FEASIBILITY OF FUEL CELLS FOR ENERGY
CONVERSION ON THE DAIRY FARM
A Thesis
Presented to the Faculty of the Graduate School
of Cornell University
in Partial Fulfillment of the Requirements for the Degree of
Master of Science
by
Stefan Jason Minott
May 2002
c Stefan Jason Minott 2002
°
ALL RIGHTS RESERVED
ABSTRACT
Fuel cells running on digester biogas appear to be a missing piece of the sustainable
dairy farming puzzle. This was indicated in the technical feasibility part of this
thesis which showed that the production of electricity and heat from a fuel cell as
well as the utilization of liquid and solid effluent streams from an anaerobic digester
(which produces the biogas) form the basis for operating a total resource recovery
(TRR) and integrated farm energy system. The results of the economic feasibility
part suggest that large dairy farms (>500 cows) are a niche market for fuel cells.
Because manure-derived biogas is pivotal for operating a fuel cell on the dairy
farm, a moving coordinate model for predicting biogas production from a plug flow
digester was developed. The model accurately predicted the approximately 1,274
m3 /day (45,000 ft3 /day) of biogas produced at AA Dairy, and does a better job of
predicting real time biogas production than the popular or commonly used model.
Modelling biogas production is the first phase toward defining each component in
an integrated farm energy system.
With about 2,549 m3 /day (90,000 ft3 /day) of biogas, produced by digesting the
manure from 1000 cows, the generating capacity of a molten carbonate fuel cells
(MCFC) would far exceed the thermal and electrical needs of the farm. Dairyderived biogas is similar to natural gas, however, in order for fuel cell to operate
with it, the H2 S and moisture content has to be greatly reduced with off the shelf
technology and such technology exists today. The 43-45% CO2 , which is usually
considered a dilutant in biogas, turns out to be essential for replenishing the carbonate electrolyte inside MCFC. Compared to a diesel engine-generator set (genset),
there are more opportunities for reducing greenhouse gas emissions (over 90% less
CH4 , NOx , SOx and particulates, except CO2 ), and reducing noise. There are also
excellent opportunities for using CHP to produce bio-derived commodities like compost, value-added dairy products, and aquaponically produced fish and vegetables.
Because most of these commodities are tradable, there is great potential for the farm
to increase financial and other benefits from selling products and saving money by
replacing propane and other externally purchased products. The water used to recover fuel cell exhaust heat at AA dairy would have to be softened to prevent fouling
of the heat exchangers which are connected to the fuel cell CHP system.
Economically, fuel cells cannot compete with diesel gensets on a 500 or 1000 cow
dairy farm with current economic conditions. However, there is an opportunity for
economical operation of a MCFC at AA dairy if biogas from 1000 cows is converted
into electrical and thermal energy. The resulting 2.70 MWh/yr of electricity can
provide for all on-farm energy needs, drive various TRR systems, and provide excess
power for sale. This possibility arises from the combination of reduced MCFC capital
cost, plus high electricity buying and selling prices. One scenario with 1/4 of today’s
capital cost and a $0.09 buying and selling price for electricity suggested a discounted
payback of 3 years, life cycle benefit of over $1.3 million, and 21% increase in net
farm income. With anticipated increased demand for fuel cells, driven by rising
concerns about reducing greenhouse gas pollution and improving energy efficiency,
the described scenario is projected to become a reality by as early as 2007.
Biographical Sketch
Stefan Minott comes from Kingston, Jamaica where he completed his elementary
and high school education. Introduced to the field of biomass renewable energy at
an early age by his father and other engineers, the author was inspired to pursue a
career in the engineering of livings systems for the manufacture of food and energy
from locally derived materials. He graduated from Swarthmore College in Pennsylvania with a Bachelors of Science in Engineering in 1997 where he focused on
Energy Systems and Mechanical Engineering. After working for a year and a half at
E.I. Dupont du Nemours and Co. Inc, as a Research Engineer in Bioinformatics and
Computational Biology, he decided to pursue graduate studies in Agricultural and
Biological Engineering at Cornell University. His present interests include sustainable development, distributed power generation, total resource recovery, and rural
eco-industrial parks.
iii
To my wife and family.
iv
Acknowledgements
I am grateful to my mentor and advisor Prof. Norm Scott for the opportunity to
pursue research in this timely Sustainable Agriculture and Energy topic. He set a
good example for engaging with experts and stakeholders from various disciplines
and encouraged me to solve problems with an integrated systems approach and a
critical eye. I thank my minor advisor Prof. Bob Thomas, for introducing me to
some of the intricacies of deregulated power systems and markets. Thanks also to
Mr. Peter Wright, for his wealth of knowledge about manure management, dairy
economics, and the importance of agricultural extension to improving farming.
To my wife and chief motivator, Sylvia Kwakye, thanks for your love and support
as we journey through graduate school together. I acknowledge the members of
my research group for their contributions and dedication: Steve Zicari, Poe Tyler,
Kanvasi Tejasin, Natalia Peranginangin, Jianguo Ma, Kristy Graf, Nic Lutsey, Kelly
Saikkonen, Peter Schloth, and Gloria Fung. For added guidance I thank the advisory
committee members: Bob Aman, Brad Anton, Dave Ludington, Tim Mount, Dick
Petersen, Richard Mattocks, Ed Heslop, Arnie Talgo, and Ed Kear.
The partial support of the New York State Energy Research and Development
Authority, NYSERDA, for Project no. 37861, ”Feasibility of Fuel Cells for Energy
Conversion on the Dairy Farm” is gratefully acknowledged.
v
TABLE OF CONTENTS
1 INTRODUCTION
1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2 Scope of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.3 Thesis Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2 MODELLING BIOGAS PRODUCTION IN AN ANAEROBIC PLUG
FLOW DIGESTER
13
2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3 Solution Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.4 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.5 Execution of Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.6 Verification of Results . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.7 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . 38
3 TECHNICAL FEASIBILITY
3.1 Background . . . . . . . . . . . . . . . . . . .
3.2 Molten carbonate fuel cells and how they work
3.3 Farm data . . . . . . . . . . . . . . . . . . . .
3.3.1 Biogas production . . . . . . . . . . . .
3.3.2 Electrical energy audit . . . . . . . . .
3.3.3 Digester heating requirement . . . . .
3.3.4 Propane usage . . . . . . . . . . . . . .
3.3.5 Other Thermal Loads . . . . . . . . . .
3.4 Impacts and benefits of fuel cell operation . .
3.4.1 Biogas compatibility . . . . . . . . . .
3.4.2 Fuel cell electric generation capacity .
3.4.3 Environmental impact of exhaust gases
3.4.4 Other environmental impacts . . . . .
3.4.5 Water compatibility . . . . . . . . . .
3.4.6 Proposed changes . . . . . . . . . . . .
3.5 Discussion . . . . . . . . . . . . . . . . . . . .
vi
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40
41
45
47
49
50
55
62
64
67
67
69
70
74
74
76
78
4 ECONOMIC FEASIBILITY
4.1 Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Execute Strategy . . . . . . . . . . . . . . . . . . . . . .
4.2.1 Selection of Distributed Generation Alternatives .
4.2.2 Cost and Benefits of Total Resource Recovery . .
4.2.3 Setting up the Net Present Value (NPV) problem.
4.2.4 Important Variables . . . . . . . . . . . . . . . .
4.2.5 Equations used in NPV and LCC Analysis . . . .
4.2.6 Constants and Assumptions . . . . . . . . . . . .
4.2.7 Farm Profitability . . . . . . . . . . . . . . . . . .
4.3 Results and Discussion . . . . . . . . . . . . . . . . . . .
4.3.1 NPV Sensitivity Analysis . . . . . . . . . . . . . .
4.3.2 Best options for increasing farm profitability . . .
4.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . .
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80
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84
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89
91
95
96
103
103
109
116
5 CONCLUSIONS
118
A Summary data for AA Dairy digester and diesel genset
124
vii
LIST OF TABLES
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
Types of Fuel Cells [32] . . . . . . . . . . . . . . . . . . . . . . . .
Advantages and disadvantages of various types of Fuel Cells [4] . .
NY Milking Operations by Herd Size and Total (1993-2000). . . . .
Comparison of Total Electric Costs at AA Dairy (1997-2000) . . .
Largest annual electric loads at AA Dairy. . . . . . . . . . . . . . .
Some of the thermal loads at AA Dairy. . . . . . . . . . . . . . . .
Comparison of farm biogas properties and MCFC requirements. . .
Comparison of Emissions from the fuel cell and other cogeneration
technologies. [adapted from: [10]] . . . . . . . . . . . . . . . . . . .
3.9 Comparion of efficiencies, noise and installed cost [adapted from: [8;
10; 20; 23]]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.10 Comparison of AA Dairy water quality and the MCFC requirements.
[adapted from: [8]] . . . . . . . . . . . . . . . . . . . . . . . . . . .
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42
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48
50
52
66
68
. 71
. 74
. 75
4.11
4.12
4.13
4.14
4.15
Energy generation from 500 cows producing 1,274 m3 biogas/day . . 87
Energy generation from 1000 cows producing 2,549 m3 biogas/day . 88
Capital Costs and useful life of the distributed generators . . . . . . 95
Constants and Assumptions in NPV Analysis [2; 8; 31] . . . . . . . . 95
Comparison of year 2000 receipts and expenses from AA Dairy and
a typical NYS 400-599 cow dairy. [2; 16] . . . . . . . . . . . . . . . . 97
Comparison of year 2000 NYS 500 and 1000 cow dairy cash flows [16] 99
Comparison of NYS dairies with 400-599 cows, in 1999 and 2000 [16] 101
Comparison of NYS dairies with >600 cows, in 1999 and 2000 [16] . 102
Discounted payback periods for varying scenarios with 500 cows . . . 103
Results of varying scenarios, showing discounted payback periods for
a 1000 cow operation . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
500 cow dairy LCCA: Actual diesel ic genset Scenarios . . . . . . . . 110
500 cow dairy LCCA: Projected 130 kW Diesel IC Genset Scenarios 111
1000 cow dairy LCCA: 225 kW Diesel Genset Scenarios . . . . . . . 112
1000 cow dairy LCCA: 8x30 kW Microturbine Scenarios . . . . . . . 113
1000 cow dairy LCCA: 250 kW MCFC Scenarios . . . . . . . . . . . 114
A.1
AA Dairy Summary data [28]
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
4.9
4.10
. . . . . . . . . . . . . . . . . . . . . 125
viii
LIST OF FIGURES
1.1
1.2
1.3
1.4
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
2.10
2.11
2.12
2.13
3.1
3.2
3.3
3.4
3.5
3.6
3.7
Missing piece of the sustainable farm puzzle, the digester and fuel cell
Conceptual dairy eco-industrial park with fuel cell running on biogas.
Paradigm Shift from Centralized Model to Distributed Resources
Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Kalundborg Eco-Industrial Park, Kalundborg, Denmark, Source: [11]
Overview sketch of on-site electricity generation from biogas. . . .
Schematic Diagram of 5565-liter pilot scale plug flow digester. (Cornell University Design [13]). . . . . . . . . . . . . . . . . . . . . . .
Pictures of the AA Dairy digester from three different views. . . . .
Diagram suggesting the similarity between a PFD and a series n of
CSTR’s [13]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Simple Mass Balance equation for one plug in a plug flow digester.
Manure flow through a control volume . . . . . . . . . . . . . . . .
Diagram showing BVS Concentration = function (position, time ) .
Diagram describing BVS degradation as a function of position at a
given point in time in the plug flow digester. The parameters on the
plot are used to derive the moving coordinate model. . . . . . . .
Diagram describing derivation of moving coordinate model of the
plug flow digester. . . . . . . . . . . . . . . . . . . . . . . . . . . .
Data from our models corresponding to the actual measurements .
Layout of the Biogas Production Model in Simulink. . . . . . . . .
Plots from the Simulink Biogas Production Model . . . . . . . . .
Graph showing Prediction of Biogas Production at AA Dairy . . .
1
2
3
7
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. 16
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20
21
22
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30
33
35
36
37
FuelCell Energy’s 250 kW MCFC unit. . . . . . . . . . . . . . . . . .
Basic chemical reactions inside the molten carbonate fuel cell [6]. . .
Change in NY Dairy population for different sized farms (1993-2001).
Total biogas production per day and per cow per day (1998-2001). .
Energy generated and net energy sold at AA Dairy (1998-2001). . . .
Example of electricity (a)purchased and (b)used at AA Dairy . . . .
Schematic diagram of the heat recovery loop keeping the digester
at 38◦ C (100◦ F). The thicker line show the hot water path to the
digester heating array. . . . . . . . . . . . . . . . . . . . . . . . . . .
46
46
47
49
53
54
ix
58
3.8
3.9
3.10
3.11
Digester thermal characteristics. [adapted from [19]] . . . .
Propane useage and propane costs, 1999-2001. . . . . . . . .
Input-Output diagram for a biogas fed fuel cell system. . . .
Future generating capacities compared to today’s farm load.
4.1
Graphical depiction of NPV, life cycle cost, and EUAC/EUAB used
in the economic analysis of the three energy converter alternatives. . 94
Price Received for Milk, (Data from same 69 farms over period: 19912000) [16] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
Sensitivity of MCFC NPV to reduced capital and net metering while
operating on a 1000 cow dairy. . . . . . . . . . . . . . . . . . . . . . 108
4.2
4.3
5.1
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60
63
71
76
Timeline showing the averaged cost estimates ($/KW) of fuel cell
systems for current projections and substantial order case. Adapted
from [5] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
x
Chapter 1
INTRODUCTION
This thesis lays the foundation for a larger group project focused on the design of
rural eco-industrial parks powered by fuel cells and other biogas-powered distributed
energy technologies. It is the belief of the group that combining of fuel cells with
digester-derived biogas may be the missing piece of the puzzle that could transform
dairy farms into truly sustainable agricultural systems. Figure 1.1 illustrates this.
Total
Resource
Recovery
Anaerobic
Digester
Fuel+Cell
CHP Plant
Rural
Economic
Development
Integrated
Farm
Energy
Systems
Reduce
Environmental
Impacts
Figure 1.1: Missing piece of the sustainable farm puzzle, the digester and fuel cell
The system input-otput diagram in Figure 1.2 demonstrates how the group plans
to engineer or re-engineer agricultural systems for the sustainable utilization of farm
energy resources and the production of bio-derived goods and services.
1
E
n
v
i
r
o
n
m
e
n
t
Sun
Water
Resources
Farm Residence
Steam Generator/
Heating System
CO2
Separator
Manure
Pretreatment
Dairy Cow
Heat Exchanger
Photovoltaic
Cells
Fuel Cell/
Cogenerator
H2S Scrubber/
Fuel processing
Digester
Screw
Separator
Inverter
Backup
generation
Compost
Heap
Greenhouse
Fish Pond
Electricity
Storage
Propane
Tank
Liquid
lagoon
Dairy Electric
Network
Refrigerator/
Absorption Chiller
Dairy Farm
Electricity
Compost
Veggies
Fresh
Fish
Milk
Figure 1.2: Conceptual dairy eco-industrial park with fuel cell running on biogas.
Sprinkler System
Cropland
Feed Parlor
Milk Parlor
NYSEG
Market
C
o
m
m
u
n
i
t
y
2
3
A number of factors are driving our interest in this area. One is the continual
need to reduce methane emissions from livestock agriculture, particularly in dairy
waste management. Another is the continual need to mitigate greenhouse gas emissions associated with generating electrical and thermal energy on dairy farms. Also,
there needs to be better utilization and conservation of biogas-derived energy to
reduce wasting this valuable byproduct of anaerobic digesters.
Additionally, there is a revolution taking place in the electrical power industry
where many power monopolies have been, or are in the process of being, dismantled
into separate generation and transmission & distribution entities. This paradigm
shift, referred to as deregulation of the power utilities, is graphically depicted in Figure 1.3. The centralized model on the left shows traditional utility power generators
(Pgx ) providing all of the power to non-utility end users (PLx ).
Centralized Model
Utility
Distributed Resources Model
Utility
Non-Utility
Pg1
Non-Utility
DR1
Pg1
PL1
Pg2
DR2
Transmission
System
Transmission
System
DR3
PL2
PL3
PL4
PL1
PL2
Figure 1.3: Paradigm Shift from Centralized Model to Distributed Resources Model
The distributed resource model on the right shows a traditional utility generation
plant (Pgx ) competing with small generators (DRx ), some of them former end-users,
to sell power onto the grid. The intent of this deregulation is to increase customer
selection of energy suppliers with the hope of reducing electric cost to the end user.
4
Consequently it is now possible for smaller, cleaner, renewable energy companies to
compete with big centralized power plants.
Energy generating technologies including diesel engines, solar photovoltaic panels, and more recently fuel cells and microturbines make it possible for end users to
generate their own electricity from renewable sources with the option of selling excess power to the grid. A number of farmers, realizing the benefit of using anaerobic
digesters, not only for odor control and animal waste management but also for biogas
production, have begun to use biogas as a viable fuel alternative for running diesel
engine generator sets, hot water heaters and in a few cases absorption chillers. The
trio products: electricity, heat and cooling, is referred to as cogeneration (cogen),
and is the primary reason for investigating fuel cells use on dairy farms.
Upon studying the energy potential and utilization of biogas on a several farms [21;
33], it became evident that fuel cells might be a reasonable alternative to diesel
gensets for following reasons: cogeneration, larger quantities of excess electricity for
sale, and lower gaseous emissions. The main deterrent to fuel cell use is the high
capital cost. In order to reap the improved benefits of converting biogas to electricity and heat with a fuel cell, the group realized that the technology had to be
analyzed for its technical and economic merits.
The type of fuel cell studied in this thesis is the molten carbonate fuel cell
(MCFC), primarily because its high operating temperature of 650◦ C (1200◦ F ) allows
it to do internal reforming of biogas to a hydrogen rich gas, eliminating the need for
additional gas reforming equipment. Other reasons for choosing the MCFC were its
high tolerance of CO2 and carbon monoxide which can poison lower temperature
fuel cells. Also, its approximately 371◦ C (700◦ F) recovered heat will increase the
number of combined heat and power(CHP) applications possible on a dairy farm.
5
The immediate plan is to study MCFC operation with dairy-derived biogas, however,
the long term plan is to run MCFCs on biogas derived from any locally produced
biomass such as crop and livestock wastes and crops grown for energy production.
Prior to the development of infrastructure for a dairy eco-industrial park, it was
deemed necessary to see if fuel cell use for energy conversion on a dairy farm is a
reasonable alternative to conventional forms of electricity generation. To this end,
the purpose of this thesis is to conduct a technical and economical feasibility study
of MCFC use on dairy farms.
From an engineering perspective, operating fuel cells on dairy-derived-biogas
seems to be a plausible solution for (1) low external input sustainable agriculture
(LEISA) and (2) dairy farm energy self sufficiency. However, an important question,
is how to accomplish these goals? The group’s answer, now, is by creating (1) total
resource recovery and (2) integrated farm energy systems around the proposed fuel
cell combined heat and power (CHP) plant. The following are additional reasons
for fuel cells on dairy farms:
• It has never done before. This would be the first application of a fuel cell CHP
plant on a farm as far as we know in the world;
• An MCFC unit is one of the highest efficiency electricity generators,
• Unique opportunities exist for using the high quality exhaust heat (371◦ C),
• CHP systems can be used to construct novel district heating arrays, and independent distributed generation systems,
• The local market for biofuels and green power is unexplored,
6
• There is great potential for rural economic development through the manufacture and sale of value added commodities, and
• Possibilities exist for reducing environmental impacts by using low polluting,
more energy efficient technologies.
Throughout the course of this thesis many of the features that make the dairy
industry into a unique niche market for fuel cells will be examined.
1.1
Background
A total resource recovery system eliminates waste by using all effluents as feed
streams for other onsite processes [12; 14; 15]. The purpose of an integrated energy system is to maximize the total energy utilization of all onsite energy resources by coupling the excess energy of particular processes to drive other processes [26; 27; 37; 39]. The Kalundborg Industrial Park, shown in Figure 1.4, is an
excellent example of an integrated energy system with effective total resource recovery [11]. It is reported that the energy conversion efficiency of the entire facility
approaches 90% with near zero emissions. We propose to engineer comparable agriculture systems that take full advantage of biogas for making the farm energy self
sufficient and more profitable due to production of more value-added commodities.
For a case study, AA Dairy in Candor, NY, was selected because it has an
operating digester, produces it own electricity from a biogas-fired diesel enginegenerator set (genset), and is located very close to Cornell. The owners plan to
expand to a 1000 cow milking operation from a present herd size of approximately
500 cows, and expressed interest in collaborating with us in upgrading their existing
diesel genset to fuel cell CHP.
Figure 1.4: Kalundborg Eco-Industrial Park, Kalundborg, Denmark, Source: [11]
7
8
Diesel gensets do convert biogas into electricity and some useful heat, but are not
very efficient in producing electricity. AA Dairy’s genset has an electrical efficiency
of about 23% and a thermal efficiency of about 67% depending on whether recovered
heat is fully utilized; at least 10% is assumed to be unrecoverable waste heat.
MCFCs are more efficient energy converters than diesel gensets and they generate electricity electrochemically without combustion [8]. Electrical efficiencies of
40-55% is the main reason fuel cells are excellent candidates for replacing diesel
engines. Other reasons include the production of negligible gaseous emissions, quiet
operation, high quality steam and hot water from heat recovery, modularity for
scaling to larger sizes, and high reliability ( six 9’s or 99.9999% reliability, ie. less
than 32 seconds downtime per year if there are no fuel and water supply issues).
We propose to use a fuel cell that is large enough to utilize AA Dairy’s target biogas production from 1000 dairy cows, which is 2.2 to 2.8 million L/day (90,000
-100,000 cfd). With at least double the efficiency of the diesel genset, the electricity
production is expected to far exceed electricity usage on the farm.
For the most part, the farm’s entire electrical load is currently met with the 70kW
generated by the diesel genset [2]. A 250kW MCFC was chosen for AA Dairy because
it is ideally sized to use all of the gas generated from the future expansion to 1000
cows, and it is able to use CH4 and CO2 , the major constituents of biogas, as fuels.
