Hybrid Biogas Photovoltaic System

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Supervised by:

Dr.-Ing. Bernd Krautkremer

University of Kassel & Fraunhofer IWES

M.Sc. Dirk Kirchner

Fraunhofer IWES

Reviewed by:Prof. Albert Claudi

University of Kassel

Prof. Ahmed Elkousy

Cairo University

Submitted to the Faculty of Engineering in Cairo University and Faculty of Electrical Engineering/Computer

Science in University of Kassel in partial fulfillment of the requirements for the degree of Master of Science in

Renewable Energy and Energy Efficiency for the Middle East and North Africa Region

February, 2012

MODELING AND PERFORMANCE ANALYSIS OF A

HYBRID BIOGAS-PHOTOVOLTAIC SYSTEMAnalysis of the Electrical and Thermal System in Eichhof Center, Germany - A Case Study in Jordan

By,

Rand Al-Zu'bi


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Submitted to the Faculty of Engineering in Cairo University and Faculty of Electrical

Engineering/Computer Science in University of Kassel in partial fulfillment of the

requirements for the degree of Master of Science in Renewable Energy and Energy Efficiency

for the Middle East and North Africa Region

Approved by the Examining Committee:

_______________________________________________

Prof. Albert Claudi University of Kassel

_______________________________________________

Prof. Ahmed Elkousy Cairo University

_______________________________________________Prof. Mohammed ElSobki Cairo University

Faculty of EngineeringCairo University

Faculty of Electrical Engineering/Computer ScienceUniversity of Kassel

February, 2012

MODELING AND PERFORMANCE ANALYSIS OF A

HYBRID BIOGAS-PHOTOVOLTAIC SYSTEMAnalysis of the Electrical and Thermal System in Eichhof Center, Germany - A Case Study in Jordan

By,Rand Al-Zu'bi


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iii

DISCLAIMER

To the best of my knowledge I do hereby declare that this thesis is my own work. It has notbeen submitted in any form of another degree or diploma to any other university or other

institution of education. Information derived from the published or unpublished work of

others has been acknowledged in the text and a list of references is given.

Rand Al-Zubi

Kassel, 22.02.2012


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iv

ABSTRACT

The integration of PV and biogas with a focus on biogas as a flexible electricity supplier isthoroughly investigated in this thesis. The performance of the biogas/PV hybrid system at

Eichhof agricultural center in Germany is evaluated. The complete electrical and thermal

energy system is analyzed and modeled using Simulink. A proposed future scenario for a

more sustainable, reliable and autonomous system is developed. Finally, a case study to

determine the feasibility of this type of hybrid systems on a dairy farm in Jordan is carried on.

The investigated system at Eichhof consists of a biogas plant which has an average biogas

production of 720 m3/day and PV system with a peak power of 130 kW. The biogas is

distributed to a dual fuel engine (30 kWel, 48 kWth), a micro-gas turbine (28 kWel, 60 kWth)

and a gas burner (50 kWth). The PV system consists of three different structures andcontributes to 36% of the total energy generation. It was found that the electricity produced

from PV and biogas is capable of providing the entire load at Eichhof to a degree slightly

higher than 101%. The electricity, however, is produced with no relation to the load and has a

correlation coefficient of 0.03. Throughout the year, biogas is available to cover just 75% of

the required biogas volume. More than 80% of the thermal demand is supplied by natural gas.

The data used were for the year 2009.

In the proposed future scenario, the system was able to cover almost the entire load (over

99%) at all times throughout the year. This was accomplished through a suitable control

strategy that operates according to the demand (both thermal and electrical). The produced

biogas was set to a monthly average value with a minimum of 480 m3/day in summer and a

maximum of 1200 m3/day in winter. This control helped in limiting the biogas storage volume

to 5000 m3 and increasing the biogas availability to 98%. The biogas withdrawal from the

storage tank was also controlled upon demand. Most of the PV output was directly consumed

at the time it is generated, leaving a small amount of excess energy to be stored in a battery

bank (33 Ah) until needed. The correlation coefficient improved to a value of 0.95.

Finally, it was concluded that in order to ensure the feasibility of a similar hybrid system in

Jordan the tariff at which the produced electricity is sold to the utility grid should be morethan 0.02 JD/kWh (0.021 /kWh) fora large size project (1.65 MW installed capacity) and

more than 0.03 JD/kWh (0.032 /kWh) for the smaller project (0.22 MW installed capacity).

The large scale system proved to be generally more feasible and had a levelized cost of

electricity of 0.136 JD/kWh (0.146 /kWh). The project has a total investment of 5.4 million

JD (5.78 million ).


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v

ACKNOWLEDGMENT

I owe my deepest appreciation to all those people who have made this thesis possible.

First of all I would like to thank my family for the enormous support, love and care that they

conveyed to me in my twenty months abroad. Without them the entire study would not have

been possible.

I would like to profoundly thank all my friends, most importantly, Maen for being my

greatest supporter from the beginning of my Masters, following each step of the thesis and for

his unbound love and support; Arabi for keeping me company in the REMENA journey and

lending me a hand whenever I needed; Hanan and Laila for their tremendous last minute

assistance and my colleagues from REMENA and in the student room at Fraunhofer IWES.

I would like to gratefully acknowledge the supervision of M.Sc. Dirk Kirchner from

Fraunhofer IWES who has been kindly helpful and has supported me in numerous ways. I

specially thank him for his continuous assistance and feedback and for providing me with

answers when I needed them.

I would like to express my appreciation to Dr. Bernd Krautkremer for giving me a part of his

time and for his keen guidance and input. I am thankful for Prof. Albert Claudi and Prof.

Ahmed Elkousy for reviewing my thesis.

Finally I would like to thank Hammoudeh Food Industries Company, especially Mr.Mohammad Eid for providing me with the information I needed and answering my questions.


