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Energy, exergy, economic analysis and optimization of polygeneration hybrid solar-biomass system
Accepted Manuscript Energy, Exergy, Economic Analysis and Optimization of Polygeneration Hybrid Solar-Biomass System U. Sahoo, R. Kumar, S.K. Singh, A.K. Tripathi PII: DOI: Reference:
S1359-4311(18)32009-X https://doi.org/10.1016/j.applthermaleng.2018.09.093 ATE 12702
To appear in:
Applied Thermal Engineering
Received Date: Revised Date: Accepted Date:
31 March 2018 7 August 2018 22 September 2018
Please cite this article as: U. Sahoo, R. Kumar, S.K. Singh, A.K. Tripathi, Energy, Exergy, Economic Analysis and Optimization of Polygeneration Hybrid Solar-Biomass System, Applied Thermal Engineering (2018), doi: https:// doi.org/10.1016/j.applthermaleng.2018.09.093
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Energy, Exergy, Economic Analysis and Optimization of Polygeneration Hybrid Solar-Biomass System 1 1 2
U.Sahoo, *2R.Kumar, 1S.K.Singh, 1A.K.Tripathi
National Institute of Solar Energy, MNRE, Gurgaon, Haryana-122003, India Mechanical Engineering Department, Delhi Technological University, New Delhi-110042,
India Corresponding Author’s Address: Prof. Rajesh Kumar, Professor, Mechanical Engineering Department, DTU, New Delhi-110042, India; Email id: [email protected] ; Phone No.: +91-8750632739
Abstract: The energy, exergy, economic analysis and optimization with the objective functions of energy efficiency, exergy efficiency, VAR cooling output, desalination output and total output of the polygeneration hybrid solar and biomass system are investigated. The optimization using the objective functions, constraints and the decision variables is carried out utilizing genetic algorithm in EES software for polygeneration hybrid solar and biomass system. In this paper, the optimized values of energy efficiency, exergy efficiency, VAR cooling output, desalination output and total output of the polygeneration system are achieved to 49.85%, 20.94%, 7278 kW, 4405 kW and 14,606 kW respectively. The payback period of polygeneration hybrid solar biomass system is 1.5 years at the electricity tariff rate of ₹7.45/kWh which is less than of solar thermal and hybrid solar biomass power plant. Key Words: Solar; Biomass; Energy; Exergy; Optimization 1. Introduction Renewable energy is one of the options to transform the energy system to less carbon intensive, sustainable and bring energy security benefits. Renewable energy encompasses a broad range of energy resources and technologies that have differing attributes and applications. Renewable energy resources include solar, biomass, geothermal, wind and hydro. These sources are abundant and widely distributed; but they are not equally easy to harness. Solar, biomass and 1
geothermal resources are used for the generation of electricity and process heat applications. Renewable energy is the world’s second largest source of electricity generation after coal based power generation plants. These sources have huge potential in meeting energy requirements for process heat in industry and transport sectors. For the first time the renewable energy based industry has achieved a major milestone in 2017, with capacity additions exceeding as compared to fossil fuels and nuclear [1]. Globally, the total renewable power capacity has reached 2195,000 MW (including hydro power) at the end of 2017 [2]. Of this renewable power capacity, wind power contributes to 21.1 % (463,145 MW), Solar photovoltaic power (SPV) 10.93 % (239,914 MW), Bio-power 7.3 % (160,235 MW), Geothermal power 0.64 % (14,048 MW), Concentrated solar thermal power 0.23 % (5,048 MW), and hydro power 59.8% (1312,610 MW). Globally the concentrated solar thermal power generation capacity increased by 2.3% over 2016 to reach nearly 5,048 MW at the end of year 2017. Spain is the highest producer of solar thermal power of 46.07% (2362 MW) followed by the United States 36.3% (1832 MW), South Africa 7.9% (400 MW), India 4.06% (203.5 MW), Morocco 3.5% (181 MW) and rest of the world is 2.17% (109.5 MW). Apart from thermal power generation, the total installed capacity of solar thermal heating, cooling other industrial process heat applications in the World is 47200 MWth. Presently in India, the total installation capacity of solar thermal power plant is achieved to 203.5 MW using various solar thermal technologies (i.e. parabolic trough collector, linear fresnel reflector and scheffler dish) across the India. 101 MW parabolic trough collectors based CSP have been commissioned in various states of this country. 100 MW compact linear fresnel reflector based CSP has been built by M/s. AREVA [3]. 2.5 MW heliostat power tower based solar thermal power plant is installed in Bikaner, Rajasthan. A 1 MW solar thermal power plant
2
based on scheffler dish solar concentrators has also been installed at Mount Abu, Rajasthan, India. Bio-power is increasing with rapid growth for power generation in the major countries i.e. Brazil, the United States, China, Germany, India, Sweden, the United Kingdom and Japan. The total installed capacity of bio power is 109,213 MW. Brazil is the largest producer of bio-power 13.35% (14,583 MW), followed by the United States 12.04% (13,151 MW), China 10.4% (11,365 MW), India 8.72% (9,533 MW), Germany 8.23% (8,990 MW) , the United Kingdom 4.87% (5,326 MW), Sweden 4.45% (4,860 MW), Japan 1.95 % (2,131 MW) and other countries 35.99% (39,306 MW). In India, the estimated potential from agricultural and agro-industrial residues is 18,000 MW. The potential of bagasse based power plant with cogeneration is estimated as 7,000 MW [4]. Although, solar thermal is having good potential to generate electricity, but in cloudy days or short transient of the day it is not possible to operate the solar thermal power plant. In this context, hybridization of solar thermal is the best combination with other source of heat like biomass. The major advantages of the hybrid system is overcoming the variation of solar insolation, continuous generation of power with higher efficiency and minimizing the cost of power generation. In other way, the energy requirements for cooling and heating applications are increasing continuously in industrial sector. It is estimated that, around 2/3rd of energy consumption is used for process heat applications across the world. A numbers of research projects work has been carried out in cooling or heating in cogeneration and trigeneration process which are able to make more output from single heat sources for various end use application but a very little research work has been attempted in polygeneration hybrid solar and biomass system for power, cooling and desalination [5-7]. Solar and biomass resources assessment and energy evaluation 3
have studied for hybrid solar-biomass power plant in various regions of India [8]. The energy output of polygeneration process in hybrid system is increased to 69.62% as compared to simple hybrid system. In this study, the energy and exergy efficiencies of the polygeneration system are 49.25% and 15.77% respectively [9]. The performance evaluation of polygeneration system for the methanol production and the power generation with the solar-biomass thermal gasification was investigated through dynamic simulation. The energy and exergy efficiencies of the system is 56.09% and 54.86% respectively [10].The optimization of a novel polygeneration system has conducted. The primary energy saving of the system is 20% with 25 kilo tons/year reduction of CO2 [11].
