Maraver D, Sin A, Royo J, Sebastián F. Assessment of CCHP systems based on biomass
878
combustion for small-scale applications through a review of the technology and analysis of
879
energy efficiency parameters. Appl. Energy 2013, 102, 1303−1313.
880
[48] Sharma S, Sheth PN. Air–steam biomass gasification: experiments, modeling and
881
simulation. Energy Convers. Manage. 2016, 110, 307−318.
882
[49] Yaws CL, Gabbula C. Yaws" Handbook of Thermodynamic and Physical Properties of
883
Chemical Compounds. Knovel, 2003.
884
[50] Svishchev DA, Kozlov AN, Donskoy IG, Ryzhkov AF. A semi-empirical approach to
885
the thermodynamic analysis of downdraft gasification. Fuel 2016, 168, 91−106.
886
[51] Ong Z, Cheng Y, Maneerung T, Yao Z, Tong YW, Wang CH, Dai, Y. Co-gasification
887
of woody biomass and sewage sludge in a fixed-bed downdraft gasifier. AIChE
888
Journal, 61(8), 2508−2521.
ACCEPTED MANUSCRIPT 43 of 55
889
[52]Chintala V, Kumar S, Pandey JK. A technical review on waste heat recovery from
890
compression ignition engines using organic Rankine cycle. Renew. Sustain. Energy Rev.
891
2018, 81, 493−509.
892
[53] Lemmon EW, Huber ML, McLinden MO. NIST standard reference database 23:
893
reference fluid thermodynamic and transport properties: REFPROP. 9.1, 2013.
894
[54] Sanaye S. Khakpaay N. Simultaneous use of MRM (maximum rectangle method) and
895
optimization methods in determining nominal capacity of gas engines in CCHP (combined
896
cooling, heating and power) systems. Energy 2014, 72, 145-158.
897
[55] Wang JJ, Xu ZL, Jin HG, Shi GH, Fu C, Yang K. Design optimization and analysis of
898
a biomass gasification based BCHP system: A case study in Harbin, China. Renew. Energy
899
2017, 71, 572−583.
900
[56] Wang C, Chang Y, Zhang L, Pang M. Hao Y. A life-cycle comparison of the energy,
901
environmental and economic impacts of coal versus wood pellets for generating heat in
902
china. Energy 2017, 120, 374−384.
903
[57] Kang L, Yang J, An Q, Deng S, Zhao J, L, Z, Wang Y. Complementary configuration
904
and performance comparison of CCHP‒ORC system with a ground source heat pump under
905
three energy management modes. Energy Convers. Manage. 2017, 135, 244‒255.
906
[58] Mago PJ, Hueffed A, Chamra LM. Analysis and optimization of the use of CHP–ORC
907
systems for small commercial buildings. Energy Build. 2010, 42(9), 1491‒1498.
908
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909
Tables
910
Table 1. Characteristics of demand areas divided by boundaries of output area of the PGU.
Area No. Characteristic 1
Operating PGU in the idle condition. On. Matching Eorc with electrical demand. Releasing excess waste heat. Sufficient Epgu and Qpgu. Operating the system at the matching point. On. Sufficient Qpgu but insufficient Epgu. Operating PGU in the full-load condition. On. Operating ORC with excess waste heat. Supplementing electricity from the grid. Sufficient Epgu but insufficient Qpgu. Matching Epgu with electrical demand. Off. Supplementing thermal energy from the auxiliary system. Insufficient Epgu and Qpgu. Operating PGU in full-load condition. Off. Supplementing electricity from the grid. Supplementing thermal energy from the auxiliary system.
4 5
911
Table 2. Heat of formation of compound species related to the gasification model [49]. Species
H0 f(kJ/mol)
CO CO2 H2O(l) CH4
−110.6 −393.8 −285.8 −74.9
Table 3. Polynomial coefficients of cp of the species related to the gasification model [49]. Species
a0
a1
a2
a3
a4
H2 CO CO2 H2O CH4 N2 C
25.399 29.566 27.437 33.933 34.942 29.342 -0.832
2.0178×10−2 -6.5807×10-3 4.2315×10-2 -8.4186×10-3 -3.9660×10-2 -3.5935×10-3 3.4846×10-2
-3.8549×10-5 2.0130×10-5 -1.9555×10-5 2.9906×10-5 1.9184×10-4 1.0076×10-5 -1.3223×10-5
3.1880×10-8 -1.2227×10-8 3.9968×10-9 -1.7825×10-8 -1.53×10-7 -4.3166×10-9 0
-8.7585×10-12 2.2617×10-12 -2.9872×10-13 3.6934×10-12 3.9321×10-11 2.5935×10-13 0
913
Table 4. Coefficients of equilibrium constant functions [48]. K1 K2 K3
914
ORC
Excess Epgu and Qpgu.
2 3
912
Strategy
b1
b2
7082.848 5870.53 -22784.85
-6.567 1.86 7.951
b3
b4
b5
3.733×10-3 -3.6067×10-7 3.505×10-6 0 -2.7×10-4 -5.82×104 -4.354×10-3 3.6067×10-7 4.85×103
b6 32.541 -18.007 -24.899
Table 5. The properties of the biomass feedstock [50] and the syngas at nominal condition. Item
Value
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biomass
a (-) b (-) w (-) LHV (kJ/mol) H0 f (kJ/mol) x1 (-) x2 (-) x5 (-) LHV (kJ/mol)
syngas
915
1.49 0.69 0.10 439.1 -122.9 0.161 0.176 0.022 106.5
Table 6. Correlation results of Epgu and Qpgu from jacket water and exhaust gas [11]. PGU J312 GS J316 GS
Epgu c0 -81.821 -100.71
Qpgu from jacket water
c1 0.430 0.421
916
R2 1.00 0.99
c0 68.433 91.782
c1 0.243 0.182
Qpgu from exhaust gas
R2
c0 -2.303 1.897
1.00 0.99
c1 0.200 0.244
R2 1.00 0.99
Table 7. Unit prices of the equipment [55].
Equipment
Gasifier
PGU
ORC
Boiler
Heating coil SAC
Electric AC
Unit price (Yuan/kW)
2500
6800
12000
375
200
970
917
1200
Table 8. Unit prices of energy resources [55]. Energy resource
Electricity
Unit price (Yuan/kWh) 0.936
918
NG
Biomass feedstock
0.385
0.345
Table 9. CO2 conversion factors of different energy sources [25, 56]. Energy resource
Electricity
NG
Biomass feedstock
μ (kg/kWh)
0.923
0.220
0.030
919
Table 10. Comparison on results from this study and Refs. [35, 57, 58]. No.
Energetic
Economic
Environmental
Annual
(1)
PESR=20.6%, ΔPESR=3.2%
CSR=11.1%, ΔCSR=7.1%
CERR=43.7%, ΔCERR=1.4%
performance
(2)
PESR=76.5%, ΔPESR=22.4%
CSR=8.3%, ΔCSR=11.8%
CERR=50.1%, ΔCERR=17.7%
PESR=29.7%‒34.9%,
CSRa=13.8%‒55.1%,
CERR=0.5%‒28.6%
ΔPESR=12.4%‒19.3%
ΔCSRa=6.7%‒24.1%
ΔCERR=-4.5%‒17.9%
(4)b, c, d
ΔPEC=14.7%
ΔCOST=26.1%
ΔCE=24.1%
Highest
(1)
PSER=24.2%, Jan.
CSR=17.7%, Jan.
CERR=45.8%, Jan.
performance
(2)
PSER=93.4%, Dec.
CSRa=49.6%, Mar.
CERR=62.1%, Mar.
(3)
PESR=34.9%, hot‒humid
CSRa=55.1%, severe cold
CERR=38.6%, very cold
(4)d
-
-
-
Lowest
(1)
PESR=18.7%, Aug.
CSR=8.4%, Oct.
CERR=35.5%, Aug.
performance
(2)
PESR=49.6%, Jul.
CSRa=25.5%, Aug.
CERR=33.7%, Jul.
PESR=29.7%, hot‒dry
CSRa=13.9%,
CERR=-0.4%, hot‒dry
and variation
(3)
(3)
mixed‒humid
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Most beneficial to integrate Least
(4)d
-
-
-
(1)
ΔPESR=8.1%, Nov.
ΔCSR=11.8%, Nov.
ΔCERR=4.6%, Mar.
ΔPESR=60.2%, Jan.
ΔCSRa=29.0%,
Mar.
ΔCERR=37.0%, Dec.
(3)
ΔPESR=19.3%, hot‒dry
ΔCSRa=24.1%,
mixed‒humid
ΔCERR=17.9%, mixed‒humid
(4)b
ΔPEC=24.8%, summer
ΔCOST=32.7%, autumn
ΔCER=34.1%, autumn
(1)
ΔPESR=-1.2%, Aug.
ΔCSR=3.1%, Aug.
ΔCERR=-1.6%, Aug. ΔCERR=0.1%, Jul.
(2)
beneficial to
(2)
ΔPESR=-0.4%, Oct.
ΔCSRa=-0.8%,
integrate
(3)
ΔPESR=12.4%, very cold
ΔCSRa=6.7%, severe cold
920 921 922 923 924 925 926 927
929
ΔCERR=-4.5%, hot‒dry
(4)b
ΔPEC=4.9%, winter ΔCOST=18.1%, winter ΔCE=15.0%, winter Refers to operational cost saving ratio. b The energy consumption of the conventional system is not provided. ΔPEC = (PEC cchp-PECcchp-orc)/ PECcchp. ΔCOST and ΔCE are calculated in the same way. c The annual performance is based on the assumption that the number of days in each season equals and the daily energy consumption equals to that of representative day. d The energy consumption of the conventional system is not provided. Therefore, the performance of the system cannot be judged just by criteria of absolute value. a
Table 11. Adopted values and ranges of the parameters for sensitivity analysis [47, 48]. No. Parameter Value 1 ER (-) 0.35 2 ηorc (-) 0.0827 3 ηb (-) 0.8 4 ηrec (-) 0.8 5 COPel (-) 3.5 6 COPSAC (-) 0.7 7 ηg (-) 0.323 8 I (-) 0.0655 9 COSTf (Yuan/kWh) 0.345 10 COSTel (Yuan/kWh) 0.936 11 COSTng (Yuan/kWh) 0.385 12 μf (g/kWh) 31.2 13 μel (g/kWh) 923 14 μng (g/kWh) 220
928
Jul.
Range 0.25−0.35 0.0758−0.0877 0.7−0.9 0.7−0.9 3.0−4.0 0.6−0.75 0.230−0.416 0.0594−0.0774 0.293−0.397 0.796−1.076 0.327−0.443 26.5−35.9 877−968 185−255
Relevant criteria PESR, CSR, CERR PESR, CSR, CERR PESR, CSR, CERR PESR, CSR, CERR PESR, CSR, CERR PESR, CSR, CERR PESR CSR CSR CSR CSR CERR CERR CERR
ACCEPTED MANUSCRIPT 47 of 55
Figures 100
30
80 60
20
40
10
20
Temperature Relative humidity
0 0
1095
2190
6570
7665
Relative humidity (%)
40
Temperature (°C)
930
0 8760
931
3285 4380 5475 Number of hours
932
Fig. 1. Hourly temperature and relative humidity of Shanghai [46].
933 3000
electricity demand
cooling demand
heating demand
Demand (kW)
2500 2000 1500 1000 500 0
934 935
0
1095
2190
3285 4380 5475 Number of hours
6570
7665
8760
Fig. 2. Energy demands of a large office building located in Shanghai.
936 937
Fig. 3. Schematic of the biomass gasification based CCHP system integrated with ORC.
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output curve of PGU boundaries of output area of PGU with ORC demand operation
Q/Epgu,max
1.5
1.0 Qaux
F Eg,el
B
G Eg,el
① Qpgu for ORC
0.5
①
Qaux
①
A matching point
①
Qrelease
0.0
0.0 ①
938 939
C
D
0.5
E/Epgu,max
1.0
1.5
Fig. 4. Operation strategy of the biomass gasification-based CCHP system integrated with an ORC.
940 941
Fig. 5. Flow chart of operation simulation of the system.
ACCEPTED MANUSCRIPT 49 of 55
942 943
Fig. 6. The methodology of this study.
944 945
Fig. 7. Comparison between simulative and experimental results [50] of the gasification model.
946 947
Fig. 8. (a) Schematic and (b) T-s diagram of the ORC module.
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3000 electricity demand cooling demand heating demand
Demand (kW)
2500 2000 1500 1000
(3583, 843.24)
Y
500 0
948 949
0
1095
2190
3285 X 4380 5475 Number of hours
6570
Fig. 9. Cumulative demands of the building.
7665
8760
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950 951
Fig. 10. Contour maps of PESR, CSR and CERR of the system in cooling mode.
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952 953
Fig. 11. Contour maps of PESR, CSR and CERR of the system in heating mode.
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system with ORC system without ORC ΔPESR
8 6
PESR(%)
20
4 2
15
ΔPESR(%)
25
0 10
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
-2
954
Month
955
Fig. 12. Monthly PESR and ∆PESR of the system with and without the ORC module.