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TABLE OF CONTENTS

DISCLAIMER .......................................................................................................................... iiiABSTRACT .............................................................................................................................. ivACKNOWLEDGMENT ............................................................................................................ vTABLE OF CONTENTS .......................................................................................................... viLIST OF FIGURES ................................................................................................................... ixLIST OF TABLES .................................................................................................................. xiv

NOMENCLATURE ................................................................................................................. xvABBREVIATIONS AND TERMS ......................................................................................... xvi1. INTRODUCTION .............................................................................................................. 1

1.1. Motivation ................................................................................................................... 11.2. Research Objectives .................................................................................................... 21.3. Scope of the Work ....................................................................................................... 2

2. A BRIEF REVIEW OF THE LITERATURE .................................................................... 43. TECHNICAL BACKGROUND ........................................................................................ 6

3.1. Hybrid Systems............................................................................................................ 63.2. Biogas .......................................................................................................................... 8

3.2.1. Substrates ............................................................................................................. 83.2.2. Biogas formation .................................................................................................. 93.2.3. Environmental conditions .................................................................................. 10

3.3. Photovoltaics ............................................................................................................. 123.3.1. Components ........................................................................................................ 123.3.2. Classifications .................................................................................................... 133.3.3. System sizing ...................................................................................................... 14

4. ANALYSIS OF THE ENERGY SYSTEM OF EICHHOF AGRICULTURE CENTER 154.1. System Description .................................................................................................... 15

4.1.1. The existing gas grid .......................................................................................... 155. SIMULATION ................................................................................................................. 20

5.1. Biogas Production ...................................................................................................... 22


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5.1.1. Feed .................................................................................................................... 225.1.2. Fermenter ........................................................................................................... 245.1.3. Fermenter temperature model ............................................................................ 355.1.4. Biogas storage .................................................................................................... 435.1.5. Distribution model .............................................................................................. 455.1.6. Biogas burner ..................................................................................................... 475.1.7. Micro-gas turbine ............................................................................................... 495.1.8. Combined heat and power unit ........................................................................... 535.1.9. Natural gas burners ............................................................................................. 54

5.2. Photovoltaic System .................................................................................................. 565.2.1. Solar radiation on a tilted angle .......................................................................... 575.2.2. PV system power output .................................................................................... 61

5.3. System Analysis ........................................................................................................ 655.3.1. Demand side analysis ......................................................................................... 655.3.2. Current status ...................................................................................................... 67

5.4. Proposed Future Scenario .......................................................................................... 745.4.1. Battery model .................................................................................................... 775.4.2. Biogas control strategy ....................................................................................... 785.4.3. Substrate management ........................................................................................ 805.4.4. Results ................................................................................................................ 81

6. CASE STUDY: HAMMOUDEH DAIRY FARM IN JORDAN .................................... 856.1. Site Description ......................................................................................................... 856.2. Input Parameters ........................................................................................................ 86

6.2.1. Biogas ................................................................................................................. 866.2.2. PV ....................................................................................................................... 88

6.3. Simulation .................................................................................................................. 896.3.1. System design ..................................................................................................... 896.3.2. Results ................................................................................................................ 91


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6.4. Economical Analysis ................................................................................................. 946.4.1. Total investment ................................................................................................. 946.4.2. Annual costs ....................................................................................................... 966.4.3. Annual revenue .................................................................................................. 966.4.4. Extra costs .......................................................................................................... 976.4.5. Levelized cost of energy (LCE) ......................................................................... 976.4.6. Cash flow diagram ............................................................................................. 99

7. CONCLUSIONS ............................................................................................................ 1028. REFERENCES ............................................................................................................... 104APPENDIX A: GAS CONSUMERS ..................................................................................... 106APPENDIX B: EQUIPMENTS SPECIFICATIONS ........................................................... 107APPENDIX C: SOLAR RADIATION .................................................................................. 108

C.1 Apparent solar time (AST) .......................................................................................... 108C.2 Solar declination ()..................................................................................................... 108C.3 Hour angle (h) .............................................................................................................. 109C.4 Incidence angle () ...................................................................................................... 109C.5 Solar altitude angle () ................................................................................................ 109

APPENDIX D: CORRELATION COEFFICIENT CALCULATIONS ................................ 110APPENDIX E: REGMODHARZ .......................................................................................... 112


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LIST OF FIGURES

Figure 3.1: Biogas-PV hybrid system. ....................................................................................... 7Figure 3.2: Stages of anaerobic fermentation process. ............................................................... 9Figure 3.3: Influence of the temperature on the time of fermentation. .................................... 11Figure 3.4: Major PV system components. .............................................................................. 12Figure 4.1: The layout of Eichhof agricultural center. ............................................................. 16Figure 4.2: Gas grid layout. ...................................................................................................... 18Figure 5.1: Eichhof energy system simulation. ........................................................................ 21

Figure 5.2:Biogas production blocks. ...................................................................................... 22Figure 5.3: Substrate input flow rate. ....................................................................................... 23Figure 5.4: Feed properties. ...................................................................................................... 24Figure 5.5: Fermenter model (Jan-Dec, 2009 feed). ................................................................ 26Figure 5.6: Kinetic parameter versus volatile solids concentration. ........................................ 27Figure 5.7: Fermenter Temperature throughout 2009. ............................................................. 28Figure 5.8: corrected fermenter temperatures. ......................................................................... 29Figure 5.9: Retention time in the fermenter. ............................................................................ 29Figure 5.10: Methan content block. ......................................................................................... 30Figure 5.11: Methane content throughout the year. ................................................................. 30Figure 5.12: Exponential biogas accumulation model. ............................................................ 31Figure 5.13: Methane production for substrate entering in 2009. ............................................ 32Figure 5.14: Biogas output from substrate which is fed in the last fifty days of 2008. ........... 32Figure 5.15: Actual biogas production block. .......................................................................... 33Figure 5.16: Daily biogas production in 2009 (a) calculated, (b) actual. ................................. 34Figure 5.17: Fermenter temperature model block. ................................................................... 35Figure 5.18: Substrate volume in fermenter ............................................................................. 35