The integrated concentrating photovoltaic and thermal collector polygeneration
system has investigated for the region of Naples, Italy. The dynamic simulation is performed using zero-dimensional transient simulation model developed in TRNSYS. The economic profitability of the system is also maximized [12-13]. The efficient use of heat energy sources is a key solution by considering the useful outputs like electricity, cooling and desalination which draw interest in this regard. Polygeneration provides opportunity to increase further efficiency through proper thermodynamic arrangement of different process or end products. In this study, the optimization scenarios with the objective functions of efficiencies, three outputs like power, cooling and desalination of hybrid system are considered. The study on optimization is being carried out on hybrid solar and coal based power plant under various operating parameters. It has studied that, the overall electric efficiency of the hybrid system is achieved to 20% [14]. The optimization of a real combined heat and power plant and a slurry drying process have been analyzed using multi objective function to maximize electrical output and heat for slurry process. The electrical efficiency is increased by 3% [15]. A Mixed-Integer Nonlinear Program (MINLP) modelling framework has been developed and optimized for coal-based polygeneration plant. By producing electricity & methanol together, the overall efficiency gets increased. In this framework, a polygeneration process consists of four functional blocks: gasification, chemical 4
synthesis, gas turbine, steam turbine and heat recovery steam generator. For each block, all alternative technologies are considered and mathematical representation has been carried out by using first-principle sub-models. And all sub-model are linked into substructure based MINLP program. This framework provides the design variables of technologies, equipment, capacity, size of each functional block, power generation for polygeneration process [16]. The parametric optimization of steam cycle in pressurized water reactor has been investigate by integrating genetic algorithm with simplex algorithm. The output of the system is increased to 23.8 MW with an efficiency of 34.69% by an average of 236 iterations [17]. A hybrid concentrated solar power and photovoltaic power plant is optimized by using genetic algorithm (multi objective functions) to increase the capacity factor at lower operational and installation cost [18]. Many of the researchers explained combined cooling, heating and power system but very little research work has been carried out on polygeneration system. The proposed system relates to energy, exergy, economic analysis and optimization using genetic algorithm in EES software for further improvement in efficiency and payback period of polygeneration process concept on hybrid solar biomass thermal power plant. In this paper, the energy, exergy, economic analysis & optimization with the objective functions of energy efficiency, exergy efficiency, VAR cooling output, desalination output and total output of the polygeneration hybrid solar and biomass system has been investigated to make the system more efficient and sustainable in the field of solar and biomass. 2. System Description In hybrid solar and biomass system, PTC field is utilized to heat the heat transfer fluid resulting in the heating of feed water using heat exchanger (HE-3). Biomass boiler depends upon the water heating from PTC field and continuously operate at full load capacity at intermittency of day & night hours. The superheated steam through biomass boiler at temperature of 500 oC and pressure 5
of 60 bar supply to turbine and produce electricity as shown in Fig. 1. The schematic diagram of the polygeneration system is divided into three cycle i.e. cycle-I, II & III. In the cycle-I, the steam is bled at temperature and pressure of 182 oC, 5 bar with mass flow rate of 4.45 kg/sec to an intermediate stage and supplied to the generator of cooling system (Cycle-II). And remaining steam is expanded at low pressure and medium temperature. The bled steam at point 6’ is sent to feed water heater. Condensed water is pumped to the feed water heater at pressure of 5 bar (State point 2) and mixed with bled steam in feed water heater. The cooling system is designed such that the condenser heat at 80.3 oC (Condenser-2) will be utilize for heating the preheated water for desalination system as shown in Fig. 1 (Cycle-III). In desalination system, the feed water is pumped (State points 21-22) to condenser chamber (State points 22-23) and supplied to the HE-2 (State point 23). The preheated water is getting further heated and supplied to the condenser-2 of VAR cooling system through storage tank (State point 24-24’) and then the hot water at 80.3 oC is sprayed on to the top of the evaporator chamber at a desired mass flow rate (State point 25). The humidified hot air from evaporator chamber is drifted toward the condenser chamber by natural convection. Then the air gets partially dehumidified in the condenser chamber and brought back to the evaporator chamber. The water vapor gets condensed in condenser chamber and collected the water droplet in desalination water collection tank. The combination of the three cycle is called polygeneration system.
6
Fig. 1 Schematic diagram of polygeneration system
7
3. Thermodynamic and Economic Analysis The thermodynamic (i.e. energy and exergy) and economic analysis has been conducted to better understand the performance of the system. 3.1 Energy and Exergy Analysis The mass and energy balance equations are being used in the polygeneration system.
1
mi me Where, mi , me are the rate of flow of fluid at inlet and exit.
Vi 2 Ve 2 Qsolar Qb mi (hi gZi ) me (he gZ e ) Wnet 2 2
2
Where Wnet is the net-work output of the HSB plant (kW),
Qb is the total heat produced from
biomass (kW), Qsolar is the solar energy falling on PTC field (kW), Vi, Ve are the bulk velocities of the working fluid at inlet and exit (m/sec), Zi, Ze is the altitudes of the stream above the sea level at inlet and exit (meters) and g is the specific gravitational force (m/sec2), hi, he are the specific enthalpies at inlet and exit (kJ/kg). The energy balance equations of polygeneration hybrid solar biomass system for power, cooling
and desalination are expressed in [5]. The energy efficiency E , polygeneration can be written as:
E , polygeneration
Wnet Qe (mDw h fg )
(3)
Qsolar Qb
Where, Qe is output of evaporator of VAR system (kW), mDw is amount of distilled water generated from distillation system (kg/sec), Qsolar and Qb are solar and biomass heat (kW). The exergy balance equations are being used in the polygeneration system.
E i
x ,i
4
T 1 o Q j Ex ,e Wnet Ex , D j e T j
Where Ex,i , Ex,e are exergy at inlet and exit (kW), To is temperature at surrounding conditions (K), T j is temperature of each state points (K) and Ex , D is exergy destruction (kW). Exergy of the flowing stream at each state point Ex of polygeneration system can be written as 8
5
Ex h ho To s so Where,
h, s , ho , so
are specific enthalpy (kJ/kg), specific entropy (kJ/kg K) of each state points,
specific enthalpy (kJ/kg), specific entropy (kJ/kg K) at surrounding conditions respectively. The exergy balance equations of polygeneration hybrid solar biomass system for power, cooling and desalination are expressed in [5]. The exergy efficiency of polygeneration process in hybrid
solar biomass (HSB) power plant Ex, polygeneration is expressed as:
Ex , polygeneration
Wnet Ex ,evaporator Ex , Dw
(6)
Ex , Solar Ex ,b
Where, Ex,evaporator is exergy of the evaporator load of VAR system (kW), Ex , Solar is exergy of solar heat (kW), Ex ,b is exergy of biomass heat (kW) and Ex , Dw exergy of distilled water (kW). 3.2 Economic Analysis The economic analysis has also been discussed to find out the actual size of the system that gives the cheapest combination of solar and biomass energy system for various applications. The complete cost analysis of the system is examined below thoroughly: The total capital cost (₹) of the system ( CCtotal ) is expressed as
CCtotal CSolar Cb CVAR Cdesalination
(7)
Where CSolar , Cb , CVAR , Cdesalination is the cost of solar, biomass, VAR system and desalination system respectively. The total cost of solar Cs can be calculated as: Cs Ca Aap
(8)
Where, Ca is the area dependent cost of components (₹/m2) (PTC collectors, piping with fitting and heat pump) and Aap aperture area of the PTC collector (m2). The total cost of biomass Cb is expressed as
9
Cb P & M ( n ) 1 F1 F2 F3
(9)
Where, P & M ( n ) is the plant and machinery cost (₹), F1 , F2 , F3 are factors for civil works, commissioning and financing of the polygeneration system. The plant and machinery cost (₹) of the system can be calculated as P & M ( n ) P & M (0) 1 d (n)
(10)
Where the value of P & M (0) (i.e base year plant & machineries) is considered as ₹ 443.61 lakhs and d (n) is the capital cost escalation factor of 3.52 %. The values of each factors are taken from central electricity regulatory commission under renewable energy tariff regulations, India and used in the formulae of capital cost of the system are given in Table 1 [21]. The capital costs of VAR cooling system and desalination system have been considered approximately as ₹ 560 lakhs and ₹ 25 lakhs respectively as per their actual capacity. The cost of VAR cooling system and desalination system as per proposed capacity are taken from various Indian manufacturers such as M/s Thermax and M/s Amba Engineers & Project [22 & 23]. Table 1 Value of each factor used for calculation of capital cost of biomass power system [21]. Variables Type of Description Value
a
Steel index
0.7
b
Machinery index
0.3
F1
Factor for civil works
0.1
F2
Factor for commissioning
0.09
F3
Factor for financing
0.14
10
In addition, the costs of fitting, piping and electronics are considered as 5 % of the total cost of each system. For calculation of overall cost per kWh of the system, the following assumptions are considered as per central electricity regulatory commission under renewable energy tariff regulations, India, in Table 2. Table 2 Assumptions for economic analysis of the system Assumption Head Units
Value
Overall System: Auxiliary consumption during the stability
%
13
Auxiliary consumption after stability
%
12
Plant load factor (stabilization for 6
%
60
months)
%
70
%
80
Years
20
Debt
%
70
Equity
%
30
Year
0
%
12.76
Years
12
Return on equity for first 10 years
%
20
Return on equity after 10 years
%
24
Weighted average of rate of Return on
%
22
Plant load factor (during 1 st year after stabilization) Plant load factor (2nd year onwards) Useful Life Financial Assumption:
Moratorium period Interest rate Repayment period
11
Discount rate
%
10.74
Income tax
%
33.99
Depreciation rate
%
5.83
Depreciation rate 13th year onwards
%
2.505
Operation and maintenance spares
%
15
Interest on working capital
%
13.26
Fuel: Biomass price escalation factor
%
5
Lakhs
50
%
5.72
Working Capital:
Operation and Maintenance: Operation and maintenance expenses Operation
and
maintenance
expenses
escalation The payback period of the polygeneration system is expressed as:
Pay back period=
Energy consumed by the system (MWh) Energy produce by the system per year (MWh/Year)
(11)
4. Optimization GA technique [19 & 20] is used for optimization the thermodynamic parameters of the system. The thermodynamic parameters of the system are optimized with energy efficiency, VAR cooling output, desalination output and the total output. The work flow of the optimization for polygeneration process in hybrid system for combined power, cooling and desalination is shown in Fig. 2.