CSR(%)
15
system with ORC system without ORC ΔCSR
12 10 8
10
6 5
4
0 -5
ΔCSR(%)
20
2 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
0
956
Month
957
Fig. 13. Monthly CSR and ∆CSR of the system with and without the ORC module.
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6 system with ORC system without ORC ΔCERR
45
40
2
ΔCERR(%)
CERR(%)
4
0 35
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
-2
958
Month
959
Fig. 14. Monthly CERR and ∆CERR of the system with and without the ORC module. 17.4%
PESR
system without ORC system with ORC
Criterion
20.6% 4.0%
CSR
11.1% 42.3%
CERR
43.7% 0
10
960 961
20
30 40 Percentage (%)
50
60
Fig. 15. Annual PESR, CSR and CERR of the system with and without the ORC module. 35 ηg
Annual PESR (%)
30
ER
25 20 21.5
15 10
21.0 20.5 20.0
5
19.5
Minimum
962
COPel ηb PESR = 20.6%
ηrec ηorc COPsac Adopted
Value of parameter
Maximum
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963
Fig. 16. The sensitivity of the PESR of the system integrated with ORC module. 20 COSTel
COSTf ER
Annual CSR (%)
15
COSTng
COPsac
10 11.6 11.4
5
11.2 11.0
I CSR = 11.1%
COPel
ηrec ηorc
10.8
0
Minimum
Maximum
Adopted
Value of parameter
964 965
ηb
Fig. 17. The sensitivity of the CSR of the system integrated with ORC module.
Annual CERR (%)
48
μf ER
46 44 42 40
COPsac
μel
ηb 43.7 43.6 43.5
COPel
ηrec
μng ηorc
38 43.4
Minimum
966 967 968
Adopted
Maximum
Value of parameter
Fig. 18. The sensitivity of the CERR of the system integrated with ORC module.
ACCEPTED MANUSCRIPT
A biomass gasification-based CCHP system integrated with ORC was investigated.
The performance of the system in cooling/heating mode was analyzed.
The system was evaluated based on operation simulation of a building in Shanghai.
Analysis and evaluation comprised energy, economic and environmental aspects.
C.Y. Li, T. Deethayat, J.Y. Wu, T. Kiatsiriroat, R.Z. Wang PII:
S0360-5442(18)31052-1
DOI:
10.1016/j.energy.2018.05.206
Reference:
EGY 13041
To appear in:
Energy
Received Date:
12 January 2018
Accepted Date:
31 May 2018
Please cite this article as: C.Y. Li, T. Deethayat, J.Y. Wu, T. Kiatsiriroat, R.Z. Wang, Simulation and evaluation of a biomass gasification-based combined cooling, heating, and power system integrated with an organic Rankine cycle, Energy (2018), doi: 10.1016/j.energy.2018.05.206
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.
ACCEPTED MANUSCRIPT 1
4
Simulation and evaluation of a biomass gasificationbased combined cooling, heating, and power system integrated with an organic Rankine cycle
5
C.Y. Lia, T. Deethayatb, J.Y. Wua,*, T. Kiatsiriroatb, and R.Z. Wanga
2 3
6
a Institute
of Refrigeration and Cryogenics, Shanghai Jiao Tong Univerasity, Dongchuan Road
7 8
800, Shanghai 200240, China b Department
of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang
9
Mai 50200, Thailand
10
11 12
*
Corresponding author. Tel.: +86 21 34206776; fax: +86 21 34206814
E-mail address: [email protected] (Jingyi Wu)
ACCEPTED MANUSCRIPT 2 of 55
13
Abstract: A biomass gasification-based combined cooling, heating, and power system
14
integrated with an organic Rankine cycle is investigated. The energetic, economic, and
15
environmental performances of the system in cooling mode and heating mode are analyzed.
16
Based on the electrical and thermal demands of a large office building in Shanghai, the
17
monthly and annual operation of the system is simulated and its performance is evaluated.
18
Results show that the system has a better performance in its heating mode than in its cooling
19
mode. Meanwhile, the advantage of this system over the system without the ORC module
20
decreases with the increase of the thermal demand. The annual primary energy saving ratio,
21
cost saving ratio, and CO2 emission reduction ratio are 20.7%, 11.1%, and 43.7%,
22
respectively. The increments of the above criteria owing to installation of the ORC module
23
are 3.2%, 7.1%, and 1.4%, respectively. Sensitivity analysis shows that the equivalence ratio
24
of biomass gasification has a relatively greater influence of the system on all aspects, which
25
can increase the above criteria by up to 6.6%, 6.3%, and 3.6%, respectively. In addition, the
26
parameters of the biomass feedstock and the public grid on different aspects affect the
27
system performance on the corresponding aspect significantly.
28
Keywords: biomass gasification; combined cooling, heating, and power system; organic
29
Rankine cycle; simulation and evaluation
30 31
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Nomenclature a, b
coefficients in chemical formula of biomass (-)
ai, bi, ci
fitting coefficients (-)
C
capacity of the equipment (kW)
CE
CO2 emission (kg)
CERR
CO2 emission reduction ratio (-)
COP
coefficient of performance (-)
cost
unit price (Yuan/kW or Yuan/kWh)
COST
cost (Yuan)
cp
specific heat capacity at constant pressure (kJ∙mol-1∙K-1)
CSR
cost saving ratio (-)
E
electric power (kW)
En
energy output per unit time(kW)
ER
equivalence ratio (-)
F
fuel energy consumption rate (kW)
h
specific enthalpy (kJ/kg)
H0 f
heat of formation (kJ/mol)
Hvap
latent heat of vaporization (kJ/kg)
I
interest rate (-)
K
equilibrium constant (-)
l
service life (year)
LHV
lower heating value (kJ/mol)
m
molar flow rate of air per mol biomass (mol/mol)
mi
molar fraction of element (-)
ṁ
mass flow rate (kg/s)
n
molar generation rate per mol biomass (mol/mol)
N
number (-)
PEC
primary energy consumption (kWh)
PESR
primary energy saving ratio (-)
Q
thermal power (kW)
R
capital recovery factor (-)
SD
standard deviation (-)
T
temperature (K)
W
work (kWh)
w
molar water content per mol biomass (mol/mol)
Y
value of data (unit of the represented data)
x
proportion of waste heat for thermal demand (-)
ACCEPTED MANUSCRIPT 4 of 55
xi
molar fraction (-)
Subscripts amb
ambient
b
boiler
bio
biomass or biomass feedstock
c
cooling
cap
capital
cchp
combined cooling, heating, and power system
cg
cold gas
conv
conventional system
d
demand
el
electrical or electricity
exp
experiment
g
public grid
gen
generator of the organic Rankine cycle
h
heating
hr
hour
max
maximum
min
minimum
model
model prediction
ng
natural gas
op
operation
orc
organic Rankine cycle
p
pump of the organic Rankine cycle
pgu
power generation unit
r
reduction zone of the gasifier
rec
waste heat recovery
release
release
s
syngas
sac
single-effect absorption chiller
t
turbine of the organic Rankine cycle
Greek symbols η
efficiency (-)
μ
CO2 emission factor (kg/kWh)
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Abbreviations
32
AC
air conditioner
CSWD
Chinese Standard Weather Data
FEL
following the electric load
G
generator
GSHP
ground source heat pump
HX
heat exchanger
ICE
internal combustion engine
LPG
liquefied petroleum gas
NG
natural gas
ORC
organic Rankine cycle
PGU
power generation unit
SAC
single-effect absorption chiller
ACCEPTED MANUSCRIPT 6 of 55
33
1. Introduction
34
A combined cooling, heating, and power (CCHP) system, in which the waste heat of the
35
prime mover is recovered for cooling and heating purposes, provides an efficient way to
36
achieve energy saving, economic saving, and greenhouse gas emission reduction [1].
37
However, due to the issues of energy crisis and environment pollutions, an even higher
38
advantage can be obtained by feeding the system with renewable energy. Biomass is an
39
attractive alternative to fossil fuels of the CCHP system owing to its abundance [2], wide
40
distribution [3], and CO2 neutrality [4]. Among the energy conversion technologies of
41
biomass, gasification is a self-sufficient thermo‒chemical process that can be used to produce
42
clean gaseous fuel [5]. Owing to the optimum utilization of available biomass feedstock,
43
since both hydrogen and carbon contribute to the heating value to a great extent, biomass
44
gasification has higher energy conversion efficiency than combustion and pyrolysis [6].
45
Furthermore, the gasification product, called syngas or producer gas, is applicable to various
46
prime movers of CCHP systems, such as solid oxide fuel cells [7], gas turbines [8], internal
47
combustion engines (ICEs) [9], and Stirling engines [10]. Therefore, biomass gasification-
48
based CCHP systems have attracted increasing attention.
49
Studies in biomass gasification-based CCHP systems were conducted on multiple
50
aspects. Puig-Arnavat et al. [11] modeled and evaluated different configurations of CCHP
51
systems based on biomass gasification. The systems of all the studied configurations could
52
be considered as “high efficiency systems” owing to relatively high overall efficiency,
53
electrical equivalent efficiency, primary energy saving, and exergy efficiency, with the
54
values of 53.2%−64.2%, 34.1%−42.7%, 7.8%−8.9%, and 18.9%−21.8%, respectively. Wang
55
et al. [12] investigated the effect of key parameters on the performance of a CCHP system
56
integrated with a biomass gasifier. The gasifier could provide syngas with a lower heating
57
value (LHV) of 5.665 MJ/m3 and the gasification efficiency is up to 77.45% when it was
58
operated at the optimized condition. Meanwhile, for the entire CCHP system, it could have
ACCEPTED MANUSCRIPT 7 of 55
59
lower CO2 and SO2 emissions with total energy efficiency and syngas yield of 75.43% and
60
2.476 m3/kg, respectively.
61
Besides changing the system itself, combined with other types of energy recourses is
62
another approach to improve the system performance. With the co-firing technology proven
63
applicable and more efficient for different types of prime movers of CCHP systems, such as
64
ICEs [13], micro turbines [14], steam turbines [15], and gas turbines [16], addition of fossil
65
fuel into syngas becomes a simple and direct way to improve the performance of biomass
66
gasification-based CCHP systems. Wang et al. [17] studied a CCHP system based on co-
67
firing of syngas and natural gas (NG). With the mixture ratio of NG to syngas increased from
68
0 to 1, the energy and exergy efficiency of the system could be improved from 70.0% to
69
79.5% and from 21.9% to 35.6%, respectively. Li et al. [18] evaluated and compared the
70
performance of a CCHP system when liquefied petroleum gas (LPG) was added into different
71
biomass-derived fuel gases. It was found that the CCHP system fueled by syngas was the
72
most attractive to improve system performance by adding LPG. The primary energy ratio,
73
exergy efficiency, and energy saving ratio could be improved by up to 7.4%, 5.1%, and
74
17.8%, respectively.
75
Although the addition of fossil fuel can help to improve the energy performance, it was
76
also pointed out that the fossil energy consumption and environment pollution would increase
77
[17], which would weaken the advantage of biomass-based systems. To overcome this
78
drawback, hybrid energy systems, in which multiple renewable energy resources were
79
integrated, were investigated. Wang and Yang [19] proposed a CCHP system integrated with
80
a biomass gasifier and a solar evacuated collector. The entire system could achieve the
81
primary energy ratio and exergy efficiency of 57.9% and 16.1%. More importantly, the
82
carbon emission reduction ratio could reach as high as 95.7%. Besides solar energy, ground
83
source heat pump (GSHP), which can be used for spacing cooling [20] and heating [21],
84
provides another option of integration. Li et al. [22] conducted research on a combined
ACCEPTED MANUSCRIPT 8 of 55
85
heating and power system coupling biomass partial gasification with GSHP. The coupling
86
with GSHP made a better use of flue gas at mid-low temperature and increased the coefficient
87
of performance (COP) of the GSHP. The overall efficiency and energy efficiency of the
88
system were 72.12% and 40.13%, respectively. In addition to theoretical analysis, Esen and
89
Yuksel [23] conducted experiments on a heating system using various renewable energy
90
sources, including biogas, solar energy, and GSHP. The system was satisfactorily operated
91
without any serious defects for heating greenhouses. Jradi and Riffat [24] tested performance
92
of a hybrid solar‒biomass ORC-based micro-CCHP system under different operation
93
conditions. The system was observed to be reliable with a smooth and quiet operation. The
94
overall efficiency of the system could be up to 83.08%.