This type of fuel cell operates at 650◦ C (1200◦ F ) and produces useable recovered
heat at 371 − 427◦ C (700 − 800◦ F ) [8], which opens up excellent opportunities for
cogeneration and trigeneration uses. Trigeneration is the simultaneous production
of heat, cooling and electricity.
The products of fuel cell trigeneration can be used to create an on-farm electric
grid, and district-heating arrays for various milk and bio-material processing on the
9
dairy. Among these uses, a significant amount of heat will remain dedicated to
keeping the digester at 40◦ C (100◦ F ) for mesophilic digestion during cold weather.
We could alternatively use some of the super heated steam from the fuel cell to
raise the digester temperature to 60◦ C for thermophilic digestion which would be
more effective in killing weed seeds and pathogens than mesophilic digestion. The
most important use of thermophilic digestion would be to increase the rate of biogas
production, provided the temperature is tightly regulated.
Recovered heat from the fuel cell can partly be used in absorption chillers to
replace electric compression refrigeration. The the recovered heat could also be used
for drying compost, providing radiant heat, and supplying hot water to wash milk
lines, all of which would help to offset the use of propane. Additionally, there is good
potential for a host of entrepreneurial activities such as greenhouses, aquaculture
facilities, and onsite pasteurization/homogenization of milk. These activities can
take advantage of the locally abundant heat, electricity and waste management
effluent streams produced by the total resource recovery system.
1.2
Scope of Thesis
In preparation for the possibility of installing a fuel cell CHP system on a dairy
farm, the thesis will document and analyze the compatibility issues of both the fuel
cell and the host dairy operation, AA Dairy. To justify paying $1.25 million for a
250 kWele MCFC, the thesis will assess whether there are sufficient benefits to using
such a system over other generating systems, and whether the payback period is
competitive. The thesis will set out to answer the following questions: How will
fuel cell integration into the farm improve the dairy operation? And, what major
changes will have to be made on the farm in order to accommodate the new fuel cell
10
CHP? This feasibility study is intended to help in deciding whether it is possible to
operate AA dairy, and dairy farms in general, more profitably and sustainably than
is currently possible.
To facilitate the progress of this thesis and supervise the overall project, a project
advisory committee was assembled which included a farmer, utility personnel, Cornell professors, energy consultants and other stake holders. The advisory committee
suggested that adopting a systems engineering approach would be the best way
to assess and design an integrated MCFC CHP system running on dairy-derived
biogas. The six main steps in this approach are:
• Define characteristics of the proposed system based on farm needs and intended products,
• Define operating system requirements
• Conduct life cycle cost-analysis (LCCA)
• Develop behavioral and structural models
• Conduct tradeoff analysis and systems optimization
• Produce a plan for implementing the proposed farm energy system.
All of these system engineering steps will be addressed in the chapters 2, 3
and 4. These chapters correspond to three recent papers written by the author
with colleagues. Chapter 2 is primarily theoretical in nature, and describes the
development of a computer model for biogas production in a anaerobic plug flow
digester. Chapter 3 compiles and analyzes relevant farm information and fuel cell
requirements to determine if it is technically feasible to run fuel cells on a dairy
11
farm. Chapter 4 addresses the economic feasibility of the proposed dairy fuel cell
CHP system.
Chapter 2 derives a Model for biogas production in an anaerobic plug flow
digester. This is useful because a long-term goal of the larger project is to simulate
all of the major components on the farm system to optimize the system and conduct
trade-off analysis. Prediction of daily biogas production is one the first things needed
to successfully run a computer simulation of the integrated farm energy system.
In Chapter 3, on Technical Feasibility, different kinds of fuel cells will be discussed, three years of collected data from AA Dairy’s biogas and electric production
will be summarized, and the farm energy audit will be examined. Compatibility
issues for siting a MCFC on the farm and examination of the environmental and
social impacts of operating a fuel cell on dairy-derived biogas are addressed.
Economic Feasibility, in chapter 4, compares fuel cells, diesel engines and
microturbines. In the comparisons, simple payback periods are calculated and sensitivity analysis is conducted using variables such as capital cost, maintenance cost,
and electricity prices. These initial calculations become part of an overall Life Cycle
Cost Analysis (LCCA) for the MCFC CHP system.
1.3
Thesis Statement
The Thesis statement is:
Farm energy self-sufficiency, sale of excess energy, and the production
of tradable bioderived commodities suggest the assessment and possible
demonstration of the technical and economical feasibility of running fuel
cells on dairy farms.
12
This statement is based on the view that using a fuel cell CHP system is the
most efficient way to leverage the energy contained in dairy-derived biogas. The
electricity, heat and exhaust from the fuel cell, as well as the liquid and solid effluent streams from the anaerobic digester (which produces the biogas), are excellent
renewable resources for operating a total resource recovery system and integrated
farm energy system. Despite prohibitive prices for fuel cells now, this integrated
systems approach towards creating and utilizing the products and byproducts of
the of the fuel cell CHP system will make the farm self-sufficient in energy and offer
unique manufacturing options for various bio-derived products.
To give some perspective of how this thesis fits the bigger project, the goals of
the overall project are to:
• Assess the technical feasibility of fuel cell technology in converting dairyderived biogas into electrical and thermal energy (heating and cooling).
• Assess the potential for fuel cell technology to work across a range of dairy
farms from small (100 cows) to large (500 cows or more). The potential success
of this technology will likely increase farm profitability by well over 20% and
reduce waste impacts on the environment by over 25%.
• Conduct a life-cycle cost analysis(LCCA) of a fuel cell combined heat and
power plant within the context of a conceptual design. The LCCA consists
of a feasibility study, system operating and maintenance requirements, and
economic analysis.
Chapter 2
MODELLING BIOGAS
PRODUCTION IN AN
ANAEROBIC PLUG FLOW
DIGESTER
2.1
Background
In the design of an energy self-sufficient dairy farm operating with a fuel cell, it is
important to model the plug flow digester (PFD) because it is the crucial subsystem
providing biogas fuel to the farm. A conceptual representation of a cogeneration
plant operating on biogas is shown in Figure 2.1 where biogas is the principal output
from the PFD that is fed into the fuel cell.
The main goal of this project is to develop a computer model of the PFD that
would eventually be incorporated into a bigger model for an entire farming operation.
With the digester model we can find the optimal digester design characteristics for
13
14
Electricit
Air
Molten
Carbon
Fuel Cell
Dairy Manure
fed into
digester
Biogas
Crops
convert
sunlight
into fodder
that cows can eat
Plug Flow
Digester (PDF)
Heat (Steam)
CO2 and other
Exhaust Gases
Digested
Manure Slurry
Figure 2.1: Overview sketch of on-site electricity generation from biogas.
biogas production on a given farm. We can also combine the digester model with
models for fuel cells, heat exchangers, pumps, lighting, milk chillers, and other dairy
farm equipment to develop full-scale models of the farm. Full-scale models of the
farm can then be used to generate theoretical results for comparison with actual
farm data, identify inefficiencies and potential areas for improvement on the farm,
and also design control systems for the integration of fuel cells and other electrical
machinery on the farm.
Before defining the problem that we are trying to model, it is useful to look at
Figure 2.2, which shows a schematic of a pilot scale, 5565 liter, PFD built here at
Cornell by Prof. Bill Jewell in the mid 1970s [13]. This Cornell design inspired
many successful digesters that are still operational today including the 1.546 million
liters PFD, shown in Figure 2.3, which is situated at AA Dairy in Candor, NY.
2.2
Problem Statement
Before modeling gas production in an entire PFD, we must first find a way to
model gas production from a ”single plug” in the PFD as a function of position
and time. We could then make improvements to this expression by accounting for
15
Figure 2.2: Schematic Diagram of 5565-liter pilot scale plug flow digester. (Cornell
University Design [13]).
variations in gas production due to changes in temperature, digester geometry and
manure composition. The following questions comprise the problem statement for
this project:
• How do you model from first principles, substrate degradation within each
plug in a digester?
• What effects do digester geometry, and temperature and pressure have on
digestion in each plug?
• Can you derive a substrate degradation expression for the whole PFD based
on characteristics of individual plugs?
• How do you predict gas production using the substrate degradation expression
for a PFD?
Mathematically, finding a solution to these questions involves modeling:
BGmethane = f (x, t)
16
(a) Side view of digester with flare in the foreground
(b) Point of entry for manure flow into the digester.
(c) View of the digester and the free stall barn.
Figure 2.3: Pictures of the AA Dairy digester from three different views.
17
before the more complex model:
BGmethane = f (x, t, A, T, p)
where :
BGmethane
A
x
T
t
p
=
=
=
=
=
=
V olume of methane gas produced by the digester,
Cross Section,
position,
digester temperature,
time,
pressure
The main objectives for this project are therefore to:
• Find current models for the plug flow digester and identify important missing
features that are needed to make an executable digester model.
• If necessary derive, from first principles, a new model that is more representative of a realistic plug flow digester, capable of predicting daily and annual
gas production trends.
• Compare new and existing models for accuracy in predicting observed gas
production based on real anaerobic PFD data.
2.3
Solution Strategy
In our initial literature review, we found a an empirical model, developed by Jewell [13], for biogas production in a PFD. The model is composed of two sets of
equations that describe (i) biomass degradation in the digester, and (ii) methane
gas production as a result of the biomass degradation:
S0 = 0.864(T S)
(2.1)
18
Sb0 = S0 (1 − R)
(2.2)
= Sb0 ∗ exp(−k ∗ HRT )
(2.3)
and
BGmethane = 0.5(Sb0 − Sb1 )/HRT
(2.4)
where:
S0
Sb0
TS
Sb1
HRT
k
R
BGmethane
=
=
=
=
=
=
=
Inf luent T otal V olatile Solids(T V S) Conc., g/L
Inf luent Biodegradable V olatile Solids(BV S) Conc., g/L
T otal Solids Conc., g/L
Ef f luent BV S Conc., g/L
Hydraulic Retention T ime, days
F irst − order decay rate coef f icient
Ref ractory f raction (ratio of inf luent ref ractory V S
to inf luent T V S)
= V olumetric methane production rate (volume of gas
produced per working digester volume per day), L/(L ∗ day)
The first set of equations is derived from the assumption [13] that a PFD is analogous
to an infinite number of Constant Stirred Tank Reactors (CSTR) that are tied
together in sequence; refer to Figure 2.4.
The second set of equations computes the expected methane gas production based
Influent
Manure
1
2
3
…
n-1
n
Effluent
Manure
Figure 2.4: Diagram suggesting the similarity between a PFD and a series n of
CSTR’s [13].
on the amount of available biodegradable substrate. This model is simple at best
and can predict gas production for a given set of parameters. However, if you
19
change any of the parameters such as digester volume, slurry temperature, and
maybe atmospheric pressure, the model no is longer reliable because the previous
assumptions would have been violated.
We know for sure that the digester heat requirement, needed to maintain constant slurry temperature, varies seasonally and every time the heat supply from
the cogeneration equipment is interrupted. So temperature is a crucial variable to
incorporate into our model. Digesters come in various sizes with various flow rates
of influent manure depending on the herd size on the farm, so geometry and slurry
flow rates must also be accounted for. We have to assume that the consistency of
the influent manure is kept within a certain range of solids concentration, which
ensures that the predominant variable affecting reaction rates is temperature. We
try to keep all other slurry and digester parameters constant.
We envision being able to supply a certain set of constraints for describing digester performance and with it get an accurate prediction of gas production regardless of digester size, temperature variations and more. With that in mind we set
about developing a new model to incorporate these new ideas mentioned above.
Professor Larry Walker [38] explained that when a plug of manure flows through
a PFD at some uniform rate, F, the only thing expected to change is the substrate
concentration, Cz , in the manure where the subscript z denotes position. Nutrient
uptake, r, occurs during the change and biogas is produced due to bacterial anaerobic conversion of biodegradable substrates into methane, CO2 and other gases.
Figure 2.5 shows how all of the above factors are interrelated within each plug of
manure. Prof. Walker further elaborated that any equation describing the chemical reaction within an individual plug needs to have a bulk flow term, a nutrient
uptake term and a storage term. Our strategy is, therefore, to come up with a set
20
Figure 2.5: Simple Mass Balance equation for one plug in a plug flow digester.
of differential equations which incorporated these three terms, translate the singleplug equation, which is analogous to an equation for a CSTR, into an batch reactor
expression, and then finally derive an overall equation for the PFD.
2.4
Assumptions
• Solids concentration of influent manure ranges between 8% and 14%.
• Manure Flowrate is uniform, only substrate concentration changes.
• 1 gram of anaerobically fermented BVS yields 0.5 liters of methane.
• The PFD is analogous to an infinite number of CSTR’s. Implicit in this
assumption is the other assumption that there is negligible interaction at the
interface between individual plugs of manure.
• The predominant variable affecting reaction rates is the slurry temperature,
which is kept constant at 35o C. Steady biogas production is expected with
constant the digester temperature.
• By analogy to Jewell’s transformation from CSTR → PFD, we can derive a
new transformation from Hashimoto’s CSTR → PFD.
21
Figure 2.6: Manure flow through a control volume
2.5
Execution of Strategy
Start off with the graphical representation in Figure 2.6 of fluid flow through a
control volume then introduce a generalized first-order differential equation, as suggested by Prof. Walker. The first-order equation describes substrate degradation
(i.e. solids concentration degradation) in a single plug of manure.
F
dC
dC
+r =
A
dz
dt
(2.5)
where:
F =
C, Cz , C∆z =
z =
r =
t =
A =
M anure slurry f lowrate, L/day
BV S conc. at position z, g/L
position of manure plug, meters
N utrient uptake, g/L
Solids retention time, days
Cross Section Area of control volume, m2
In Equation 2.5 above, bulk flow is the 1st term, nutrientuptake is the 2nd term and
storage is the 3rd term. We showed Professor Jean-Yves Parlange [24] this equation,
and he said it would be much simpler to solve the problem as a one- dimensional
problem instead of the three-dimensional problem that Equation refeq:mflow1 turns
out to be. By simply dividing through by the cross-sectional area, A, changing the
r term to a term which is proportional to BVS conc., and introducing two initial
22
BVS Conc., (g/L)
Position (meters)
Time (days)
Figure 2.7: Diagram showing BVS Concentration = function (position, time )
conditions for a individual manure plug in the digester, we end up with Equation 2.6.
∂C
∂C
+V
= −KC
∂t
∂x
(2.6)
initial conditions:
x = 0,
C = C0 (constant)
t = 0,
C = C0 (constant)
The general solution that Prof. Parlange suggested for Equation 2.6 was:
C = C0 e−kt · f (x − tv)
(2.7)
which contains a simple exponential expression like the one Prof. Jewell’s used to
model the behavior of a plug element inside a plug flow digester. If we plot BVS
degradation in the digester as a function of position and time, we end up with the
exponential curve shown in Figure 2.7, which is connected by dots.
The crosshatched slice from the previous figure depicts degradation of BVS concentration as a function of position at a given time inside the digester, which we
annotated in Figure 2.8.
23
BVS Concentration, C
C0
x- tv < 0
x- tv > 0
C= (Co) exp (-kx/v)
C=(Co) exp (-kt)
0
Position,X
x = L, t= 30 days
x = tv
Figure 2.8: Diagram describing BVS degradation as a function of position at a given
point in time in the plug flow digester. The parameters on the plot are used to derive
the moving coordinate model.
As indicated in the graph, the solution to Equation 2.7 is divided into two parts,
one for each segment of the curve.
C = C0 e
−kx
v
x − tv < 0
C = C0 e−kt
(2.8)
x − tv > 0
In Tasks 1-3 below, I am going to derive this solution to the differential equation in
Equation 2.6, from first principles. This derivation will demonstrate how we arrive
at Equation refeq:mflow3-sol from Equation 2.6. In a nutshell the new model we
are developing treats manure as individual plugs which are undergoing change on a
moving coordinate. The moving coordinate model is an integral tool for transforming the characteristics of a CSTR into that of a plug flow reactor.
Task 1: Overview of Jewell (traditional) and Hashimoto(newer) models.
We begin with the simple batch reactor empirical model that Prof.
Jewel
24
used [13].
Cb1 = Cb0 e−kt
(2.9)
where:
Cb1
Cb0
k
t
=
=
=
=
Ef f luent BV S conc., g/L
Inf luent BV S conc., g/L
F irst − order rate constant
Solids retention time, days
He showed that each plug element in a plug flow digester is analogous to a batch unit
travelling through the system with time. This is derived from the assumption that
the characteristics of a plug flow digester can be closely approximated by several
completely stirred tank reactors (CSTR) connected in series.
Using the substrate degradation model for a single CSTR, and the same variables
as Equation 2.9, Effluent BVS concentration for a CTSR is expressed as:
Cb1 =
Cb0
1 + kt
(2.10)
and a series of n CSTR is represented by
Cbn = ³
Cb0
´n
kt
1 + /n
(2.11)
When dealing with an infinite number of CSTRs , Jewell claims that we can use the
approximation
·
kt
lim 1 +
n→∞
n
¸n
= ekt
(2.12)
which serves as the basis for of Jewell’s representation of the plug flow digester as
an infinite set of CSTR’s
Cbn = Cb0 e−kt
This time dependent model allows us to choose the exponential constant, k, so
that we can curve fit a digester performance curve through inflexible empirical data
25
points. Changing any one of the conditions, however, will invalidate the model for
prediction purposes.
Another model by Prof. David Hashimoto [38] incorporates temperature variations into prediction of substrate degradation. The Hashimoto model is as follows
C1 =
µm
µ
C0 k
+k−1
(2.13)
where:
C1 = V olatile Solids(V S) conc., g/L
C0 = Initial V S conc., g/L
µm = specif ic growth rate, days
= 0.013T − 0.129
T = digester temperature, o C
µ = 1/(Hydraulic retention time), days−1
k = dimensionless kinetic param.
Rearranging that equation gives
C1 =
µm
µk
C0
+1−
1
k
and using the definition HRT = 1/µ, turns Eq 2.13 into:
C1 =
C0
1 + (µm HRT − 1)
1
k
(2.14)
Here goes a big assumption: By analogy to Eq 2.11, a series on n Hashimoto CSTRs
should possibly be modeled as:
Cn = ³
C0
1+
1 (µm HRT −1)
k
n
´n
(2.15)
where the limit of the denominator as n goes to infinity produces an exponential
similar to the one in Eq 2.12:
26
µ
lim
n→∞
1 (µm HRT − 1)
1+
k
n
¶n
1
= e k (µm HRT −1)
(2.16)
but notice we can always replace HRT with time variable, t, to simplify our annotations. So, with regards to substrate removal, the equation:
Cn = C0 e−
(µm t−1)
k
(2.17)
describes our improved model for substrate degradation in n plugs of manure moving down a PFD with respect to time, based on Hashimoto’s single CSTR model.
Task 2: To prove that Eqs 2.7 and 2.8 are solutions to Eq 2.6.
Return to the differential equation in Eq 2.6, which describes the behavior inside
a plug flow digester
∂C
∂C
+V
= −KC
∂t
∂x
initial conditions:
x = 0 , C = 1 (constant)
t = 0 , C = 1 (constant)
take the Laplace transform of Eq 2.6 with respect to t:
½
¾
z }| {
dC (x, t)
= −kL {C(x, t)}
[sL{C (x, t)} − C(x, 0)] + vL
dx
=1
(2.18)
In the second term we assume we may interchange integration and differentiation,
ie.
½ ¾ Z ∞
Z ∞
d
dC
−st dC
dt =
e−st C(x, t)dt
L
=
e
dx
dx
dx 0
0
½ ¾
dC
d
∴L
L {C(x, t)}
(2.19)
=
dx
dx
27
Using the notation, Eq 2.18 becomes
dC
= −kC
dx
dC
=1
(s + k)C + v
dx
sC − 1 + v
dC (s + k)
1
+
C=
dx
v
v
Rewriting Eq 2.20 as
·
(2.20)
¸
(s + k)
1
dx + 1.dC = 0
C−
v
v
we can solve for by integrating by parts:
Z
dC
¤
£¡ s+k ¢
1
C
−
v
v
µ
¶ ·µ
¶
¸
v
s+k
1
ln
C−
s+k
v
v
·µ
¶
¸
s+k
1
ln
C−
v
v
µ
¶
s+k
1
C−
v
v
µ
(2.21)
Z
= −
dx
(2.22)
= −x + B
µ
¶
s+k
= −
(x + B)
v
= e−(
s+k
v
)(x+B)
(2.23)
(2.24)
(2.25)
constant,A,with
respect to time
¶
z }| {
s+k
s+k
1
−( s+k
x
)
v
e−( v )B
C−
= e
v
v
µ
¶
s+k
s+k
C = A e−( v )x
v
s+k
1
=
+ A e−( v )x
v
(2.26)
(2.27)
(2.28)
constant,D
z }| {
1
Av −( s+k
e v )x
∴C =
+
s+k s+k
s+k
1
C(x, s) =
+ D e−( v )x
s+k
(2.29)
(2.30)
so
C(x, s) =
s+k
1
+ D e−( v )x
s+k
Now since
C (0, t) = 1
(2.31)
28
and
L {C (0, t)} = L {1} = 1/s
the constant D can be found by
C (0, s) = L {C (0, t)}
C (0, s) = 1/s,
=1
z }| {
s+k
+ D e−( v )0
1
1
=
s
s+k
1
1
⇒D =
−
s s+k
so Eq 2.31 can be rewritten as
1
C(x, s) =
+
s+k
1
C(x, s) =
−
s+k
µ
µ
1
1
−
s s+k
1
1
−
s+k s
¶
e−(
s+k
v
)x
constant
¶
e
− sx
v
z}|{
kx
e− v
(2.32)
Finding the inverse Laplace transform of Eq 2.32 and applying the time shifting
theorem gives
³
´ ³
©
ª
x
−kx
x´
C(x, t) = L−1 C(x, s) = e−kt − e v e−k(t− v ) −1 u t −
v
(2.33)
and finally
C(x, t) = e
−kt
³
´ ³
−kx
x´
−kt
v
− e −e
u t−
v
The unit impulse function, u(t − xv ), only takes effect after, t =
³ h
x i´
= e−kt
C x, t <
v
³ h
x
x i´
C x, t >
= e−k v
v
(2.34)
x
v
so
(2.35)
(2.36)
29
which confirms Parlange’s solution in Eqs 2.7 and 2.8:
C(x, t) = C0 e−kt .f (x − tv)
x − tv < 0
C = C0 e
−kx
v
x − tv > 0
C = C0 e−kt
Of course, we replace the implicit 1 in front of the exponents in Eq 2.35 with the
generic constant for influent solids concentration C0. Last but not least, since we
have chosen to base our new model on Hashimoto’s temperature varying equations,
we need to substitute the exponents in Eqs 2.7 and 2.8 with new exponential terms
as follows:
C(x, t) = C0 e−
(µm t−1)
k
.f (x − tv)
x − tv < 0 ⇒ C = C0 e−
x − tv > 0 ⇒ C = C0 e−
(2.37)
(µm x
v −1)
k
(µm t−1)
k
Task 3: To develop a description of the plug flow digester using a proven model for
a batch reactor.