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Figure 5.19: Area calculations of heating system components. ............................................... 37Figure 5.20: Agitator block. ..................................................................................................... 37Figure 5.21: Heat of agitation. ................................................................................................. 38Figure 5.22: Density and specific heat capacity calculation. ................................................... 38Figure 5.23: Overall heat transfer coefficient calculations. ..................................................... 39Figure 5.24: Outlet temperature of water. ................................................................................ 41Figure 5.25: Fermenter temperature model. ............................................................................. 42Figure 5.26: Water flow rate required for heating of fermenter. .............................................. 43Figure 5.27: Biogas storage modelling. ................................................................................... 44Figure 5.28: Comparing biogas out to needed and available biogas in storage ....................... 44Figure 5.29: Used storage volume for biogas storage throughout the year. ............................. 45Figure 5.30: Biogas distribution block. .................................................................................... 46Figure 5.31: Biogas flow rate in for biogas consumers. ........................................................... 46Figure 5.32: Biogas burner model. ........................................................................................... 47Figure 5.33: burner load and temperature dependency. ........................................................... 48

Figure 5.34: Biogas needed for burner ..................................................................................... 48Figure 5.35: Gas consumption for burner. ............................................................................... 49Figure 5.36: Heat output of burner. .......................................................................................... 49Figure 5.37: Micro-gas turbine model. ..................................................................................... 50Figure 5.38: Biogas needed for turbine. ................................................................................... 50Figure 5.39: Efficiency and power of micro-gas turbine and their relation with temperaturethrough 2009. ........................................................................................................................... 51Figure 5.40: Power output from micro-gas turbine. ................................................................. 52Figure 5.41: Actual and calculated power output from the micro-gas turbine......................... 52Figure 5.42: CHP unit model. .................................................................................................. 53Figure 5.43: CHP electrical power output. ............................................................................... 54Figure 5.44: Comparison between actual and calculated power out from CHP unit. .............. 54


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Figure 5.45: Natural gas consumers block. .............................................................................. 55Figure 5.46: Natural gas needed for natural gas burners. ......................................................... 55Figure 5.47: PV system model. ................................................................................................ 56Figure 5.48: PV installations .................................................................................................... 57Figure 5.49: Global solar radiation at Grebenau in 2009. ........................................................ 57Figure 5.50: Equation of time block. ........................................................................................ 58Figure 5.51: Apparent solar time block. ................................................................................... 58Figure 5.52: Radiation calculations block. ............................................................................... 59Figure 5.53: Solar radiation on PV panels in the 15th of January, 2009.................................. 60 Figure 5.54: Solar radiation on PV panels in the 27th of July, 2009. ...................................... 61Figure 5.55: PV power model. ................................................................................................. 62Figure 5.56: Power output from PV systems in 2009, (a) PV1a, (b) PV1b, (c) PV2, (d) PV3.63 Figure 5.57: Actual measured PV output (black) compared to the calculated output for (a)

three days in 2009 for PV1b (b) four days in 2009 for PV2. ................................................... 63Figure 5.58: Total PV power output in 2009. .......................................................................... 64Figure 5.59: Load profile. ........................................................................................................ 65Figure 5.60: Simplified electrical infrastructure at Eichhof showing production and

consumption loads. ................................................................................................................... 66Figure 5.61: Estimated heat demand for 2009. ........................................................................ 66Figure 5.62: Energy system analysis block. ............................................................................. 67Figure 5.63: Inside energy analysis block. ............................................................................... 67Figure 5.64: Penetration level calculations. ............................................................................. 68Figure 5.65: Penetration level of electrical power from RE resources throughout the year

2009. ......................................................................................................................................... 68Figure 5.66: Penetration rate for heat produced from RE resources throughout 2009. ........... 69Figure 5.67: Load profile versus production profile in 2009. .................................................. 71Figure 5.68: Heat produced from CHP versus heat consumed for fermenter heating. ............ 72Figure 5.69: Biogas availability in 2009. ................................................................................. 73


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Figure 5.70: Future scenario block. .......................................................................................... 74Figure 5.71: PV residual load calculations block. .................................................................... 76Figure 5.72: Residual load from PV produced electricity. ....................................................... 76Figure 5.73: Battery model. ...................................................................................................... 77Figure 5.74: Battery utilization block. ..................................................................................... 78Figure 5.75: Number of days for heat storage calculations sample. ........................................ 78Figure 5.76: Biogas control strategy flow diagram. ................................................................. 79Figure 5.77: Needed biogas calculations after applying the control strategy. ......................... 80Figure 5.78: Calculating monthly average needed biogas (for January here). ......................... 81Figure 5.79: Comparison between inflow and outflow and needed biogas for the future case

scenario. .................................................................................................................................... 81Figure 5.80: Storage volume occupied by biogas in the future case scenario......................... 82Figure 5.81: Production profile versus load profile in the future case scenario (excluding

batteries). .................................................................................................................................. 83Figure 5.82: Fermenter heat demand versus CHP heat generation in the future case scenario.