95
However, for a system that supplies multiple types of energy, if the analysis only focused
96
on the system itself, then it should be considered as an analysis of an ideal condition where
97
all outputs of the system are fully used. In practice, on the contrary, the mismatch between
98
the output and the demand occurs constantly, which leads to the generation of excess energy
99
and requirement for auxiliary energy from time to time [25]. Therefore, demands are a
100
significant factor that affects the performance of the system. According to the study by Wang
101
et al. [26] on a typical biomass gasification-based CCHP system when supplying energy to a
102
hotel building, the energy efficiency of the system had the highest value of 37% during
103
summer and the lowest value of only 16% in transitional seasons. Some other studies, such
104
as Yang et al.’s research on a biomass gasification-based CCHP system [27], Li et al.’s
105
research on a solar/autothermal hybrid gasification CCHP system [28], and Palomba et al.’s
106
research on a gasification‒SOFC based tri-generation system [29], also indicated the
107
importance of energy demands to the performance of the CCHP systems based on biomass
108
gasification. The studies on optimization of biomass gasification-based CCHP systems in
109
consideration of energy demands are rare, but there are many researches on methods to
110
weaken the effect of mismatch between outputs and demands on the CCHP system driven by
ACCEPTED MANUSCRIPT 9 of 55
111
fossil fuels. In general, to deal with the energy shortage situation, supplementing energy, such
112
as NG and electricity, is a common treatment. For the excess energy, the possible choices
113
can be divided into two types. One is to optimize the operation strategies of the CCHP system
114
in order to avoid excess energy as much as possible. Some researchers optimized existing
115
operation strategies using different methods including dynamic programming [30], multi-
116
objective decision-making methods [31], and some other novel optimization methods [32];
117
others came up with novel strategies, such as the minimum distance operation strategy [25],
118
and the compromised electric–thermal load strategy [33]. The other way is to make full use
119
of the excess energy. If the thermal demand were taken as priority, there would be excess
120
electricity. The common approach is to sell it back to public grid, which is strictly limited by
121
the local policy and thus not available for many places [34]. Similarly, excess thermal energy
122
would be produced if the system were operated in the mode with electrical demand taken as
123
priority. To make full use of the excess thermal energy, researchers have focused on two
124
approaches: one is to store it in thermal storage units
125
with it. The first approach is direct but passive, as the system can only store and release
126
thermal energy when conditions permit [35]; whereas the second one is indirect but flexible,
127
as it can dynamically adjust the electrical and thermal output of the system to match user
128
demands.
and the other is to generate electricity
129
With development and maturity of organic Rankine cycle (ORC) technology on utilizing
130
waste heat from CCHP system’s prime movers, including the gas turbine [36], the NG engine
131
[37], and the marine diesel engine [38], studies about the effect of ORC integration with
132
CCHP systems were carried out. Fang et al. [35] evaluated a CCHP system integrated ORC
133
and electric chiller using simulated demands of representative days in different seasons. It
134
was found that comparing with the CCHP system without ORC and electric chiller, the
135
primary energy consumption, CO2 emission, and total cost could all be reduced in the
136
representative days owing to favorable match between outputs and demands. Hajabdollahi
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137
[39] introduced a method to determine the optimum prime mover of a CCHP−ORC system
138
and to find its benefit at different load demands. Results showed that the highest annual
139
benefit was obtained when there was the most heating load demand. Comparatively, the gas
140
turbine was more profitable with lower electrical and higher heating demands, while the
141
diesel engine was more advantageous when both electrical and heating demands were high.
142
Wu et al. [40] proposed and evaluated CCHP−ORC systems based on natural gas, solar
143
energy, and a biomass boiler. Results indicated that the ORC unit had higher potential to
144
reduce CO2 emission than annual total cost. The solar−ORC system and the biomass−ORC
145
system had the best economic and environmental performance, respectively. Meanwhile, it
146
was also observed that the variation of different energy demands had different effects on the
147
three CCHP−ORC systems.
148
According to the literature review above, it can be found that the CCHP system based
149
on biomass gasification suffers from the issue of mismatch between energy outputs and
150
demands. The ORC integration provides an option for this system to solve this problem by
151
enabling the original system to recover excess waste heat, which can be deduced from Refs.
152
[35, 39, 40]. However, there is no research on the biomass gasification-based CCHP system
153
integrated with an ORC module. On the other hand, as indicated in Ref. [40], the effect of
154
ORC integration varies with the energy resources and configurations of the system. As for
155
the system based on biomass gasification, for instance, the parameters of gasification, the
156
cost factors involving the gasifier and biomass feedstock, and the environmental benefit from
157
usage of biomass, have a great effect on the energy, economic, and environmental
158
performances of the system, respectively. Therefore, the conclusions of existed studies
159
cannot provide effective reference quantitatively for ORC integration with a biomass
160
gasification-based CCHP system. In addition, as a result of relatively low LHV of syngas, the
161
efficiency of the prime mover of the system is low [41], and the proportion of the waste heat
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162
is high accordingly, which makes the investigation on making full use of excess waste heat
163
more necessary.
164
For the above reasons, in this study, a biomass gasification-based CCHP system
165
integrated with an ORC is evaluated. In this system, other than being the prime mover as in
166
many biomass systems, the ORC module is powered by excess waste heat of the prime mover
167
as a complement component to obtain higher energy conversion ratio. The main objective of
168
the present work is to analyze the energetic, economic, and environmental performance of
169
the proposed system considering characteristics of both biomass and CCHP systems. The
170
specific objectives are:
171
To evaluate hourly performance of the system under different demand conditions and to
172
analyze the advantages and disadvantages of the system over the conventional system.
173
To evaluate monthly and annual performance of the system based on operation simulation.
174
To compare performance of the proposed system and the system without the ORC module,
175
and find out under what seasonal conditions the proposed system performs better than the
176
system without the ORC module.
177
To analyze the effect of variations of key parameters on the performance of the system.
178
In order to achieve the objectives above, the paper is organized as follows. First, the
179
configuration and operation strategy of the system is introduced. Second, the key components
180
of the system are modeled and the criteria on energetic, economic, and environmental aspects
181
are presented. Third, the performances of the system in cooling and heating modes are
182
analyzed respectively. Fourth, the monthly and annual performances of the system are
183
evaluated based on operation simulations, and the sensitivity analysis of the annual
184
performance is carried out. Finally, the main conclusions are presented.
185
2. System description
186
2.1. Energy demands of the building
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187
In this study, a hypothetic office building with a cover area of 55584 m2, located in
188
Shanghai, is selected as a case for analysis and evaluation. Shanghai is a city with a typical
189
subtropical monsoon climate. According to the database of Chinese Standard Weather Data
190
(CSWD) [42], the annual average temperature is 15.6°C, and the temperature range of the
191
whole year is from −4.5°C to 36.8°C [43]. The monthly relative humidity ranges from 67%
192
to 83%. In summary, Shanghai has a hot summer, a not too cold winter and mild transient
193
seasons. The humidity is high all year round. The hourly temperature and relative humidity
194
[43] are shown in Fig. 1. In this building, the energy demands compromise electricity supply,
195
cooling energy for space cooling, and heating energy for space heating and domestic hot
196
water. Electricity supply and domestic hot water are required in each season. Space heating
197
and cooling are required mainly in winter and summer, respectively, and also in a little time
198
in transient seasons. The hourly energy demands of the building are obtained from the
199
simulation software EnergyPlus V8.8.0 [44] using meteorological data from CSWD. The
200
simulation results are shown in Fig. 2. It can be found out that generally the heating load in
201
winter is much smaller than the cooling load in summer. Besides the weather factors, the
202
operation pattern of the building also has an impact on this result. In an office building, the
203
operation time is mainly during the daytime, which has higher temperature among the hours
204
in a day. As a result, the cooling requirement in summer gets higher and the heating demand
205
in winter gets lower. The peak electrical, cooling, and heating demands are 1515.3 kW,
206
2695.5 kW and 1484.2 kW, respectively.
207
2.2. The biomass gasification-based CCHP system integrated with an ORC
208
As shown in Fig. 3, the CCHP system consists of a main system and an auxiliary system.
209
The main system comprises a biomass gasifier, an ICE, a generator, an ORC module, a
210
single-effect absorption chiller (SAC), a fan coil and some heat exchangers. In this subsystem,
211
biomass is converted into fuel gas, called syngas, in the gasifier. The ICE and generator
212
constitute the power generation unit (PGU), which is fueled by the syngas. During the power
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213
generation process, waste heat is produced in the jacket water and exhaust gas. The waste
214
heat is recovered through hot water in exchangers in series. Then the hot water is used as a
215
heat source of the ORC module to generate more power, of the SAC for space cooling, of the
216
fan coils for space heating and of the domestic hot water. When the main system fails to
217
provide sufficient energy for the building, the auxiliary system will activate. The electricity
218
is supplemented from the grid, and the cooling energy is supplied by an electric air
219
conditioner (AC), which also consumes the electricity from the grid. Heating load for space
220
heating and domestic hot water is supplemented by a boiler fueled by NG. It is noted that the
221
conventional system, used as the base case, shares the same configuration as the auxiliary
222
system.
223
2.3. The operation strategy of the system
224
Generally, the system is operated in following the electrical load (FEL) mode with some
225
modifications, which means that the system satisfies the demand of electricity prior to
226
thermal energy. The operation status of the system is mainly determined by electrical
227
demand. Unlike common CCHP systems, in this system, if there is excess waste heat, instead
228
of being released, it is converted into electricity through the ORC module. Specifically, the
229
dimensionless energy demand area is divided into five areas according to the relation between
230
heat and power, as shown in Fig. 4. AB is the output curve of a typical PGU from ASHRAE
231
Handbook [45]. In the idle operation condition of the PGU, part or all of the waste heat can
232
be used for power generation through the ORC module, which forms the curve of AD.
233
Similarly, each operation point of the PGU becomes a curve through distributing waste heat
234
for electrical and thermal usage. Ultimately, the output curve of the PGU turns into an output
235
area, with BC as the right boundary, which is the output curve of the PGU in full-load
236
condition. BF and BG are two rays, as the boundaries of area that both electrical and thermal
237
demands exceed the maximum of corresponding output of the PGU. Thus, the whole demand
238
area is divided by the boundaries of this output area of the PGU and the rays of BF and BG.
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239
The characteristics of each area are shown in Table 1, and the detailed operation strategy of
240
the system in different areas is as follows.
241
(1) When the demand point is located in Area 1, both thermal and electrical demands are
242
small. The PGU runs in the idle condition. The electrical demand is satisfied with ORC, the
243
thermal demand is satisfied with the waste heat, and the remaining waste heat is released to
244
the environment, as expressed in Eqs. (1) and (2): Eorc Ed ,
(1)
Qrelease Q pgu,min Eorc / ηorc Qd ,
(2)
245
where Eorc is the power generation by ORC. Ed and Qd are the electrical and thermal demand,
246
respectively. Qd is the sum of waste heat demand for both heating load and cooling load, as
247
expressed in Eq. (3). Qrelease is the excess waste heat of PGU which is released to the
248
environment. Qpgu,min is the recoverable waste heat of the PGU in the idle condition. ηorc is
249
the efficiency of ORC. Qd
Qh, d Q c, d , ηh, rec COPsac
(3)
250
where Qh,d, and Qc,d are demands of heating and cooling, respectively. ηh,rec is the efficiency
251
of waste heat recovery for heating, and is assumed as 0.8 [46]. COPsac is the COP of the
252
SAC, and is assumed as 0.7 [47].
253 254
(2) Area 2 is the output area of the PGU. In this area, the system operates at the matching point between demand and output, which satisfies the following relations:
E pgu Eorc Ed ,
(4)
x Q pgu Qd ,
(5)
Eorc (1 x) Q pgu ηorc ,
(6)
255
where x is the proportion of waste heat for thermal demand. Epgu and Qpgu are electrical output
256
and recoverable waste heat of the PGU, respectively.
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257
(3) In Area 3, the main system is capable of satisfying thermal demand by operating the
258
PGU in full-load condition. However, in case that the thermal demand is satisfied, even if all
259
the remaining waste heat is used for ORC, there is not enough power for electrical demand.
260
Due to the priority of electrical demand, the PGU is operated in full-load mode to generated
261
power as much as possible. Meantime, the excess waste heat is also used for power generation
262
with ORC. The insufficient part of electrical demand is supplemented from the grid. The
263
relevant relations are expressed in Eqs. (7−9):
E pgu,max Eorc E g , el Ed ,
(7)
x Q pgu,max Qd ,
(8)
Eorc (1 x) Q pgu,max ηorc ,
(9)
264
where Eg,el is the electricity from the grid as supplement. Epgu.max and Qpgu.max are the electrical
265
and thermal output of the PGU as full-load condition.
266
(4) Area 4 represents the demand situation that the electrical demand is less than the
267
maximum power output of the PGU but the thermal output is insufficient when matching
268
electrical output with demand. In this case, the short part of thermal energy is supplemented
269
from the auxiliary system. Due to the higher efficiency to generate heating energy than
270
cooling energy, the heating demand takes the priority over cooling demand. Because the
271
thermal energy is insufficient, the ORC module is off. The relevant relations are expressed
272
as Eqs. (10−15).
273
If Q pgu
E pgu Ed ,
(10)
Eorc 0 .