We have just shown how to predict substrate degradation in individual plug
elements at any given time. In order to expand the model to predict total substrate
degradation in an entire plug flow digester we have to come up with a way to sum
the reactions of all plugs in the digester. To do that we integrate over every plug
in the digester both with respect to time and position of each plug element. Note
in Figure 2.9, that the substrate degradation vs. position curve only represents the
reaction that has transpired in one manure plug up to a certain point in time, t.
30
Biogas
GCH4
Effluent:
Digested
Manure
Influent:
Untreated
Manure
C
C0
x- tv > 0
x- tv < 0
C=Co.exp (-1/k[µmτ-1])
C=Co. exp (-1/k[µmx/v-1])
0
X
x = L, t= 30 days
x = tv
Figure 2.9: Diagram describing derivation of moving coordinate model of the plug
flow digester.
The graph is a slice of the three- dimensional plot in Figure 2.7, at a specific time.
Something interesting happens at the point x=tv. Up until that point, BVS conc.
decreases exponentially. However, at x=tv and beyond, BVS conc. as a function
of position stays constant since no time dependent reaction can occur, explaining
the flatness of the curve beyond x=tv. Total substrate degradation in the plug flow
digester is represented by:
Z
τ =HRT
Z
x=L
T otalC(x, t) =
0
C0 e−
(µm τ −1)
k
.f (x − τ v)dx.dτ
(2.38)
0
which is the integration of Eq 2.37 with respect to time and position. This integration process sums the reactions in all plugs as nutrient uptake occurs in each plug
on a moving coordinate system. This summing process transforms the batch reactor
representation of simple plug elements into the total representation of the plug flow
digester.
Rewriting Eq 2.38 as an integration over the two sections of the degradation
31
curve shown in Figure 1.4:
Z τ =t µZ
T otalC(x, t) = C0
0
τv
−
e
(µm x
v −1)
k
Z
L
dx +
¶
−
e
0
(µm τ −1)
k
dx dτ
(2.39)
tv
starts us down the path to deriving the new substrate degradation model for the
PFD.
Z
τ =t
T otalC(x, t) = C0
0
=
=
=
¯τ v
¯ ¶
(µm τ −1) ¯L
vk − (µm xv −1) ¯¯
−
k
k
dτ
−
e
¯
¯ + xe
µm
τv
0
(2.40)
µ
¸
¶
·
0 −1)
(µm v
(µ τ −1)
vk − (µm τvv −1)
−
− mk
k
k
C0
−
e
−e
+ (L − τ v) e
dτ
µm
0
¶
Z τ =t µ
i
(µm τ −1)
1
vk h − (µm τ −1)
−
k
k
C0
−
e
− e k + (L − τ v) e
dτ
µm
0
¶
¸
Z τ =t µ·
vk − (µm τ −1)
vk 1
k
C0
L − τv −
e
+
e k dτ
µm
µm
0
¸
¶
Z τ =t µ·
vk 1
vk − µm τ 1
k
k
k
e
e +
e dτ
C0
L − τv −
µm
µm
0
Z
=
µ
τ =t
Z
= C0 e
h
let U = L − τ v −
vk
µm
τ =t
1
k
i
0
µ·
¸
¶
vk
vk − µm τ
L − τv −
e k +
dτ
µm
µm
and dV = e−
µm τ
k
(2.41)
, therefore from
µm τ
k
dU = −vdτ, V = − e− k
µm
·
¸ −µm τ
Z
vk ke k
vk 2 − µm τ
U dV = − L − τ v −
+ 2 e k
µm
µm
µm
and substituting this into Eq 2.41 we get:


ignore τ =t
1
mk
T otalC(x, t) = C0 e k  τ vµ
−
µ2
m
=
1
C0 e k
µ2m
£
(Lµm −τ vµm −vk) ke −
. µm
µm
µm τ
k
µm τ ¤τ =t
2vk) ke− k 0
+
τ
vk2 − µm
e k
µ2m
z}|{
+ cont 
τ vµm k − (Lµm − τ vµm −
hn
o
i
µm t
=
tvµm k − (Lµm − tvµm − 2vk) ke− k + (Lµm − 2vk) k
i
1 h
t
k
− µm
k
−
(Lµ
−
tvµ
−
2vk)
e
+
(Lµ
+
tvµ
−
2vk)
= C0µke
m
m
m
m
2
1
C0 e k
2
µm
m
0
32
1
³
´
³
´i
t
t
C0 ke k h
− µm
− µm
k
k
(Lµm − 2vk) 1 − e
=
+ tvµm 1 + e
µ2m
2.6
(2.42)
Verification of Results
In order to check if Eq 2.42 successfully models biomass degradation for the prediction of biogas production in a PFD, results from Jewell’s model and the newer
moving coordinate model were compared with actual measurements from a pilotscale digester. The kinetics and operation of the 5565 liter pilot-scale PFD [13] are
well documented. The operating variables from the pilot-scale digester study were
used in the two models to plot (i)Total Volatile Solids Conc. Vs. Time, (ii)Total
Solid Conc. Vs. Time, and (iii)Methane Production vs. Time graphs of actual,
Jewell’s model and new model data. Those graphs are shown in Figure 2.10 and the
most pertinent input data for generating those graphs were:
Co
K
µm
L
V
T
=
=
=
=
=
=
%V olatile solids ∗ T otal Solids conc. = 0.863 ∗ 87.6 = 75.5988
Hashimoto0 s ideal plug f low constant = 1.26
growth rate at 35 degrees◦ C = 0.326
20 f eet, should be volume (L ∗ Area) of tank = 5565 liters
or daily f low rate of manure slurry into the digester = 185.5 L/day
max HRT f or digestion up to the point you are examining = 17 days
To get the model results and actual measurements on the same graphs, the
Total Solids concentration had to be divided by HRT and digester volume to get
solids degradation per day per unit digester volume. The sign of the 1/k exponent
in the first term of Eq 2.42 also had to be changed from positive to negative to
get the Solids degradation model to work properly. This change was necessary
33
Effluent Total Volatile Solids vs Time
80
Effluent TVS(g/l
70
60
Eff TVS
50
Eff_TVS_new
40
30
20
10
Actual Data-TVS
0
0
10
20
30
40
Time
(a) Total Volatile Solids Concentration vs. Time.
Eff. TS Conc.
Effluent Total Solids Conc. vs HRT
100
90
80
70
60
50
40
30
20
10
0
Eff TS
Eff TS_new
Actual Data-TS
0
5
10
15
20
25
30
35
HRT(days)
(b) Total Solids Concentration vs. Time.
Methane production vs. HRT
Methane production (litres/day)
4000
3500
G_old
3000
2500
G_new
2000
1500
1000
500
0
0
5
10
15
20
25
30
35
HRT (Days)
(c) Gas Production vs. Time
Figure 2.10: Data from our models corresponding to the actual measurements
34
because the unadjusted theoretical curve was exponentially increasing instead of
decreasing, which is wrong since bacterial nutrient uptake causes a decrease in solids
concentration, not vice versa.
The MATLAB Simulation package, Simulink, was used to develop a computer
simulation of the biogas production model. The flowchart-like arrangement of the
Simulink model is demonstrated in Appendix 2.11. All of the input parameters are
located on the left hand side of the model, and the graphical displays representing
the output for the model are on the right hand side. Looking at Figure 2.12, one
should realize that Simulink produced identical graphs to the ones that we get in
Excel: (i) Total Volatile Solids Conc. vs. Time, (ii) Total Solids vs. HRT, and (iii)
Total Gas Production vs. HRT.
Figure 2.11: Layout of the Biogas Production Model in Simulink.
35
36
(a) Total Volatile Solids Concentration vs. Time.
(b) Total Solids Concentration vs. Time
(c) Gas Production vs. Time
Figure 2.12: Plots from the Simulink Biogas Production Model
37
The final step was to test the model with real data from AA Dairy. This would
verify if the model was capable of predicting the farm’s already known biogas production rate of 1,274 m3 /day (45,000 ft3 /day).
Co
K
µm
L
V
=
=
=
=
=
=
T =
%V olatile solids ∗ T otal Solids conc. = 0.863 ∗ 87.6 = 75.5988
Hashimoto0 s ideal plug f low constant = 1.26
growth rate at 32.5 degrees◦ C = 0.2935
volume of tank = 130f t ∗ 31f t ∗ 14f t = 1, 546, 099.82 liters
daily f low rate of manure slurry into the digester
9, 892.08 gallons/day = 38, 335.9079 liters/day
max HRT f or digestion up to the point you are examining = 40 days
The Excel spreadsheet, that was originally used to model biogas production from
the pilot scale digester, was modified to use data specific to AA Dairy. The resulting
biogas production vs. retention time graph in Figure 2.13 gives testament to the
good prediction capabilities of the moving coordinate model.
Biogas(ft^3) @ digester temp 32.5degrees C
60000
50000
Biogas Production (ft^3/day)
biogas(ft^3)
40000
30000
20000
10000
0
0
20
40
60
80
100
120
140
160
Time(days)
Figure 2.13: Graph showing Prediction of Biogas Production at AA Dairy
38
2.7
Discussion and Conclusion
Close examination of the first two plots in Figure 2.10 shows a significant improvement in the ability to predict solids degradation with the moving coordinate model
over the Jewell model. Derived from first principles, the only change need in Eq 2.42
was replacing the positive exponent with a negative one instead. After making the
modification the resulting equation was:
T otalC(x, t) =
³
´
³
´i
1 h
t
t
C0 ke− k
− µm
− µm
k
k
(Lµ
−
2vk)
1
−
e
+
tvµ
1
+
e
m
m
µ2 .L.t
(2.43)
m
Biogas production/day/digester volume was calculated at Standard Temperature
and Pressure (STP) with: 0.5*(solid degradation)/HRT*Volume*273.2K/308.2K (
i.e. at 35◦ C) such that:
T otal gas production =
µ
³
´
1 h
t
C0 ke− k
0.5
− µm
k
·
Inf
luentT
V
S
−
(Lµ
−
2vk)
1
−
e
m
HRT
µ2m .L.t
³
´i´
µm t
+tvµm 1 + e− k
· L · 273.2K
308.2K
(2.44)
One thing that is clear from all the graphs is that Jewell’s model is accurate in
predicting the final solids concentration and final gas production values. This makes
it very appropriate for predicting gas production values when you only care about
fixed residence time (HRT) for the manure in the digester. The benefit of the new
model is its ability to predict daily values before and after a chosen HRT.
39
All in all, the project was successful in developing a computer model for dairy
biogas production in plug flow digesters. The next step is to continue applying the
model as an alternative to universally accepted model used by Jewell for PFDs.
That way livestock farmers, not only dairy farmers, can use this moving coordinate
model predict biogas production rates for their digesters. The bonus to using this
model is the possibility of with monitoring equipment to produce real time estimates
of gas production if necessary.
Chapter 3
TECHNICAL FEASIBILITY
Dairy farms occupy a special niche market for distributed power from fuel cells.
This was evident to Pfeffer [30] who, in meetings with rural energy focus groups in
New York and Pennsylvania, observed that:
”... fuel cells would be economically viable in rural areas, if they were
put in specialized uses that operate on a continuous basis, [utilize the]
heat generated and ... [receive] a premium for such characteristics as low
emissions, low noise levels, high quality power, modularity of design and
capacity to use different types of hydrogen-based fuels.”
Of these special criteria, many are met by dairy farms, but not without challenges.
Biogas cleanup and the effective, efficient utilization of biogas-derived heat, electricity, and exhaust gases are some of the challenges that must be examined to determine
the technical feasibility of the fuel cell. Despite these challenges, however, the high
electricity costs and the availability of biogas from anaerobic digesters make the
siting of a fuel cell in rural New York (excluding a small number of electric cooperatives and municipalities) very advantageous. With no fuel supply costs resulting
40
41
from locally produced biogas, the payback period for a fuel cell system is expected to
be much shorter than similar installations in urban areas which pay for natural gas.
Value-added products and reductions in environmental impacts represent long-term
benefits of using fuel cell CHP on the dairy farm.
This chapter examines the Technical Feasibility of operating fuel cells on dairy
farms and begins with an overview of available fuel cell technology. It then proceeds
by summarizing the farm energy audit, analyzing three years of biogas and electric
production data, and estimates corresponding changes to the farm energy loads when
the farm expands from 500 milking cows to 1000 cows. Compatibility issues related
to siting a fuel cell on the farm, as well as, the environmental and social impacts of
running a fuel cell on dairy-derived biogas are assessed. On the expanded 1000 cow
farm, some new opportunities that arise only from using a fuel cell, as opposed to
a diesel engine, are examined. Ideas for improving current energy utilization on the
farm are also proposed in this chapter.
3.1
Background
There are several different kinds of fuel cells, as shown in Table 3.1, which are
differentiated by the types of electrolyte they use. Instead of producing electricity by
combustion, as in the case of internal combustion engines (ICEs), fuel cells operate
like batteries and convert chemical energy directly into electricity and heat as long
as there is an ample supply of fuel and air. Electricity generation in every kind
of fuel cell results from a controlled oxidation-reduction reaction of hydrogen and
oxygen (or air) in the presence of a catalyst. Hydrogen charge carriers are forced
through an electrolyte where they eventually combine with electrons that have flowed
between electrodes (anodes and cathodes) to create electricity. This electrochemical
* Projected
Some Major
Producers
Operating
Temperature,◦ C
Power
Efficiency,η(%)
Power Density
(mW/cm2 )
Charge Carrier
Catalyst
Prime
Components
Major
Applications
Electrolyte
Graphite based
Stationary
Power;
Cogeneration/
On-site power;
Peak Shaving;
Transportation
Carbon-based
Transportation;
Small Scale
Combined heat
and Power(CHP);
Portable/leisure;
Residential use;
Standby power
Ballard Power
Systems; Plug
Power; H Power
Space
(Apollo→Shutte);
Standby
Power
100(200*)
H+
Platinum
350(650*)
H+
Platinum
100-200(300*)
H+
Platinum/Gold
ONSI
40-45
40-50
40-50
180-210
50-125
50-90
Alkaline
Potassium
Hydroxide
Phosphoric
Acid
(PAFC)
Immobilized liquid
Phosphoric Acid
(eg. Orthophosphoric
Acid)
Table 3.1: Types of Fuel Cells [32]
Proton Exchange
Membrane
(PEMFC)
Ion Exchange
Membrane/
Solid Polymer
(eg. Perflouriated
Sulphonic Acid)
FuelCell Energy;
M-C Power
Stainless Steel
Stationary
Power;
Cogeneration/
On-site power;
Peak Shaving;
Transportation
100(200*)
CO=
3
Nickel
50-60
630-650
Molten
Carbonate
(MCFC)
Immobilized liquid
Molten Cabonate
(eg. Lithium +
Potassium
Carbonate)
SeimensWestinghouse
Ceramic
Stationary
Power;
Cogeneration;
Intermediate/
Baselaod
Power
240(300*)
O=
Perovskites
50-60
900-1500
(eg.
Stabilized
Zirconia)
Solid
Oxide
(SOFC)
Ceramic
42
43
reaction is the driving force behind all fuel cells, therefore selecting a fuel cell depends
primarily on the operating temperature of the underlying electrochemical reaction,
the purity the hydrogen or hydrocarbon fuel supply, and the desired temperature of
the fuel cell application.
In stationary power applications, where electrical efficiency is very important,
both low temperature (PEMFC and PAFC) and high temperature (MCFC and
SOFC) compare well with conventional generating methods below 500kWelec . For
transportation applications, the PEMFC has a clear advantage due to its low operating temperature, which enables it to start up rapidly, have high power density,
and makes it well suited for tight spaces and weight requirements.
For large multi-megawatt niche markets and power plants, the higher temperature MCFCs and SOFCs outperform other fuel cells because generated heat can
be used with turbines and modified microturbines to boost total efficiencies to over
80%, which is higher than than the best conventional technology [32]. Table 3.2
shows some of the major advantages and disadvantages of using the four major
kinds of fuel cells. Christenson[4] identified the following locations as favorable for
fuel cells power generation:
• where fuel (natural gas, biogas, landfill gas, etc.) is readily available or low
cost and electrical demands are high;
• where waste heat can be recovered from processes because system efficiency is
greatly improved in such a situation;
• where compliance with stringent environmental air quality regulations limits
the options available for meeting electric and thermal requirements;
• where power delivery is expensive because of long-distance transmission; and
Disadvantages
Advantages
Fuel cleanup and
external gas
reformer required
if pure hydrogen
not available
Pt catalyst is
deactivated
by CO
Fuel cleanup and
external gas
reformer required
if pure hydrogen
not available
Water Management
in cell is difficult
Only low grade heat
is available
Catalyst needs
precious metal
Most advanced
stage
Small capacity
plants and
vehicular use
Low working
temperature
Very high
current
density
CO content is
strictly prohibited
Phosphoric
Acid
(PAFC)
Tolerant of CO2
Proton
Exchange
Membrane
(PEMFC)
Tolerant of CO2
Phase change
between working
and ambient
temperature
CO2 source
needed
Molten
Carbonate
(MCFC)
No precious
metals required
CO is a
useable fuel
Internal reforming
in cells is
feasible
High grade heat
is available
Solid
Oxide
(SOFC)
No precious
metals required
CO is a
useable fuel
Internal reforming
in cells is
feasible
High grade heat
is available
CO2 recycling
not required
High temperature
puts severe
constraint on cell
material
Relatively high
electrode
resistivity
Table 3.2: Advantages and disadvantages of various types of Fuel Cells [4]
44
45
• where critical electrical loads are supplied by high-cost uninteruptable power
supplies(UPS), motor-generator sets, or backup generators running on fossil
fuels. (Such locations may have sophisticated computer and electronic systems, which demand noise free, highly reliable, high-quality electrical supply.)
3.2
Molten carbonate fuel cells and how they work
Comparing the requirements of different kinds of fuel cells with the the chemical
constituents of biogas, the need for high temperature steam and hot water on the
farm, and the commercial availability of each fuel cell, narrowed the selection of fuel
cells to just MCFCs.
One reason for this selection was the ability to internally reform biogas into
hydrogen-rich gas using the MCFC 630 - 650◦ C operating temperature; lower temperature fuel cells require relatively costly and high-maintenance external gas reforming equipment. Also, according to tables 3.1 and 3.2, carbon dioxide in the
biogas should not affect the MCFC catalyst. However, high levels of H2 S and moisture present in biogas will definitely affect and poison the catalyst. Lastly, the main
reason for choosing MCFCs was the opportunity to harness 370 - 426◦ C (700-800◦ F)
exhaust gases from the MCFC to produce cogeneration heat and electricity on the
farm. Found below are explanations about how MCFCs work and why this kind of
fuel cell is the best selection for biogas energy conversion on the dairy farm.
The commercial molten carbonate fuel cell (MCFC), manufactured by FuelCell
Energy (FCE) of Danbury Connecticut, is composed of three main parts, which is
shown in Figure 3.1: the power conditioning and plant controls (Electrical/ Control
Skid), the fuel cell stacks (MTU Hot Module skid), and balance of plant (the gas
processing/mechanical skid) [8; 25].
46
Power
Conditioning
and Controls
Fuel Cell
Stack
Module
Balance of Plant
10.5’
11.5’
33’
Figure 3.1: FuelCell Energy’s 250 kW MCFC unit.
Inside the MCFC, hydrocarbons are reformed with steam in the presence of
a catalyst to produce a hydrogen rich gas. This hydrogen rich gas reacts with
carbonate (CO2−
3 ) ions at the anode to produce carbon dioxide (CO2 ) and water [6].
The electron flow from the anode to cathode is the basis for electricity generation
in this fuel cell. Figure 3.2 demonstrates the chemical reactions involved in the
process. Before looking at the compatibility issues for interconnecting fuel cells on
CH4
H20
H2
CO
Catalyst
Anode:H2+C032- --> H20
CO+C032- --> 2C02 +2eC032-
H20
C02
H20 C02
+C02 +2e-
electrolyte
eC032-
Cathode: ½O2+C02 +2e- --> C032Air(O 2)+ C02
Some recycle
C02
+Air
Operating temperature: 650 °C
Figure 3.2: Basic chemical reactions inside the molten carbonate fuel cell [6].
47
dairy farms, data collected from AA Dairy’s operation will be assessed for use in
this feasibility study.
3.3
Farm data
NY dairy demographics for 1993 to 2001 shows a shift in dairy population from being
mostly on small farms (<100 cows) to being mostly on medium (100-500cows) and
large (> 500 cows) farms. This trend is clearly demonstrated in Figure 3.3, which
shows in 2001, that 60% of the cow population resided on medium to large farms.
Milk Cows on NY Farms by Herd Size
1993-2001
350
300
Thousand Head
250
1-29
30-49
50-99
100-199
200 plus
200
150
100
50
0
1993
1994
1995
1996
1997
1998
1999
2000
2001
Figure 3.3: Change in NY Dairy population for different sized farms (1993-2001).