.................................................................................................................................................. 83Figure 5.83: Load profile versus power production with battery usage in the future case

scenario. .................................................................................................................................... 84Figure 5.84: biogas availability in the proposed future scenario. ............................................ 84Figure 6.1:Hammoudeh Food Industries Company's (a) dairy farm (b) dairy plant. ............... 86Figure 6.2: Global solar radiation in Mafraq. ........................................................................... 88Figure 6.3: Temperature profile throughout 2008 in Mafraq. .................................................. 88Figure 6.4: Hybrid system model for Hammoudeh dairy farm and plant in Jordan. ............... 90Figure 6.5: Micro-gas turbine power output changes throughout the year. ............................. 92Figure 6.6: Solar radiation absorbed by the PV systems in its four configurations. ................ 92 Figure 6.7: PV system power output. ....................................................................................... 93Figure 6.8: Satellite image of Hammoudeh dairy farm. ........................................................... 93Figure 6.9: Total investment calculations. ............................................................................... 94


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Figure 6.10: Annual costs and revenues. .................................................................................. 97Figure 6.11: Economical analysis block. ................................................................................. 98Figure 6.12: Discounted cash flow diagram, P: price for selling electricity. ......................... 100Figure A.1: Natural and biogas consumers, nominal capacity and variable output. .............. 106Figure B.1: Burner output range. (source: manufacturer) ...................................................... 107Figure B.2: Net power and efficiency at ambient temperature. (source: manufacturer) ........ 107Figure C.1: Solar declination. ................................................................................................ 108Figure D.1: Correlation coefficient block for current situation and proposed future scenario.

................................................................................................................................................ 111


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LIST OF TABLES

Table 3.1: Typical properties of some substrates. ...................................................................... 8Table 5.1: Simulation parameters. ............................................................................................ 20Table 5.2: Substrate properties. ................................................................................................ 22Table 5.3: Ultimate methane yield of substrates. ..................................................................... 27Table 5.4: Mean biogas yield for the substrates. ...................................................................... 33Table 5.5: Biogas production prediction evaluation. ............................................................... 33Table 5.6: Properties of fermenter heating system components. ............................................. 36Table 5.7: Dimensions and details of PV installations. ............................................................ 56Table 5.8: Overall efficiency for PV systems. ......................................................................... 62Table 6.1: Location of Hammoudeh dairy farm. ...................................................................... 85Table 6.2: Properties of dairy manure. ..................................................................................... 86Table 6.3: Simulation results for the Jordan study case. .......................................................... 87Table 6.4: Simulation results over one year for the Jordan study case (scenario one: more

biogas). ..................................................................................................................................... 91Table 6.5: Simulation results over one year for the Jordan study case (scenario two: more PV).

.................................................................................................................................................. 91Table 6.6: PV system pricing indices. ...................................................................................... 95Table 6.7: Factors for annual costs calculations. ..................................................................... 96Table 6.8: suggested scenarios for Hammoudeh plant hybrid system. .................................... 99Table 6.9: economical indicators for scenario one: more biogas. .......................................... 100Table 6.10: economical indicators for scenario two: more PV. ............................................. 101


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NOMENCLATURE

A Area (m2) Greek lettersB methane yield of organic waste (m3

CH4/kg VS),

solar altitude angle

surface tilt angle from the horizontalCC correlation coefficient

C Capacity hydraulic retention time (day)

cpspecific heat capacity (J/kg.K) incidence angle

CV calorific value (kWh/m3) mean value

C(x,x) variance of the data sequence viscosity (N.s/m2)

d Diameter (m) the maximum specific growth rate

(day-1)DOD battery depth of discharge

Ds battery autonomy (day) volume fraction

E Energy (Wh) solar zenith angle

h the heat transfers coefficients

(W/m2.C)

Density (kg/m3)

h Hour angle Efficiency

i discount rate Solar declination

I Radiation (W/m2)

k thermal conductivities (W/m.C) Subscripts

k the first order kinetic constant (day-1) a Ambient

K kinetic parameter which indicates the

overall performance of the digester

ag Agitator

B battery

m

mass (kg) BG Biogas

m mass flow rate (kg/day) c concreten number of the years of a project c cell

N Number of readings cf correction factor

Nu Nusselt number e Equivalent

P Power (kW) el electrical

Pr Prandtl number f Fermenter

Q rate of heat flow (W) G global

Re Reynolds number i Each substrate

St volatile solids concentration (kg/m3) ins insulation

t time L Load

T temperature loss losses

Tm log mean temperature difference NOCT nominal operating cell temperature

(oC)U overall heat transfer coefficient

(W/m2.C) m Microorganisms

V Volume (m3) o Ultimate value

V Voltage p Peak

V volumetric flow rate (m3/day) ref reference

VHR

volumetric heat release (W/m3) s Substrate

w mass fraction s Surface

y Biogas accumulation (m3/kg) th thermal

x

Thickness (m) tot total

Z azimuth angle w water


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ABBREVIATIONS AND TERMS

AC Alternating Current

AC Annual Cost

ADM1 Anaerobic Digestion Model no 1

BG Biogas

CC Capital Cost

CHP Combined Heat and Power

C/N Carbon Nitrogen ratio

CRF Capital Recovery Factor

DC Direct Current

DER Distributed Energy Resources

DS Daylight Saving

ET Equation of TimeGSM Global System for Mobile communications

HDPE High-Density Polyethylene

ISET Institut fr SolareEnergieversorgungstechnik

IWA International Water Association

IWES Institute for Wind Energy and Energy systems

JD Jordanian Dinar

LCE Levelized Cost Of Energy

LL Local Longitude

LST Local Standard TimeNERC National Center for Research & Development Energy Research Program

MENA Middle East and North Africa

SL Standard Longitude

SoDa Solar data

oTS organische Trockensubstanz

PV Photovoltaic

RE Renewable Energy

RegModHarz Regenative Modellregion Harz

ROI Return On Investment

ROR Rate Of ReturnTAC Total Annualized Cost

TS Total Solids

VFA Volatile Fatty Acids

VS Volatile Solids


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1. INTRODUCTION

1

1. INTRODUCTIONAs the world is moving to a higher share of renewable energy, new questions and challenges

in the integration of these resources arise and novel answers are required to ensure an

economically and technically feasible integration.