(11)
Q pgu ηh,rec Fng ηb Qh,d ,
(12)
Qh, d , then ηh, rec
ACCEPTED MANUSCRIPT 16 of 55
E g,c COPel Qc ,d ,
(13)
274
where Fng is the energy consumption of NG. ηb is the efficiency of the auxiliary boiler. Eg,c
275
and COPel are the grid electricity consumption and the COP of electric AC. The value of
276
COPel is assumed as 3.5.
277
278 279 280
If
Qh, d Q Q Q pgu h, d c , d , then ηh, rec ηh, rec COPsac Q pgu , h ηh, rec Qh, d ,
(14)
(Q pgu Q pgu ,h ) COPsac E g ,c COPel Qc ,d ,
(15)
where Qpgu,h is the waste heat of the PGU used for heating. (5) In Area 5, both electrical and thermal demands are more than output of the PGU in full-load condition. In this case, the PGU operates in full-load condition: E pgu E pgu , max ,
(16)
Q pgu Q pgu ,max .
(17)
281
Because there is no excess waste heat, the ORC module is also off. Both electricity and
282
thermal energy are required from the auxiliary system. The electricity from the grid as
283
supplement is calculated by Eq. (18), and the energy consumption of the auxiliary system for
284
thermal demand is calculated by Eqs. (12−15).
E pgu E g , el Ed . 285 286
(18)
For all the above cases, the cooling and heating demands satisfied by recovering waste heat of the PGU (Qc,pgu and Qh,pgu, respectively) can be calculated as Qc , pgu Qc , d E g , c COPel ,
(19)
Qh, pgu Qh,d Fng ηboiler .
(20)
287
In this way, except for minor operating points in Area 1, there is no excess energy for all
288
the other operating points in other areas, which can further improve the energy utilization
289
rate of the whole system. Meantime, in the area below the curve A-B-G, because a part of
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290
the electrical demand is satisfied with the ORC module, the biomass consumption is reduced.
291
Based on the operation strategy above, a MATLAB code has been developed to simulate the
292
operation of the system with the energy demand data obtained in Section 2.1. The flow chart
293
of the code is shown in Fig. 5. Besides the energy demand of the building and necessary
294
parameters of the operation parameters, the following models of the components are also
295
required: (1) the model of the PGU, to calculate power and thermal output when heating
296
value of syngas input (Qs) changes; (2) the model of biomass gasifier, to reveal the relation
297
between Qs production and various gasification parameters, including heating value of the
298
biomass feedstock (Qs), molar fraction of water (w) and dry-basis mass fraction of the
299
elements (mi) of the biomass feedstock, and equivalence ratio (ER); (3) the model of the ORC
300
model, to obtain efficiency of ORC at different operational temperatures (T). The above-
301
mentioned models are introduced in the next section in detail. Ultimately, through the
302
operation simulation, the detailed input, output and process parameters of the whole system
303
can be obtained.
304
To analyze the effect of addition of the ORC module on this system, the operation of a
305
CCHP system without ORC is also simulated. Except that there is no ORC module in this
306
comparative system, it has an identical layout to the other parts of the proposed system. The
307
comparative system is operated with standard FEL strategy, which can be referred to Ref.
308
[46].
309
3. Mathematical models
310
The methodology of this study is outlined in Fig. 6. It consists of demand simulation,
311
operation simulation, and evaluation. The input and output data of each process can be found
312
in Fig. 5. The simulations of demand and operation have been introduced in the previous
313
section. The criteria can be calculated with simulation results and characteristic parameters
314
of corresponding energy sources. The simulation models and evaluation criteria are
315
introduced in this section.
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316
3.1. Model of the biomass gasifier [48]
317
The equilibrium model developed by Shama and Sheth, called “3-reaction model”, is
318
adopted to simulate gasification process in this study. The detailed basic assumptions of this
319
model can be referred to Ref. [48]. This model comprises elemental molar balances,
320
thermodynamic balance and equilibrium balances. The former two are based on the global
321
gasification reaction, which can be written as CH a O b wH 2 O m(O 2 3.76 N 2 ) ns ( x1H 2 x2 CO x3CO 2 x4 H 2 O x5CH 4 x6 N 2 ) nc C , (21)
322
where CHaOb is the chemical formula of biomass feedstock based on a single atom of carbon,
323
which can be calculated with weight percentage of the element C, H and O; m is the molar
324
flow rate of air per mol of biomass feedstock. 3.76 represents the approximate molar ratio of
325
N2 to O2 of air. ns and nc are production of syngas and char, respectively, in mol per mol
326
CHaOb. x1 to x6 are the molar fraction of the corresponding gaseous compositions of syngas,
327
respectively. Therefore, the elemental molar balance for C, H, O and N can be written as
1 ns ( x2 x3 x5 ) nc ,
(22)
2 w a ns (2 x1 2 x4 4 x5 ) ,
(23)
w b 2m ns ( x2 2 x3 x4 ) ,
(24)
2 3.76 m ns 2 x6 .
(25)
328
The gasification is assumed adiabatic, which means the sum of enthalpy heat of
329
formation of all compositions at the initial state is equal to that at equilibrium state.
330
Thermodynamic balance can be expressed as H 0f ,bio w( H 0f ,H 2O(l) H vap ) m( H 0f ,O2 3.76 H 0f , N 2 ) ΔQ n g
x H i
0 f ,i
nc H 0f ,C
Tr
Tamb
(ns
x c
i p ,i
nc c p ,C ) , (26)
331
where H0 f,bio is the heat of formation of biomass feedstock; H0 f,H2o(l) is the heat of
332
formation of liquid water; Hvap is the vaporization heat of water. It is noted that the heat of
333
formation is defined as the change of enthalpy during formation from its constituent elements.
334
Therefore, all elements, such as O2, N2, H2 and C in this case, have a heat of formation of
ACCEPTED MANUSCRIPT 19 of 55
335
zero, as there is no change of enthalpy involved in their formation. The H0 f of compounds
336
involved in this article can be found in Table 2. ∆Q is the heat input (such as by increasing
337
the temperature of the inlet air or the biomass feedstock) to the gasifier. cp,i is the specific
338
heat capacity of component i. Tamb and Tr are temperature of the ambient and the reduction
339
zone of the gasifier, respectively. Tamb is also considered as the temperature of biomass
340
feedstock and air input to the system. The polynomial fittings of cp,i are adopted, as expressed
341
in Eq. (27). The polynomial coefficients are obtained from Ref. [49], as listed in Table 3. c p (a0 a1 T a2 T 2 a3 T 3 a4 T 4 ) 103 .
342 343
(27)
The equilibrium balances are based on three reversible reactions of the gasification process: C 2H 2 CH 4 ,
(28)
CO H 2 O CO 2 H 2 ,
(29)
CH 4 H 2 O CO 3H 2 .
(30)
344
In the equilibrium state, the molar fractions of gaseous species of the reversible reactions
345
are bounded by equilibrium constant (K). The relations between xi and K of the above-
346
mentioned three reactions are given by Eqs. (31−33) in turn: K1
x5 x12
,
(31)
K2
x1 x3 x2 x4
,
(32)
K3
x13 x2 . x5 x4
(33)
347
The equilibrium constant is a function of reaction temperature T, as expressed in Eq.
348
(34). The coefficients of the functions of the three reactions are listed in Table 4. In this model,
349
Tr is considered as the reaction temperature of the three related reversible reactions.
ln K b1 T 1 b2 ln T b3 T b4 T 2 b5 T 2 b6 .
(34)
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350
The molar fractions of gaseous species are bounded by Dalton’s law of partial pressure: 6
x
i
1.
(35)
i 1
351
Thus, after obtaining the parameters of biomass feedstock such as a, b and w, and the
352
ambient temperature, there are ten variables (Tr, m, x1~x6, ns, and nc) and nine equations.
353
Therefore, the gasification temperature and syngas production per mol of biomass feedstock
354
can be calculated after determining the air input. The flow chart of gasification process
355
simulation can be referred to Ref. [48]. Generally, ER, defined as the ratio of the actual air
356
input to the air required for complete combustion, is used to measure the air input. The
357
relation between ER and m is expressed as ER
358 359
m . 1 0.25 a 0.5 b
(36)
The gasification is evaluated by cold gas efficiency (ηcg), which is defined as the lower heating value ratio of syngas to biomass feedstock, as expressed in Eq. (37).
ηcg
( x1 LHV1 x2 LHV2 x5 LHV5 ) ns . LHVbio
(37)
360
The model is validated with the experimental data reported in Ref. [50]. The validation
361
results are shown in Fig. 7. The standard deviation (SD) [48], which is defined as Eq. (38), is
362
used to assess the error of each data group.
SD
[(Yexp Ymodel ) / Yexp ]2 i 1 N 1 N
1/ 2
.
(38)
363
where Yexp and Ymodel are the values from the experiment and the model, respectively. N is the
364
number of data. It can be observed that the model agrees well with the experiment results
365
when ER changes. The predictions for molar fractions of CO and H2, which are the main
366
components of the syngas, are accurate. The SDS of molar fractions of CO2 and CH4 are
367
relatively larger, but the ranges of them are still within 10%. Moreover, as noncombustible
ACCEPTED MANUSCRIPT 21 of 55
368
and minor component respectively, they have little effect on the prediction of ηcg. As a result,
369
ηcg has a satisfying SD with the value of 1.26%.
370
Eventually, the biomass energy input to the system can be estimated by
Qbio Qs / ηcg .
(39)
371 372
In this study, it is assumed that the gasifier is operated with a fixed ER of 0.35. The input
373
rate of air and biomass feedstock can be adjusted to stabilize operation of the gasifier on the
374
fixed ER and to satisfy syngas requirement of PGU. The influence of the changing of the set
375
value of ER is discussed in the sensitivity analysis section. Meanwhile, in Shanghai, the
376
ambient temperature ranges from about 270 K to 310 K. According to the simulation results
377
and summary in Ref. [51], Tr ranges from 973 K to 1373 K. Therefore, the ambient
378
temperature is assumed as a constant value of 290 K and the error caused by this is considered
379
negligible. The properties of the biomass feedstock [50] adopted in this article and the
380
simulation results of syngas at nominal condition are listed in Table 5.
381
3.2. Model of the organic Rankine cycle
382
According to the results from Ref. [52], R245fa is a low temperature working fluid and
383
commercially available for waste heat recovery. It also has comparatively high molecular
384
weight, which can make the system compact due to reducing the mass flow rate of working
385
fluid. Besides, as a hydrofluorocarbon, it has zero value of ozone depletion potential.
386
Therefore, R245fa is selected as the working fluid of the ORC module in this article.
387
Thermodynamic model [52] is widely used in simulating the performance of the ORC
388
module. As shown in Fig. 8(a), the ORC module is composed of an evaporator, a turbine with
389
a generator, a condenser, and a pump. Four processes are included in the ideal ORC:
390
isentropic compression (1→2), isobaric heating (2→3), isentropic expansion (3→4) and
391
isobaric heat rejection (4→1), as can be seen in Fig. 8(b). Due to the energy loss in the pump
ACCEPTED MANUSCRIPT 22 of 55
392
and turbine, the process of 3→4 and 1→2 become non-isentropic. As a result, the actual cycle
393
is 1→2a→3→4a→1. The work output of the ORC module (Worc) is the difference between
394
work output of turbine (Wt) and the work consumption of the pump (Wp):
Worc Wt W p . 395
(40)
Wt and Wp can be calculated with Eqs. (41) and (42): orc ( h3 h4 ) , Wt ηt m
Wp
(41)
m orc (h2 h1 ) , ηp
(42)
396
where ηt and ηp are the efficiencies of the turbine and pump, respectively, and are both
397
assumed as 0.8 [40]. hi is the enthalpy of the working fluid at state point i, which is obtained
398
from REFPROP 9.1 [53] developed by the National Institute of Standards and Technology
399
of the United States. ṁorc is the mass flow rate of the ORC fluid, which is determined by
m orc 400
Qorc , h3 h2
(43)
where Qorc is heat obtained from waste heat of the PGU in the evaporator.
401
According to the equations above and considering the efficiency of the electric generator
402
(ηgen), the total efficiency of the ORC module can be written as Eq. (44). The value of ηgen is
403
assumed as 0.85 [35].
ηorc
404
[(h3 h4 ) ηt (h2 h1 ) η p1 ] ηgen h3 h2
.
(44)
3.3. Model of the power generation unit
405
The performance of the PGU is related to its size. Therefore, the size of the PGU is
406
determined first. As mentioned in the previous section, the operation strategy of the system
407
is actually a modified FEL, so the size of the PGU is determined by the maximum power
408
output of the system. The maximum rectangle method is adopted to choose the size of the
409
PGU [54]. The maximum area XY of the cumulative electricity demand curve in Fig. 9,
ACCEPTED MANUSCRIPT 23 of 55
410
determines the size of the PGU, and Y, with the value of 843.24 kW, is the maximum power
411
output of the system. In this system, electricity is generated by PGU and ORC module.
412
Therefore, the energy output of the PGU should satisfy following equation: E pgu , max Q pgu , max ηorc 843.24 kW .