12
Fig. 2 Work flow of the optimization process in polygeneration in HSB thermal power plant 4.1 Objective Functions In this study, objective functions are taken to maximize the energy efficiency and exergy efficiency, VAR cooling output, desalination output and the total output. The heat inputs are taken as solar and biomass heat and the total output in polygeneration process are output of turbine, output of evaporator and output of desalination water. 4.2 Decision Variable and Constraints In this study, various inequality constraint are taken to define the feasibility regions for the engineering optimization problem and feasible operating conditions for an optimal performance. The relevant parameters are selected as decision variables for the analysis i.e. P6 (kPa); f ; T25 (0C) are given in Table 3. Table 3 Ranges of constraints defined for the decision variable Decision Variable
Range of Variation
Extraction pressure from turbine P6 , kPa Fraction of steam extraction from turbine Desalination
inlet
heated
water
390 P6 600
f
temperature 13
0.1 P6 0.89 80 T25 90
T
25,
0
C
5. Results & Discussions Fig. 3 shows the variation in efficiency (i.e. energy & exergy) of the polygeneration system with respect to extraction pressure at different fraction of steam. The system energy efficiency slightly increases from 24.69% to 27.77% with the change of bled steam pressure from 390 to 600 kPa at steam fraction of 0.1. It is also observed that, the energy efficiency of the system increases to 49.35% at bled steam pressure of 555 kPa and steam fraction of 0.9 and exergy efficiency follows the same trend as energy efficiency against different bled steam pressure and steam fraction. Although the energy efficiency increases to 49.35%, but it is not sufficient and to find the higher energy efficiency, VAR cooling & desalination output of the polygeneration system. In view of this, optimization results is shown in Table 4.
Fig. 3 Variation of polygeneration system efficiency (i.e. energy & exergy) with respect to extraction pressure at different fraction of steam 14
Table 4 shows the values of the decision variables in the base case design along with different optimization criteria. In addition, the results of analyses for each optimization criteria are also shown in this table. It is seen that the values for the decision variables are considered (i.e. P6 (kPa); f ; T25 (0C)) to be continuous over the determined constraints for the optimization problem. The various optimization decision variables are closest values in the analysis of energy efficiency, VAR cooling system output, desalination output and total output of the polygeneration process in the system, which is giving the most optimal results. In this analysis, the optimization scenarios with the objective functions of energy efficiency, exergy efficiency, VAR cooling system output, desalination output and total output of the polygeneration process in HSB thermal power plant are considered. Table 4 Optimized values at various decision variables [P6 (kPa); f ; T25 (0C)] Parameters Optimized value Decision variables Energy Efficiency
49.85%
P6: 596.5 kPa ; f : 0.89; T25 : 80.08 0C
Exergy Efficiency
20.94%
P6: 598 kPa ; f : 0.101; T25 : 89.83 0C
VAR Cooling System
7278 kW
P6: 598.6 kPa ; f : 0.89; T25 : 80.08 0C
Desalination Output
4405 kW
P6: 596.8 kPa ; f : 0.89; T25 : 80.02 0C
Total Output
14606 kW
P6: 597 kPa ;
f : 0.89; T25 : 80.08 0C
The base case design parameters are taken as P6: 500 kPa ; f : 0.17; T25 : 82 0C Fig. 4 to 8 shows the corresponding optimization scenarios on energy efficiency, exergy efficiency, VAR cooling system output, desalination output and total output. The energy efficiency, exergy efficiency, VAR cooling output, desalination output and total output are optimized in 120 numbers of generation. The optimized energy efficiency is achieved to 49.85% at decision variables of P6 = 596.5 kPa; f = 0.89; T25= 80.08 0 C respectively at 120th number of generation. It is also observed that the optimized value of VAR cooling output is 7278 kW at decision variables of P6 = 598.6 kPa; f = 0.89; T25= 80.08 0 C respectively. The optimized value 15
of desalination output is 4405 kW at closest value of decision variables of total output and energy efficiency of polygeneration process in HSB system as shown in Table 4. The optimized exergy efficiency of the system is 20.94% at decision variable of extraction pressure, fraction of steam and desalination inlet heated water temperature of 598 kPa, 0.101 & 89.83 0 C respectively as shown in Fig. 8.
Fig. 4 Optimization of the system over generations with respect to the energy efficiency
16
Fig. 5 Optimization of the system over generations with respect to the exergy efficiency
Fig. 6 Optimization of the system over generations with respect to the VAR cooling production
17
Fig. 7 Optimization of the system over generations with respect to the output of the desalination system
Fig. 8 Optimization of the system over generations with respect to the output of the system The various optimization decision variables are closest values in the analysis of energy efficiency, VAR cooling output, desalination output and total output of the polygeneration 18
process in the system, which is giving the most optimal results generated from EES software. For the proposed subsystem, the optimized various parametric state points of the system are given in Table 5. Table 5 Optimized various parametric state points properties of the system State Fluid Pressure Temperature Enthalpy Mass flow Points (bar) (oC) (kJ/kg K) rate (kg/sec) 1 Water 0.1 45.8 191.8 5.0 2 Water 5 45.8 192.3 5.0 3 Water 5 102.3 429.1 5.0 4,c Water 60 102.7 434.9 5.0 5 Water 60 500 3422 5.0 6 Water 5 182 2810 4.45 6’ Water 5 158.8 670.5 4.45 7 Water 0.1 45.8 2237 0.55 8 Water 60 102.7 434.9 5.0 9,a Water 60 275.6 1235 5.0 X Therminol 12 301.6 554.6 25.0 VP-1 Y Therminol 12 240 420.5 25.0 VP-1 10 LiBr-Water 0.001313 37 81.43 28.67 11 LiBr-Water 0.08453 37 81.43 28.67 12 LiBr-Water 0.08453 132 288 28.67 13 LiBr 0.08453 148.8 331.4 25.24 14 LiBr 0.08453 30 96.75 25.24 15 LiBr 0.001313 30 96.75 25.24 16 Water 0.08453 148.8 2745 3.429 17 Water 0.08453 95 398 3.429 18 Water 0.08453 40 167.6 3.429 19 Water 0.001313 11 398 3.429 20 Water 0.001313 11 2521 3.429 21 Water 0.1 35 192.4 56.98 22 Water 0.1 35 146.7 56.98 23 Water 0.1 37.5 157.1 56.98 24 Water 0.1 50 209.4 56.98 25 Water 0.1 80.3 336.5 56.98 26 Water 0.1 50.2 210.4 55.09
Table 6 shows the comparison of payback period among solar thermal power plant, HSB thermal power plant, cogeneration in HSB thermal power plant and polygeneration HSB thermal power plant. Form the Table 6 it is observed that, the payback period of polygeneration in HSB thermal 19
power plant is 1.52 years, while the tariff is ₹ 7.45/kWh and the capital cost of ₹ 7460.72 lakhs. The payback period for the solar thermal power plant is 18.7 years for the same site, when the tariff is ₹ 12.08/kWh [21] and the capital cost of ₹ 7128/kWh. It is seen that, the Polygeneration in HSB Thermal Power Plant is the most cost effective and efficient as compared to the solar thermal power plant, HSB thermal power plant and cogeneration in HSB Thermal Power Plant. Table 6 Comparison of payback period among solar thermal power plant, HSB thermal power plant, cogeneration in HSB thermal power plant and polygeneration in HSB thermal power plant.