Accompanying this shift in the dairy population was a decline in the number
of small NY dairy farms from 83% to 75%, and a corresponding increase in the
number of medium to large farms, as shown in Table 3.3. One plausible explanation
for these industry changes is small farmers giving up farming all together or selling
their farms to larger dairy operations.
48
Table 3.3: NY Milking Operations by Herd Size and Total (1993-2000).
Number of Milk
cows per herd
1-29
30-49
50-99
100-199
200 plus
total
small farms
1993
2,400
2,500
4,200
1,500
400
11,000
83%
1994
2,400
2,200
4,200
1,500
400
10,700
82%
1995
2,100
2,200
4,000
1,300
400
10,000
83%
1996
2,800
2,000
3,700
1,300
400
10,200
83%
1997
1,700
1,900
3,600
1,300
500
9,000
80%
1998
1,600
1,800
3,500
1,300
500
8,700
79%
1999
1,400
1,600
3,200
1,400
600
8,200
76%
2000
1,400
1,500
3,000
1,400
600
7,900
75%
With the majority of cows residing on medium to large farms, widespread use
of anaerobic digesters seems more possible because at least 400 cows are needed to
economically run an anaerobic digester [40]. Unfortunately, 75% of the NY dairy
operations in year 2000 cannot take advantage of digester technology because they
are small farms with fewer than 100 cows. Dairy waste from small farms need not
and cannot be ignored, however, because effluent from livestock agriculture accounts
for a significant portion of the drinking water pollution in NY waters [21]. One of the
main goals of this project is to examine the potential for using fuel cells on different
sized farms. The focus in this thesis is on farms with greater than 400 milking cows
because they have a greater potential for economical waste management and biogas
production. In the near future, however, small farms which own their own digester
or share a community digester will be explored to see if they can also benefit from
fuel cell technology.
AA Dairy, a medium size farm in Candor NY, has approximately 500 milking
cows, an operating digester with diesel genset, and is located 20 miles from Cornell
University. This combination of characteristics, along with the farmers’ track record
of maintaining data records, made AA Dairy a desirable location for conducting this
feasibility study on a medium-sized dairy farm. On advise of RCM Digesters, Inc.
49
of Berkeley, CA, who designed the digester-diesel genset system at AA Dairy, the
dairy owners (Bob Aman and family) have diligently recorded day-to-day operating
data, biogas meter readings, and electric generation levels for almost 4 years.
3.3.1
Biogas production
Figure 3.4 shows that biogas from AA Dairy’s approximate 500 cows is produced at
a rate of 1.1 million to 1.4 million liters per day (L/day) (40,000-50,000 cubic feet
per day [ft3 /day]), or 2,265 to 2,832 L/day/cow (80-100 ft3 /day/cow).
120
120,000
Total biogas
100
100,000
80
80,000
60
60,000
40
40,000
20
20,000
8/24/01
5/26/01
2/25/01
11/27/00
8/29/00
5/31/00
3/2/00
12/3/99
9/4/99
6/6/99
3/8/99
12/8/98
9/9/98
0
6/11/98
0
Total biogas production (cubic feet/day)
Biogas production (cubic feet/day/cow)
Biogas/cow
Date
Figure 3.4: Total biogas production per day and per cow per day (1998-2001).
It is interesting to note that in both graphs, particularly the graph for total biogas,
that biogas production seems to be increasing, with more recent data indicating as
high as 1.4 million L/day (48,000 cfd) or 2,718 L/day/cow (96 cfd/cow). Planned
expansion of the farm from 500 to 1000 milking cows could, therefore, double the
biogas production to 2.2 million - 2.8 million L/day (90,000 - 100,000 cfd) if the
50
production rate per cow per day stays the same.
3.3.2
Electrical energy audit
Prior to the installation of the diesel engine generation facility, AA Dairy paid approximately $37,000/year for electricity to meet the farm energy needs [2]. In 1997,
AA Dairy received a grant from the EPA AgStar program to partially support the
installation of an anaerobic digester for dairy waste management, and an accompanying diesel engine-generator set (diesel genset) to convert digester biogas into electricity. Table 3.4 shows the gradual reduction of AA Dairy’s electricty consumption
Table 3.4: Comparison of Total Electric Costs at AA Dairy (1997-2000)
Month
Dec+Jan
1997
$6,215
Month
Dec+Jan
1998
$6,059
Feb+Mar
$5,748
Feb+Mar*
$5,394
Apr+May
$5,620
Apr+May*
$5,323
Jun+Jul
$6,271
Aug+Sep
$6,734
Oct+Nov
$6,097
Jun
Jul
Aug
Sep
Oct
Nov
Dec
TOTAL:
$36,685
$939
$1,361
$1,096
$635
$596
$669
$574
$22,646
Month
1999
Month
2000
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
$727
$537
$1,235
$616
$962
$1,443
$1,477
$1,621
$1,794
$900
$555
$182
$12,049
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
$350
$102
$89
$1,145
$737
$249
$908
$373
$164
$311
$197
$200
$4,116
*pilot project conducted during these months
and associated costs as the farm changed from being solely an electricity consumer
to a source of distributed generation to the local utility, New York State Electric &
Gas (NYSEG). Demand charges and insurance were the primary reasons why AA
Dairy paid utility bills in 1999 and 2000. In the middle of 1998 the diesel genset
was brought online.
51
The diesel genset consists on a synchronous reciprocating generator connected
to a spark plug modified diesel engine which allows the genset to generate electricity
with low energy/low pressure biogas. Diesel generation levels have been approximately 70 to 75 kW. This level of generation exceeds the farm electric demand most
of the time, making it possible for the farmer to get a monthly payment for selling
energy to the grid. On average AA Dairy purchases electricity from its local utility,
NYSEG, at $0.09/kWh and earns $0.025/kWh for supplying excess to the grid.
In June 1999, there was a jump in AA Dairy’s selling price for electricity to as
much as $0.0525/kWh. This reflected a change from the original contract where
NYSEG bought electricity at the avoided cost of $0.029/kWh during peak hours
and $0.014/kWh during off peak hours, to transactions determined by the New York
Independent Service Operator(NYISO). According to Petersen [29], the switch by
the NYISO allows real time metering instead of fixed rate metering, and supposedly
reflects the real demand for electricity. Higher prices are possible during warm
summer months due to typical high demand for electricity. However during cooler
months and regular demand period, no better than avoided cost should be expected.
52
Ludington [17] conducted an energy audit of AA dairy’s electrical demand. As
indicated in Table 3.5, the five largest electric loads in decreasing order are: ventilation fans, vacuum pumps, refrigeration, lighting, and air compressors totaling 91%
of the annual electric load. While many loads are on year round 12-24 hrs a day,
certain loads like free stall fans only get turned on during the warmer months to
cool the cows. Inadequate lighting during months with increased cloud cover and
shorter daylight hours also increases the need for lighting in and around the free
stall area. Fortunately peak months for lighting and ventilation don’t coincide.
Table 3.5: Largest annual electric loads at AA Dairy.
Rank
1
Equipment
Vent Fans
(free stall fans
kWh
85,906
64,907
%
27.04
20.43)
Vacuum Pump
Refrigeration
Lighting
(free stall lighting
83,220
54,355
44,324
31,262
26.19
17.11
13.95
9.84)
5
Air Compressor
21,462
6.76
6
7
8
9
Manure Handling
Water Pump
Milk Pump
Milk Pump AFD
(misc./unaccounted
Total farm electric usage
14,529
6,439
2,862
179
4,424
317,700
4.57
2.03
0.90
0.06
1.39)
100.00
2
3
4
Notes
to cool cows,
from May-Oct
supplements day light,
from Sept-May
usage for top 5 top loads
= 289,267kWh or 91%
53
We can estimate electrical use on the farm as the electricity generated at the
diesel genset minus the net energy sold to the grid in kWh, which is depicted graphically in Figure 3.5. The local utility NYSEG provides access to time-of-day meter-
2000
kWh/day
1500
Energy Produced on Farm
Difference = Energy Used on the Farm
1000
500
Net Energy to the Grid
0
- 50 point moving average
-500
7/31/98
10/31/98
1/31/99
4/30/99
7/31/99
10/31/99
1/31/00
4/30/00
7/31/00
10/31/00
1/31/01
4/30/01
7/31/01
Date
Figure 3.5: Energy generated and net energy sold at AA Dairy (1998-2001).
ing records for AA Dairy through the Energy Profiler Online web service, (source:
http://www.nyseg.com/epo). The service provides readings at five-minute intervals
for electricity sold to and purchased from the grid. Figure 3.6 shows the amount of
electricity (a)purchased, and (b)used on the farm during four days in March 2001.
As shown in Figure 3.6(b), March 9th was the only day of the four days where AA
Dairy exceeded its 70 kW generating capacity. The farmer confirmed the electricity
demand spike as coinciding with a routine engine oil change, done every weekend, as
part of maintaining the diesel genset. Frequent oil changes helps to prevent engine
corrosion because it reduces acidity in the oil resulting from H2 S and CO2 dissolving
into the engine oil.
54
(a)
Electricity Used on March 9-12, 2001
Electricity used (kW)
Generating
Capacity ~70 kW
Time of day@ 5 min. intervals
(b)
Figure 3.6: Example of electricity (a)purchased and (b)used at AA Dairy
55
3.3.3
Digester heating requirement
Producing biogas continuously and efficiently requires maintaining the digester temperature at approximately 38◦ C (100◦ F) year round. Heat recovered from the diesel
genset cooling system, is circulated through the digester to maintain the digester
temperature. As would be expected, winter months require the most digester heating, while during the summer there is little or no need for heat to maintain the
digester temperature. Just how much heat is required to maintain the digester at
38◦ C is the subject of the calculations below.
The original plan was to use some of the recovered heat form the diesel genset for
washing the milk lines, warming the milk parlor floors, and providing radiant heat
in employee work spaces. However, once the system was implemented it was quickly
abandoned, because winter conditions only resulted in low temperature ”hot” water
for heating the digester. Consequently, heat for everything but the digester was
switched to propane or electricity and this practice still holds today.
It is safe to assume that every percent of methane in biogas yields about 103.4971
Wh/%/m3 (10 Btu/%/ft3 ). Also biogas production at the 500-cow AA Dairy is reported to be 1,274.26 to 1,415.84 m3 /day (45,000 to 50,000 cubic feet per day (cfd)).
Therefore, biogas with 55% to 60% methane represents a daily heat equivalent of:
(5.69234 to 6.20983 kW h/m3biogas ) ∗ (1, 274.26 to 1, 415.84m3biogas /day)
→ 7, 254 to 8, 792kW h/day
or
302.23 to 366.34kW
or
(550 to 600Btu/f t3biogas ) ∗ (45, 000 to 50, 000cf dbiogas )
→ 24.75 to 30.00 M Btu/day
56
This daily heat equivalent of 302 kWheat is better known as the lower heating
value (LHV) of biogas. The LHV of a fuel is the potential combustible energy after
expending the heat of condensation of water. LHV is different from higher heating
value (HHV) which is the maximum energy available from a fuel. With given LHV
efficiencies of a diesel genset and fuel cell, it was possible to determine the electrical, recoverable thermal, and unrecoverable thermal energy conversion capabilities
of both systems. While the primary interest now is on determining diesel genset
recovered heat, fuel cell calculations are also listed for future reference.
Diesel Genset: 80-90% cogen. efficiency with 20% elec. efficiency.
For 55% methane biogas @ 45,000 ft3 /day( 7,254 kWh/day [24.75 MBtu/day]):
Electricity :
(20%) ∗ (7, 254kW h/day)
= 1, 451 kW h/day
Heatrecoverable : (60 to 70%) ∗ (7, 254kW h/day) = 4, 352 to 5, 078 kW h/day
Heatwasted : (10 to 20%) ∗ (7, 254kW h/day) = 725 to 1, 451 kW h/day
For 60% methane biogas @ 50,000 ft3 /day( 8,792 kWh/day [30 MBtu/day] ):
Electricity :
(20%) ∗ (8, 792kW h/day)
= 1, 758kW h/day
Heatrecoverable : (60 to 70%) ∗ (8, 792kW h/day) = 5, 275 to 6, 155 kW h/day
Heatwasted : (10 to 20%) ∗ (8, 792kW h/day) = 879 to 1, 758 kW h/day
Thus for the full range:
500 cows
1, 000 cows
Electricity : 1, 451 to 1, 758kW h/day 2, 901 to 3, 517 kW h/day
Heatrecoverable : 4, 352 to 6, 153 kW h/day 8, 704 to 12, 308 kW h/day
Heatwasted :
725 to 1, 758 kW h/day
1, 451 to 3, 517 kW h/day
57
MCFC: 85-95% cogen. efficiency with 40-50% elec. efficiency. For 55% methane
biogas @ 45,000 cfd ( 7,254 kWh/day [24.75 MBtu/day]):
Electricity : (40 to 50%) ∗ (7, 254kW h/day) = 2, 901 to 3, 627 kW h/day
Heatrecoverable : (35 to 55%) ∗ (7, 254kW h/day) = 2, 539 to 3, 989 kW h/day
Heatwasted :
(5 to 15%) ∗ (7, 254kW h/day)
= 363 to 1, 088 kW h/day
For 60% methane biogas @ 50,000 cfd ( 8,792 kWh/day [30 MBtu/day] ):
Electricity : (40 to 50%) ∗ (8, 792kW h/day) = 3, 517 to 4, 396 kW h/day
Heatrecoverable : (35 to 55%) ∗ (8, 792kW h/day) = 3, 077 to 4, 836 kW h/day
Heatwasted :
(5 to 15%) ∗ (8, 792kW h/day)
= 440 to 1, 319kW h/day
Thus for the full range:
500 cows
1000 cows
Electricity : 2, 901 to 4, 396 kW h/day 5, 803 to 8, 792 kW h/day
Heatrecoverable : 2, 539 to 4, 836 kW h/day 5, 078 to 9, 671 kW h/day
Heatwasted :
363 to 1, 319 kW h/day
725 to 2, 638 kW h/day
There are two ways to calculate the heat requirement of the digester and they should
both be equal: 1) calculate the heat lost from the hot water array in the digester
which heats up the digester, or 2) sum the heat needed to warm up manure and
the heat need to maintain digester temperature by replacing heat lost through the
walls, floor and roofing. Each method starts with the heat transfer equation
q = ṁ ∗ c ∗ ∆T
where ṁ is the mass flow rate, c is the specific heat of manure which we assume
to be the same as water, 4200 J/kg.◦ C (1 Btu/lb/◦ F), and ∆T is the temperature
change from the influent and effluent water flows.
58
Task 1: To calculate heat lost from the hot water array to the digester.
The heat recovery system used to maintain the digester temperature consists of a
number of heat exchangers coupled with the diesel engine cooling system, a hot
water tank, a radiator, Holby mixing valve, and a Grundfos(UPC80-160, series 200,
3 phase) HVAC circulating pump. All of these component are shown in Figure 3.7.
115 °F
F
Digester
104 °F
F
165 gpm
15psi
115 °F
Holby
Mixing
valve
Hot water tank
Grundfos UPC80-160 203V, 3ph
circulating pump @speed 3;
Pump operates 12hrs/day.
Radiator
Diesel
Engine
Figure 3.7: Schematic diagram of the heat recovery loop keeping the digester at 38◦ C
(100◦ F). The thicker line show the hot water path to the digester heating array.
q = 165gallon/min ∗ 8.3lb/gallon ∗ 60min
∗12hr ∗ 1Btu/lb/◦ F ∗ (115◦ F − 104◦ F )
= 10.85M Btu/day = 3, 179.8kW hheat /day
= (3, 179.8kW h/day)/(4, 352 to 5, 275kW h)
= 73.1% recoverable heat f rom 1, 274.26 m3 /day biogas assuming 55% CH4
or 51.7% recoverable heat f rom 1, 415.84 m3 /day biogas assuming 60% CH4
alternatively,
= (3, 179.8kW h/day)/(7, 254 to 8, 792kW h/day)
= 43.8% total energy f rom 1, 274.26 m3 /day biogas assuming 55% CH4
or 36.2% total energy f rom 1, 415.84 m3 /day biogas assuming 60% CH4 !!!
59
Published data on an operational New York anaerobic digester [36] claims that
the digester needs around 35% of the produced biogas heating value to maintain the
digester temperature. The value calculated above, 3,179.8kWhheat /day, is around
36.2% of the energy content in 1415.84 m3 /day of biogas at 60% methane, therefore
it appears that a good estimate was made for AA Dairy’s current digester heating
requirement.
Task 2: To sum the heat to warm up influent manure from 34 to 100◦ F
and heat loss replacement.
For 500 cows (7,254 kWh/day):
500 cows * (75 kgmanure /cow/day) = 37,500 kgmanure /day
qincoming manure = 37, 500 kg/day ∗ 4200 J/kg/◦ C ∗ 1hr/3600s
∗1kW ∗ s/1000J ∗ (37.78◦ C − 1.11◦ C)
= 1, 599 kW hheat /day (5.445 M Btu/day)
this represents:
1, 599/7, 254 = 22% LHV
1, 599/(4, 352 to 6, 153) = 25.99 to 36.74% of diesel genset heatrecovered
5.445/(2, 539 to 4, 836) = 30.07 to 62.98% of f uel cell heatrecovered
For 1000 cows(14,508 kWh/day):
1000 cows * (75 kgmanure /cow/day) = 75,000 kgmanure /day
qincomingm anure = 75, 000 kg/day ∗ 4200 J/kg/◦ C ∗ 1hr/3600s
∗(37.78◦ C − 1.11◦ C)
= 3, 198kW hheat /day (10.890M Btu/day)
60
this represents:
3, 198/14, 508 = 22% LHV
3, 198/(8, 704 to 12, 308) = 25.99 to 36.74% of diesel heatrecovered
3, 198/(5, 078 to 9, 671) = 30.07 to 62.98% of f uel cell heatrecovered
Now:
qheatl oss = qwall + qf loor + qceiling
where q = U ∗ A ∗ ∆T,
U = Conduction heat transf er coef f icient,
A = Area, and
∆T = T emperature dif f erence
Assuming the following thermal characteristics for the digester plastic covering, walls
and floor [19]:
air:-5°C (23°F)
plastic covering
with no insulation
uniform average
temperature outside
the digester wall:
0°C(32 F)
manure
slurry at
38°C (100°F)
plain 12 inch concrete
wall below ground
surrounded by
moist earth
12 inch thick concrete
floor with no insulation
moist earth below floor:-12.78° C (23°F)
Figure 3.8: Digester thermal characteristics. [adapted from [19]]
61
Temperatures:
Sludge Contents Inside Digester = 38◦ C(100◦ F )
Air temperature = −5◦ C(23◦ F )
Earth next to walls = 0◦ C(32◦ F )
Incoming sludge = 1.11◦ C(34◦ F )
Earth below f loor = 12.78◦ C(55◦ F )
Concrete digester dimensions:
130f t ∗ 30f t ∗ 14f t = 54, 600f t3 = 1, 546m3
Surface Areas:
Acover = 130f t ∗ 30f t = 3, 900f t2 = 110.44m2
Af loor = 130 ∗ 30f t = 3, 900f t2 = 110.44m2
Awalls = 2 ∗ (130f t ∗ 14f t + 30f t ∗ 14f t) = 4, 480f t2 = 126.86m2
Heat transfer Coefficients:
Ucover (f lexible plastic covering, no insulation) = 0.35Btu/(f t2 .hr.◦ F )
Uf loor (12 inch plain concrete f loor, ) = 0.12Btu/(f t2 .hr.◦ F )
in contact with moist earth)
Uwalls (12 inch plain concrete walls, = 0.25Btu/(f t2 .hr.◦ F )
below ground surrounded by moist earth)
Compute the heat loss by conduction:
Qcover = .35Btu/(f t2 .hr.◦ F ) ∗ (3, 900f t2 ) ∗ (100 − 23)◦ F = 105, 105Btu/hr
Qf loor = .12Btu/(f t2 .hr.◦ F ) ∗ (3, 900f t2 ) ∗ (100 − 55)◦ F = 21, 060Btu/hr
Qwalls = .25Btu/(f t2 .hr.◦ F ) ∗ (4, 480f t2 ) ∗ (100 − 40)◦ F = 67, 200Btu/hr
62
T otal losses = (67, 200 + 21, 060 + 105, 105)Btu/hr ∗ 24hr/day
= 4.641M Btu/day = 1, 360.14kW h/day
∴ Required heating array capacity
= heat to warm up sludge + heat to replace digester heat loss
= (1, 595.77 + 1, 360.14)kW h/day(ie.5.445 + 4.641)M Btu/day
= 2, 955.91kW h/day(10.09M Btu/day) which is similar to
3, 179.8kW hheat /day(10.85M Btu/day), reasonable!!
The similarity between the two methods is striking. Since there is less uncertainty
about the first method, where heat is transferred to the disgester with a heating
array, 3,179.82 kWhheat /day will be used as the best estimate for digester heating.
To summarize, assuming biogas production is 1.27 million L/day (45,000 cfd)
with 55% methane, it was calculated that approximately 3,179.82 kWhheat /day
(10.85 MBtu/day) would be needed to maintain the digester at 38◦ C(100◦ F), which
is 73% of the 4,352.11 kWhheat /day (14.85 MBtu/day) recovered from the engine exhaust. The 3,179.82 kWhheat /day represents 43% of the 7,254 kWhheat /day
(24.75 MBtu/day) lower heating value (LHV) of biogas. An estimated 1,172.29
kWhheat /day (ie. 4,352.11 kWhheat /day - 3,179.82 kWhheat /day ), remaining after
the heating the digester, can be used elsewhere on the farm, provided the temperature of the hot water supply matches up with required process temperatures.
3.3.4
Propane usage
Propane is purchased monthly [2] for heating the water used to wash the milk lines,
and it is also used to provide radiant heat in the milking parlor during the cold
months. Propane usage data are shown below in Figure 3.9.
700
1.4
600
1.2
500
1
400
0.8
300
0.6
200
0.4
Propane use
Propane cost
100
Propane cost ($/gallon)
Propane use (gallons)
63
0.2
0
0
12/30/99
2/21/00
4/3/00
6/5/00
8/7/00
10/12/00 11/29/00
1/22/01
3/19/01
Date
Figure 3.9: Propane useage and propane costs, 1999-2001.