The creation of an efficient energy infrastructure with optimal share of renewable energies is

the goal of any renewable energy integration scheme; this can be done by combining various

renewable energy producers and bringing out the strength of each resource.

Through the coordination of production, storage and consumption, a stable, reliable and

consumer-oriented supply of electrical energy should be possible, even with a high proportion

of renewable energy sources.

Bioenergy is gaining an increasing significance in the distributed energy resources (DER)

field as a storable form of energy. Bioenergy should be able to serve important roles in the

future in stabilizing the energy supply structure, decreasing the overall cost of a system and

utilizing the full potential of a given location. The strength of bioenergy comes from the fact

that it can supply energy exactly when needed and when other sources of renewable energy

cannot (such as wind and solar energy). On the other hand bioenergy has the ability of being

cut off when the supply is enough from other resources and therefore should not be

considered as a competitor to other energy resources but as a building block for base load and

smoothing of an energy system.

1.1.MotivationThis research is part of the RegModHarz project (Regenative Modellregion Harz)* which

aims at creating a virtual power plant depending on the maximum penetration of renewable

energy through supply, demand and storage management. Wind and solar energy are the most

abundant and economical renewable energy technologies available, however they also present

the biggest challenge of being intermittent and unpredictable on the long run. This is where

the bioenergys role comes, representing a storable, controllable and economical source in

areas that have good potential.

The Eichhof agricultural center is a perfect opportunity for optimizing, developing the

experience and more understanding of the function of biogas in completing the picture of the

maximum renewable energy mix in a country, city or society. Eichhof has the potential and

matches an energy consumption profile of a village and is equipped with the appropriate

measurement equipments and follows procedures that help in understanding the system,

improving it and taking it as a reference case for future development.


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Moreover, biogas, hybrid and off-grid systems are being considered increasingly as a

significant option for rural areas, agricultural sites and developing countries especially in the

MENA region (Middle East & North Africa) where the solar and wind potential is highly

available and desirable storage, integration and control options need to be studied and

optimized.

The results of the Eichhof center simulation and analysis, can be employed to further improve

the system and realize the maximum potential of the location within the environmental

conditions that portray a region with high bio potential, moderate solar potential and high heat

demand and also, by using the simulation itself in a location with the almost opposite

environmental conditions portraying a region with significant bio potential, high solar

potential and low heat demand. This all represents an excellent opportunity to build

innovative conclusions, understandings and maturity in the concept of integration of biogas as

a flexible energy supplier in hybrid energy systems under variant conditions.

1.2.Research ObjectivesThe objectives of this research can be summarized in two core ones:

1. To realize the optimum scenario in which a mix of two kinds of renewable energyresources one of which is an intermittent source (solar in our case ) and the second is a

provider of base load (bioenergy) through maximizing the coverage of the electrical

and heating demands and minimizing the storage volumes and hence reducing the

overall costs.

2. To determine whether the studied system and the integration of biogas and PV can beimplemented in different environmental conditions.

1.3.Scope of the WorkIn order to achieve the previous objectives, the scope of research will be divided into three

main stages:

1. Modeling the energy system of the Eichhof center through simulating the electricalenergy and heat producers (biogas and PV) and the electrical and heat energy

consumers. The simulation should be able to estimate and predict the half-hourly

electrical and thermal energy produced from the biogas plant by estimating the biogas

volume produced and modeling the electrical devices and heaters that consume the

biogas and also estimating the electrical energy produced by the PV system. Moreover

the model is continuously validated by comparing the results of each step with actual


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1. INTRODUCTION

3

data from the site throughout the year 2009 which is described in details throughout

chapter five.

2. Evaluating the performance of the system in Eichhof at the current situation andproposing an improved scenario that would bring the system closer to the goals stated

previously. This is done by using statistical analysis techniques that evaluate the

relations between consumption and production, through determining the renewable

energy penetration level in the given year and other evaluation methods. And by

modifying the existing system by controlling the energy production through

introducing a control scheme to the system, by adding units such as electrical engines

and storage units and by suggesting slight demand side management measures.

3. Applying the simulation that is created for Eichhof on the system that is chosen in theMENA region. The location investigated is a dairy farm and plant in Jordan. The

electrical and thermal energy production will be calculated using the model that was

created for Eichhof and will be compared and analyzed in reference to the plants

electrical consumption and finally concluding whether the system and the potential are

able to meet the demand in the year as a whole and whether it is financially feasible or

not.


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2. A BRIEF REVIEW OF THE LITERATURE

4

2. A BRIEF REVIEW OF THE LITERATURE

Many studies are available on hybrid systems including well-established renewable energyresources such as PV and wind. The main focus of such studies is usually minimizing the cost

and storage requirements by enhancing the wind speed and direction and solar radiation

prediction techniques, developing control mechanisms and other measures. Many of the

studies include a diesel generator or another conventional fuel as base load provider in order

to fulfill the load requirements while reducing the cost. Biogas is being increasingly

introduced as an alternative to fossil fuel generators and large storage requirements and

therefore is starting to be a target of many more studies and papers.

Most of the researches about hybrid systems which contain biogas as a main component focus

on off-grid hybrid systems and their applications in rural areas and present the hybrid systemas a solution to provide electricity at relatively low prices for such locations. A few of these

studies and their main conclusions are summarized in this chapter.

Gupta and Sharma[23]

proposed a system consisting of a photovoltaic array, biomass (fuel

wood), biogas, small/micro-hydro, a battery bank and a fossil fuel generator. They proposed a

mixed integer linear mathematical programming model that can be used in planning studies to

determine the optimum design of an autonomous hybrid energy system. They also applied the

model on a cluster of off-grid villages in India and concluded that hybrid energy systems can

theoretically reduce generation costs and increase the reliability of energy supply, the

calculated cost of energy had a mean value of 0.10 Euro per kWh. They also proposed an

optimized operation control algorithm of hybrid energy system based upon the combined

dispatch strategies.