Capital Cost (Lakhs) Electricity Tariff (₹/kWh) Payback Period (Years)
Solar Thermal Power Plant 7128
HSB Thermal Power Plant
Cogeneration in HSB Thermal Power Plant
Polygeneration in HSB Thermal Power Plant
6875.72
7375.72
7460.72
12.08
7.45
7.45
7.45
18.7
2.55
2.36
1.52
6. Conclusion The energy efficiency, exergy efficiency, VAR cooling output, desalination output and total output of polygeneration system in hybrid solar and biomass system for power, cooling and desalination are optimized in 120 numbers of generation of genetic algorithm. It has been investigated and found that, the energy efficiency increases up to 49.85% at decision variable of bled steam pressure, desalination inlet heated water temperature and steam fraction of 598.6 kPa, 80.08 0 C , 0.89 respectively. It is expected that the optimized data generated from the polygeneration system will be useful for commercial establishment and development of future road map for hybrid solar and biomass polygeneration system for power, cooling and desalination. It is also investigated that, although the instant investment of the polygeneration 20
system is higher than solar thermal and hybrid solar biomass power plant, cogeneration hybrid solar biomass power plant, but the payback and tariff cost of electricity are lower (i.e. 1.5 years and ₹7.45/kWh) than the other systems. This polygeneration system is most cost effective and efficient as compared to solar thermal and hybrid solar biomass power plant, cogeneration hybrid solar biomass power plant. The polygeneration hybrid solar biomass system for power, cooling and desalination will constitute one of the best option to fulfill the energy demand in the global energy transition. It is expected that in the near future the polygeneration hybrid solar biomass system will constitute one of the competitive options of electricity generation from renewable energy.
Nomenclature Symbols Aap
:
Aperture area of the PTC collector (m2)
C
:
Cost of system (₹)
Ex
:
Exergy (kW)
f
:
Fraction of steam bled from turbine
g
:
Specific gravitational force (m/sec2)
h
:
Specific enthalpy (kJ/kg)
m
:
Mass flow rate of working fluid (kg/sec)
Q
:
Rate of heat transfer (kW)
s
:
Specific entropy (kJ/kg K)
T
:
Temperature (K)
V
:
Bulk velocity of the working fluid (m/sec)
W
:
Rate of work transfer (kW) 21
Z
:
Altitude of the stream above the sea level (m)
Greek
E , polygeneration
: Energy efficiency of polygeneration system %
Ex, polygeneration : Exergy efficiency of polygeneration system % Subscripts a
:
area dependent quantity
b
:
Biomass
D
:
Destruction
Dw
:
Distilled water
evaporator
:
Evaporator of VAR system
i, e
:
Inlet and exit of flowing stream
f, g
:
Liquid and gaseous state of steam
j
:
state points of the system
o
:
Surrounding (or reference environment) condition
w
:
Water
CC
:
Capital Cost
EES
:
Engineering Equation Solver
GA
:
Genetic algorithm
HSB
:
Hybrid solar and biomass
P&M
:
Plant and machinery
PTC
:
Parabolic trough collector
TRNSYS
:
Transient System Simulation
VAR
:
Vapor absorption refrigeration system
Acronyms
22
References [1] International Energy Agency (IEA), World Energy Outlook, Paris, WEO 2017. [2] Renewable energy policy network for the 21st century, Global status report on renewables, Paris,2017. [3] Annual Report 2015-16, Ministry of New and Renewable Energy, Government of India, 2016. [4] C. Bhushan, A. Kumarankandath and N. Goswami. The State of Concentrated Solar Power in India: A Roadmap to Developing Solar Thermal Technologies in India, Centre for Science and Environment, New Delhi, India;2015. [5] U.Sahoo, R.Kumar, P.C.Pant, R. Chaudhary, Scope and sustainability of hybrid solarbiomass power plant with cooling, desalination in polygeneration process in India, Renew. Sustain. Energy Rev. 51 (2015) 304-316. [6] C.P. Jawahar, R. Saravanan, J.C. Bruno, A. Coronas, Simulation studies on GAX based Kalina cycle for both power and cooling applications, Appl. Therm. Eng. 50 (2013)15221529. [7] M. Rivarolo, A.Cuneo, A.Traverso, A.F.Massardo, Design optimization of smart polygeneration energy districts through a model based approach, Appl. Therm. Eng. 99 (2016) 291-301. [8] U.Sahoo, R.Kumar, P.C.Pant, R. Chaudhary, Resource assessment for hybrid Solar-Biomass power plant and its thermodynamic evaluation in India, Sol. Energy 139 (2016) 47-57. [9] U.Sahoo, R.Kumar, P.C.Pant, R. Chaudhary, Development of an innovative polygeneration process in hybrid solar-biomass system for combined power, cooling and desalination, Appl. Therm. Eng.120 (2017) 560-567. [10] Z.Bai, Q.Liu, J.Lei, H.Li, H.Jin, A polygeneration system for the methanol production and the power generation with the solar-biomass thermal gasification, Energy Convers. Manage. 102 (2015) 190201.
[11] K.Jana, S.De, Sustainable polygeneration design and assessment through combined thermodynamic, economic and environmental analysis, Energy 91 (2015) 540-555. [12] A.Buonomano, F.Calise, G.Ferruzzi, L.Vanoli, A novel renewable polygen- eration system for hospital buildings: Design, simulation and thermo-economic optimization, Appl. Therm. Eng. 67 (2014) 43-60.
24
[13] F. Calise, M. Dentice d'Accadia, L. Libertini, E. Quiriti, M. Vicidomini, A novel tool for thermoeconomic analysis and optimization of trigeneration systems: A case study for a hospital building in Italy, Energy 126 (2017) 64-87. [14] Y.Wang, J. Xu, Z. Chen, H. Cao, B. Zhang, Technical and economical optimization for a typical solar hybrid coal-fired power plant in China, Appl. Therm. Eng. 115 (2017) 549-557. [15] P. Danti, L. Pezzola, S. Magnani, Performance analysis of an optimization management algorithm on a multi-generation small size power plant, Energy Procedia 101 (2016) 566573. [16] L. Pei, N.P. Efstratios, L. Zheng, A mixed-integer optimization approach for polygeneration energy systems design, Computers and Chemical Engg. 33 (2009) 759-768. [17] C. Wang, C. Yan, J. Wang, C. Tian, S. Yu, Parametric optimization of steam cycle in PWR nuclear power plant using improved genetic-simplex algorithm, Appl. Therm. Eng.125 (2017) 830-845. [18] A. R. Starke, J. M. Cardemil, R.A. Escobar, S. Colle, Multi-objective optimization of hybrid CSP+PV system using genetic algorithm, Energy 147 (2018) 490-503. [19] A. Konak, D. W. Coit, A. E. Smith, Multi-objective optimization using genetic algorithms: a tutorial, Reliability Engineering and System Safety 9 (2006) 992-1007. [20] S. S. Rao, T.S.Pan, V.B.Venkayya, Optimal placement of actuators in actively controlled structures using genetic algorithms, AIAA Journal 29 (1991) 942-943. [21] Central electricity regulatory commission, New Delhi, India. [http://www. cercind .gov.in/2016/orders/SORE.pdf], 2016. [22] Thermax private
limited,
[http://www.thermaxglobal.com/thermax-absorption-cooling-
systems/], December 22, 2016. [23] Water and waste water treatment, [http://www.ambaenggs.com/], December 22, 2016.