The following list summarizes the overall picture of propane use on the farm.
Total Usage: 18,571 Liters/year (4,906 gallons/yr)
Total Cost:
Total Heating Value (HHV):
$5,039/yr
484.43 kWhheat /yr (1.653 MBtu/yr)
Average propane use/day: 1.3263 kWhheat /day (4,525 Btu/day)
Heaviest: Feb/2000: 2.771 kWhheat /day (9,456 Btu/day)
Lightest: July/2000:
0.674 kWhheat /day (2,298 Btu/day)
Comparing 2.771 kWhheat /day with the remaining 1,172.29 kWhheat /day recovered heat indicates that there is ample heat to replace propane use. The quantity of
heat recovered from the diesel genset is too low in the winter months to successfully
heat the digester, provide radiant heat and heat the wash line water. That is why
the farmer uses propane for these heating loads. With recovered heat temperatures
ranging from 371 - 426◦ C (700-800◦ F), propane is expected to be the first thing
replaced by fuel cell cogen heat.
64
3.3.5
Other Thermal Loads
With the availability of large quantities of high temperature exhaust gases from
a fuel cell, electrical compressor refrigeration of milk at AA Dairy can easily be
replaced with thermal energy driven absorption chillers. To facilitate this change,
the current size of the milk refrigeration load has to be estimated.
The energy audit carried out by Ludington (2001), listed refrigeration as the
third largest electrical load on the farm(Refer to Table 3.5). The thing that makes
refrigeration different from the two larger, seasonal loads –ventilation and lighting,
is its all year long requirement, for an average of 12.7 hours per day, in order to
hold the milk at the proper temperature. During warmer months the refrigeration
load is on approximately 15 to 16 hours per day. The electric consumption of milk
cooling, is reported to be 11.76 kW. Assuming Coefficient of Performance(COP) of
2.5 for AA Dairy’s Tecumseh/Copeland electric compressors, the tonnage of cooling
for the present operation is calculated by
kW
3.516
=
T ons
COP
11.76 kW ∗ 2.5
T ons =
3.516
= 8.36 tons
which is rounded to 9 tons of cooling.
Diesel fuel is used at AA dairy to run almost all of the trucks, tractors, heavy
farm machinery and a backup diesel generator. Records are not kept on how much
diesel fuel is used to run the backup diesel generator specifically, however, Aman
explains that this generator is typically turned on for a couple of hours, maximum,
during days when the biogas genset is taken off line for repairs. Operating the
diesel backup generator instead of drawing power from the grid is a cost effective
65
way that AA Dairy uses to provide temporary electricity because maintenance and
diesel repairs to the genset typically occurs during peak times when electricity cost
is high (e.g. $0.12kWh).
Heating oil is kept on hand, just in case the diesel genset heat recovery system
under performs or breaks down for a prolonged period. The oil is used to fire a
backup boiler, which is connected to the hot water tank that circulates hot water
to the digester-heating array.
Lastly, precooling of milk with well water through a plate heat exchanger, constitutes another load, albeit a readily available cooling load. This load, which requires
some electric load for pumping well water to the precooling heat-exchanger, can be
calculated by using the following data [17] from the AA Dairy energy audit:
average milk production = 33.053 kg/day/cow(/72.87 lbs/cow/day)
average daily herd size = 501.90 cows
milk inf low temperature = 34.44◦ C(94◦ F )
milk outf low temperature = 16.67◦ C(62◦ F )
∴ W ell water precooling of milk = 33.053 kg/day/cow ∗ 501.90 cows
∗ 4, 200 J/kg/◦ C ∗ 1hr/3600s
∗ 1kW ∗ s/1, 000J ∗ (34.44◦ C − 16.67◦ C)
= 14.34 kWcooling
which is equivalent to an extra 10.19 tons of cooling if the Tecumseh/Copeland
electric compressors were used. Table 3.6 summaries the thermal loads discussed in
this section.
66
Table 3.6: Some of the thermal loads at AA Dairy.
Equipment
Thermal Load,units
Digester heating:
-daily requirement, water at 115◦ F
3,179.82 kWhheat /day
-remaining recovered heat, water at 104◦ F
1,172.29 kWhheat /day
Propane usage:
-total heating value (HHV)
484.43 kWhheat /yr
-average propane use/day:
1.3263 kWhheat /day
-heaviest: Feb/2000:
2.771 kWhheat /day
-lightest: July/2000:
0.674 kWhheat /day
Refrigeration:
-average(12hrs)
141.12 kWh/day
-heavy(summer, 16hrs)
188.16 kWh/day
Precooling:
-average(12 hrs)
172.04 kWh/day
-heavy(summer, 16hrs)
229.38 kWh/day
Diesel fuel
n/a
Heating oil
n/a
67
3.4
Impacts and benefits of fuel cell operation
The 250 kW fuel cell plant is designed for the following interconnections at the
plant: pipeline natural gas supply, municipal potable water supply, waste water
discharge, and exhaust gas discharge to a safe location. In order for the fuel cell
to work properly on the dairy farm it has to be compatible with the biogas, be
environmental friendly, and meet actual farm energy load profiles. Accordingly
biogas, water, fuel cell exhaust and power generation issues are discussed below.
3.4.1
Biogas compatibility
FCE’s performance requirement for natural gas flowrate [8] is 1.305 million L/day
(32 ft3 /minute), which fall within AA dairy’s current biogas production range of
1.274-1.416 million L/day (45,000 to 50,000 cf/day) from 500 cows. Biogas is a
good fuel for the MCFC, but it has to go through a biogas cleanup process to
remove corrosive hydrogen sulfide and moisture. A major portion of the overall
project is devoted to gas cleanup of 3,000-4,000 ppm Hydrogen Sulfide (H2 S) found
in AA Dairy’s biogas stream. Scrubbing options, including Zinc oxide and the Mehra
process that are used in the chemical industry and oil refineries, exist for reducing
the high levels of H2 S in ”sour” gas to parts per billion levels required of pipeline
quality natural gas. These options, however, are too expensive or impractical for
smaller operations like dairy farms. An immediate goal of our gas cleanup group is
to use proven technology including Iron sponge and bio-filters to reduce H2 S levels
to less than 10 ppm. To meet our total resource recovery objective, we hope to
use some of the digested solids as part of the H2 S scrubbing medium, instead of
buying woodchips or other packing material. Table 3.7 compares the unscrubbed
and scrubbed biogas with the MCFC pipeline natural gas requirements.
68
Table 3.7: Comparison of farm biogas properties and MCFC requirements.
FUEL CELL
PERFORMANCE
AA DAIRY
ACCEPTABLE
AT FCE TEST
TARGET FOR
RANGE/LIMITS
POWER PLANT
CLEAN BIOGAS
80-100
93.8
55-60
Ethane
0-10
1.9
0
Propane
0-3
1.6
0
Butanes
0-1.25
0.5
0
Nitrogen
0-3
1.1
0
Carbon Dioxide
0-3
1.1
40-45
0-0.8
0
0-10
PROPERTY
Composition (vol %)
Methane
Impurities
H2 S, ppm
(current levels
> 3,000)
CO, ppm
0-2
0
0
Odorants, ppm
0-12
6
0
Halogens(Cl,Fl,etc.)
None
None
None
Particulates/Gum
None
None
None
Water vapor, ppm
0-7
Dry
Dry
375 - 431
402
237 - 259
(870 - 1000)
(933)
(550 - 600)
418 - 475
446
263 - 287
(970 - 1100)
1035
(610 - 665)
15
15
15
Physical Properties
Heating Values:
LHV: kW/L x 10−6
(Btu/ft3 )
HHV: kW/L x 10−6
(Btu/ft3 )
Pressure, psig
69
3.4.2
Fuel cell electric generation capacity
As stated before, biogas is different from pipeline natural gas with 55-60% methane
as opposed to 90-99% found in pipeline natural gas. This difference should logically
translate into a different exhaust composition when converting biogas to electricity
and heat. A rule of thumb is to expect 4.3124 x 10−6 kWheat /L (10 Btu/ft3 ) for
every percent of methane in the biogas stream.
FCE performance calculations for an operating 250kW MCFC are based on
pipeline natural gas composed of 93.8% CH4 , 21.7 kWheat /L (933 Btu/ft3 ) LHV,
and a flowrate of 906.14 L/min (32 ft3 /min). Assuming we used a similar 906.14
L/min biogas flow rate, with 55% CH4 , and 45%CO2 we can estimate the generation
capacity and exhaust composition from a 250 kW fuel cell.
CH4 Composition
LHV
Generation
93.8% → 4.02 ∗ 10−4 kW/L (933Btu/scf ) → 250kW
55.0% → 2.37 ∗ 10−4 kW/L (550Btu/scf ) → x
60.0% → 2.59 ∗ 10−4 kW/L (600Btu/scf ) → (x + ∆x) > x
Ratio = 4.02/2.37 = 1.696
or 4.02/2.59 = 1.555
∴ 1.696 ∗ f lowrate units (906.14L/min) of Biogas (55%CH4 ) → 250kWelec
or 1 f lowrate unit (906.14L/min) of biogas → 147 kW of electricity
similarly :
1.555 ∗ f lowrate units (906.14L/min) of Biogas(60%CH4 ) → 250kWelec
or 1 f lowrate unit (906.14L/min) of biogas → 161 kW of electricity
70
FCE’s natural gas flowrate of 32 scfm = 46,080 scf/day which is almost identical to
AA dairy’s current gas production of 45,000-50,000 cf/day from 500 cows!!!!
With AA Dairy’s intended expansion to 1000 cows, the gas production will double to 90,000 - 100,000 cf/day, and likewise double the electricity generation to 322
kW. Until expansion of the cow population takes place the 250 kW MCFC will be
underutilized, however, the doubling to 1000 cows will exceed the 250 kW unit’s
generating capacity. Tripling the manure production to that of 1500 would produce
enough biogas to almost max out two(2) 250 kW units. This tripling of the biogas
production is very possible at AA Dairy if the manure from the farm’s 400-500 dry
cows are piped over to the digester instead of direct spreading the dry cow manure
onto crop land, which is currently done. Another alternative is to use the next generation 300 kW fuel cell from FCE which is about to be available in 2002. 1000 cows
worth of manure will max out the 300 kW fuel cell, with excess gas to spare. This
excess gas can be stored, utilized in natural gas vehicles, or used to run a peaking
unit like a small PEM fuel cell or microturbine.
3.4.3
Environmental impact of exhaust gases
Once moisture and all sulfur compounds such as H2 S and mercapatans are scrubbed
from the biogas, the fuel cell gas stream will be an almost pure mixture of CH4 and
CO2 . With 906.14 L/min biogas flowing into the fuel cell, the exhaust characteristics
are detailed in Figure 3.10 and Table 3.8:
71
Biogas:
55-60% CH4
40-45% CO2
906.14 L/min
3000-4000 ppm H 2S
Air:
78 % N2
21% O2
Water:
Exhaust gas:
Temperature, 370 - 427 °C(700-800 °F)
Flowrate, 19,226 L/min (680 scfm)
Relative humidity ~20% vol, or 64.41 kg/hr(142 lbs/hr)
CO2 , 235 lbs/hr
Low NOx, SOx , CO, and VOC levels (see Table 5)
250 kW MCFC
83 L/hr
Moisture
to exhaus
5 C(40 °F)
170 L/hr
o
Electricity:
147-161kW A
Discharge Water: 87
L/hr
Figure 3.10: Input-Output diagram for a biogas fed fuel cell system.
Table 3.8: Comparison of Emissions from the fuel cell and other cogeneration technologies. [adapted from: [10]]
Power
Emissions lbs/kWh
Generator type
(kW)
NOx
SOx
CO
CO2
VOC
MCFC*
250
.0000041
.00000057
.00025
.94
.00015
Diesel Engine
500
.017
.005
.01
1.7
.002
Otto/Spark Engine*
500
.0026
.00001
.008
.97
.0015
Microturbine
45
.00115
.00003
.00265
1.188
.00004
99.84
94.3
96.88
3.09
90
% reduction*
* reductions compare emissions from the MCFC to the diesel spark genset
All of the above pollutants in the exhaust stream are expected to be reduced
except CO2 which should not change significantly. To show why CO2 content does
not get reduced much, the expected CO2 production due to the methane and CO2
content in biogas is calculated below.
72
CO2 Material balance for diesel genset and fuel cell
Assume that all carbon in pipeline natural gas(png) or biogas will leave as exhaust
CO2 from diesel genset and fuel cell systems alike.
93.8%CH4 in png = 93.8% ∗ 32 cf m ∗ 60 min/hr ∗ (28.31685 l/cf )
= 50, 997.51 liters/hr of CH4
M olecular mass of CO2 = 12.01 + 2(16.00) = 44.01g/mole
M olecular mass of CH4 = 12.01 + 4(1) = 16.01g/mole
1 mole of any gas at STP(0◦ C, 1 atm) occupies 22.4 liters
@ 70F, and 15 psia(or 1 atm),
1 mole of gas = 22.4liters/mole ∗ 294.26K/273.15K
= 24.1312 liters/mole
@ 700F, and 15 psia,
1 mole of any gas = 22.4 liters/mole ∗ 644.26K/273.15K
= 52.8334 liters/mole
moles of carbon in png input = (50, 997.51 liter/hr)/(24.1312 liter/mole)
= 2113.34 moles/hour
= same moles of carbon in CO2
∴ volume of carbon in exhaust = 2113.34 moles/hour ∗ 52.8334 liters/mole
= 111, 654.9 liters/hr of CO2 in exhaust
and mass of carbon in exhaust = 2, 113 moles/hour ∗ 44.01g/mole
= 93, 008.09 g/hr
= 205 lbs of carbon f rom methane input alone
73
with 98.4% of the png being hydrocarbon (93.8%methane,1.9%ethane,1.6%butane)
or CO2 (1.1%), the moles of carbon expected in the exhaust is close to 100% utilization of the carbon which is 218.62 lb/hr CO2 in the exhaust.
Note, because all of the CO2 is derived from biogas which ultimately came from
plant uptake of atmospheric CO2 , the net production of CO2 for the biogas cogeneration system is almost zero. All CO2 emitted from the machinery and transportation
involved in producing biogas is excluded because these are unavoidable costs for
manure waste management anyway. With or without the electric generator, AA
dairy intended to implement anaerobic digestion to reduce odors, eliminate most
of the weed seeds and pathogens, sell composted manure, and irrigate crops with
separated digester liquid.
With 32 scfm biogas flowing into the fuel cell:
55% CH4 = 55% ∗ 32 scf m ∗ 60 min/hr ∗ (28.31685 l/cf )
= 29, 902.59 liter/hr
= 29, 902.59 l/hr ∗ 24.13 l/mole
= (1239.2 moles/hr ∗ 44.01 g/mole)/(483.59 g/lb)
= 120.24 lb/hr of exhaust CO2 due to CH4 in biogas
45% CO2 = 45% ∗ 32 scf m ∗ 60 min/hr ∗ (28.31685 l/cf )
= 24, 465.76 liter/hr
= 24, 465.76 l/hr ∗ 24.13 l/mole
= (1013.9 moles/hr ∗ 44.01 g/mole)/(483.59 g/lb)
= 98.38 lb/hour exhaust CO2 due to CO2 in biogas
100% carbon in exhaust = 218.62 lb/hourof CO2
74
3.4.4
Other environmental impacts
The most important environmental benefit of the fuel cell is its high electrical efficiency, which is assumed to be 47% for the 250 kW MCFC Unit; See Table 3.9.
With proper utilization of recovered heat the overall efficiency can approach 80%.
Table 3.9: Comparion of efficiencies, noise and installed cost [adapted from: [8; 10;
20; 23]].
Generator
type
MCFC
Power
(kW)
250
Diesel
Engine
500
Otto/
Spark
Engine
Microturbine
500
45
Noise Level
(qualitative
and db)
low (no
enclosure required)
∼60db @ 100 ft
moderate to high
(enclosure required)
∼110db
moderate to high
(enclosure required)
∼110 db
moderate(enclosure
supplied with it),
∼70 db @10 ft
Installed
Cost
($/kW)
3000
Electric
Efficiency
(LHV)
45-55%
Heat Rate
kWheat /
(kWelec )
2.0005
410
20-50%
2.28595
425
15-40%
2.84279
575
20-30%
3.51685
Higher efficiency translates into better fuel utilization, that is, less fuel is needed
to produce electricity and heat. With few moving parts, and many solid state
components, the fuel cell is understandably one of the quieter forms of electricity
generation. The level of noise is comparable to that of a vacuum cleaner, as opposed
to the bulldozer levels currently experienced with the diesel genset.
3.4.5
Water compatibility
Water samples from AA Dairy were analyzed for water quality. The results, shown
in Table 3.10, compares FCE’s recommendations with the actual water hardness,
minerals, and other factors affecting water quality. The analysis indicated that AA
75
Dairy’s water hardness is high, around 378mg/liter, which is unacceptable for fuel
cell operation and most processing applications including other forms of distributed
generation, district heating, and laundry facilities. The high water hardness level
is due to elevated levels of calcium carbonate, magnesium carbonate, and silica in
the water which needs to be reduced to prevent that fouling of the fuel cell. Fouling
would shorten the 5 year expected life-span of the fuel cell stacks, and definitely
increase maintenance costs. Filters and water softeners have to be installed upstream
from the fuel cell for reducing water hardness to 0-60ppm.
Table 3.10: Comparison of AA Dairy water quality and the MCFC requirements.
[adapted from: [8]]
ITEM
Quality(mg/liter)
Calcium
Magnesium
Sodium
Bicarbonate
Chloride
Sulfate
Tot. Diss. Solids
Tot. Hardness,
as CaCO3
Silica
Iron
Copper
Properties
Silt Density Index
Spec. Conductance,
(µmho/cm)
pH
Temperature, ◦ F
Pressure, psig
FUEL CELL
ACCEPTABLE
RANGE/LIMITS
PERFORMANCE
AT FCE TEST
POWER PLANT
AA DAIRY
MEASUREMENTS
0 - 120
0 - 30
N/A
0 - 90
0 - 50
0 - 90
0 - 350
0 - 150
44
16
16
50
17
20
135
60
0 - 10
0 - 0.1
0 - 0.05
3
0.03
0.01
48
9.97
11.74
73.11
14.98
800
378(=22 grains
of hardness)
9.85
0.0026
0.0474
0 - 10
0 - 580
2
205
357
7 - 8.5
40 - 90
50 - 65
7.6
59
60
7.56
40
60
76
3.4.6
Proposed changes
As previously stated the diesel genset produces enough electricity to sell excess to the
grid but occasionally AA Dairy purchases electricity to meet higher than generating
capacity demand. This will change with the fuel cell, particularly because higher
efficiencies will generate more than double or triple current generating capacities.
As shown in Figure 3.11, biogas produced from the manure of 1000 cows will be
converted to 250 kWelec when the fuel cell is operating.
Figure 3.11: Future generating capacities compared to today’s farm load.
In 5 years time, when the fuel cell stack is due for normal replacement, a 300
kW stack would be installed. Both the 250kW and 300 kW configurations will
utilize all of the biogas to generate enough electricity so that the farm load should
never exceed the fuel cell capacity, given similar activities on the dairy now. The
expansion to 1000 cows is expected to double most of the electrical loads, because
77
two free stall barns will need twice the free stall fans, twice the free stall lighting,
and twice the milk processing electrical demand. The electrical demands that will
remain unchanged, include lighting and fans not related to milk processing and the
free stall areas, such as those in the offices and mechanical shops.
Refrigeration can undergo a big change in the dairy operation, because the installed electric chillers could be replaced with absorption chillers. As mentioned
before, tonnage of cooling for the present operation is 8.36 tons which is rounded to
9 tons of cooling. With 1000 cows instead of 500, the new absorption chillers will be
sized to handle twice the milk cooling load of the present operation, which happens
to be approximately 20 tons. Although this is small for a typical chiller system,
there are at least three manufactures of small gas-fired or waste-heat driven absorption chillers, namely: Robur Servel Corportion, American Yazaki Inc, and Broad
USA Inc. Broad USA’s smallest absorption chiller operates at 33 tons, which of the
three manufactures, has the unique capability of utilizing heat from exhaust gases
to drive absorption chilling. Robur and American Yakazi manufacture direct-fired
(hot water/steam) chillers and indirect-fired (natural gas-fired) chillers, which do
not recover thermal energy straight from the hot exhaust gases. To use these types
of chillers, we would have to either utilize some of the biogas to run the indirect-fired
chillers, or pass exhaust gases from the fuel cell through a gas-liquid heat exchanger
to create steam or hot water for operating the direct-fired chillers. The most compatible selection for absorption refrigeration at AA Dairy would be the Broad USA
33 ton system, because it can utilize the hot exhaust gases from the fuel cell, which
is a high temperature source of waste heat.
78
3.5
Discussion
This technical feasibility part of the thesis discussed the potential use of 250 kW
MCFCs in leveraging the energy contained in manure-derived biogas. The electricity,
heat and exhaust from MCFC, as well as the liquid and solid effluent streams from
the biogas-producing anaerobic digester, are excellent renewable resources for total
resource recovery and integrated energy systems on a dairy farm.
From a fuel cell compatibility perspective the only things required to get the fuel
cell running on the farm are removal of H2 S from the biogas to less than 10 ppm
down from current 3,000-4,000ppm levels, and a good water softener to prevent salt
deposits from fouling of the fuel cell system. If the calcium carbonate levels are not
reduced in the water there will be fouling and eventual shortening of the fuel cell
life and associated operating and maintenance costs will increase.
From the perspective of potential changes to the farm arising from using the
MCFC, all seem positive at this point. Electricity generation could potentially rise
to 250kW from about 70 kW. With a projected electric use of 483,781kWh/year,
the remaining 2,214,765 would be available for further use on farm or sale to the
grid. Only about 18% of total energy content of the biogas or about 50% of the
370◦C(700 ◦F) recovered heat would be used directly to heat the digester, leaving
6155kWh/day (21 MBtu/day) as high temperature heat for other farm applications.