Berglund and Brjesson[24]

performed a life cycle assessment for large scale biogas plants.

They concluded that the overall energy input into biogas systems corresponds to 20-40% of

the energy content in the biogas produced. Large variations were found in energy efficiency

depending both on the properties of the raw materials and on the system design for the

different biogas plants studied. Therefore, an improved system design, more energy efficient

processes and energy-rich substrates were found to reduce the energy input of the system.

Thomas, Post, Durand and Rosenthal [25] conducted a performance analysis over three PV

hybrid systems representing three distinct types of remote electrical loads: large mini-grid

systems; single small residential systems and telecommunications repeaters. The hybrid

systems are integrated with diesel and propane generators. Compared to the generator-only

power system, each of the hybrid systems had a much larger initial cost, while reflecting

reduced fuel, maintenance, and replacement costs for the life of the system. The large system

demonstrated very high availability (99.6%) while the small system had the highest cost

effectiveness of the three systems.


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3. TECHNICAL BACKGROUND

6


3.1.Hybrid SystemsThrough combining different sources of renewable energies such as solar, bio, hydro or wind

energy together with appropriate storage and control systems it is possible to be able to

feasibly produce energy in a reliable manner. Such arrangements are defined as hybrid energy

systems and are frequently used to provide electrical and thermal energy to rural areas,

agricultural communities and other off-grid locations.

The hybrid systems can also be connected to the grid to create a virtual power plant thatrepresents a large scale hybrid system. Many hybrid technological options are available in

practice, most commonly including diesel generator sets, renewable energies and storage.

Moreover, for certain locations such as areas at a high distance from the grid or locations with

high renewable energy potential and high electrical and heat consumption, the decentralized

or approach distributed energy resources or local supply can provide a competitive

economical option due to the lower overall costs compared to purchasing electricity from the

grid over a long life time and to large scale electrification projects.

Hybrid systems can create market opportunities for emerging energy technologies through thecombination of the strengths of each resource and overcoming its limitations. The goals of

such combinations include increasing the efficiency of the system and its reliability, reducing

storage requirements and emissions and decreasing the costs among other goals.

The hybrid system examined in this research is a PV-biogas hybrid system with a focus on the

biogas system. A graphical presentation of such system is shown in Figure 3.1. The figure

shows the main components of each of the biogas and PV systems and the flow of material

and energy throughout the system.

The inputs are solar radiation and substrate which includes animal waste (manure) and energy

crops (corn silage and grain wheat). The outputs are electricity and heat. This system also

solves the problem of disposing the manure in such an agricultural site, with opportunity of

utilizing the by-product from the fermentation process as a fertilizer.

The PV-biogas hybrid systems are supposed to rely on biogas for heat production, on PV for

regular load coverage and biogas for base load and for emergencies. Treated and conditioned

biogas can be fed into an existing natural gas grid to fulfill the heating purposes.


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7

Nevertheless, in order to achieve day-time and seasonal stability amid production and

consumption; storage of somewhat large quantities of biogas and other energy management

measures are necessary.

The hybrid system studied in this research is still connected to the electrical grid and supplied

by natural gas. This gives more opportunities to investigate a real-life-scenario with its peaks

and fluctuations and presents more ways to control the system on both sides, demand and

supply.

Figure 3.1: Biogas-PV hybrid system.


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8

3.2.Biogas

The formation of methane is a biological process that takes place naturally when organicmaterial (biomass) decomposes in a humid atmosphere in the absence of air and in the

presence of natural microorganisms which are metabolically active, i.e. methane bacteria. In

nature, methane is formed as marsh gas (or swamp gas), in the digestive system of ruminants,

in plants for wet composting, and in flooded rice fields. Biomass which is suitable to be

fermented is named substrate. The most currently used methane-rich gas is natural gas and it

has various differences depending on the origin.

3.2.1. SubstratesIn general, any type of biomass can be used as a substrate for biogas production as long as itcontains carbohydrates, proteins, fats, cellulose, and hemi-cellulose as main components.

Table 3.1 shows the typical maximum gas yields per kg total solids for different substrates.

Total Solids (TS) is a measure of the total content of all inorganic and organic substances

contained in a liquid and is measured through drying the material to 103-105C and

measuring the mass of the sample before and after the drying process. The total solids value

(%) would be the mass of the dry matter divided by the wet matter. The same abbreviation is

widely used for total solids in the German literature and it is an abbreviation of

(Trockensubstanz).

The volatile Solids (VS) is the quantity of the organic constituents of a substance after

complete removal of all water and mineral components. This value is calculated by

contrasting the difference in weight between the dry state by heating the sample to a

temperature of 105 C and the weight after further heating it at 550 C to 600 C. The

corresponding term in German literature is (organische Trockensubstanz) abbreviated as

(oTS). This value is of high significance to biogas production.

Table 3.1: Typical properties of some substrates.[1]

Substrate for biogas

production

TS (%) VS in TS

(%)

Biogas yield

(m3

kg-1

TS)

Retention

time (d)

Vegetable wastes 5-20 76-90 0.4 8-20

Hay 86 90-93 0.5 -

Maize straw 86 72 0.4-1.0 -

Bio waste 40-75 30-70 0.3-1.0 27

Leftovers (canteen kitchen) 9-37 75-98 0.4-1.0 -

Sewage sludge - - 0.2-0.75 17

Liquid dairy manure 6-11 68-85 0.1-0.8 -

Excreta from dairy 25-30 80 0.6-0.8 -

Liquid pig manure 3-10 77-85 0.3-0.8 -

Excreta from pigs 20-25 75-80 0.27-0.45 -

Excreta from chicken 10-29 67-77 0.3-0.8 -


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3.2.2. Biogas formation [1]If the chemical composition of the substrate is known, the theoretical yield of methane from

biomass is calculated according to the following the equation:

22342snohc x)CO(cSsHnNHxCHOyHSNOHC 3.1Having,

3s)3n20h(4c1/4y

2s)3n20h(4c1/8x

Methane fermentation is a complex process that inculudes many intermidiant steps between

inputs and outputs, it can be principally divided up into four stages of decomposotion:

hydrolysis, acidogenesis, acetogenesis, and methanation (Figure 3.2).