25
Figure captions: Fig.1 Schematic diagram of polygeneration system Fig.2 Work flow of the optimization process in polygeneration in HSB thermal power plant Fig. 3 Variation of polygeneration system efficiency (i.e. energy & exergy) with respect to extraction pressure at different fraction of steam Fig. 4 Optimization of the system over generations with respect to the energy efficiency Fig. 5 Optimization of the system over generations with respect to the exergy efficiency Fig. 6 Optimization of the system over generations with respect to the VAR cooling production Fig. 7 Optimization of the system over generations with respect to the output of the desalination system Fig. 8 Optimization of the system over generations with respect to the output of the system
Table captions: Table 1 Value of each factor used for calculation of capital cost of biomass power system Table 2 Assumptions for economic analysis of the system Table 3 Ranges of constraints defined for the decision variable Table 4 Optimized values at various decision variables [P6 (kPa); f ; T25 (0C)] Table 5 Optimized various parametric state points properties of the system Table 6 Comparison of payback period among solar thermal power plant, HSB thermal power plant, cogeneration in HSB thermal power plant and polygeneration in HSB thermal power plant.
26
Highlights:
Modelling of polygeneration hybrid solar and biomass system is carried out.
Optimization of various output parameters is considered using Genetic Algorithm.
Optimized values in various decision variables.
Efficiency of polygeneration hybrid solar and biomass system is achieved to 49.85 %.
The payback period is 1.5 years which is less than of solar thermal and hybrid system.
27
S1359-4311(18)32009-X https://doi.org/10.1016/j.applthermaleng.2018.09.093 ATE 12702
To appear in:
Applied Thermal Engineering
Received Date: Revised Date: Accepted Date:
31 March 2018 7 August 2018 22 September 2018
Please cite this article as: U. Sahoo, R. Kumar, S.K. Singh, A.K. Tripathi, Energy, Exergy, Economic Analysis and Optimization of Polygeneration Hybrid Solar-Biomass System, Applied Thermal Engineering (2018), doi: https:// doi.org/10.1016/j.applthermaleng.2018.09.093
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Energy, Exergy, Economic Analysis and Optimization of Polygeneration Hybrid Solar-Biomass System 1 1 2
U.Sahoo, *2R.Kumar, 1S.K.Singh, 1A.K.Tripathi
National Institute of Solar Energy, MNRE, Gurgaon, Haryana-122003, India Mechanical Engineering Department, Delhi Technological University, New Delhi-110042,
India Corresponding Author’s Address: Prof. Rajesh Kumar, Professor, Mechanical Engineering Department, DTU, New Delhi-110042, India; Email id: [email protected] ; Phone No.: +91-8750632739
Abstract: The energy, exergy, economic analysis and optimization with the objective functions of energy efficiency, exergy efficiency, VAR cooling output, desalination output and total output of the polygeneration hybrid solar and biomass system are investigated. The optimization using the objective functions, constraints and the decision variables is carried out utilizing genetic algorithm in EES software for polygeneration hybrid solar and biomass system. In this paper, the optimized values of energy efficiency, exergy efficiency, VAR cooling output, desalination output and total output of the polygeneration system are achieved to 49.85%, 20.94%, 7278 kW, 4405 kW and 14,606 kW respectively. The payback period of polygeneration hybrid solar biomass system is 1.5 years at the electricity tariff rate of ₹7.45/kWh which is less than of solar thermal and hybrid solar biomass power plant. Key Words: Solar; Biomass; Energy; Exergy; Optimization 1. Introduction Renewable energy is one of the options to transform the energy system to less carbon intensive, sustainable and bring energy security benefits. Renewable energy encompasses a broad range of energy resources and technologies that have differing attributes and applications. Renewable energy resources include solar, biomass, geothermal, wind and hydro. These sources are abundant and widely distributed; but they are not equally easy to harness. Solar, biomass and 1
geothermal resources are used for the generation of electricity and process heat applications. Renewable energy is the world’s second largest source of electricity generation after coal based power generation plants. These sources have huge potential in meeting energy requirements for process heat in industry and transport sectors. For the first time the renewable energy based industry has achieved a major milestone in 2017, with capacity additions exceeding as compared to fossil fuels and nuclear [1]. Globally, the total renewable power capacity has reached 2195,000 MW (including hydro power) at the end of 2017 [2]. Of this renewable power capacity, wind power contributes to 21.1 % (463,145 MW), Solar photovoltaic power (SPV) 10.93 % (239,914 MW), Bio-power 7.3 % (160,235 MW), Geothermal power 0.64 % (14,048 MW), Concentrated solar thermal power 0.23 % (5,048 MW), and hydro power 59.8% (1312,610 MW). Globally the concentrated solar thermal power generation capacity increased by 2.3% over 2016 to reach nearly 5,048 MW at the end of year 2017. Spain is the highest producer of solar thermal power of 46.07% (2362 MW) followed by the United States 36.3% (1832 MW), South Africa 7.9% (400 MW), India 4.06% (203.5 MW), Morocco 3.5% (181 MW) and rest of the world is 2.17% (109.5 MW). Apart from thermal power generation, the total installed capacity of solar thermal heating, cooling other industrial process heat applications in the World is 47200 MWth. Presently in India, the total installation capacity of solar thermal power plant is achieved to 203.5 MW using various solar thermal technologies (i.e. parabolic trough collector, linear fresnel reflector and scheffler dish) across the India. 101 MW parabolic trough collectors based CSP have been commissioned in various states of this country. 100 MW compact linear fresnel reflector based CSP has been built by M/s. AREVA [3]. 2.5 MW heliostat power tower based solar thermal power plant is installed in Bikaner, Rajasthan. A 1 MW solar thermal power plant
2
based on scheffler dish solar concentrators has also been installed at Mount Abu, Rajasthan, India. Bio-power is increasing with rapid growth for power generation in the major countries i.e. Brazil, the United States, China, Germany, India, Sweden, the United Kingdom and Japan. The total installed capacity of bio power is 109,213 MW. Brazil is the largest producer of bio-power 13.35% (14,583 MW), followed by the United States 12.04% (13,151 MW), China 10.4% (11,365 MW), India 8.72% (9,533 MW), Germany 8.23% (8,990 MW) , the United Kingdom 4.87% (5,326 MW), Sweden 4.45% (4,860 MW), Japan 1.95 % (2,131 MW) and other countries 35.99% (39,306 MW). In India, the estimated potential from agricultural and agro-industrial residues is 18,000 MW. The potential of bagasse based power plant with cogeneration is estimated as 7,000 MW [4]. Although, solar thermal is having good potential to generate electricity, but in cloudy days or short transient of the day it is not possible to operate the solar thermal power plant. In this context, hybridization of solar thermal is the best combination with other source of heat like biomass. The major advantages of the hybrid system is overcoming the variation of solar insolation, continuous generation of power with higher efficiency and minimizing the cost of power generation. In other way, the energy requirements for cooling and heating applications are increasing continuously in industrial sector. It is estimated that, around 2/3rd of energy consumption is used for process heat applications across the world. A numbers of research projects work has been carried out in cooling or heating in cogeneration and trigeneration process which are able to make more output from single heat sources for various end use application but a very little research work has been attempted in polygeneration hybrid solar and biomass system for power, cooling and desalination [5-7]. Solar and biomass resources assessment and energy evaluation 3
have studied for hybrid solar-biomass power plant in various regions of India [8]. The energy output of polygeneration process in hybrid system is increased to 69.62% as compared to simple hybrid system. In this study, the energy and exergy efficiencies of the polygeneration system are 49.25% and 15.77% respectively [9]. The performance evaluation of polygeneration system for the methanol production and the power generation with the solar-biomass thermal gasification was investigated through dynamic simulation. The energy and exergy efficiencies of the system is 56.09% and 54.86% respectively [10].The optimization of a novel polygeneration system has conducted. The primary energy saving of the system is 20% with 25 kilo tons/year reduction of CO2 [11].