One sure application is the replacement of milk electric refrigeration with indirect
fired absorption coolers, which would transform what is currently the third largest
electric load on the farm into a non-income spending thermal load. This move
alone would significantly increase fuel utilization and boost overall farm efficiency.
Another is the replacement of propane use for deicing parlors floors and walkways,
heating milk line wash water, and proving radiant heat in employee work spaces.
79
There are a number of tangible environmental benefits for using MCFC over the
current diesel genset. Emission levels, for example are expected to shrink significantly by more than 90% for all pollutants except CO2 . The argument for CO2
is that it is produced by a MCFC running on biogas and is net neutral, since the
source of carbon in the biogas comes from farm crops sequestering CO2 from the atmosphere. Noise levels are expected to be minuscule compared to the bulldozer-like
noise of a diesel genset which requires a sound dampening enclosure. Also, adjoining properties could have access to an inexpensive supply of electricity if power is
delivered ”over the fence” without the use of utility grid lines.
The applications listed so far would only use approximately 50% of the fuel cell
thermal energy, therefore there is plenty room for improving the overall efficiency on
the farm and the readily available heat creates new opportunities for cogeneration
and the production of other farm value-added commodity. Additionally, more fuel
cells in the market will help drive down the capital cost for fuel cells and make them
more available to the dairy and livestock farming community.
To recap, this chapter shows how electricity generation from biogas and the onsite
utilization of at least one bio-derived commodity, heat for absorption refrigeration,
is sufficient for demonstrating the technical feasibility of operating fuel cells on dairy
farms. In order for the net benefits of an integrated farm energy and total resource
recovery approach to have great appeal to farmers and other potential adopters,
economic feasibility of this approach needs to be done, which also happens to be the
topic of the next chapter.
Chapter 4
ECONOMIC FEASIBILITY
In this chapter fuel cells are compared with microturbines and diesel gensets, to determine if fuel cells are feasible for providing innovative, economical energy solutions
within the framework of agricultural waste management and total resource-recovery
(TRR). Life Cycle Cost Analysis (LCCA) is conducted on the three distributed
generation technologies using data gathered from a real operating dairy, and NYS
2000-2001 Dairy Farm statistics [16]. Some of the variables used in the analysis are:
time value of money, capital costs, operating costs, replacement costs, insurance,
real and electricity inflation, electricity buying and selling prices, and environmental benefits.
The analysis begins with the three distributed generators using AA Dairy operating conditions and then it generalizes to typical upstate NY 500 and 1000 cow
operations. The 1000 cow fuel cell alternative is then further analyzed to identify
changes that could level the playing field for fuel cells to compete with mainstream
diesel gensets. Sensitivity analysis is used to highlight variables that have the biggest
effect on net present value and payback period. The final step assesses the income
earning potential of each alternative and its impact on overall farm profitability.
80
81
4.1
Strategy
Data from AA Dairy’s current operation with a diesel genset was used as a reference in this study. NYS statistical data for similar sized 400-599 cow dairies was
contrasted with AA Dairy, to see if there were clear differences between typical NYS
dairies without CHP and AA Dairy which has CHP. The objective of this economic
feasibility study is to use a net present value (NPV) approach to determine the life
cycle costs and benefits to a farm using a fuel cell. By including capital costs, operating and maintenance costs, replacement costs and various financial and energy
benefits of each CHP system, an estimate of the life cycle cost of each of the three
systems is determined. Life cycle cost, in this thesis, is calculated as the overall
cradle to grave NPV of a system over a predetermined useful life span.
Other economic feasibility indicators derived from this NPV and life cycle cost
analysis (LCCA) include: discounted payback period, equivalent annual cost, and
internal rate of return. Because of uncertainties associated with forecasting system performance and costs, it was informative to look at a range of results. Nonmonetary benefits and other factors which were difficult or impossible to include in
this quantitative analysis are discussed briefly at the end of the chapter.
While this thesis has tended to focus primarily on the electric and thermal generating opportunities for medium- to large- sized farms, the NPV analysis would be
incomplete and misleading if the costs and benefits of the digester and associated
TRR were not included. Consequently, the extra cost of the digester, solid and
liquid separators, grid intertie and heating array must be covered with income from
each distributed generation alternative in order to ensure that the energy and waste
management cash flows do not interfere with the milk related cash flows. The NPV
analysis therefore assumes that the joint CHP and TRR farm subsystem will pay
82
for itself in a reasonable time frame and actually contribute additional farm income
in order for the fuel cell, microturbine or diesel engine to be considered economically
feasible.
4.2
4.2.1
Execute Strategy
Selection of Distributed Generation Alternatives
AA Dairy and a few other farms have implemented biogas-utilizing electricity generating systems but, to our knowledge, there are no farms operating with innovative
and more energy efficient fuel cell technology. Until recently, the only distributed
generator that was available for farms to buy was the diesel genset, but things
changed recently with the advent of commercial microturbine systems. Wisconsin
based, Alliant Energy, for example, has installed a microturbine and a diesel genset
to generate electricity from digester biogas on the 700-head Top Deck Farm, in Westgate, Iowa [1]. In NY as well, at least two dairy farms, one at Dairy Development
Institute(DDI) and another at SUNY Cobleskill, have installed microturbines to run
on dairy biogas. Both NY farms plan to begin generating with pipeline natural gas
and gradually switch to biogas, as gas cleanup technology permits.
A survey of the commercially available fuel cell systems narrowed the selection to
Phosphoric Acid Fuel Cells (PAFC) and the Molten Carbonate Fuel Cells (MCFC)
because of their potential for cogeneration and high electivity output on a dairy farm.
With higher quality heat generation, higher electric efficiency, lower capital cost, and
the ability to consume large amounts of carbon dioxide as a fuel (refer to Figure 3.2
in the previous chapter), the MCFC was chosen as the sole fuel cell alternative
for this study (Christenson, 1997 and Lloyd, 1999). The smallest commercially
83
available MCFC is FuelCell Energy’s 250 kW System(patel00), which is ideally sized
for operations of AA Dairy’s size and biogas production.
The microturbines studied here are the Capstone 30kW systems [31], but the
original plan was to include the Parrallon 75kW microturbine which is no longer
produced by Honeywell. It is relatively easy to buy Capstone microturbines these
days because they are in full production now, and the distribution chain is already
well established. A nearby farm, Dairy Development Institute(DDI)in Homer, NY,
actually received four 30kW units for a small sum of money from its local utility, NYSEG, for mutual testing of this distributed generation technology on dairy-derived
biogas.
AA Dairy’s Caterpillar 3306 Diesel genset was used as the reference model for
diesel engine gensets in this study. This kind of genset is relatively inexpensive
and widely used for CHP applications on farms, landfills, and marine vessels. The
Caterpillar 3304 diesel genset is another popular model used on farms for CHP (ref:
Haubenschild Farm). An interesting feature of these gensets is the ability to increase
their generating capacity by just replacing the coupled generator (eg. 130kW) with
bigger, higher-winding generator modules (225kW, 450 kW), at a fraction of the
cost of buying a whole new engine-genset. There is a huge market for used diesel
gensets and genset parts throughout the world, which helps to keep genset prices and
maintenance affordable. The mass production of diesel gensets, and their virtual
ubiquity as the primary source of small distributed cogeneration in the world today,
immensely contributes to affordable cost of diesel gensets.
84
4.2.2
Cost and Benefits of Total Resource Recovery
The NPV analysis incorporates costs and benefits of each of the three distributed
generators, the anaerobic plug-flow digester, and the accompanying resource recovery system. The decision to include these additional costs beyond energy generation,
came from realizing that the digester and resource recovery equipment cannot directly pay for themselves without dipping into milk profits. While the benefits of
odor and waste management are hard to put a price on, there are other quantifiable
benefits of the resource recovery system that can be treated as commodities because
they can generate income and savings.
The following commodities are included in the NPV analysis:
• separated solids, which can be sold as compost,
• separated liquids, which are stored in a lagoon for land application as irrigated
fertilizer, and
• in the case of fuel cells, reduced gaseous emission, which can be traded for air
pollution credits.
Biogas, if produced in excess, is another tradable commodity. Once purged of CO2 ,
H2 S and moisture content, the excess biogas could possibly be fed into a community
natural gas pipeline at an agreed price . To simplify analysis, however, it is assumed
that not enough biogas is produced in excess to guarantee reliable pipeline delivery.
The distributed generators will most likely be optimized to use all the biogas to
match thermal and electrical load changes on host farms.
Value-adding milk processes such as pasteurization and homogenization, and the
production of skim milk, ice cream, butter, cheese and yogurt, are not included in
the NPV at this time. These value-adding steps, however, have the potential to
85
enhance farm profitability and fulfil Max Pfeffer’s description of utilizing readily
available CHP heat and electricity. Three other benefits that were omitted, but
would definitely increase the farm’s net income, are: the production of bedding
material from pasteurized separated solids; the sale of vegetables grown in greenhouses which utilize readily available heat, electricity, and CO2 ; and fish raised by
aquaculture utilizing the heat, electricity, and some of the nutrients contained in
the separated liquid lagoon.
Because the primary focus here is on the income generation capabilities of the
three alternative systems, many of the costs and benefits that are similar to all the
alternatives have been ignored. Also, potential problems and costs pertaining to the
technical, business, and regulatory barriers of interconnection have been neglected,
despite their importance to any project. Benefits such as manure pathogen reduction, odor elimination, relative noisiness, and good neighbourly relations, are also
not included. Although these are all certainly valid benefits, this analysis focuses
on more easily definable and quantifiable economic parameters. Knowing that these
benefits also require large investments in specialized equipment and personnel to
bring them to fruition, attention to these benefits is deferred until later projects.
4.2.3
Setting up the Net Present Value (NPV) problem.
With knowledge of the biogas energy content and farm energy needs it is possible
to compare the thermal and electrical generating capacities of the diesel genset,
microturbine and fuel cell. A meter on the 130kW diesel genset at AA Dairy reveals
electricity generation of approximately 70kW. This level of generation would yield
1,680 kWh/day, assuming the electrical efficiency of the genset is 22%. A biogas
supply of 1,274.26 m3 /day (45,000 ft3 /day) is currently used by the diesel genset to
86
generate 1,680 kWhelec /day. The energy content of this amount of biogas would be
able to run one 250kW MCFC, or four 30kW microturbines.
The microturbine manufacturer states 31% electric efficiency plus about 50%
recoverable thermal efficiency for their systems. The 250kW MCFC is expected
to yield a 52% electric efficiency, along with 40% recoverable heat efficiency. AA
Dairy’s total electric usage of 317,700 kWh/yr for year 2000, based on NYSEG
electric bills, was assumed to be the electric usage in all generation scenarios with
500 cows, while excess electricity was sold to the grid. A summary of the three
systems’ ability to deliver electricity and recoverable heat to a 500 cow operation is
shown in Table 4.1.
In Table 4.1, the microturbine generation capability almost matches the devices’
power ratings, however, the fuel cell is drastically oversized. Instead of generating
250kWelec , which is the capacity rating of the MCFC, it is only possible to generate
157kWelec due to the limited biogas energy content. This oversizing of the fuel
cell (or under utilizing of its capacity) will soon be alleviated however, since AA
Dairy plans to increase its milking herd size from 500 cows to 1000, and effectively
double the biogas fuel supply to the 250-kW MCFC. A 225 kW generator can be
substituted for the current 130kW generator on the Caterpillar 3306 diesel genset to
accommodate increased biogas production. To match the bigger quantity of biogas,
however, the number of microturbines would need to double to eight 30-kW systems.
Table 4.2 shows these data.
0.50
30-kW (heat)
0.40
MCFC (heat)
250
120
130
120.83
157.17
151.03
93.55
70
(kW)
Generated
Power
1,058,427
1,376,809
1,323,034
819,529
582,540
(kWh/yr)
Generated
Energy
Annual
317,700
317,700
317,700
(kWh/yr)
Use on Farm
Electricity
Annual
1,059,109
501,829
264,840
(kWh/yr)
Sold to Grid
Energy
Annual
1,058,427
1,323,034
**
(kWh/yr)
Energy
Heat
Available
not included.
**Although the diesel-generated heat is significant, it is of a lower temperature than that of the other systems, therefore it is
however, such effects are neither known at this time, nor reported in the literature review.
*The generating efficiencies of each alternative might vary due to the high amount of carbon dioxide (∼43%) in the system,
0.52
MCFC (elec)
Fuel Cell
0.31
30-kW (elec)
Microturbine
130 kW (elec)
0.22
(kW)
System
Diesel IC
Power
Conversion
Efficiency*
Electric
Energy
Rated
Table 4.1: Energy generation from 500 cows producing 1,274 m3 biogas/day
87
0.50
30-kW (heat)
0.40
MCFC (heat)
250
240
225
241.80
314.32
302.23
187.09
140
(kW)
Generated
Power
2,118,000
2,698,546
2,647,710
1,639,059
1,165,080
(kWh/yr)
Generated
Energy
Annual
483,781
483,781
592,491
(kWh/yr)
Use on Farm
Electricity
Annual
2,214,765
1,155,278
572,589
(kWh/yr)
Sold to Grid
Energy
Annual
2,118,000
2,647,710
**
(kWh/yr)
Energy
Heat
Available
not included.
**Although the diesel-generated heat is significant, it is of a lower temperature than that of the other systems, therefore it is
however, such effects are neither known at this time, nor reported in the literature review.
*The generating efficiencies of each alternative might vary due to the high amount of carbon dioxide (∼43%) in the system,
0.52
MCFC (elec)
Fuel Cell
0.31
30-kW (elec)
Microturbine
130 kW (elec)
0.22
(kW)
System
Diesel IC
Power
Conversion
Efficiency*
Electric
Energy
Rated
Table 4.2: Energy generation from 1000 cows producing 2,549 m3 biogas/day
88
89
4.2.4
Important Variables
It is possible to proceed with the economic feasibility of each system, with the
farm’s electricity demands and the generation capabilities, however, there are many
variables that require estimation and further refinement to describe the proposed
systems. For each variable a best estimate for the reference case was determined, and
these estimates were varied to see approximately how sensitive our economic analysis
was to each particular variable. Some notes on the values used in this analysis
are given below. Each value was varied roughly in accordance with the degree of
its uncertainty. Parameter estimation was aided by personal communication with
manufacturers, as well as the cited sources.
• Time Value of Money(TV), or discount rate: This is the rate at which one
compares one investment to other alternative. It includes inflation, risk, depreciation, interest, and other related factors, so obviously it is apt to vary
widely. Fortunately, whether it is 10% or 16%, the analysis is not very sensitive
to this term, relative to the other factors. Here, a TV of 14% was used [18].
• Inflation(i): The inflation rate, which applies to the taxes, administrative,
operating and maintenance cost, of 3% was used. Like for the discount rate,
if this rate was varied within a reasonable range of 2-5%, the effects on the
analysis were not drastic.
• Capital Costs (K0 ): These include the cost of the energy converter (i.e. diesel
genset, microturbine, fuel cell) plus all the equipment needed to run the system, the electricity generator, the cogeneration unit, electrical interconnect to
utility, and any other accessories. For the diesel engine, our best estimates
have hovered around $26,000; for the four 30-kW microturbines, $210,000;
90
and for the 250-kW MCFC, $1,250,000. Approximately one quarter of the
fuel cell cost, that is $1,250/kW, pays for the generating stack. According to
the manufacturer, the generating stack needs replacement every 5 years. The
other three quarters of the fuel cell cost comes from the balance of plant, site
preparation, and interconnection infrastructure.
• Operating Costs (OC): These costs include those needed for day-to-day labor
and maintenance on the system, as well as for new parts and oil (if necessary).
For the diesel genset, O&M cost was calculated as a percentage of the initial
capital cost, K0 . The reference cases used here for the diesel, microturbine,
and fuel cell were 12.5% ,$12,500, and $20,000, respectively. Also, the cost of
adding and replacing oil in the diesel engine was included. The fuel cell stack,
which costs about one quarter of the capital cost, needs replacement after
the first 5 years, then every 7 years thereafter (due to expected technology
improvement).
• Insurance, Taxes, other costs (T): This category takes into account insurance,
taxes, and any other yearly costs that are overlooked in the operating costs.
Similar to operating costs, this is calculated here as a percentage of the original
capital cost. This analysis varied this cost between 1% and 5%.
• Electricity Cost (ec ): This is the amount paid by the farm when it buys
electricity from a utility. It is used to determine how much the farm saves
when it avoids purchasing power from the grid. This price varies widely,
depending on location, between $0.03/kWh and $0.12/kWh.
• Electricity Price (ep ): This is the price the farm expects to receive when it
sells electricity to a utility. It is used to determine the potential profit a farmer
91
could receive for selling excess energy. The going rate for small commercial operations like AA Dairy in Candor, NY is $0.025/kWh, however, the AA Dairy
reported receiving $0.0525/kWh during June 2000, when the NY Independent
Service Operator (NYISO) replace fixed prices with real time electric prices
in the zone where AA Dairy is located. Some municipalities, which manage
their own electricity distribution, have been know to pay higher fixed prices to
generating farms when they sell excess electricity to the municipality. Moreover, future government-induced ”green power” incentives and net-metering
are expected to push prices higher in the state.
• Electricity Inflation (ie ): History has shown electricity prices to be very steady.
Yet there are always some fears of rate increases in the future. Here, yearly
electricity inflation rates from 0-1% were used.
4.2.5
Equations used in NPV and LCC Analysis
With all applicable terms defined, an economic model can be formed to determine
if investment in any of the distributed generation technologies is recoverable, and
if so what the payback period for investing in each distributed generation system
is. These questions can be answered by calculating the net present value (NPV) of
investing in a system.
An investment in an distributed generation technology is said to pay for itself,
if it’s NPV for a given period of n years is greater than zero. If the investment pays
for itself before its useful life span, the investment can be considered worthwhile and
economically feasible. A less than zero life cycle cost, or positive life cycle benefit,
corresponds to a positive NPV during the year an investment reaches its useful life.
92
The NPV of an investment today is worth:
N P V0 = K0
NPV after one year of operation is calculated by:
P
P
(Benef itsf oryear1) − (Costsf oryear1)
N P V1 = N P V0 +
(1 + T V )1
This means, NPV1 is equal to the original investment, NPV0 , plus the sum of one
year’s costs and benefits that is discounted by the time value of money. Expanding
the equation to include electricity generation costs and benefits, one gets:
N P V1 = N P V0 +
[ep (εto grid ) + ec (εonf arm )] (1 + ie )1 − (OC + T )(K0 )(1 + i)1
(1 + T V )1
and more generally, the NPV formula for n year of operation is,
N P Vn = N P Vn−1 +
[ep (εto grid ) + ec (εonf arm )] (1 + ie )n − (OC + T )(K0 )(1 + i)n
(1 + T V )n
NPV is calculated this way in order for net benefits to gradually offset the net
costs and initial capital investment every year. Using this framework, life cycle cost
is calculated as the cradle to grave NPV of a system, where n equals the system
useful life, that is:
LCCinvestment = N P Vn=usef ul lif e
Three systems being assessed have unequal useful life spans. The lifetime of a
diesel engine, running continuously, can vary greatly depending on the source of
fuel, degree of use, and maintenance. An upper bound of 15 years was used for the
diesel engines useful life. For microturbines, the useful life is much lower, between
3 and 5 years; 4 years was used for the reference case. The life span for the fuel
cell is much more elusive, though, because few fuel cells have not been running long
enough to get a reasonable estimate.
93
The MCFC manufacturer claim that it can operate for well over a decade with
proper maintenance and replacement of fuel cell components as needed. Assuming
scheduled stacks replacement at years 5, 12, and 19 (in which the first fuel cell stack
is replaced after 5 years of operation followed by seven year replacements thereafter
due to expected technology improvements), the useful life of a MCFC system was
chosen to be 19 years. This facilitates buying a new fuel cell at year 19, instead of
just replacing the fuel cell stacks that year.
The 15, 4, and 19 year useful life cycles of the diesel genset, microturbine, and
MCFC makes comparing NPVs as the selection criteria for choosing the most economical system, misleading, particularly when scheduled changes such as component replacements, salvage values, and product disposals, are included. It is inevitable that one or more scheduled changes will be ignored when comparing system
NPVs with unequal life cycles, therefore, another criteria called the equivalent uniform annual cost(EUAC) criteria, is more appropriate. One formula for calculating
EUAC [22] is
EU AC = [P resent value of all costs and benef its](A/P, i, n)
+[Replacements − Salvage value](A/F, i, n)
Essentially, EUAC converts all cash flow streams over a common analysis period
into equivalent annual costs as shown in Figure 4.1. This EUAC economic equivalence criteria becomes important when calculating the benefits of each distributed
generator on the profitability of a farm.
94
Diesel Genset:
EAUB
+NPV19
15
30
Microturbine:
EAUC
4
-NPV19
Fuel Cell:
EAUC
-NPV19
= Life Cycle Cost
+NPV19
= Life Cycle Benefit
8
12
16
-NPV19
19
EAUB = Equivalent Uniform Annual Benefit
EUAC = Equivalent Uniform Annual Cost
Figure 4.1: Graphical depiction of NPV, life cycle cost, and EUAC/EUAB used in
the economic analysis of the three energy converter alternatives.