(45)
413
The output characteristics of the PGU with different sizes are based on the manufacture’s
414
database of GE Jenbacher modules especially adapted to work with syngas. According to
415
comparing output characteristics of PGU in database and Eq. (44), the J312 GS, with
416
maximum electrical output of 765 kW by itself and maximum electrical output of 80 kW by
417
ORC is selected as the PGU of this system, which is close to the restriction of Eq. (44). J316
418
GS, with maximum electrical output of 850 kW is selected as the PGU of the comparative
419
system. The part-load performance, including electrical output and recoverable waste heat
420
from jacket water and exhaust gas with respect to syngas heating value input, is obtained
421
from Ref. [11]. They are all fitted into linear relations, as expressed in Eq. (46), and the
422
correlation results are list in Table. 6.
En pgu c0 c1 Qs , 423
where Enpgu stands for energy of electricity or waste heat.
424
3.4. Evaluation criteria
(46)
425
The advantage or disadvantage of a CCHP system is usually reflected by comparing with
426
the conventional system. In this article, the evaluation includes energetic, economic, and
427
environmental aspects. The related criteria are as follows.
428
3.4.1. Primary energy saving ratio (PESR)
429 430
Primary energy saving ratio is defined as the ratio of primary energy saved by the CCHP system to that consumed by the conventional system, which is expressed as
ACCEPTED MANUSCRIPT 24 of 55
PECconv PECcchp
PESR
(47)
,
PECconv
431
where PECconv and PECcchp are primary energy consumption of the conventional system and
432
the CCHP system in a period of time, respectively. They can be calculated as
PECconv
((E
PECcchp
(Q
d
Qc / COPel ) / ηg Qh / ηb )
,
(48)
( E g ,el E g ,c ) / ηg Fng )
433
(49) , where ηg is the energy conversion efficiency of the grid regarding the energy loss during
434
power generation and transmission. The value of ηg is 0.323 in China [46].
435
3.4.2. Cost saving ratio (CSR)
bio
436
The cost of the system includes the capital cost and operation cost, where capital cost is
437
the sum of all equipment in the system considering capital recovery, and operation cost
438
includes cost of all types of energy resources, as expressed in Eqs. (50−52):
COST COSTcap COSTop COSTcap R
COSTop
(cost
bio
N hr 365 24
cost
(50)
,
cap ,i
Ci ,
Qbio costel E g costng Fng ) ,
(51) (52)
439
where COST, COSTcap and COSTop are total cost of the system, capital cost, and operation
440
cost, respectively. R is the capital recovery factor, which can be calculated with Eq. (53).
441
Conventionally, R is an annualized factor. Therefore, Nhr/(365∙24) is added to calculate the
442
cost of any period of time such as a month or an hour, where Nhr is the number of hours in
443
this period of time. costcap,i and Ci are the unit cost and the capacity of equipment i,
444
respectively. The unit prices of the equipment are listed in Table 7. costbio, costel, and costng
445
are the unit price of biomass, electricity from the public grid and NG, which are listed in
446
Table 8.
ACCEPTED MANUSCRIPT 25 of 55
R
I (1 I )l (1 I )l 1 ,
(53)
447
where I is the interest rate, which is 6.55% in China. n is the service life of the equipment, in
448
year. In this article, the service lives of all equipment are assumed as 20 years.
449 450
The cost of the conventional system can also be calculated with Eqs. (50−52), with some changes on the calculation of electricity and NG consumption in Eq. (52):
COSTop ,conv 451
(COST
el
( Ed Qc / COPel ) / ηg COSTng Qh / ηb )
(54)
Thus, the cost saving ratio (CSR) can be calculated as CSR
452
.
COSTconv COSTcchp COSTconv
.
(55)
3.4.3. CO2 emission reduction ratio (CERR)
453
The issue of global warming is one of the world’s greatest concerns. As was previously
454
mentioned, this system has great potential in reducing CO2 due to utilization of biomass and
455
recovery of waste heat. Therefore, CO2 emission of the system is evaluated in this article.
456
Similarly, CO2 emission reduction ratio is adopted for the evaluation: CERR
CEconv CEcchp CEconv
,
(56)
457
where CEcchp and CEconv are the CO2 emission of the conventional system and the CCHP
458
system, respectively, which are estimated as
CEconv CEcchp
(μ
(μ
( Ed Qc / COPel ) / ηg μng Qh / ηb ) ,
(57)
Qbio μel ( E g ,el E g ,c ) / ηg μng Fng ) ,
(58)
el
bio
459
where μel, μng and μbio are the CO2 conversion factors of the electricity from the grid, NG and
460
biomass feedstock, respectively, whose values are listed in Table 9. It should be noted that,
461
owing to the neutrality of biomass in relation to global warming, the CO2 emission of biomass
462
feedstock is estimated with the effect of production and transportation of it [59].
ACCEPTED MANUSCRIPT 26 of 55
463
4. Results and discussion
464
4.1. Performance analysis of the system
465
In this section, the performance of the system along with the change of electrical and
466
thermal demands is analyzed. The performance of the system in cooling and heating mode is
467
studied respectively. It is noted that although domestic hot water is required all year round,
468
in the most time of summer, the cooling demand is much more than this part of heating
469
demand. Therefore, the system is considered to be operated in cooling mode in summer.
470
4.1.1. Comparison among the criteria
471
The PESR, CSR and CERR of the system in cooling and heating mode are shown in Figs.
472
10 and 11, respectively. Among the three criteria, CERR has the highest value because of the
473
neutrality of biomass in relation to global warming. The CO2 emission of biomass is only
474
derived from production and transportation, which results in much lower CO2 conversion
475
factor of biomass than other energy resources. Therefore, the value of CERR is high in the
476
whole map. CSR has the lowest value, and is the only criteria with negative values because
477
the capital cost of the system is very high. Meanwhile, as the cost of the biomass has no
478
significant advantage over other energy resources, the difference of capital cost is even more
479
difficult to offset.
480
4.1.2. The variations of criteria with respect to demands
481
It can be observed that each of the three criteria has a maximum value as a center and
482
the value of the criterion decreases from the center to the border. The features of decrease of
483
the three criteria along with energy demands are different. Generally, PESR and CSR
484
decrease more sharply as energy demands decrease, whereas CERR decreases more evidently
485
as energy demands increase. This reflects that the advantages of the system on different
486
aspects result from different factors. The saving of primary energy and cost is mainly because
ACCEPTED MANUSCRIPT 27 of 55
487
of the waste heat utilization. Therefore, with low energy demand, due to the low efficiency
488
of the PGU and high capital cost of the system, it is difficult to save primary energy and cost.
489
As the increase of energy demands, the efficiency of the PGU is higher; this results in higher
490
efficiency of the main system. Meanwhile, the capital cost is easier to offset with the increase
491
of the energy saving, owing to utilization of the waste heat. In this occasion, the effect of
492
supplementary energy from the auxiliary system is weak. On the contrary, the CO2 reduction
493
is mainly because of the replacement of fossil fuel with biomass. When energy demands are
494
low, even if the efficiency of the system is low, the system demand can be satisfied with
495
biomass alone. Because the fossil fuel is fully replaced by biomass, the CO2 reduction ratio
496
is still high. However, when energy demands exceed the output capacity of the main system,
497
electricity and NG consumption from the auxiliary system make CO2 emission increase
498
greatly with the growth of energy demands. Therefore, CERR decreases slowly on the lower
499
direction of the preferable zone, and decreases sharply on the upper right direction. To
500
summarize, it is more important to improve waste heat utilization efficiency for the better
501
performance of the system on energy and economic aspects, while it is more effective to
502
utilize biomass as energy recourse to improve environmental performance of the system.
503
4.1.3. Comparison between cooling and heating modes
504
Comparatively, the system performs better in heating mode than in cooling mode in
505
consideration of all three criteria. Because for the conventional system, the cooling demand
506
and heating demand are satisfied by electric AC and gas furnace, respectively. The COP of
507
SAC is lower than the efficiency of waste heat recovery of heating. On the other hand, in the
508
conventional system, the cooling energy is generated by electric AC, whose COP is 3.5. The
509
efficiency of the public grid is 0.323, and therefore, the efficiency of generating cooling
510
energy is greater than 1. However, owing to the heat loss in the boiler, the efficiency of
511
heating energy generation in the conventional system is only 0.8. To sum up, in the CCHP
ACCEPTED MANUSCRIPT 28 of 55
512
system, the waste heat is more efficiently used in heating mode, whereas in the conventional
513
system, the efficiency of cooling energy generation is higher. As a result, the effect of the
514
waste heat recovery is more apparent for heating than for cooling. Energy price is another
515
factor for the difference of the economic performance of the system between heating mode
516
and cooling mode. According to the corresponding ways to generate energy, energy
517
generation efficiency and energy source price, the prices of the cooling and heating energy
518
per unit are 0.267 Yuan/kWh and 0.481 Yuan/kWh, respectively, in the conventional system.
519
That is to say, the conventional system has lower efficiency and much higher cost when
520
generating heating energy than cooling energy. Therefore, economic advantage of CCHP
521
system in heating mode over cooling mode is the greatest among the three aspects. However,
522
according to the calculation with corresponding efficiencies and CO2 conversion factors, the
523
CO2 emission rates of generating cooling energy and heating energy are 0.264 kg/kWh and
524
0.275 kg/kWh, respectively, which are very close. Meanwhile, as analyzed previously, the
525
replace ratio of fossil fuel with biomass is the key to reduce CO2 emission. Therefore, the
526
gap of CERR between the cooling mode and the heating mode is the narrowest, and the
527
descending trends from the areas with maximum values are similar. In addition, it can be
528
observed that the areas with better performance, which are colored red and yellow, are larger
529
in heating mode than in cooling mode. Therefore, it is indicated that the system also has
530
higher possibility to have better performance in heating mode than in cooling mode.
531
4.2. Evaluation of the system based on operation simulation
532
In this section, the system is evaluated based on operation simulation and is compared
533
with the system without the ORC module.
534
4.2.1. Monthly performance on the energetic aspect
535
As shown in Fig. 12, the PESR of the system with and without the ORC module ranges
536
from 18.7% to 24.2% and from 11.8% to 20.0%, respectively. The system with ORC module
ACCEPTED MANUSCRIPT 29 of 55
537
has better performance from January to May and from October to December, and has a
538
performance that is not as good as in the rest months. Besides the efficiency factor as analyzed
539
before, the demand factor is also important. As shown in Fig. 2, the cooling demand of the
540
system is relatively high. In cooling mode, the system is operated in thermally overloaded
541
condition for many hours. For the system with the ORC module, in these hours, there is no
542
excess waste heat for the ORC module. Thus, the energy performance cannot be improved
543
by the ORC module. On the contrary, because of the smaller capacity of the PGU, more
544
electricity is required from the grid for the electrical and cooling demands, which leads to a
545
decrease of primary energy that can be saved. Although, as can be observed from Fig. 2, there
546
are also many hours with a low cooling demand, in which there is excess waste heat for ORC
547
to improve the energy performance of the system, the value of monthly PESR still decreases.
548
Therefore, the energy performance of the system with the ORC module is not as good as than
549
that of the system without the ORC module in summer. However, in winter and transient
550
seasons, the circumstance is the opposite. Owing to the low thermal demand, there is much
551
excess waste heat. The system with the ORC module performs better by generating electricity
552
with the excess waste heat. Therefore, it can be concluded that the energy performance of the
553
system is improved by the ORC module when there is much excess waste heat, and is
554
deteriorated when the system is thermally overloaded. By using ORC module, the energy
555
performance is improved by 6.1%−8.1% in the months with relatively low thermal demand,
556
and the variations of PESR are from −1.2% to 0.8% in the months with relatively high thermal
557
demand. Therefore, it can be concluded that the addition of ORC module can satisfactorily
558
solve the problem of recovering excess waste heat and thus, improve the energy performance
559
of the system.
560
4.2.2. Monthly performance on the economic aspect
ACCEPTED MANUSCRIPT 30 of 55
561
As shown in Fig. 13, the CSR ranges from 8.4% to 17.7% and from −3.2% to 8.6% for
562
the system with and without the ORC module, respectively. The variation trend of the CSR
563
in each system is similar to that of the PESR. Because of the purchase of grid electricity for
564
cooling demand during thermally overloaded hours, the CSR is lower from May to September
565
than in other months, ranging from 9.6% to 10.3%. Unlike the PESR, the CSR can be
566
improved in every month by adding the ORC module, with the variation ranging from 3.1%
567
to 11.8%. As previously mentioned, even in the months with high cooling demand, there are
568
many hours with low cooling demand. In these hours, owing to the power generation of the
569
ORC, the consumption of biomass feedstock decreases, thus, the operation cost of the system
570
is reduced. Meanwhile, in the thermally overloaded hours, the operation cost increases as the
571
increase of cooling demand. However, as can be observed from Fig. 10, the value of the CSR
572
decreases little with the increase of the cooling demand. Thus, the saved operation cost in the
573
hours with low thermal load exceeds the incremental cost in thermally overloaded hours,
574
which leads to increase of CSR in the month with more thermal demand. From the analysis
575
above and results shown in Fig. 13, it can be deduced that, the increase of the grid electricity
576
for cooling affects less significantly on the economic performance of the system. It is more
577
important to reduce biomass consumption by making use of the waste heat. For this reason,
578
the increment of the CSR by installation of the ORC is the largest among the three criteria.