The integrated concentrating photovoltaic and thermal collector polygeneration
system has investigated for the region of Naples, Italy. The dynamic simulation is performed using zero-dimensional transient simulation model developed in TRNSYS. The economic profitability of the system is also maximized [12-13]. The efficient use of heat energy sources is a key solution by considering the useful outputs like electricity, cooling and desalination which draw interest in this regard. Polygeneration provides opportunity to increase further efficiency through proper thermodynamic arrangement of different process or end products. In this study, the optimization scenarios with the objective functions of efficiencies, three outputs like power, cooling and desalination of hybrid system are considered. The study on optimization is being carried out on hybrid solar and coal based power plant under various operating parameters. It has studied that, the overall electric efficiency of the hybrid system is achieved to 20% [14]. The optimization of a real combined heat and power plant and a slurry drying process have been analyzed using multi objective function to maximize electrical output and heat for slurry process. The electrical efficiency is increased by 3% [15]. A Mixed-Integer Nonlinear Program (MINLP) modelling framework has been developed and optimized for coal-based polygeneration plant. By producing electricity & methanol together, the overall efficiency gets increased. In this framework, a polygeneration process consists of four functional blocks: gasification, chemical 4
synthesis, gas turbine, steam turbine and heat recovery steam generator. For each block, all alternative technologies are considered and mathematical representation has been carried out by using first-principle sub-models. And all sub-model are linked into substructure based MINLP program. This framework provides the design variables of technologies, equipment, capacity, size of each functional block, power generation for polygeneration process [16]. The parametric optimization of steam cycle in pressurized water reactor has been investigate by integrating genetic algorithm with simplex algorithm. The output of the system is increased to 23.8 MW with an efficiency of 34.69% by an average of 236 iterations [17]. A hybrid concentrated solar power and photovoltaic power plant is optimized by using genetic algorithm (multi objective functions) to increase the capacity factor at lower operational and installation cost [18]. Many of the researchers explained combined cooling, heating and power system but very little research work has been carried out on polygeneration system. The proposed system relates to energy, exergy, economic analysis and optimization using genetic algorithm in EES software for further improvement in efficiency and payback period of polygeneration process concept on hybrid solar biomass thermal power plant. In this paper, the energy, exergy, economic analysis & optimization with the objective functions of energy efficiency, exergy efficiency, VAR cooling output, desalination output and total output of the polygeneration hybrid solar and biomass system has been investigated to make the system more efficient and sustainable in the field of solar and biomass. 2. System Description In hybrid solar and biomass system, PTC field is utilized to heat the heat transfer fluid resulting in the heating of feed water using heat exchanger (HE-3). Biomass boiler depends upon the water heating from PTC field and continuously operate at full load capacity at intermittency of day & night hours. The superheated steam through biomass boiler at temperature of 500 oC and pressure 5
of 60 bar supply to turbine and produce electricity as shown in Fig. 1. The schematic diagram of the polygeneration system is divided into three cycle i.e. cycle-I, II & III. In the cycle-I, the steam is bled at temperature and pressure of 182 oC, 5 bar with mass flow rate of 4.45 kg/sec to an intermediate stage and supplied to the generator of cooling system (Cycle-II). And remaining steam is expanded at low pressure and medium temperature. The bled steam at point 6’ is sent to feed water heater. Condensed water is pumped to the feed water heater at pressure of 5 bar (State point 2) and mixed with bled steam in feed water heater. The cooling system is designed such that the condenser heat at 80.3 oC (Condenser-2) will be utilize for heating the preheated water for desalination system as shown in Fig. 1 (Cycle-III). In desalination system, the feed water is pumped (State points 21-22) to condenser chamber (State points 22-23) and supplied to the HE-2 (State point 23). The preheated water is getting further heated and supplied to the condenser-2 of VAR cooling system through storage tank (State point 24-24’) and then the hot water at 80.3 oC is sprayed on to the top of the evaporator chamber at a desired mass flow rate (State point 25). The humidified hot air from evaporator chamber is drifted toward the condenser chamber by natural convection. Then the air gets partially dehumidified in the condenser chamber and brought back to the evaporator chamber. The water vapor gets condensed in condenser chamber and collected the water droplet in desalination water collection tank. The combination of the three cycle is called polygeneration system.
6
Fig. 1 Schematic diagram of polygeneration system
7
3. Thermodynamic and Economic Analysis The thermodynamic (i.e. energy and exergy) and economic analysis has been conducted to better understand the performance of the system. 3.1 Energy and Exergy Analysis The mass and energy balance equations are being used in the polygeneration system.
1
mi me Where, mi , me are the rate of flow of fluid at inlet and exit.
Vi 2 Ve 2 Qsolar Qb mi (hi gZi ) me (he gZ e ) Wnet 2 2
2
Where Wnet is the net-work output of the HSB plant (kW),
Qb is the total heat produced from
biomass (kW), Qsolar is the solar energy falling on PTC field (kW), Vi, Ve are the bulk velocities of the working fluid at inlet and exit (m/sec), Zi, Ze is the altitudes of the stream above the sea level at inlet and exit (meters) and g is the specific gravitational force (m/sec2), hi, he are the specific enthalpies at inlet and exit (kJ/kg). The energy balance equations of polygeneration hybrid solar biomass system for power, cooling
and desalination are expressed in [5]. The energy efficiency E , polygeneration can be written as:
E , polygeneration
Wnet Qe (mDw h fg )
(3)
Qsolar Qb
Where, Qe is output of evaporator of VAR system (kW), mDw is amount of distilled water generated from distillation system (kg/sec), Qsolar and Qb are solar and biomass heat (kW). The exergy balance equations are being used in the polygeneration system.