The individual phases are carried out by different groups of microorganisms, which partlystand in syntrophic interrelation and place different requirements on the environment.

Figure 3.2: Stages of anaerobic fermentation process.

In the hydrolysis phase, undissolved compounds, like cellulose, proteins, and fats are cracked

into monomers by exoenzymes of facultative and obligatorily anaerobic bacteria. The


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hydrolysis of carbohydrates takes place within a few hours and the hydrolysis of proteins and

lipids within few days. Hydrolysis is the rate-limiting step in the acid-forming phase.

In the second phase; acidogenesis, the monomers formed in the hydrolysis phase are taken up

by different facultative and obligatorily anaerobic bacteria and are degraded to short-chain,

alcohols, hydrogen, and carbon dioxide.

The acetogenesis reactions are endergonic (absorbing energy in the form of work) through

which acetate is produced by anaerobic bacteria. In this phase the homoacetogenic

microorganisms constantly reduce exergonic H2 and CO2 to acetic acid. The acetogenic phase

limits the rate of degradation in the final stage. From the quantity and the composition of the

biogas, a conclusion can be drawn about the activity of the acetogenic bacteria.

In the final stage, the methanation phase;the methane formation takes place under anaerobic

conditions and the reaction is classified as an exergonic reaction. The products of the acid

fermentation are converted into CO2 and CH4. 70% of the methane arises from acetate duringthe methanation phase. The energy released in the reaction of the four phases is partially used

for synthesis of the anaerobic bacteria populations.

3.2.3. Environmental conditionsThe provision of nutrients, an optimum temperature, pH, frequent agitation and other

environmental factors are vital in order to maximize the activity of the bacteria. The type of

substrate also determines the rate of the anaerobic degradation; therefore process operation

and technology must take into consideration what substrate is dealt with. The operating

conditions thus must be monitored and controlled in order to enhance the microbial activity

and increase the efficiency and performance of the digester.

Sudden and severe changes in environmental conditions can cause process failure; for

example, fluctuations in fermenter temperature or in the case of hydraulic or organic shock

loading.

The formation of hydrogen and volatile acids occur simultaneously with their conversion to

methane and carbon dioxides, meaning that acidogenesis and methanogenesis organisms work

at the same time and rate and are in dynamic equilibrium.

This means that the levels of acids and hydrogen in a correctly working fermenter should

remain low. However when the methanation is disturbed, over acidification occurs, hydrogen

accumulates, methane production lags behind and the pH value drops.

The main challenge is that methanogenesis bacteria are inherently slow-growing, with their

doubling times in days and are affected by small changes in temperature and pH. On the other

hand, the doubling time of acidogenesis bacteria is measured in hours and they can work over

a wider range of environmental conditions than the methanogenesis bacteria.


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For instance, temperature requirements for acidogenesis range between 25-35 oC and a pH

value between 5.2-6.3, whereas for the range for methane formation is between 32-43 oC for

mesophilic microorganisms and 50-58 oC for thermophilic microorganisms (see Figure 3.3)

and a pH in the range of 6.7-7.5. Most of the methanogenic microorganisms however belong

to the mesophilic microorganisms and only a few are thermophilic.

The optimum temperature at which the fermenter should be kept varies with the substrate

composition and the type of the digester; however it should be remained relatively constant

throughout the retention time. Also, in reality, pH values are held within the neutral range by

natural procedures in the digester.

Figure 3.3: Influence of the temperature on the time of fermentation.[1]

Other important environmental parameters are retention time, Carbon to Nitrogen ratio (C:N),

mixing, organic loading rate, ammonium (NH4+) and ammonia (NH3), Nitrate (NO3

-), redox

potential, foaming and scum, among other parameters.


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12

3.3.Photovoltaics

Photovoltaic (PV) is the well known technology in which direct current (DC) electrical poweris generated from semiconductors when illuminated by photons. Photovoltaic modules are

highly reliable, having no moving parts and requiring almost no maintenance and no external

inputs such as fuel but only a flux of solar energy. Solar cells produce direct current electricity

from sun light, which can be used to power equipment or to recharge a battery.

The first uses of PV as stand-alone systems were applications in which conventional resources

of energy were not available or costly to get such as space satellites, meteorological

measurement stations and marine warning lights.

3.3.1. ComponentsThe major components of a PV system are (see Figure 3.4):

PV module: converts sunlight instantly into DC electricity. Solar charge controller: regulates the voltage and current that is going to the battery

bank, it prevents battery from overcharging and prolongs the battery life.

Inverter: converts DC output of PV panels into standard AC current for AC appliancesor feeds it back into grid line.

Battery: stores energy when there is an excess and supplies electrical appliances whenthere is a demand.

Others: such as auxiliary energy sources (for example diesel generator), connectedload or utility meters.

Figure 3.4: Major PV system components.


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3.3.2. ClassificationsThe two main classifications of PV-based systems are grid-connected and stand-alone

systems. The two types of systems differ in their functional operational requirements, theircomponents and how they deliver the produced power, i.e. how they are connected the load.

Grid Connected

When the solar array generates more power than is being used, the surplus is exported to the

grid and the difference is imported from the grid when it generates less power than the load.

Grid-connected systems use the PV array to produce electricity; it is designed to feed the

electric utility grid through a bi-directional interface. The inverter converts the DC power

produced by the PV array into AC power consistent with the voltage and power quality

required by the utility grid. The bi-directional interface allows the power produced by the PV

system to either supply on-site electrical loads, to back feed the grid when the PV system

output is greater than the on-site load demand or to take form the grid.