579
The smallest and largest increment is in August and November respectively. In addition, after
580
installing the ORC module, the economic benefit can be achieved in every month.
581
4.2.3. Monthly performance on the environmental aspect
582
The CERRs of the system with and without the ORC module and their difference are
583
shown in Fig. 14. As discussed previously, the CO2 reduction of the CCHP system based on
584
biomass gasification is mostly due to the replacement of fossil fuel with biomass. Because
585
both systems have high ratios of the replacement, they both own relatively high monthly
ACCEPTED MANUSCRIPT 31 of 55
586
CERR, ranging from 35.5% to 45.8% and from 36.8% to 41.7%, respectively, for the system
587
with and without the ORC module. In the months with high thermal demand, under the dual
588
influence of lower efficiency and lower ratio of replacement of fossil fuel with biomass, the
589
decrease of the CERR due to the installation of the ORC module is higher than that of the
590
PESR, ranging from 0.5% to 1.3%. Furthermore, for the same reason, the effect of thermal-
591
demand overload is more noticeable on the environmental performance than on energetic and
592
economic performance for both systems. In the system with the ORC module, the decrease
593
of the CERR due to the overload of thermal demand is the sharpest, with the difference of
594
10.3% between the maximum and minimum value. Meanwhile, the CERRs of the system
595
without the ORC module in the thermally overloaded month are much lower than the other
596
months except November. In the months with low thermal demand, there is excess waste heat
597
for ORC, thus the environmental performance is improved. However, because the ratio of
598
replacement of fossil fuel with biomass in the CCHP system is relatively high, the
599
improvement by the ORC module is limited. As a result, the increment of CERR in these
600
months is the smallest among the three criteria, ranging from 4.0% to 5.8%.
601
4.2.4. Summary of monthly performance and analysis of annual performance
602
In summary, from May to September, the system is operated in cooling mode, which is
603
at a disadvantage on all the three aspects compared with heating mode. More importantly,
604
the system is in thermally overloaded condition in these months, which leads to even poorer
605
performances in the system with ORC, and little increase or even decrease of the criteria by
606
installing the ORC module. However, in the other months, owing to the utilization of excess
607
waste heat by ORC, the system has greater improvement in terms of all the three aspects. The
608
improvement is different due to the different influence factors of each aspect. The economic
609
performance is influenced by efficiency and economic factors simultaneously. The improve
610
the CSR, the key is to reduce biomass consumption. The ORC module has a positive effect
ACCEPTED MANUSCRIPT 32 of 55
611
on CSR in every month. As such, the installation of ORC module has the most significant
612
improvement of the annual CSR, which is improved from 4.0% to 11.1%, as shown in Fig.
613
15. Nevertheless, due to the capital cost to be set off, the annual CSR has lowest value among
614
all the three criteria. Owing to the much lower CO2 conversion factor of biomass than that of
615
fossil fuel and relatively high ratio of replacement of fossil fuel with biomass, the value of
616
the annual CERR is much higher than PESR and CSR. However, because of relatively high
617
decrease in overload condition and low increase in low-thermal-demand condition of the
618
CERR, the installation of the ORC module can only help improve the annual CERR from
619
42.3% to 43.7%, which is the least increment among the three criteria. The annual PESR can
620
be improved from 17.4% to 20.6%. It is only influenced by the efficiency factor. However,
621
similar to CERR, the most effective way to improve PESR is also to reduce the energy
622
consumption from the auxiliary system. By installing the ORC module, the decrease of PESR
623
in overload condition is mostly due to the reduction of the PGU capacity, and the increase in
624
the months with low thermal demand is only due to the utilization of excess waste heat. As a
625
result, the increment of annual PESR is between the annual CSR and CERR.
626
Although the results are obtained only from the operation simulation in Shanghai, the
627
results can also be expanded. From the seasonal comparison between the system with and
628
without the ORC module, the feasibility to integrate an ORC module with biomass
629
gasification-based CCHP system in different climate regions can be deduced. Generally, the
630
effectiveness of integration decreases with the increase of thermal demands. The integration
631
is relatively effective in the regions with a wild climatic condition as there is plenty of excess
632
waste heat. Meanwhile, as analyzed previously, the system has better performance and larger
633
advantage to integrate an ORC module in heating mode than in cooling mode. Therefore, for
634
the same amount of thermal demand, the system in hot zones is preferable to add an ORC
635
module than in cold zones.
636
4.2.5. Result comparison with references
ACCEPTED MANUSCRIPT 33 of 55
637
The comparison among results from this study and those from some references are
638
presented in Table 10. The system is compared with CCHP‒ORC systems of other types
639
before and after integrating the ORC module. Herein, in all these studies, the ORC module
640
is a supplementary device. The comparison comprises annual performance, highest/lowest
641
performance on a single aspect, and most/least beneficial to integrate an ORC module in
642
consideration of variation of each criterion. The numbers of study and the corresponding
643
system type are:
644
(1) This study. A CCHP‒ORC system based on biomass gasification.
645
(2) Ref. [57]. A CCHP‒ORC system fueled by natural gas and coupled with GSHP.
646
(3) Ref. [58]. A combined heating and power‒ORC system fueled by natural gas.
647
(4) Ref. [35]. A CCHP‒ORC system fueled by natural gas.
648
As can be observed from Table 10, among the four systems, the ORC integration makes
649
the least beneficial for the system in this study. One reason is that some energy is lost in the
650
process of biomass gasification, which cannot be recovered by the ORC module. On the other
651
hand, during the selection of the ORC fluid in this study, environment and commercial
652
availability are also taken into consideration. As a result, the efficiency of the ORC module
653
is lower than that in other studies. It is indicated that to improve the performance of this
654
system, to recover heat from gasification is one of the approaches. Overall, except for the
655
CCHP‒ORC system assisted by GSHP, the addition of the ORC module makes the most
656
benefit on economic aspect. For the performance of the entire system, the biomass
657
gasification based CCHP‒ORC system has higher performance on the environmental aspect,
658
but lower performance on the energetic and economic aspects. Due to the energy loss during
659
biomass gasification, as mentioned previously, and the low efficiency of the PGU caused by
660
low heating value of the syngas, the efficiency of the system is lower than the other ones.
661
This also affects the economic performance of the system. However, the main reason of lower
662
economic performance is the high capital cost of the additional components, the gasification
ACCEPTED MANUSCRIPT 34 of 55
663
device of the system. The same situation can also be observed in the CCHP‒ORC system
664
assisted by GSHP, which can save more on the operation cost (monthly CSR) but less on the
665
total cost (annual CSR) due to the expense on GSHP. Actually, high initial investment is a
666
common problem for renewable energies. Meanwhile, because the relatively high expense
667
on the treatment of biomass feedstock, the cost of the biomass feedstock has no distinct
668
advantage. For the above reasons, the overall economic performance of the investigated
669
system is worse than the CCHP‒ORC system driven by fossil fuels. Comparatively, the major
670
advantage of the investigated system lies in its environmental performance, owing to the
671
large difference of CO2 emission characteristics between biomass and fossil fuels. In terms
672
of operation mode, it can be found that all the systems perform better in heating mode, such
673
as in cold months or places, than in cooling mode, which reflects the advantage of the system
674
in cooling mode, as analyzed previously. The most benefits are obtained mainly in spring,
675
autumn, and places with mild climate, while the least amounts of benefits appear mainly in
676
summer, winter, and cold places. This agrees with the relative findings in previous sections.
677
In summary, to make more benefit, the ORC module should be integrated with the system
678
where there is less thermal demand, which agrees with the conclusion in this study.
679
4.3. Sensitivity analysis
680
In previous simulation and evaluation, some key parameters were assumed as constant.
681
Therefore, a sensitivity analysis is carried out to assess the effect of variation of the relevant
682
parameters on performances of the system. This can also help to find out the improvement
683
potential of the system by changing the parameters. The parameters involved in the sensitivity
684
analysis and the ranges of their possible values are listed in Table 11. It is noted that, to
685
reduce the number of involved parameters, the change of performance of the ORC module is
686
represented by the change of the working fluid. The change of other parameters of the ORC
687
module, including efficiencies of the pump, turbine and generator, are considered equivalent
688
to that of the working fluid, for they all influence ηorc ultimately. The efficiencies of the ORC
ACCEPTED MANUSCRIPT 35 of 55
689
module when using working fluids from Ref. [43], which are proven available for low-grade
690
waste heat, are calculated. According to the results, R134a and R11, with the ηorc values of
691
7.58% and 8.77%, respectively, are the least and most efficient working fluids. Therefore,
692
their values are considered as the range of the ηorc.
693
The results of the sensitivity analysis for PESR, CSR, and CERR are shown in Figs.
694
16−18, respectively. Generally, with the variations of various parameters, the advantage of
695
the system on energetic and economic aspects may be close to non-existent. However, owing
696
to the quite low CO2 conversion factor of biomass, the influence of the parameter variations
697
on CERR is relatively small. In the worst case, the value of CERR can still be as high as
698
38.0%, which means the integration would be environmentally effective in any circumstance
699
that one of the parameters was changed. Specifically, it can be observed that among the first
700
six parameters, which are relevant to all the three criteria, the gasification parameter, ER, has
701
the most noticeable effect on the performances of the system. If the ER were set as 0.25, the
702
PESR, CSR, and CERR could be improved by 6.6%, 6.3% and 3.6%, respectively. This is
703
because biomass is the major energy source of the system; therefore, reducing the
704
consumption of the biomass feedstock can effectively improve the performance of the system
705
on all aspects. Without considering some technical problems such as relatively low reaction
706
speed and high tendency to agglomerate, operating the gasifier under lower ER has great
707
potential to make comprehensive improvement on the system performance. Besides the
708
efficiency factor, the cost and CO2 emission factor, COSTf and μf respectively, also have large
709
effect on the corresponding criterion. Due to the change of COSTf and μf, the variations of
710
CSR and CERR range from −7.5% to 7.5% and −4.4% to 4.3%, respectively. The parameters
711
of the grid can also have great influence on the performance of the system. With the increase
712
of the grid efficiency, the value of PESR can be reduced to as low as 6.3%. Meanwhile, the
713
cost saving ratio would be only 1.5% if the cost of electricity were at the minimal value.
714
Owing to the much lower CO2 conversion factor of biomass than the other energy resources,
ACCEPTED MANUSCRIPT 36 of 55
715
the minimum CERR is still as high as 42.2% when the minimum value of μf is adopted. In
716
other words, in terms of energetic and economic aspects, the integration would not be
717
effective for the system located in places with high grid efficiency. It is noted that the
718
efficiency of the ORC module, with a relatively low value and a narrow variation range, can
719
barely influence the performance of the system on any aspect. The relevant variations are
720
within ±0.3% for all three criteria. This means, in terms of the existing ORC fluids that are
721
mature and commercially available, any change should not be considered as an improvement
722
the performance of the entire system. The parameters relevant to thermal energy, such as
723
COPs of chillers and some other efficiencies, only affect on the waste heat utilization. The
724
influence of their variations on the performance of the system is also very limited. In
725
summary, efficiency of the grid, cost of the grid electricity and biomass feedstock, and
726
emission factor of biomass feedstock have noticeable effect on energetic, economic, and
727
environmental aspects, respectively. Meanwhile, all three criteria are sensitivity to the
728
variation of equivalence ratio of biomass gasification.
729
5. Conclusions
730
In this study, a biomass gasification-based combined cooling, heating, and power system
731
integrated with organic Rankine cycle is simulated and evaluated. The simulation is based on
732
a large office building located in Shanghai and the evaluation is composed of energetic,
733
economic, and environmental aspects. The main conclusions that can be drawn are as follows:
734
(1) Generally, this system can achieve benefits on energetic, economic and
735
environmental aspects. The primary energy saving ratio, cost saving ratio, and CO2
736
emission reduction ratio are 20.6%, 11.1%, and 43.7%, respectively. Comparing with
737
the system without the ORC module, the increments of the above criteria are 3.2%,
738
7.1%, and 1.4%, respectively.
ACCEPTED MANUSCRIPT 37 of 55
739
(2) Because of different energy conversion efficiencies of generating heating and
740
cooling energy in this system and the conventional system, the performance of this
741
system is better in heating mode than in cooling mode on all the three aspects.
742
(3) Thermal demand is a decisive factor for the performance of the system. In the month
743
that the system is operated in thermally overloaded condition, it performs poorly;
744
meanwhile, the effect of installation of ORC module is slightly positive or even
745
negative. When the thermal load is low, the system has good performance as well as
746
obvious improvement owing to the ORC module. Therefore, the climate regions in
747
preferable order to install an ORC module are mild-climate regions > cold regions>
748
hot regions.