E i
x ,i
4
T 1 o Q j Ex ,e Wnet Ex , D j e T j
Where Ex,i , Ex,e are exergy at inlet and exit (kW), To is temperature at surrounding conditions (K), T j is temperature of each state points (K) and Ex , D is exergy destruction (kW). Exergy of the flowing stream at each state point Ex of polygeneration system can be written as 8
5
Ex h ho To s so Where,
h, s , ho , so
are specific enthalpy (kJ/kg), specific entropy (kJ/kg K) of each state points,
specific enthalpy (kJ/kg), specific entropy (kJ/kg K) at surrounding conditions respectively. The exergy balance equations of polygeneration hybrid solar biomass system for power, cooling and desalination are expressed in [5]. The exergy efficiency of polygeneration process in hybrid
solar biomass (HSB) power plant Ex, polygeneration is expressed as:
Ex , polygeneration
Wnet Ex ,evaporator Ex , Dw
(6)
Ex , Solar Ex ,b
Where, Ex,evaporator is exergy of the evaporator load of VAR system (kW), Ex , Solar is exergy of solar heat (kW), Ex ,b is exergy of biomass heat (kW) and Ex , Dw exergy of distilled water (kW). 3.2 Economic Analysis The economic analysis has also been discussed to find out the actual size of the system that gives the cheapest combination of solar and biomass energy system for various applications. The complete cost analysis of the system is examined below thoroughly: The total capital cost (₹) of the system ( CCtotal ) is expressed as
CCtotal CSolar Cb CVAR Cdesalination
(7)
Where CSolar , Cb , CVAR , Cdesalination is the cost of solar, biomass, VAR system and desalination system respectively. The total cost of solar Cs can be calculated as: Cs Ca Aap
(8)
Where, Ca is the area dependent cost of components (₹/m2) (PTC collectors, piping with fitting and heat pump) and Aap aperture area of the PTC collector (m2). The total cost of biomass Cb is expressed as
9
Cb P & M ( n ) 1 F1 F2 F3
(9)
Where, P & M ( n ) is the plant and machinery cost (₹), F1 , F2 , F3 are factors for civil works, commissioning and financing of the polygeneration system. The plant and machinery cost (₹) of the system can be calculated as P & M ( n ) P & M (0) 1 d (n)
(10)
Where the value of P & M (0) (i.e base year plant & machineries) is considered as ₹ 443.61 lakhs and d (n) is the capital cost escalation factor of 3.52 %. The values of each factors are taken from central electricity regulatory commission under renewable energy tariff regulations, India and used in the formulae of capital cost of the system are given in Table 1 [21]. The capital costs of VAR cooling system and desalination system have been considered approximately as ₹ 560 lakhs and ₹ 25 lakhs respectively as per their actual capacity. The cost of VAR cooling system and desalination system as per proposed capacity are taken from various Indian manufacturers such as M/s Thermax and M/s Amba Engineers & Project [22 & 23]. Table 1 Value of each factor used for calculation of capital cost of biomass power system [21]. Variables Type of Description Value
a
Steel index
0.7
b
Machinery index
0.3
F1
Factor for civil works
0.1
F2
Factor for commissioning
0.09
F3
Factor for financing
0.14
10
In addition, the costs of fitting, piping and electronics are considered as 5 % of the total cost of each system. For calculation of overall cost per kWh of the system, the following assumptions are considered as per central electricity regulatory commission under renewable energy tariff regulations, India, in Table 2. Table 2 Assumptions for economic analysis of the system Assumption Head Units
Value
Overall System: Auxiliary consumption during the stability
%
13
Auxiliary consumption after stability
%
12
Plant load factor (stabilization for 6
%
60
months)
%
70
%
80
Years
20
Debt
%
70
Equity
%
30
Year
0
%
12.76
Years
12
Return on equity for first 10 years
%
20
Return on equity after 10 years
%
24
Weighted average of rate of Return on
%
22
Plant load factor (during 1 st year after stabilization) Plant load factor (2nd year onwards) Useful Life Financial Assumption:
Moratorium period Interest rate Repayment period
11
Discount rate
%
10.74
Income tax
%
33.99
Depreciation rate
%
5.83
Depreciation rate 13th year onwards
%
2.505
Operation and maintenance spares
%
15
Interest on working capital
%
13.26
Fuel: Biomass price escalation factor
%
5
Lakhs
50
%
5.72
Working Capital:
Operation and Maintenance: Operation and maintenance expenses Operation
and
maintenance
expenses
escalation The payback period of the polygeneration system is expressed as:
Pay back period=
Energy consumed by the system (MWh) Energy produce by the system per year (MWh/Year)
(11)
4. Optimization GA technique [19 & 20] is used for optimization the thermodynamic parameters of the system. The thermodynamic parameters of the system are optimized with energy efficiency, VAR cooling output, desalination output and the total output. The work flow of the optimization for polygeneration process in hybrid system for combined power, cooling and desalination is shown in Fig. 2.
12
Fig. 2 Work flow of the optimization process in polygeneration in HSB thermal power plant 4.1 Objective Functions In this study, objective functions are taken to maximize the energy efficiency and exergy efficiency, VAR cooling output, desalination output and the total output. The heat inputs are taken as solar and biomass heat and the total output in polygeneration process are output of turbine, output of evaporator and output of desalination water. 4.2 Decision Variable and Constraints In this study, various inequality constraint are taken to define the feasibility regions for the engineering optimization problem and feasible operating conditions for an optimal performance. The relevant parameters are selected as decision variables for the analysis i.e. P6 (kPa); f ; T25 (0C) are given in Table 3. Table 3 Ranges of constraints defined for the decision variable Decision Variable
Range of Variation
Extraction pressure from turbine P6 , kPa Fraction of steam extraction from turbine Desalination
inlet
heated
water
390 P6 600
f
temperature 13
0.1 P6 0.89 80 T25 90
T
25,
0
C
5. Results & Discussions Fig. 3 shows the variation in efficiency (i.e. energy & exergy) of the polygeneration system with respect to extraction pressure at different fraction of steam. The system energy efficiency slightly increases from 24.69% to 27.77% with the change of bled steam pressure from 390 to 600 kPa at steam fraction of 0.1. It is also observed that, the energy efficiency of the system increases to 49.35% at bled steam pressure of 555 kPa and steam fraction of 0.9 and exergy efficiency follows the same trend as energy efficiency against different bled steam pressure and steam fraction. Although the energy efficiency increases to 49.35%, but it is not sufficient and to find the higher energy efficiency, VAR cooling & desalination output of the polygeneration system. In view of this, optimization results is shown in Table 4.
Fig. 3 Variation of polygeneration system efficiency (i.e. energy & exergy) with respect to extraction pressure at different fraction of steam 14
Table 4 shows the values of the decision variables in the base case design along with different optimization criteria. In addition, the results of analyses for each optimization criteria are also shown in this table. It is seen that the values for the decision variables are considered (i.e. P6 (kPa); f ; T25 (0C)) to be continuous over the determined constraints for the optimization problem. The various optimization decision variables are closest values in the analysis of energy efficiency, VAR cooling system output, desalination output and total output of the polygeneration process in the system, which is giving the most optimal results. In this analysis, the optimization scenarios with the objective functions of energy efficiency, exergy efficiency, VAR cooling system output, desalination output and total output of the polygeneration process in HSB thermal power plant are considered. Table 4 Optimized values at various decision variables [P6 (kPa); f ; T25 (0C)] Parameters Optimized value Decision variables Energy Efficiency
49.85%
P6: 596.5 kPa ; f : 0.89; T25 : 80.08 0C
Exergy Efficiency
20.94%
P6: 598 kPa ; f : 0.101; T25 : 89.83 0C
VAR Cooling System
7278 kW
P6: 598.6 kPa ; f : 0.89; T25 : 80.08 0C
Desalination Output
4405 kW
P6: 596.8 kPa ; f : 0.89; T25 : 80.02 0C
Total Output
14606 kW
P6: 597 kPa ;
f : 0.89; T25 : 80.08 0C
The base case design parameters are taken as P6: 500 kPa ; f : 0.17; T25 : 82 0C Fig. 4 to 8 shows the corresponding optimization scenarios on energy efficiency, exergy efficiency, VAR cooling system output, desalination output and total output. The energy efficiency, exergy efficiency, VAR cooling output, desalination output and total output are optimized in 120 numbers of generation. The optimized energy efficiency is achieved to 49.85% at decision variables of P6 = 596.5 kPa; f = 0.89; T25= 80.08 0 C respectively at 120th number of generation. It is also observed that the optimized value of VAR cooling output is 7278 kW at decision variables of P6 = 598.6 kPa; f = 0.89; T25= 80.08 0 C respectively. The optimized value 15
of desalination output is 4405 kW at closest value of decision variables of total output and energy efficiency of polygeneration process in HSB system as shown in Table 4. The optimized exergy efficiency of the system is 20.94% at decision variable of extraction pressure, fraction of steam and desalination inlet heated water temperature of 598 kPa, 0.101 & 89.83 0 C respectively as shown in Fig. 8.