95
4.2.6
Constants and Assumptions
Table 4.3: Capital Costs and useful life of the distributed generators
Type
130 kw Diesel engine
225 kW Diesel Genset
4 x Capstone Microturbine System
8 x Capstone Microturbine System
250 kW MCFC
33 Ton Absorption Chiller
(for MT and MCFC with 1000 cows)
Digester
Solids Separation
consultant,plumbing, electrical
hookup & mechanical system
Capital Costs
$26,000
$31,000
$210,000
$380,000
$1,250,000
$35,000
Useful Life
15
15
4
4
19
$189,000
$53,000
$35,000
Table 4.4: Constants and Assumptions in NPV Analysis [2; 8; 31]
1 CF biogas
Annual downtime
Electricity price($/kWh)
Inflation
MCFC stack replacement
Salvage value
Time Value of money, TV
Insurance
Diesel O&M Costs
Microturbine O&M Costs($/Annum)
Fuel cell O&M Costs($/Annum)
Net Compost Sales($/yard)
Fertilizer Savings($/Annum)
Propane Savings($/Annum)
Cooling Savings($/Annum)
*double with 1000 cows
0.1612
5%, 2%, 0.1%
$0.09, $0.025
3%, 0%
25.00%
20.00%
14%
1.50%
0.015
$12,360
$20,000
$13
$24,000
$5,030.00
$9,784
kWh, or 550 Btu
Diesel, MCFC, MT
buying, selling
regular, electrical
in yrs 5,12 & 19
$/kWh generated
*
$20-$22 possible
*
*
*, $0 for diesel genset
96
4.2.7
Farm Profitability
AA dairy has done a good job implementing processes on the farm for total resource
recovery and energy self sufficiency. One way to see the impact of such implementations, is to compare it with the average NYS dairy farm of the same size, as done
in Table 4.5. One can see in that table that AA Dairy enjoyed savings from buying
less fertilizer and utilities, which is expected. However, the added infrastructure and
complexity of the dairy operation also contributed to higher insurance costs, fuel &
oil costs and paid interest to list a few.
Note from the line item for net farm income in Table 4.5 that AA dairy made
a loss instead of a profit. When you consider that depreciation, which is mainly
used for tax purposes, is included in the calculation of total accrual expenses, the
situation looks different. Since it is not in the best interest of business survival to
operate at a loss, a more telling picture of AA Dairy cash flow situation would be a
net farm income that did not deduct depreciation as an expense. That turned the
income from a loss to a profit, so we can look at the effects CHP on pre tax net
farm income, to compare profitability.
The fact that AA Dairy depreciation is 109% bigger than the average NYS 500
cow dairy indicates that higher than normal capital investment at AA Dairy is involved. Besides traditional capital costs for dairy buildings, machinery, and animals,
AA Dairy capital costs include the diesel genset, the digester, the composting machinery, and all associated CHP and TRR capital costs. That is why it is important
to look at net income before depreciation is deducted, because higher capital expenditure will lead to high depreciation which will subsequently diminish the net
income figure. For the rest of this economic analysis, the value for depreciation is
added back to the net farm income for profitability calculations.
97
Table 4.5: Comparison of year 2000 receipts and expenses from AA Dairy and a
typical NYS 400-599 cow dairy. [2; 16]
Item
Average No. of cows
ACCRUAL EXPENSES
Hired Labour
Dairy Grain, Roughage
& Concentrate
Taxes,lease & rent
Repairs and maintenance
Fuel, oil & grease
Utilities
Interest paid
Depreciation
Fertilizer & lime
Seeds & plants
Spray & other crop expenses
Breeding
Veterinary & Medicine
BST
Milk marketing
Milk Supplies
Custom boarding
Bedding
Other expenses*
Total Accrual Expenses
ACCRUAL RECEIPTS
Milk sales
Dairy cattle
Dairy calves
Other livestock
Crops
Misc. receipts**
Total Accrual Receipts
PROFITABILITY ANALYSIS
Net farm income
Net farm income with
depreciation added
NY 400-599 Dairy
481
AA Dairy
502
Change
4%
$ 260,413.00
$ 247,427.00
-5%
$ 432,650.00
$ 85,445.00
$ 93,519.00
$ 36,131.00
$ 30,711.00
$ 98,656.00
$ 133,652.00
$ 26,781.00
$ 23,070.00
$ 19,115.00
$ 18,066.00
$ 56,523.00
$ 28,070.00
$ 71,042.00
$ 33,726
$ 12,506.00
$ 23,261.00
$ 116,339
$ 1,599,676.00
$ 621,579.98
$ 88,557.00
$ 93,331.00
$ 59,854.00
$ 26,691.00
$ 137,947.00
$ 279,961.00
$ 2,326.00
$ 1,412.00
$ 32,369.00
$ 21,916.00
$ 42,411.00
$ 46,637.25
$ 27,676.67
$ 40,737.63
$$ 24,070.50
$ 169,042.98
$ 1,963,947.01
44%
4%
0%
66%
-13%
40%
109%
-91%
-94%
69%
21%
-25%
66%
-61%
21%
-100%
3%
45%
23%
$ 1,443,991.00
$ 113,375.00
$ 24,442.00
$ 5,910.00
$ 37,561.00
$ 85,371.00
$ 1,710,650.00
$ 1,588,298.00
$ 92,996.76
$ 11,666.45
$$$ 190,597.00
$ 1,883,558.21
10%
-18%
-52%
-100%
-100%
123%
10%
$ 110,974.00
$ (80,388.80)
-172%
$ 244,626.00
$ 199,572.20
-18%
* Other Expenses include: Insurance, employee benefit, freight and trucking, Interest
Expense, office supplies, livestock suppies, Milk marketing, Bedding, BSt, employee
supplies;
** Misc Receipts include: ASCS Payment, Total Cooperative Distributions, Total
Electric Receipts, Interest, Rent reduction, Ordinary Income from Compost.
98
AA Dairy’s expenses and receipts will not simply double due to expansion from
500 to 1000 cows. As indicated in Table 4.6, the dairy expenses and receipts may
actually increase, decrease, or remain roughly the same, depending on circumstances
such as changed milk prices, fuel costs, and greater overhead costs. That said, it was
necessary to investigate how the additional CHP and TRR cash flow could improve
traditional dairy farm profitability.
The impact of CHP and TRR on dairy profitability is calculated below using cash
flows from typical NYS 500 and 1000 cow dairy farms, instead of AA Dairy’s cash
flows. This should be informative to farmers and other investors who are considering
using anaerobic digesters with technologies like fuel cells and microturbines.
Year 2000 business summary information [16] showed something very unusual,
that is, a reduction in net income as farm sized increased from 500 cows to 1000 cows.
Data from previous years indicated that year 2000 milk prices were bad compared
with previous milk prices in 1996 to 1999 which is shown in Figure 4.2.
Price Recieved for Milk
$/cwt
(Same 69 New York Dairy Farms, 1991-2000)
16
15.5
15
14.5
14
13.5
13
1990 1992 1994 1996 1998 2000 2002
Year
Milk
Price
Figure 4.2: Price Received for Milk, (Data from same 69 farms over period: 19912000) [16]
99
Table 4.6: Comparison of year 2000 NYS 500 and 1000 cow dairy cash flows [16]
Item
ACCRUAL EXPENSES
Hired Labour
Dairy Grain, Roughage
& Concentrate
Taxes,lease & rent
Repairs and maintenance
Fuel, oil & grease
Utilities
Interest paid
Depreciation
Fertilizer & lime
Seeds & plants
Spray & other crop expenses
Breeding
Veterinary & Medicine
BST
Milk marketing
Milk Supplies
Custom boarding
Bedding
Other expenses
Total Accrual Expenses
ACCRUAL RECEIPTS
Milk sales
Dairy cattle
Dairy calves
Other livestock
Crops
Misc. receipts
Total Accrual Receipts
PROFITABILITY ANALYSIS
Net farm income
Net farm income with
depreciation added
Avg. 500
Cow Dairy
Avg. 1000
Cow Dairy
Change
$ 260,413.00
$ 608,001.00
133%
$ 432,650.00
$ 85,445.00
$ 93,519.00
$ 36,131.00
$ 30,711.00
$ 98,656.00
$ 133,652.00
$ 26,781.00
$ 23,070.00
$ 19,115.00
$ 18,066.00
$ 56,523.00
$ 28,070.00
$ 71,042.00
$ 33,726
$ 12,506.00
$ 23,261.00
$ 116,339
$ 1,599,676.00
$ 864,670.00
$ 204,104.00
$ 176,756.00
$ 62,048.00
$ 59,814.00
$ 214,097.00
$ 245,243.00
$ 57,918.00
$ 40,968.00
$ 51,540.00
$ 36,082.00
$ 122,748.00
$ 69,414.00
$ 134,463.00
$ 73,526.00
$ 58,029.00
$ 55,332.00
$ 236,928.00
$ 3,371,681.00
100%
139%
89%
72%
95%
117%
83%
116%
78%
170%
100%
117%
147%
89%
118%
364%
138%
104%
111%
$ 1,443,991.00
$ 113,375.00
$ 24,442.00
$ 5,910.00
$ 37,561.00
$ 85,371.00
$ 1,710,650.00
$ 2,950,785.00
$ 251,760.00
$ 33,579.00
$ 3,320.00
$ 44,841.00
$ 144,658.00
$ 3,428,943.00
104%
122%
37%
-44%
19%
69%
100%
$ 110,974.00
$ 57,262.00
-48%
$ 244,626.00
$ 302,505.00
24%
100
During the ten year period 1991-2000, 1998 had the best milk prices ( actually
the all time best prices) and 1999 was the best year net farm income. Year 2000 milk
prices dropped to pre-1995 levels, however, with average herd size increasing by...
since 1991, and associated capital investment to operate the large dairies increasing
too, plus the high fuel prices, year 2000 was a bad year for big dairy farms. While
economies of scale was evident for small to medium sized farms (<600 cows) big
farms did not benefit from it in 2000.
Consequently, the decision was made to average the net farm income from 2000,
which was a bad year, with 1999 which was a great year. This two year average was
agreed to produce the best estimate for projecting the benefits of CHP to improve
farm profitability. Tables 4.7 and 4.8 shows the expenses and receipts for dairy
farms with 500 cows and 1000 cows respectively, for 1999 and 2000.
101
Table 4.7: Comparison of NYS dairies with 400-599 cows, in 1999 and 2000 [16]
Year
Number of farms in study
Average No. of cows
ACCRUAL EXPENSES
Hired Labour
Dairy Grain, Roughage
& Concentrate
Taxes,lease & rent
Repairs and maintenance
Fuel, oil & grease
Utilities
Interest paid
Depreciation
Fertilizer & lime
Seeds & plants
Spray & other crop expense
Breeding
Veterinary & Medicine
BST
Milk marketing
Milk Supplies
Custom boarding
Bedding
Replacement Livestock
Expansion Livestock
Other livestock expenses
Misc. expenses
(including insurance)
Total Accrual Expenses
ACCRUAL RECEIPTS
Milk sales
Dairy cattle
Dairy calves
Other livestock
Crops
Misc. receipts
Total Accrual Receipts
PROFITABILITY ANALYSIS
Net farm income
Net farm income
before depreciation
1999
27
491
2000
26
481
Average
26.5
486
$ 261,155
$ 260,413
$ 260,784
$ 442,181
$ 84,999
$ 105,892
$ 24,519
$ 28,586
$ 103,843
$ 131,550
$ 36,678
$ 21,879
$ 29,032
$ 18,921
$ 54,592
$ 30,860
$ 51,977
$ 30,305
$ 15,488
$ 23,977
$ 36,826
$ 46,806
$ 18,392
$ 432,650
$ 85,445
$ 93,519
$ 36,131
$ 30,711
$ 98,656
$ 133,652
$ 26,781
$ 23,070
$ 19,115
$ 18,066
$ 56,523
$ 28,070
$ 71,042
$ 33,726
$ 12,506
$ 23,261
$ 23,164
$ 48,396
$ 13,365
$ 437,416
$ 85,222
$ 99,706
$ 30,325
$ 29,649
$ 101,250
$ 132,601
$ 31,730
$ 22,475
$ 24,074
$ 18,494
$ 55,558
$ 29,465
$ 61,510
$ 32,016
$ 13,997
$ 23,619
$ 29,995
$ 47,601
$ 15,879
$ 36,754
$1,635,212
$ 31,414
$1,599,676
$ 34,084
$ 1,617,444
$1,630,212
$ 111,694
$ 14,440
$ 1,823
$ 49,437
$ 57,373
$1,864,979
$1,443,991
$ 113,375
$ 24,442
$ 5,910
$ 37,561
$ 85,371
$1,710,650
$ 1,537,102
$ 112,535
$ 19,441
$ 3,867
$ 43,499
$ 71,372
$ 1,787,815
$ 229,767
$ 110,974
$ 170,371
$ 361,317
$ 244,626
$ 302,972
102
Table 4.8: Comparison of NYS dairies with >600 cows, in 1999 and 2000 [16]
Year
Number of farms in study
Average No. of cows
ACCRUAL EXPENSES
Hired Labour
Dairy Grain, Roughage
& Concentrate
Taxes,lease & rent
Repairs and maintenance
Fuel, oil & grease
Utilities
Interest paid
Depreciation
Fertilizer & lime
Seeds & plants
Spray & other crop expense
Breeding
Veterinary & Medicine
BST
Milk marketing
Milk Supplies
Custom boarding
Bedding
Replacement Livestock
Expansion Livestock
Other livestock expenses
Misc. expenses
(including insurance)
Total Accrual Expenses
ACCRUAL RECEIPTS
Milk sales
Dairy cattle
Dairy calves
Other livestock
Crops
Misc. receipts
Total Accrual Receipts
PROFITABILITY ANALYSIS
Net farm income
Net farm income
before depreciation
1999
21
986
2000
29
957
Average
25
972
$ 614,648
$ 608,001
$ 611,325
$ 927,539
$ 224,902
$ 201,685
$ 45,218
$ 57,747
$ 116,103
$ 237,153
$ 69,119
$ 39,012
$ 47,522
$ 35,521
$ 120,027
$ 72,113
$ 83,120
$ 72,801
$ 45,594
$ 59,854
$ 31,105
$ 89,746
$ 16,965
$ 864,670
$ 204,104
$ 176,756
$ 62,048
$ 59,814
$ 214,097
$ 245,243
$ 57,918
$ 40,968
$ 51,540
$ 36,082
$ 122,748
$ 69,414
$ 134,463
$ 73,526
$ 58,029
$ 55,332
$ 44,752
$ 105,617
$ 18,279
$ 896,105
$ 214,503
$ 189,221
$ 53,633
$ 58,781
$ 165,100
$ 241,198
$ 63,519
$ 39,990
$ 49,531
$ 35,802
$ 121,388
$ 70,764
$ 108,792
$ 73,164
$ 51,812
$ 57,593
$ 37,929
$ 97,682
$ 17,622
$ 59,237
$ 3,266,731
$ 68,280
$ 3,371,681
$ 63,759
$ 3,319,206
$ 3,460,884
$ 196,131
$ 24,031
$ 3,716
$ 104,113
$ 117,529
$ 3,906,404
$ 2,950,785
$ 251,760
$ 33,579
$ 3,320
$ 44,841
$ 144,658
$ 3,428,943
$ 3,205,835
$ 223,946
$ 28,805
$ 3,518
$ 74,477
$ 131,094
$ 3,667,674
$ 639,673
$ 57,262
$ 348,468
$ 876,826
$ 302,505
$ 589,666
103
4.3
Results and Discussion
4.3.1
NPV Sensitivity Analysis
Tables 4.9 and 4.10 contain the results of NPV sensitivity analysis for the three
distributed generation alternatives. Table 4.9 shows the sensitivity analysis for an
operation with 500 cows, and Table 4.10 with 1000 cows. The values in the tables
are discounted payback periods resulting from varying specific economic parameters.
Remember, the analysis includes the costs and benefits of the digester, CHP and
TRR systems. A reference case is provided for each alternative to indicate the what
current 2002 economic conditions would yield in the analysis.
Table 4.9: Discounted payback periods for varying scenarios with 500 cows
Varying Parameters
Reference Case
Capital costs:
Energy converter . . .
Digester . . .
Tot. Res. Recovery
and Gas Cleanup . . .
Purchased electricity
cost cheaper than $0.9/kWh. . .
Purchased electricity cost
higher than $0.9/kWh. . .
Selling electricity price
higher than $.025/kWh. . .
Propane savings
varying widely. . .
O & M costs vary widely. . .
Insurance vary widely. . .
General inflation
3.2% +/- 1% . . .
Electric inflation
higher than 0% . . .
Time value of money:
11% - 16% . . .
Compost Sales
varies widely . . .
Fertilizer Savings
less than $23,611.00 . . .
Actual
Diesel
Genset
13
Projected
Diesel
Genset
10
4x30kW
Microturbines
over 30
Fuel Cell
over 30
12 - 14
9 - 22
10 - 11
8 - 15
over 30
over 30
over 30
over 30
9 - over 30
8 - 22
over 30
over 30
25 - over 30
16 - over 30
over 30
over 30
9
8
over 30
over 30
7 - 10
6-9
over 30
over 30
10 - 12
12 - 14
12 - 14
9 - 10
10 -11
10 - 11
over 30
over 30
over 30
over 30
over 30
over 30
12 - 14
10 - 11
over 30
over 30
12
10
over 30
over 30
11 - 18
9 - 12
over 30
over 30
11 - 14
9 - 11
over 30
over 30
21 - over 30
14 - 23
over 30
over 30
104
Table 4.10: Results of varying scenarios, showing discounted payback periods for a
1000 cow operation
Varying Parameters
Reference Case
Capital costs:
Energy converter . . .
Digester . . .
Tot. Res. Recovery
and Gas Cleanup . . .
Purchased electricity
cost cheaper than $0.9/kWh. . .
Purchased electricity cost
higher than $0.9/kWh. . .
Selling electricity price
higher than $.025/kWh. . .
Propane savings
varying widely. . .
Refrigeration saving
varying widely. . .
O & M costs vary widely. . .
Insurance vary widely. . .
General inflation
3.2% +/- 1% . . .
Electric inflation
higher than 0% . . .
Time value of money:
11% - 16% . . .
Compost Sales
varies widely . . .
Fertilizer Savings
less than $23,611.00 . . .
Reduced Fuel Cell capital plus
capital reduced,
and net metering . . .
Projected
Diesel
Genset
3
8x30kW
Microturbines
over 30
Fuel Cell
over 30
3-4
3-4
9 -over 30
over 30
7 - 25
over 30
3-4
over 30
over 30
4-5
over 30
over 30
3
over 30
over 30
2-3
6 - over 30
9 - over 30
3
over 30
over 30
n/a
3
3
over 30
over 30
over 30
over 30
over 30
over 30
3
over 30
over 30
3
over 30
over 30
3-4
over 30
over 30
3-4
over 30
over 30
4
over 30
over 30
n/a
n/a
3-5
105
The NPV sensitivity analysis in Table 4.9 suggest that the MCFC and the microturbines are undesirable options for a 500 cow dairy. None of the scenarios for
the 4x30kW microturbines showed the possibility of getting a payback during the
30 year assessment period. The 250-kW MCFC, after two stack replacements at 5
and 12 years and a new system purchase in year 19, also did not return a positive
yield on its investment after 30 years.
With a useful life of 15 years, the diesel genset under nearly every considered
scenario, is a positive investment. As shown in Table 4.9, the diesel genset at AA
Dairy pays for itself after 13 years unless the cost of buying electricity from the
grid is lower than $0.09/kWh. The situation where propane costs of $5,030/year
are replaced with CHP heat from the genset, is referred to as the ”Projected Diesel
Genset” in the second column of that table. Propane savings dropped the discounted
payback period from 13 years to 10 years, assuming all other conditions were the
same as the current AA Dairy genset. The NPV for either the actual or projected
genset was most sensitive to the buying and selling price of electricity but it was
also sensitive to fertilizer savings, digester capital cost, and the overall cost for TRR
and gas cleanup.
When the milking herd size is doubled to 1000, many of the results change.
However, similar investment returns can be expected for the diesel alternative. A
225 kW-diesel genset, as shown in Table 4.10, would likely return its investment
early within its expected lifetime, on the order of 3 years. NPV for the diesel genset
operating on a 1,000 cow dairy is still most sensitive to the farm’s buying and selling
price of electricity. The results suggest it is a good idea to install diesel distributed
generators in areas faced with high prices to purchase electricity while receiving
high prices for selling ”green power” to the grid. It also suggests net metering at
106
AA Dairy, that is $0.09 for both buying and selling electricity, would be good for
distributed generation. Economic payback periods for diesel gensets would be much
longer in municipalities with low electric utility prices, therefore, one should not
expect genset power to be attractive to farms located in such municipalities.
The prospects of the 8x30kW microturbines on a 1,000 cow dairy improves a
bit over the 4x30kw microturbines. The additional generating capacity showed fast
pay back time when the capital cost of the microturbines was a little less than half
the $380,000 reference capital cost for the 8 units. The next best pay back period
was when the price of selling the excess electricity was higher than $0.025/kWh. In
all the other scenarios it would take longer than 30 years for the microturbine to
payback.
The fuel cell becomes much more attractive with the larger herd size. As mentioned earlier, the amount of biogas energy from a 1000 cows is much more compatible with the fuel cell generating capacity of 250 kW. With the AA Dairy reference
case, the payback is still over 30 years. However, with a more progressive distributed
electricity buyback rate, economic net metering–ie buying and selling electricity at
the same price of $0.09/kWh, the fuel cell system returns its investment in 16 years,
while netting $129,488 during its lifespan. Likewise, the investment prospects get
brighter with reduced capital costs of the system. At the capital cost of $625,000,
the system will net a positive return in year 25 and almost $10,618 for NPV over
its lifespan. At a more optimistic 1/4 of the reference capital cost or $312,250, the
system could possibly paying back in year 7 with a NPV of $394,854 over its lifespan
of 19 years. However, with lower electricity buying and selling prices, the fuel cell
is not attractive.
For a real test of the potential of fuel cell technology, the capital cost for fuel
107
cells was reduced and net metering at $0.09 was added. Notice that CHP use for all
heating needs and absorption chilling of milk, was already factored into the economic
model. As a result, it may be possible to see pay back periods as little as 3 to 5
years when the 250kW MCFC potential is fully utilized! The corresponding NPVs
for those 3 and 5 year payback periods are $909,215 and $1,375,205, respectively.
Figure 4.3 gives a graphical illustration of these results.
Depending on relevant state regulations, the system may offer benefits for emissions trading, under the EPA’s Acid Rain Program. It is uncertain whether emissions of nitrogen oxides (NOx ) and sulfur dioxide (SO2 ) from the diesel and microturbine systems would be low enough to sell credits to larger power generators;
with the fuel cell; however, these emissions would presumably be substantially lower
than any power plant. Greenhouse gases, except CO2 , would see a reduction both
as compared with other manure management methods as well as in comparison with
other power plants.