749
(4) Sensitivity analysis shows that the equivalence ratio of gasification has large effect
750
on the performances of the system on all the three aspects. The increment of PESR,
751
CSR, and CERR with the decrease of the ER can be up to 6.6%, 6.3%, and 3.6%,
752
respectively. The parameters of the grid and biomass feedstock can also affect the
753
corresponding criteria significantly. The efficiencies of the components of the system
754
have little influence on the performance of the system.
755
Acknowledgments: This work is supported by the Key Project of the National Science
756
Foundation of China for international academic exchanges under the contract No.
757
51561145012. The support from the Sino-Thai Cooperation Research Project under National
758
Research Council of Thailand is also appreciated.
759
References
760
[1] Li M, Mu H, Li N, Ma B. Optimal design and operation strategy for integrated evaluation
761
of CCHP (combined cooling heating and power) system. Energy 2016, 99, 202−220.
762
[2] De Meyer A, Snoeck M, Cattrysse D, Van Orshoven J. A reference data model to support
763
biomass supply chain modelling and optimisation. Environ. Modell. Softw. 2016, 83, 1‒11.
ACCEPTED MANUSCRIPT 38 of 55
764
[3] Chen WH, Peng J, Bi, XT. A state-of-the-art review of biomass torrefaction,
765
densification and applications. Renew. Sustain. Energy Rev. 2015, 44, 847‒866.
766
[4] Mikulčić H, Von Berg E, Vujanović M, Duić N. Numerical study of co-firing pulverized
767
coal and biomass inside a cement calciner. Waste Manage. Res. 2014, 32(7), 661−669.
768
[5] Shabbar S, Janajreh I. Thermodynamic equilibrium analysis of coal gasification using
769
Gibbs energy minimization method. Energy Convers. Manag. 2013, 65, 755−763.
770
[6] Sansaniwal SK, Pal K, Rosen MA, Tyagi S K. Recent advances in the development of
771
biomass gasification technology: A comprehensive review. Renew. Sustain. Energy Rev.
772
2017, 72, 363−384.
773
[7] Minutillo M, Perna A, Jannelli E, Cigolotti V, Nam SW, Yoon SP, Kwon BW. Coupling
774
of Biomass Gasification and SOFC–Gas Turbine Hybrid System for Small Scale
775
Cogeneration Applications. Energy Procedia 2017, 105, 730−737.
776
[8] Lv X, Gu C, Liu X, Weng Y. Effect of gasified biomass fuel on load characteristics of
777
an intermediate-temperature solid oxide fuel cell and gas turbine hybrid system. Int. J.
778
Hydrog. Energy 2016, 41(22), 9563−9576.
779
[9] Elsner W, Wysocki M, Niegodajew P, Borecki R. Experimental and economic study of
780
small-scale CHP installation equipped with downdraft gasifier and internal combustion
781
engine. Appl. Energy 2017, 202, 213−227.
782
[10] Skorek-Osikowska A, Kotowicz J, Uchman W. Thermodynamic assessment of the
783
operation of a self-sufficient, biomass based district heating system integrated with a Stirling
784
engine and biomass gasification. Energy 2017, 141, 1764−1778.
785
[11] Puig-Arnavat M, Bruno JC, Coronas A. Modeling of trigeneration configurations based
786
on biomass gasification and comparison of performance. Appl. Energy 2014, 114, 845−856.
787
[12] Wang H, Yan J, Dong L. Simulation and economic evaluation of biomass gasification
788
with sets for heating, cooling and power production. Renew. Energy 2016, 99, 360−368.
ACCEPTED MANUSCRIPT 39 of 55
789
[13]
Barik D, Murugan S. Investigation on combustion performance and emission
790
characteristics of a DI (direct injection) diesel engine fueled with biogas–diesel in dual fuel
791
mode. Energy 2014, 72, 760-771.
792
[14] Pantaleo AM, Camporeale S, Shah N. Natural gas–biomass dual fuelled microturbines:
793
Comparison of operating strategies in the Italian residential sector. Appl. Therm. Eng. 2014,
794
71(2), 686−696.
795
[15]
796
cycles through primary steam reforming of the natural gas. Energy 2007, 32(4), 371-377.
797
[16] Pihl E, Heyne S, Thunman H, Johnsson F. Highly efficient electricity generation from
798
biomass by integration and hybridization with combined cycle gas turbine (CCGT) plants for
799
natural gas. Energy 2010, 35(10), 4042−4052.
800
[17] Wang J, Mao T, Sui J, Jin H. Modeling and performance analysis of CCHP (combined
801
cooling, heating and power) system based on co-firing of natural gas and biomass gasification
802
gas. Energy 2015, 93, 801−815.
803
[18] Li CY, Wu JY, Zheng CY, Wang, RZ. Effect of LPG addition on a CCHP system based
804
on different biomass-derived gases in cooling and power mode. Appl. Therm. Eng. 2017,
805
115, 315−325.
806
[19] Wang J, Yang Y. Energy, exergy and environmental analysis of a hybrid combined
807
cooling heating and power system utilizing biomass and solar energy. Energy Convers.
808
Manage. 2016, 124, 566−577.
809
[20] Esen H, Inalli M, Esen M. A techno-economic comparison of ground-coupled and air-
810
coupled heat pump system for space cooling. Build. Environ. 2007, 42(5), 1955-1965.
811
[21] Esen H, Inalli M, Esen M. Technoeconomic appraisal of a ground source heat pump
812
system for a heating season in eastern Turkey. Energy Convers. Manage. 2006, 47(9-10),
813
1281-1297.
De Ruyck J, Delattin F, Bram S. Co-utilization of biomass and natural gas in combined
ACCEPTED MANUSCRIPT 40 of 55
814
[22]Li H, Zhang X, Liu L, Wang S, Zhang G. Proposal and research on a combined heating
815
and power system integrating biomass partial gasification with ground source heat pump.
816
Energy Convers. Manage. 2017, 145, 158−168.
817
[23] Esen M, Yuksel T. Experimental evaluation of using various renewable energy sources
818
for heating a greenhouse. Energy Build. 2013, 65, 340‒351.
819
[24] Jradi M, Riffat S. Modelling and testing of a hybrid solar‒biomass ORC-based micro-
820
CHP system. Int. J. Energy Res. 2014, 38(8), 1039‒1052.
821
[25]Zheng CY, Wu JY, Zhai XQ. A novel operation strategy for CCHP systems based on
822
minimum distance. Appl. Energy 2014; 128; 325−335.
823
[26] Wang JJ, Yang K, Xu ZL, Fu C. Energy and exergy analyses of an integrated CCHP
824
system with biomass air gasification. Appl. Energy 2015, 142, 317−327.
825
[27] Yang K, Zhu N, Yuan T. Analysis of optimum scale of biomass gasification combined
826
cooling heating and power (CCHP) system based on life cycle assessment (LCA). Procedia
827
Eng. 2017, 205, 145−152.
828
[28] Li X, Shen Y, Kan X, Hardiman TK, Dai Y, Wang CH. Thermodynamic assessment of
829
a solar/autothermal hybrid gasification CCHP system with an indirectly radiative
830
reactor. Energy 2017, 142, 201−214.
831
[29] Palomba V, Prestipino M, Galvagno A. Tri-generation for industrial applications:
832
Development of a simulation model for a gasification-SOFC based system. Int. J. Hydrog.
833
Energy 2017, 42(46), 27866−27883.
834
[30] Facci AL, Andreassi L, Ubertini S. Optimization of CHCP (combined heat power and
835
cooling) systems operation strategy using dynamic programming. Energy 2014, 66,
836
387−400.
837
[31] Wu JY, Wang J L, Li S. Multi-objective optimal operation strategy study of micro-
838
CCHP system. Energy 2012, 48(1), 472−483.
ACCEPTED MANUSCRIPT 41 of 55
839
[32] Teng X, Wang X, Chen Y, Shi W. A simple method to determine the optimal gas turbine
840
capacity and operating strategy in building cooling, heating and power system. Energy Build.
841
2014, 80, 623−630.
842
[33] Han G, You S, Ye T, Sun P, Zhang H. Analysis of combined cooling, heating, and power
843
systems under a compromised electric–thermal load strategy. Energy Build. 2014, 84,
844
586−594.
845
[34] Zheng CY, Wu JY, Zhai XQ, Wang RZ. Impacts of feed-in tariff policies on design and
846
performance of CCHP system in different climate zones. Appl. Energy 2016, 175, 168−179.
847 848
[35] Fang F, Wei L, Liu J, Zhang J, Hou G. Complementary configuration and operation of
849
a CCHP‒ORC system. Energy 2012, 46(1), 211−220.
850
[36] Mohammadi A, Kasaeian A, Pourfayaz F, Ahmadi MH. Thermodynamic analysis of a
851
combined gas turbine, ORC cycle and absorption refrigeration for a CCHP system. Appl.
852
Therm. Eng. 2017, 111, 397−406.
853
[37] Seyedkavoosi S, Javan S, Kota K. Exergy-based optimization of an organic Rankine
854
cycle (ORC) for waste heat recovery from an internal combustion engine (ICE). Appl.
855
Therm. Eng. 2017, 126, 447−457.
856
[38] Song J, Song Y, Gu CW. Thermodynamic analysis and performance optimization of an
857
Organic Rankine Cycle (ORC) waste heat recovery system for marine diesel engines. Energy
858
2015, 82, 976−985.
859
[39] Hajabdollahi H. Investigating the effects of load demands on selection of optimum
860
CCHP‒ORC plant. Appl. Therm. Eng. 2015, 87, 547−558.
861
[40] Wu Q, Ren H, Gao W, Weng P, Ren J. Design and operation optimization of organic
862
Rankine cycle coupled trigeneration systems. Energy 2017, 142, 666−677.
ACCEPTED MANUSCRIPT 42 of 55
863
[41] Raman P, Ram NK. Performance analysis of an internal combustion engine operated on
864
producer gas, in comparison with the performance of the natural gas and diesel
865
engines. Energy 2013, 63, 317−333.
866
[42] US Department of Energy. Introduction to weather data sources. https://www.energyp
867
lus.net/weather/sources#CSWD. 2017.
868
[43] US Department of Energy. Weather data by location, Shanghai, Chinese Standard
869
Weather Data (CSWD). https://www.energyplus.net/weather-location/asia_wmo_region_2/C
870
HN//CHN_Shanghai.Shanghai.583620_CSWD. 2017.
871
[44]US Department of Energy. Energy plus. http://www.energyplus.gov. 2017.
872
[45]ASHRAE, 2012 ASHRAE Handbook -- HVAC Systems and Equipment (I-P), Har/Cdr
873
ed.; ASHRAE: Atlanta, the United States, 2012.
874
[46]Wang JJ, Jing YY, Zhang CF, Zhai ZJ. Performance comparison of combined cooling
875
heating and power system in different operation modes. Appl. Energy 2011, 88(12),
876
4621−4631.
877
[47] Maraver D, Sin A, Royo J, Sebastián F. Assessment of CCHP systems based on biomass
878
combustion for small-scale applications through a review of the technology and analysis of
879
energy efficiency parameters. Appl. Energy 2013, 102, 1303−1313.
880
[48] Sharma S, Sheth PN. Air–steam biomass gasification: experiments, modeling and
881
simulation. Energy Convers. Manage. 2016, 110, 307−318.
882
[49] Yaws CL, Gabbula C. Yaws" Handbook of Thermodynamic and Physical Properties of
883
Chemical Compounds. Knovel, 2003.
884
[50] Svishchev DA, Kozlov AN, Donskoy IG, Ryzhkov AF. A semi-empirical approach to
885
the thermodynamic analysis of downdraft gasification. Fuel 2016, 168, 91−106.
886
[51] Ong Z, Cheng Y, Maneerung T, Yao Z, Tong YW, Wang CH, Dai, Y. Co-gasification
887
of woody biomass and sewage sludge in a fixed-bed downdraft gasifier. AIChE
888
Journal, 61(8), 2508−2521.
ACCEPTED MANUSCRIPT 43 of 55
889
[52]Chintala V, Kumar S, Pandey JK. A technical review on waste heat recovery from
890
compression ignition engines using organic Rankine cycle. Renew. Sustain. Energy Rev.
891
2018, 81, 493−509.
892
[53] Lemmon EW, Huber ML, McLinden MO. NIST standard reference database 23:
893
reference fluid thermodynamic and transport properties: REFPROP. 9.1, 2013.
894
[54] Sanaye S. Khakpaay N. Simultaneous use of MRM (maximum rectangle method) and
895
optimization methods in determining nominal capacity of gas engines in CCHP (combined
896
cooling, heating and power) systems. Energy 2014, 72, 145-158.
897
[55] Wang JJ, Xu ZL, Jin HG, Shi GH, Fu C, Yang K. Design optimization and analysis of
898
a biomass gasification based BCHP system: A case study in Harbin, China. Renew. Energy
899
2017, 71, 572−583.
900
[56] Wang C, Chang Y, Zhang L, Pang M. Hao Y. A life-cycle comparison of the energy,
901
environmental and economic impacts of coal versus wood pellets for generating heat in
902
china. Energy 2017, 120, 374−384.