Fig. 4 Optimization of the system over generations with respect to the energy efficiency
16
Fig. 5 Optimization of the system over generations with respect to the exergy efficiency
Fig. 6 Optimization of the system over generations with respect to the VAR cooling production
17
Fig. 7 Optimization of the system over generations with respect to the output of the desalination system
Fig. 8 Optimization of the system over generations with respect to the output of the system The various optimization decision variables are closest values in the analysis of energy efficiency, VAR cooling output, desalination output and total output of the polygeneration 18
process in the system, which is giving the most optimal results generated from EES software. For the proposed subsystem, the optimized various parametric state points of the system are given in Table 5. Table 5 Optimized various parametric state points properties of the system State Fluid Pressure Temperature Enthalpy Mass flow Points (bar) (oC) (kJ/kg K) rate (kg/sec) 1 Water 0.1 45.8 191.8 5.0 2 Water 5 45.8 192.3 5.0 3 Water 5 102.3 429.1 5.0 4,c Water 60 102.7 434.9 5.0 5 Water 60 500 3422 5.0 6 Water 5 182 2810 4.45 6’ Water 5 158.8 670.5 4.45 7 Water 0.1 45.8 2237 0.55 8 Water 60 102.7 434.9 5.0 9,a Water 60 275.6 1235 5.0 X Therminol 12 301.6 554.6 25.0 VP-1 Y Therminol 12 240 420.5 25.0 VP-1 10 LiBr-Water 0.001313 37 81.43 28.67 11 LiBr-Water 0.08453 37 81.43 28.67 12 LiBr-Water 0.08453 132 288 28.67 13 LiBr 0.08453 148.8 331.4 25.24 14 LiBr 0.08453 30 96.75 25.24 15 LiBr 0.001313 30 96.75 25.24 16 Water 0.08453 148.8 2745 3.429 17 Water 0.08453 95 398 3.429 18 Water 0.08453 40 167.6 3.429 19 Water 0.001313 11 398 3.429 20 Water 0.001313 11 2521 3.429 21 Water 0.1 35 192.4 56.98 22 Water 0.1 35 146.7 56.98 23 Water 0.1 37.5 157.1 56.98 24 Water 0.1 50 209.4 56.98 25 Water 0.1 80.3 336.5 56.98 26 Water 0.1 50.2 210.4 55.09
Table 6 shows the comparison of payback period among solar thermal power plant, HSB thermal power plant, cogeneration in HSB thermal power plant and polygeneration HSB thermal power plant. Form the Table 6 it is observed that, the payback period of polygeneration in HSB thermal 19
power plant is 1.52 years, while the tariff is ₹ 7.45/kWh and the capital cost of ₹ 7460.72 lakhs. The payback period for the solar thermal power plant is 18.7 years for the same site, when the tariff is ₹ 12.08/kWh [21] and the capital cost of ₹ 7128/kWh. It is seen that, the Polygeneration in HSB Thermal Power Plant is the most cost effective and efficient as compared to the solar thermal power plant, HSB thermal power plant and cogeneration in HSB Thermal Power Plant. Table 6 Comparison of payback period among solar thermal power plant, HSB thermal power plant, cogeneration in HSB thermal power plant and polygeneration in HSB thermal power plant.
Capital Cost (Lakhs) Electricity Tariff (₹/kWh) Payback Period (Years)
Solar Thermal Power Plant 7128
HSB Thermal Power Plant
Cogeneration in HSB Thermal Power Plant
Polygeneration in HSB Thermal Power Plant
6875.72
7375.72
7460.72
12.08
7.45
7.45
7.45
18.7
2.55
2.36
1.52
6. Conclusion The energy efficiency, exergy efficiency, VAR cooling output, desalination output and total output of polygeneration system in hybrid solar and biomass system for power, cooling and desalination are optimized in 120 numbers of generation of genetic algorithm. It has been investigated and found that, the energy efficiency increases up to 49.85% at decision variable of bled steam pressure, desalination inlet heated water temperature and steam fraction of 598.6 kPa, 80.08 0 C , 0.89 respectively. It is expected that the optimized data generated from the polygeneration system will be useful for commercial establishment and development of future road map for hybrid solar and biomass polygeneration system for power, cooling and desalination. It is also investigated that, although the instant investment of the polygeneration 20
system is higher than solar thermal and hybrid solar biomass power plant, cogeneration hybrid solar biomass power plant, but the payback and tariff cost of electricity are lower (i.e. 1.5 years and ₹7.45/kWh) than the other systems. This polygeneration system is most cost effective and efficient as compared to solar thermal and hybrid solar biomass power plant, cogeneration hybrid solar biomass power plant. The polygeneration hybrid solar biomass system for power, cooling and desalination will constitute one of the best option to fulfill the energy demand in the global energy transition. It is expected that in the near future the polygeneration hybrid solar biomass system will constitute one of the competitive options of electricity generation from renewable energy.
Nomenclature Symbols Aap
:
Aperture area of the PTC collector (m2)
C
:
Cost of system (₹)
Ex
:
Exergy (kW)
f
:
Fraction of steam bled from turbine
g
:
Specific gravitational force (m/sec2)
h
:
Specific enthalpy (kJ/kg)
m
:
Mass flow rate of working fluid (kg/sec)
Q
:
Rate of heat transfer (kW)
s
:
Specific entropy (kJ/kg K)
T
:
Temperature (K)
V
:
Bulk velocity of the working fluid (m/sec)
W
:
Rate of work transfer (kW) 21
Z
:
Altitude of the stream above the sea level (m)
Greek
E , polygeneration
: Energy efficiency of polygeneration system %
Ex, polygeneration : Exergy efficiency of polygeneration system % Subscripts a
:
area dependent quantity
b
:
Biomass
D
:
Destruction
Dw
:
Distilled water
evaporator
:
Evaporator of VAR system
i, e
:
Inlet and exit of flowing stream
f, g
:
Liquid and gaseous state of steam
j
:
state points of the system
o
:
Surrounding (or reference environment) condition
w
:
Water
CC
:
Capital Cost
EES
:
Engineering Equation Solver
GA
:
Genetic algorithm
HSB
:
Hybrid solar and biomass
P&M
:
Plant and machinery
PTC
:
Parabolic trough collector
TRNSYS
:
Transient System Simulation
VAR
:
Vapor absorption refrigeration system
Acronyms
22
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Figure captions: Fig.1 Schematic diagram of polygeneration system Fig.2 Work flow of the optimization process in polygeneration in HSB thermal power plant Fig. 3 Variation of polygeneration system efficiency (i.e. energy & exergy) with respect to extraction pressure at different fraction of steam Fig. 4 Optimization of the system over generations with respect to the energy efficiency Fig. 5 Optimization of the system over generations with respect to the exergy efficiency Fig. 6 Optimization of the system over generations with respect to the VAR cooling production Fig. 7 Optimization of the system over generations with respect to the output of the desalination system Fig. 8 Optimization of the system over generations with respect to the output of the system
Table captions: Table 1 Value of each factor used for calculation of capital cost of biomass power system Table 2 Assumptions for economic analysis of the system Table 3 Ranges of constraints defined for the decision variable Table 4 Optimized values at various decision variables [P6 (kPa); f ; T25 (0C)] Table 5 Optimized various parametric state points properties of the system Table 6 Comparison of payback period among solar thermal power plant, HSB thermal power plant, cogeneration in HSB thermal power plant and polygeneration in HSB thermal power plant.
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Highlights:
Modelling of polygeneration hybrid solar and biomass system is carried out.
Optimization of various output parameters is considered using Genetic Algorithm.
Optimized values in various decision variables.
Efficiency of polygeneration hybrid solar and biomass system is achieved to 49.85 %.
The payback period is 1.5 years which is less than of solar thermal and hybrid system.
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