Aside from the more easily calculable factors addressed above, there are other
costs and benefits to energy generation that are worth discussing. Sizable tax incentives are possible in the near future. An energy bill on the Senate floor proposes tax
credits for anaerobic-digester-generated energy, efficient co-generation of heat and
electricity, and fuel cells [35]. Another bill, in the House, would address net-metering
for energy-efficient distributed power sources [34]. From previous calculations, it is
clear that offering higher rates for selling excess electricity through net-metering
would make fuel cells and indeed any of the energy conversion alternatives explored
here more attractive.
-$2,000,000
-$1,500,000
-$1,000,000
-$500,000
$0
$500,000
$1,000,000
$1,500,000
0
5
10
15
Time(years)
20
25
30
35
1/4 capital cost
1/2 capital cost
full cost
reference case
Figure 4.3: Sensitivity of MCFC NPV to reduced capital and net metering while operating on a 1000 cow dairy.
NPV($)
$2,000,000
Effect of reduced capital and net metering on Fuel Cell NPV
108
109
4.3.2
Best options for increasing farm profitability
The following tables summarize the top five or six options for improving profitability
with each distributed generator operations using annual costs and benefits. By
adding EUUCs or EUABs annual costs to typical farm net income, the effect of
CHP and TRR on farm profitability can be estimated. For the 500 cow scenarios,
neither the microturbine nor the fuel cell was able to turn a profit, therefore, no
tables were made for them. Only results for operating diesel gensets at AA Dairy
and a average NYS 500 cow operation are shown in Tables 4.11 and 4.12. For the
1000 cow sensitivity analysis, tables ( 4.13, 4.14 and 4.15) show the results for the
225 kW diesel genset, 8 x 30 kW microturbines, and 250 kW fuel cell.
Each table consist of a reference column and other columns corresponding to
a particular change. The reference column is filled with variables representing the
current best estimate for operating with the given distributed generator. In the
other columns, only the variables that cause an increase in profitability are shown,
assuming that the variables that are not shown are identical to the reference case.
To summarize Tables 4.11 through 4.15, they basically repeat the observations
perviously discussed in the Sensitivity Analysis results, namely net present value
improves by reducing costs and increasing benefits. In all the tables, two things
that always contributed additional profit to net farm income were: net metering
at $0.09/kWh, and the farm selling at a ”green power price” of $0.12. The added
piece of information in this results subsection is the effect of each system on farm
profitability.
Profitability in all the tables was measured by using EUAB/(EUAB + net farm
income), where depreciation was added back to the net farm income. It must be
mentioned that EUABs are used in this analysis for comparing alternative invest-
AA Dairy Genset
Digester
Tot. Res. Recovery
and Gas Cleanup
Maintenance Cost
Insurance
Electric Selling Price
Electric Buying Price
Heating Benefits
Compost Sales
Useful life(years)
Gen. Inflation Rate
Electric Inflation Rate
Fertilizer Savings
Environ. Credits
Total Capital
TV or Nominal
Discount Rate
NPV/Life Cycle
Cost, 19 yrs
EUAB
Discounted Payback
Net farm income
before depreciation
Contribution of profit
$253,000
$88,000
$8,738
$0.02
$0.025
$0.09
$0
$3,573
15
3%
0%
$23,611
$0
$303,000
$101,625
$12,149
9
3.86%
$45,351
$5,422
13
$302,972
1.76%
14%
$139,000
$26,000
$189,000
3.61%
$94,872
$11,342
9
$259,000
$44,000
3.59%
$94,379
$11,283
10
$0.05
5.98%
$161,235
$19,275
8
$0.09
7.81%
$214,720
$25,669
7
$0.12
Table 4.11: 500 cow dairy LCCA: Actual diesel ic genset Scenarios
4.14%
$109,511
$13,092
9
$0.12
110
$253,000
$88,000.00
$8,738.10
$0.02
$0.03
$0.09
$5,029.00
$0.00
$3,573
15
3%
0%
$23,611
0
$303,000
$143,692
$17,178
8
5.37%
$87,418
$10,450.59
10
$302,972
3.33%
14%
$139,000.00
$26,000.00
$189,000.00
5.13%
$136,939
$16,371
8
$259,000
$44,000.00
5.11%
$136,446
$16,312
9
$0.05
7.43%
$203,302
$24,304
7
$0.09
9.20%
$256,787
$30,698
6
$0.12
Table 4.12: 500 cow dairy LCCA: Projected 130 kW Diesel IC Genset Scenarios
Projected
130kW Genset
Digester
Tot. Res. Recovery
and Gas Cleanup
Maintenance Cost
Insurance
Electric Selling Price
Electric Buying Price
Heating Benefits
Cooling Benefits
Compost Sales
Useful life(years)
Gen. Inflation Rate
Electric Inflation Rate
Fertilizer Savings
Environ. Credits
Total Capital
TV or Nominal
Discount Rate
NPV/Life Cycle
Cost, 19 yrs
EUAB
Discounted Payback
Net farm income
before depreciation
Contribution of profit
5.64%
$151,578
$18,121
8
$0.12
111
$784,866
$93,828
3
13.73%
$589,665.50
12.10%
$0.05
$678,867
$81,156
3
14%
$88,000
$17,476
1.5%
$0.025
$0.09
$10,058
$0
$29,070
15
3.2%
0.0%
$47,222
$0
$308,000
$31,000
$189,000
15.86%
$929,410
$111,108
3
$0.09
17.48%
$1,045,045
$124,932
2
$0.12
13.93%
$798,521
$95,461
3
$0.12
Table 4.13: 1000 cow dairy LCCA: 225 kW Diesel Genset Scenarios
225kW Diesel
IC Genset
Digester
Tot. Res.Recovery
and Gas Cleanup
Maintenance Cost
Insurance
Electric Selling Price
Electric Buying Price
Heating Benefits
Cooling Benefits
Compost Sales
Useful life(years)
Gen. Inflation Rate
Electric Inflation Rate
Fertilizer Savings
Environ. Credits
Total Capital
TV or Nominal
Discount Rate
NPV/Life Cycle
Cost, 19 yrs
EUAB
Discounted Payback
Net farm income
before depreciation
Contribution of profit
15.75%
$922,035
$110,226
3
$58,140
112
8x30kW
Microturbines
Digester
Tot. Res. Recovery
and Gas Cleanup
Maintenance Cost
Insurance
Electric Selling Price
Electric Buying Price
Heating Benefits
Cooling Benefits
Compost Sales
Useful life(years)
Gen. Inflation Rate
Electric Inflation Rate
Fertilizer Savings
Environ. Credits
Total Capital
TV or Nominal
Discount Rate
NPV/Life Cycle
Cost, 19 yrs
Effective Annual Benefit
Discounted Payback
Net farm income
before depreciation
Contribution of profit
-$49,690
$5,940
n
-1.02%
$589,666
-6.41%
$602,000
$280,000
-$297,100
$35,517
23
14%
$98,000
$34,217
1.5%
$0.025
$0.09
$10,058
$9,784
$29,070
4
3.2%
0.0%
$47,222
$0
$702,000
$380,000
$189,000
3.85%
$197,720
-$23,637
9
$502,000
$180,000
-1.72%
-$83,233
$9,950
n
$0.0525
4.05%
$208,405
-$24,914
10
$0.09
8.22%
$441,715
-$52,806
6
$0.12
Table 4.14: 1000 cow dairy LCCA: 8x30 kW Microturbine Scenarios
-1.11%
-$53,932
$6,447
n
$58,140
113
-$71,136
-$8,504
25
-1.46%
$589,666
-25.53%
$947,000
$625,000
-$1,003,116
-$119,919
n
14%
$98,000
$29,497
1.5%
$0.025
$0.09
$10,058
$9,784
$29,070
19
3.2%
0
$47,222
0
$1,572,000
$1,250,000
$189,000
$35,000
7.41%
$394,854
$47,204
7
$634,500
$312,500
-0.69%
-$34,020
-$4,067
16
$0.09
7.73%
$413,255
$49,403
9
$0.12
15.56%
$909,215
$108,694
5
$21,494
$937,000
$625,000
Table 4.15: 1000 cow dairy LCCA: 250 kW MCFC Scenarios
250 kW MCFC
Digester
Absorption chiller
Tot. Res.Recovery
and Gas Cleanup
Maintenance Cost
Insurance
Electric Selling Price
Electric Buying Price
Heating Benefits
Cooling Benefits
Compost Sales
Useful life(years)
Gen. Inflation Rate
Electric Inflation Rate
Fertilizer Savings
Environ. Credits
Total Capital
TV or Nominal
Discount Rate
NPV/Life Cycle
Cost, 19 yrs
EUAB
Discounted Payback
Net farm income
before depreciation
Contribution of profit
21.80%
$1,375,204
$164,401
3
$21,494
$624,500
$312,500
114
115
ments and must not be interpreted as actual receipts that a farm would get each
year. EUABs should therefore be interpreted as a sort of average of a business’s
cash flows over a given analysis period. EUABs are negative EUACs.
Since the big contributing variables for improving profit were implied in the
Sensitivity Analysis, the focus for the rest of this analysis will solely be on the
MCFC. The reference case for the MCFC on a 1000 cow dairy does not pay back
during a 30 year period, unless the capital cost was reduced or net metering was
received. With just net metering at $0.09/kWh, the payback period was 16 years,
however, the cost of replacing the whole MCFC system after 19 year caused the
NPV to dip back into negative territory (please refer to table 4.15 and figure ??
for explanation). Consequently, the life cycle cost of this net metering scenario
was -$34,020, the equivalent annualized uniform cost was -$4,067 and the effect on
profitability was -0.69% over the MCFC useful life. Although the MCFC pay back
period was 16 years, the life cycle cost, or NPV at year 19, still turned out to be
negative.
The two single changes that made the fuel cell option profitable in the analysis
were a reduction of the capital cost of the fuel cell to $312,500 (that is 1/4 of
the reference), and selling electricity at the green power price of $0.12/kWh. Green
power pricing, is already offered in some states like California and Pennsylvania [7; 9]
to encourage the use and mass production of distributed generation technologies.
However it is unlikely NYS farmers will get them in the near future.
What would make the MCFC a reality on the 1000 cow dairy is not one single
change but a combination of changes. One obvious combination is reducing capital
cost and receiving net metering for buying and selling electricity. By reducing the
capital by half and getting net metering at $0.09/kWh, the result was a payback
116
period of 5 years, life cycle cost of $909,215 and EUAB of $108,694/year. That
translates into possibly increasing net farm income by 15.56%. Similarly, and more
optimistically, reducing the entire capital cost of the MCFC to a quarter of the
quoted $1,250,000 (that is, $312,500), resulted in a 3 year payback period, life cycle
cost of $1,375,204, EUAB of $164,401/year and the potential to improve net farm
income by 21.80%!
4.4
Conclusions
Generally speaking, of all the costs and benefits addressed, the feasibility of any energy conversion system on comparable farms is most sensitive to three parameters:
(1) the cost of purchased electricity, (2) the electricity price received for supplying
power to the grid, and (3) the capital cost of the new energy conversion system.
Because all energy conversion systems are inclined toward financial failure when
purchased electricity rates are low, higher electricity rates are vital for a rural, distributed power project, unless other incentives, such as environmental benefits, odor
control, or water pollution, are prevalent. The price received for selling excess generation becomes more pivotal for larger systems, like the 250-kW fuel cell system. Due
to the relative immaturity of fuel cell technology, MCFC capital cost is an important
variable to the feasibility of this option on dairy farm and other applications.
The diesel genset is a particularly attractive investment, regardless of the variables. For the 250-kW fuel cell system, if the capital costs were reduced to about
$625,000 or $312,400, and if net metering comes to farms that sell excess power,
it becomes an attractive option on farms that produce biogas from 1000 cows or
greater. Indeed, technological advances and government subsidies would greatly
improve the Fuel Cell scenario on the dairy farm.
117
From a rural eco-industrial park perspective, electricity rates could become less
of a barrier to making fuel cells competitive with less expensive technologies like
diesel gensets. Energy utility companies are not likely to increase their rates and
government mandates to encourage ”green” energy project may not come to fruition;
however, the large amount of excess energy may open the door to business partnership opportunities near the farm. Using the energy (electricity and the abundant
co-generated heat) could be a much more attractive way of getting a better rate
for the energy output from the microturbine or fuel cells. Fisheries, greenhouses,
and algal farms each deserve further study. Each one may be able to use the heat
and electricity, as well as other agricultural byproducts, such as the nitrogen liquid
effluent or the carbon dioxide exhaust. If such entrepreneurial ventures or partnerships were to pay as little as $0.05/kWh, the fuel cell would become a feasible and
profitable idea for the farm.
Chapter 5
CONCLUSIONS
The purpose of this thesis was to investigate the feasibility of fuel cells for energy
conversion on the dairy farm. It was divided into a theoretical part and two analytical parts. The part on modelling biogas production in a plug flow digester was
theoretical in nature but was the first step in modelling the various components of
an integrated farm energy system. The technical and economic feasibility parts, set
about analyzing the features of the fuel cell and dairy farm that would make them
compatible with each other.
Modelling biogas production is relevant because it can be used in studies about
the production and reliability of this farm-derived fuel supply. Soon other models for
gas cleanup, whole farm nutrient cycling, and optimum placement of the digester,
fuel cells, compost heaps, greenhouses, fish farms, on-farm heat and electricity distribution grids, will be studied together to model an entire farm, so it can be operated
with all the recycling, controls, and efficiency of a fine tuned manufacturing plant.
A theoretical model was developed to predict biogas production in a horizontal,
mesophilic, anaerobic plug flow digester. The model was based on the solution to
a simple substrate degradation equation, and it described from first principles how
118
119
biologically volatile solids in the dairy manure is converted into biogas. After many
steps, a moving coordinate model for PDF biogas production was derived, followed
by a spreadsheet that incorporated all relevant digester parameters, and then finally
a computer simulation for PFD biogas production was written in Matlab/Simulink.
The model accurately predicted the 1,274 m3 /day (∼ 45,000 ft3 /day) produced at
AA Dairy, and it did a better job of predicting real time biogas production than the
popular model. To further verify the usefulness of the model, additional data from
similar PFD digesters will be compared in the near future.
Before fuel cells find a home on dairy farms, technical issues mainly involving
compatibility have to be understood. A MCFC was selected because it can use the
55% methane and 45% CO2 main constituents of biogas as fuel. The only problem
is the H2 S and moisture in the gas, requiring gas cleanup. 2,549 m3 /day (90,000
ft3 /day) from 1000 cows will fully meet the generating capacity of the fuel cell, and
the electricity generation should exceed the electric energy needs of the farm. Three
years of data on the electrical and thermal load patterns at AA Dairy show that a
250 kW MCFC, running on digester biogas, has more than enough capacity to make
the farm completely grid independent, but in the interest of receiving additional
income, the farm will most likely continue to sell excess power to the grid.
There is enough thermal energy in the high temperature MCFC exhaust to maintain the diester temperature at 100◦ F, provide milk cooling by absorption chilling,
replace propane heating throughout the dairy operation, and still have plenty CHP
left for additional applications. Water needs to be softened to prevent fouling of the
fuel cell heat recovery system. More studies needs to be done to fully utilize the
thermal and electrical energy from the MCFC to make value added-products derived from the solid, liquid nutrient streams from the digester. Also, the benefits of
120
reduced greenhouse gas emissions, lower noise, higher efficiency, and high reliability,
will reduce the environmental impact of the farm, and probably become a source of
income through the trading of environmental credits. Evidence was given for the
dairy industry being a niche market for fuel cell distributed generation.
While 1000 cow dairy farms equipped with fuel cell and digesters have the potential to boost net farm income with electric sales, energy self-sufficiency, and trading
various bio-derived commodities, the primary obstacle facing the adoption of fuel
cells on dairy farms, is the high capital cost of the currently hand built systems. A
2001 survey by the California Stationary Fuel Cell Collaborative to leading manufacturers of stationary fuel cell showed that the installed cost ($/kW) of fuel cell
systems is expected to fall from a current average of $4,500 in 2002 to $2,000 by
2005 to $1,000 by 2010 [5]. The price drop from 2002 to 2010 is associated with
the expected increase in sales and subsequent adoption of mass production methods
over hand built systems. Better deals are offered for placing big orders for fuel cell
systems, as shown in the Figure 5.1.
Last year, the manufacturer of the 250kW MCFC, FuelCell Energy, quoted a
price of $1,250,000 in 2001 for purchasing an entire system. This works out to be
$5,000/kW installed cost in 2001 which generally agrees with the average installed
cost, $4,500/kW for 2002. The two best scenarios for improving farm profitability with the MCFC involve net metering and further reduction in capital costs y
approximately a factor of 2 or 4. That translates into $2,500/kW and $1,250/kW
respectively, which from the chart is possible by June 2004 and close to 2010 respectively. Assuming similar deals to the ones quoted by manufacturers to the California
Stationary Fuel Cell Consortium for placing large orders on MCFC systems today,
it may be possible to see capital prices fall to a half or to a quarter by June 2003
121
Present Capital Cost
June-02
Current
Projections
June-03
Substanitial
California
Power Plant
Orders
1/2 Capital
June-04
June-05
Dec-02
Oct-04
2007
Aug-06
2008
1/4 Capital
2004
$5,000
$4,500
$4,000
$3,500
$3,000
$2,500
$2,000
$1,500
$1,000
$500
$0
Mar-01
2003
Installed capital Cost
Timeline for Reducing Fuel Cell Installed
Cost ($/kW)
Jun-08
June-10
Mar-10
Jan-12
Date
Figure 5.1: Timeline showing the averaged cost estimates ($/KW) of fuel cell systems for current projections and substantial order case. Adapted from [5]
and June 2007 respectively according to the chart.
Two scenarios combining the the most sensitive variables from the NPV calculation: reduced MCFC capital cost, high electricity buying price, and high electricity
selling price, were analyzed to make the best realistic projections for economic feasibility of fuel cells on the dairy farm. They were identical in every way except for
MCFC capital costs. One was half the original $1,250,000, that is $625,000 and the
other was a quarter, or $312,500. High buying and selling price was incorporated
in a net metering price of $0.09/kWh for both scenarios. The results: a 5 and 3
year payback periods, life cycle cost of $909,215 and $1,375,204, and the potential
to improve profitability by 15.56% and 21% respectively. The net farm income used
in this analysis was the average of 1999 (great year) and 2000(bad year) data. Short
of reducing capital cost, net metering alone resulted in a pay back period of about
16 years for the MCFC. All farm electricity, heating, and cooling loads was factor
122
into the economic analysis as being met by fuel cell CHP.
Projections of fuel cell manufacturer timelines suggest that the scenario involving the reduction of MCFC capital cost by half to $625,000 with net metering at
$0.09/kW could probably become reality by summer 2004. However, if NY investors
were to place a big order for MCFC systems, and if the digester gas-fired distributed
generators get net metering status, it could actually become a reality by summer
2003.
Many farmers hope that a bill proposed to the NYS senate in 2001 will allow
net metering provisions [35], like the one currently enjoyed by 10kW photovolataic
systems, will be passed for digester gas-fired distributed generators. The current
governor of NY State, George E. Pataki, endorsed this view when he proposed
legislation [3] in April 2002 to assist farmers with net metering.
If net metering on dairy farms becomes a reality within the next two year, and
if MCFC capital costs are reduced to half by 2003, it would be a good time for
farmers invest in MCFC. In other words, such conditions would make it feasible to
operate a MCFC on the dairy farm since the potential for a 5 year payback period
could then be realized. Note, to get this attractive 5 year payback period, requires
that at least 1,000 cows worth of manure be digested to produce biogas, and all of
propane heating and electrical loads on the farm must be met with the Fuel cell
CHP. There should be more than enough energy generation to sell the excess to the
grid for profit.
On-farm energy self-sufficiency, the sale of energy to utilities, and the production
of tradable bio-derived commodities is the basis for demonstrating potential technical and economical feasibility of utilizing fuel cells on dairy farms. Whether utilities
like it or not, fuel cell will become commonplace in the future. Continued support
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for net metering, green pricing, micro grids, and renewable environmentally friendly
energy generation, will make it possible for fuel cells to come to market sooner.
When dairy farms start to use fuel cell CHP for energy self-sufficiency and total
resource recovery it may then be possible to see dairy eco-industrial park eclipse
Kalundborg with an efficiency of over 90%.
Appendix A
Summary data for AA Dairy
digester and diesel genset
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125
Table A.1: AA Dairy Summary data [28]
Component
Cows (nominal)
Manure production (gallons/cow/day)
Digester:
volume (cubic feet)
volume (gallons)
retention time (days)
temperature (inlet) (◦ C/◦ F)
temperature (outlet) (◦ C/◦ F)
Lagoon size (volume in million gallons)
Average biogas production
per cow (cubic feet per day)
total (cubic feet per day)
Biogas composition
methane
CO2
H2 S
Average electrical output
per cow (kWh/day)
total (kWh/day)
yearly output (kWh/year)
generator capacity (kW)
generator reliability
electrical efficiency(*4)
Average electricity used (kWh/day)
Average net electricity sold to the grid (kWh/day)(*5)
Average propane use (gallons/year)
Solid content from raw manure
Solid content from digested effluent
Solid content from separated solids
Average oil added weekly (quarts)
Average milk production (lbs/cow/day)
Average water consumption (lbs/cow/day)
Values(1*)
502
30
30
352,000
40(2*)
35.22/95.4
37.66/99.8
2.4
83
43,447
55%-65%
43%
0.3-0.36%(3*)
3.4
1,702
621,189
130
95%
21%
1,331
367
4,900
12%
8.60%
24%
35
73
22
(1*)Average water consumption (lbs/cow/day)
(2*)Designed hydraulic retention time is 20 days
(3*)Measured by ”Draeger Tube” method
(4*)This assumes an energy value of 0.161kWh/cubic feet (550Btu/cubic feet)
(5*)Net electricity sold to the grid is electricity sold to the grid minus electricity
purchased from the grid
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