903
[57] Kang L, Yang J, An Q, Deng S, Zhao J, L, Z, Wang Y. Complementary configuration
904
and performance comparison of CCHP‒ORC system with a ground source heat pump under
905
three energy management modes. Energy Convers. Manage. 2017, 135, 244‒255.
906
[58] Mago PJ, Hueffed A, Chamra LM. Analysis and optimization of the use of CHP–ORC
907
systems for small commercial buildings. Energy Build. 2010, 42(9), 1491‒1498.
908
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909
Tables
910
Table 1. Characteristics of demand areas divided by boundaries of output area of the PGU.
Area No. Characteristic 1
Operating PGU in the idle condition. On. Matching Eorc with electrical demand. Releasing excess waste heat. Sufficient Epgu and Qpgu. Operating the system at the matching point. On. Sufficient Qpgu but insufficient Epgu. Operating PGU in the full-load condition. On. Operating ORC with excess waste heat. Supplementing electricity from the grid. Sufficient Epgu but insufficient Qpgu. Matching Epgu with electrical demand. Off. Supplementing thermal energy from the auxiliary system. Insufficient Epgu and Qpgu. Operating PGU in full-load condition. Off. Supplementing electricity from the grid. Supplementing thermal energy from the auxiliary system.
4 5
911
Table 2. Heat of formation of compound species related to the gasification model [49]. Species
H0 f(kJ/mol)
CO CO2 H2O(l) CH4
−110.6 −393.8 −285.8 −74.9
Table 3. Polynomial coefficients of cp of the species related to the gasification model [49]. Species
a0
a1
a2
a3
a4
H2 CO CO2 H2O CH4 N2 C
25.399 29.566 27.437 33.933 34.942 29.342 -0.832
2.0178×10−2 -6.5807×10-3 4.2315×10-2 -8.4186×10-3 -3.9660×10-2 -3.5935×10-3 3.4846×10-2
-3.8549×10-5 2.0130×10-5 -1.9555×10-5 2.9906×10-5 1.9184×10-4 1.0076×10-5 -1.3223×10-5
3.1880×10-8 -1.2227×10-8 3.9968×10-9 -1.7825×10-8 -1.53×10-7 -4.3166×10-9 0
-8.7585×10-12 2.2617×10-12 -2.9872×10-13 3.6934×10-12 3.9321×10-11 2.5935×10-13 0
913
Table 4. Coefficients of equilibrium constant functions [48]. K1 K2 K3
914
ORC
Excess Epgu and Qpgu.
2 3
912
Strategy
b1
b2
7082.848 5870.53 -22784.85
-6.567 1.86 7.951
b3
b4
b5
3.733×10-3 -3.6067×10-7 3.505×10-6 0 -2.7×10-4 -5.82×104 -4.354×10-3 3.6067×10-7 4.85×103
b6 32.541 -18.007 -24.899
Table 5. The properties of the biomass feedstock [50] and the syngas at nominal condition. Item
Value
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biomass
a (-) b (-) w (-) LHV (kJ/mol) H0 f (kJ/mol) x1 (-) x2 (-) x5 (-) LHV (kJ/mol)
syngas
915
1.49 0.69 0.10 439.1 -122.9 0.161 0.176 0.022 106.5
Table 6. Correlation results of Epgu and Qpgu from jacket water and exhaust gas [11]. PGU J312 GS J316 GS
Epgu c0 -81.821 -100.71
Qpgu from jacket water
c1 0.430 0.421
916
R2 1.00 0.99
c0 68.433 91.782
c1 0.243 0.182
Qpgu from exhaust gas
R2
c0 -2.303 1.897
1.00 0.99
c1 0.200 0.244
R2 1.00 0.99
Table 7. Unit prices of the equipment [55].
Equipment
Gasifier
PGU
ORC
Boiler
Heating coil SAC
Electric AC
Unit price (Yuan/kW)
2500
6800
12000
375
200
970
917
1200
Table 8. Unit prices of energy resources [55]. Energy resource
Electricity
Unit price (Yuan/kWh) 0.936
918
NG
Biomass feedstock
0.385
0.345
Table 9. CO2 conversion factors of different energy sources [25, 56]. Energy resource
Electricity
NG
Biomass feedstock
μ (kg/kWh)
0.923
0.220
0.030
919
Table 10. Comparison on results from this study and Refs. [35, 57, 58]. No.
Energetic
Economic
Environmental
Annual
(1)
PESR=20.6%, ΔPESR=3.2%
CSR=11.1%, ΔCSR=7.1%
CERR=43.7%, ΔCERR=1.4%
performance
(2)
PESR=76.5%, ΔPESR=22.4%
CSR=8.3%, ΔCSR=11.8%
CERR=50.1%, ΔCERR=17.7%
PESR=29.7%‒34.9%,
CSRa=13.8%‒55.1%,
CERR=0.5%‒28.6%
ΔPESR=12.4%‒19.3%
ΔCSRa=6.7%‒24.1%
ΔCERR=-4.5%‒17.9%
(4)b, c, d
ΔPEC=14.7%
ΔCOST=26.1%
ΔCE=24.1%
Highest
(1)
PSER=24.2%, Jan.
CSR=17.7%, Jan.
CERR=45.8%, Jan.
performance
(2)
PSER=93.4%, Dec.
CSRa=49.6%, Mar.
CERR=62.1%, Mar.
(3)
PESR=34.9%, hot‒humid
CSRa=55.1%, severe cold
CERR=38.6%, very cold
(4)d
-
-
-
Lowest
(1)
PESR=18.7%, Aug.
CSR=8.4%, Oct.
CERR=35.5%, Aug.
performance
(2)
PESR=49.6%, Jul.
CSRa=25.5%, Aug.
CERR=33.7%, Jul.
PESR=29.7%, hot‒dry
CSRa=13.9%,
CERR=-0.4%, hot‒dry
and variation
(3)
(3)
mixed‒humid
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Most beneficial to integrate Least
(4)d
-
-
-
(1)
ΔPESR=8.1%, Nov.
ΔCSR=11.8%, Nov.
ΔCERR=4.6%, Mar.
ΔPESR=60.2%, Jan.
ΔCSRa=29.0%,
Mar.
ΔCERR=37.0%, Dec.
(3)
ΔPESR=19.3%, hot‒dry
ΔCSRa=24.1%,
mixed‒humid
ΔCERR=17.9%, mixed‒humid
(4)b
ΔPEC=24.8%, summer
ΔCOST=32.7%, autumn
ΔCER=34.1%, autumn
(1)
ΔPESR=-1.2%, Aug.
ΔCSR=3.1%, Aug.
ΔCERR=-1.6%, Aug. ΔCERR=0.1%, Jul.
(2)
beneficial to
(2)
ΔPESR=-0.4%, Oct.
ΔCSRa=-0.8%,
integrate
(3)
ΔPESR=12.4%, very cold
ΔCSRa=6.7%, severe cold
920 921 922 923 924 925 926 927
929
ΔCERR=-4.5%, hot‒dry
(4)b
ΔPEC=4.9%, winter ΔCOST=18.1%, winter ΔCE=15.0%, winter Refers to operational cost saving ratio. b The energy consumption of the conventional system is not provided. ΔPEC = (PEC cchp-PECcchp-orc)/ PECcchp. ΔCOST and ΔCE are calculated in the same way. c The annual performance is based on the assumption that the number of days in each season equals and the daily energy consumption equals to that of representative day. d The energy consumption of the conventional system is not provided. Therefore, the performance of the system cannot be judged just by criteria of absolute value. a
Table 11. Adopted values and ranges of the parameters for sensitivity analysis [47, 48]. No. Parameter Value 1 ER (-) 0.35 2 ηorc (-) 0.0827 3 ηb (-) 0.8 4 ηrec (-) 0.8 5 COPel (-) 3.5 6 COPSAC (-) 0.7 7 ηg (-) 0.323 8 I (-) 0.0655 9 COSTf (Yuan/kWh) 0.345 10 COSTel (Yuan/kWh) 0.936 11 COSTng (Yuan/kWh) 0.385 12 μf (g/kWh) 31.2 13 μel (g/kWh) 923 14 μng (g/kWh) 220
928
Jul.
Range 0.25−0.35 0.0758−0.0877 0.7−0.9 0.7−0.9 3.0−4.0 0.6−0.75 0.230−0.416 0.0594−0.0774 0.293−0.397 0.796−1.076 0.327−0.443 26.5−35.9 877−968 185−255
Relevant criteria PESR, CSR, CERR PESR, CSR, CERR PESR, CSR, CERR PESR, CSR, CERR PESR, CSR, CERR PESR, CSR, CERR PESR CSR CSR CSR CSR CERR CERR CERR
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Figures 100
30
80 60
20
40
10
20
Temperature Relative humidity
0 0
1095
2190
6570
7665
Relative humidity (%)
40
Temperature (°C)
930
0 8760
931
3285 4380 5475 Number of hours
932
Fig. 1. Hourly temperature and relative humidity of Shanghai [46].
933 3000
electricity demand
cooling demand
heating demand
Demand (kW)
2500 2000 1500 1000 500 0
934 935
0
1095
2190
3285 4380 5475 Number of hours
6570
7665
8760
Fig. 2. Energy demands of a large office building located in Shanghai.
936 937
Fig. 3. Schematic of the biomass gasification based CCHP system integrated with ORC.
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output curve of PGU boundaries of output area of PGU with ORC demand operation
Q/Epgu,max
1.5
1.0 Qaux
F Eg,el
B
G Eg,el
① Qpgu for ORC
0.5
①
Qaux
①
A matching point
①
Qrelease
0.0
0.0 ①
938 939
C
D
0.5
E/Epgu,max
1.0
1.5
Fig. 4. Operation strategy of the biomass gasification-based CCHP system integrated with an ORC.
940 941
Fig. 5. Flow chart of operation simulation of the system.
ACCEPTED MANUSCRIPT 49 of 55
942 943
Fig. 6. The methodology of this study.
944 945
Fig. 7. Comparison between simulative and experimental results [50] of the gasification model.
946 947
Fig. 8. (a) Schematic and (b) T-s diagram of the ORC module.
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3000 electricity demand cooling demand heating demand
Demand (kW)
2500 2000 1500 1000
(3583, 843.24)
Y
500 0
948 949
0
1095
2190
3285 X 4380 5475 Number of hours
6570
Fig. 9. Cumulative demands of the building.
7665
8760
ACCEPTED MANUSCRIPT 51 of 55
950 951
Fig. 10. Contour maps of PESR, CSR and CERR of the system in cooling mode.
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952 953
Fig. 11. Contour maps of PESR, CSR and CERR of the system in heating mode.
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system with ORC system without ORC ΔPESR
8 6
PESR(%)
20
4 2
15
ΔPESR(%)
25
0 10
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
-2
954
Month
955
Fig. 12. Monthly PESR and ∆PESR of the system with and without the ORC module.
CSR(%)
15
system with ORC system without ORC ΔCSR
12 10 8
10
6 5
4
0 -5
ΔCSR(%)
20
2 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
0
956
Month
957
Fig. 13. Monthly CSR and ∆CSR of the system with and without the ORC module.
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6 system with ORC system without ORC ΔCERR
45
40
2
ΔCERR(%)
CERR(%)
4
0 35
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
-2
958
Month
959
Fig. 14. Monthly CERR and ∆CERR of the system with and without the ORC module. 17.4%
PESR
system without ORC system with ORC
Criterion
20.6% 4.0%
CSR
11.1% 42.3%
CERR
43.7% 0
10
960 961
20
30 40 Percentage (%)
50
60
Fig. 15. Annual PESR, CSR and CERR of the system with and without the ORC module. 35 ηg
Annual PESR (%)
30
ER
25 20 21.5
15 10
21.0 20.5 20.0
5
19.5
Minimum
962
COPel ηb PESR = 20.6%
ηrec ηorc COPsac Adopted
Value of parameter
Maximum
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963
Fig. 16. The sensitivity of the PESR of the system integrated with ORC module. 20 COSTel
COSTf ER
Annual CSR (%)
15
COSTng
COPsac
10 11.6 11.4
5
11.2 11.0
I CSR = 11.1%
COPel
ηrec ηorc
10.8
0
Minimum
Maximum
Adopted
Value of parameter
964 965
ηb
Fig. 17. The sensitivity of the CSR of the system integrated with ORC module.
Annual CERR (%)
48
μf ER
46 44 42 40
COPsac
μel
ηb 43.7 43.6 43.5
COPel
ηrec
μng ηorc
38 43.4
Minimum
966 967 968
Adopted
Maximum
Value of parameter
Fig. 18. The sensitivity of the CERR of the system integrated with ORC module.
ACCEPTED MANUSCRIPT
A biomass gasification-based CCHP system integrated with ORC was investigated.
The performance of the system in cooling/heating mode was analyzed.
The system was evaluated based on operation simulation of a building in Shanghai.
Analysis and evaluation comprised energy, economic and environmental aspects.