Grid connected PV systems can be categorized into centralized and decentralized systems.

Applications of the centralized ones are in utility power, joint ownership and sound barriers,

while decentralized systems are used for private rooftops, facade integration and institutes.

Stand Alone Systems

Stand-alone systems (off-grid) use PV systems to directly supply electricity to a consumerunit or through a battery. These systems are intended to function independently of the electric

utility grid, and are designed to supply certain DC and/or AC electrical loads.

The simplest type of off-grid PV system is a direct-coupled system, where the DC output of a

PV panel is directly connected to a DC load. In this kind of off-grid systems, energy storage is

not available; therefore the load operates only during sufficient sunlight hours. Uses of direct-

coupled systems include ventilation fans and small circulation pumps for solar thermal water

heating systems. Many stand-alone PV systems require energy storage (mainly batteries) to

compensate for periods without sufficient solar irradiation, such as during the night or during

cloudy weather

Applications of stand-alone PV systems vary from remote location uses to industrial

applications and consumer applications. In remote areas such systems can be used for water

purification, irrigation, battery charging, lighting or village power supply. Industrial systems

are used for telecommunication applications, displays, remote monitoring, hotels restaurants,

etc. Other small-scale consumer applications include mobile phones, calculators, charging

devices and watches.


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14

3.3.3. System sizingThe power produced by PV systems depends on several factors. The annual PV performance

is mainly determined by the cumulative PV irradiance, module power rating at standard

conditions, the operating temperature, the maximum power point voltage dependence on

irradiance level, soiling effects and optical losses caused by high angles of incidence (angle

between the suns rays and the normal on a surface).

The sizing of the solar generator and the storage devices plays an important role in the cost

reduction and reliability of a photovoltaic power supply. Major sizing and design steps

include:

Determining the load and optimizing the consumption Choosing the system type Analyzing the solar radiation for the site location Estimating the array Size Sizing the battery storage Dimensioning the solar charge controller


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4. ANALYSIS OF THE ENERGY SYSTEM OF EICHHOF AGRICULTURE CENTER

16

Figure 4.1: The layout of Eichhof agricultural center.


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17

A galvanized steel pipe carries the biogas from the biogas system to the laboratories building.

A blower in the CHP unit room pushes the biogas through the pipes. Thereafter the biogas is

compressed and stored in a pressure vessel with a volume of 6 m 3. The micro-gas turbine and

the pressure vessel are connected by a stainless steel pipe. The total length of the biogas

system piping is around 400 m.

4.1.3. Gas consumptionFigure 4.2 shows the natural and biogas consumers in Eichhof.

4.1.3.1. Natural gas consumptionThe natural gas grid includes four radiators for the heating of halls and the stable. Each

radiator has a nominal power of 10.89 kW. For cooking purposes, an oven with a maximum

nominal capacity of 31 kW is available with six burners.

For heating and warm water dissemination, three 12 kW boilers can be found in each of the

three living houses (9 in total), each resident is billed separately for the use of the boiler. Two

more systems are found in the former managers house and the former workplace. A 200 kW

gas burner located in the basement of the castle is used for the castles heating.

The gas burners that produce heat for the local grid are located in the basement of the

laboratories building and have a nominal capacity of 400 kW and 200 kW. The two burners

supply the residential buildings, the green house, the class rooms and the laboratories with

heat and hot water.

An additional gas burner is located in the biogas plant building and has currently a nominal

capacity of 50 kW and can be alternatively operated by biogas, for that the burner setting

should be changed. The burner runs mainly on biogas.

The inlet natural gas pressure for all consumers is controlled with a pressure valve with a limit

of 20 mbar.

A gas meter is connected to each house, the measurement period is 1 hour and 10 impulses

produce 1 m

3

of consumption. The information read by the meter is sent and stored through aGSM-sender.


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18

Figure 4.2: Gas grid layout.


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19

4.1.3.2. Micro-gas-turbineThe Micro-gas-turbine is located in the laboratories building and is designed for low calorific

value gases. The turbine started operation in December, 2004 and was a research and study

subject for ISET (IWES). The electrical output is controlled between 10 and 28 kW el. A heat

exchanger is connected to the exhaust gas of the turbine; in this heat exchanger the water for

the hot water loop is heated. At full load, the micro-gas-turbine system produces thermal

energy of 60 kWth. Since commissioning 591.27 MW heat energy was produced.

4.1.3.3. CHP unitThe ignition engine CHP unit is located in the area of the biogas plant. It has a generator with

installed capacity of 30 kWel. The waste heat from the CHP unit (around 48 kW th) is used for

preheating the recycled water that is used in the heat loop of the feeding station. The heat loop

of the feeding station includes a stable, an office and a seminar room. The waste heat is not

used for cooling of units in summer time in the current situation.

4.1.3.4. Biogas burnerThe biogas burner is operated by using an adjusted gas burner with a nominal heat capacity of

50 kW and a variable output that ranges from 60 kW to 300 kW. It is located next to the CHP

unit in the feeding station and can be alternatively operated by natural gas. When operated

with natural gas, the burner produces a higher output.


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5. SIMULATION

20

5. SIMULATION

To evaluate the performance and analyse the hybrid system in Eichhof, full processsimulation is initially carried out. The difference between the simulation results and the actual

process outcomes are expected especially for biochemical processes where microorganism

and many other parameters affect the output. The simulation software, Simulink developed by

MathWorks, is used in this research.

The main simulation parameters are summarized below:

Table 5.1: Simulation parameters.

Simulation parameter Value

Solver Ode45

Relative tolerance 1 10-

Maximum step 1

Minimum step 0.001

Initial step 0.01

Maximum order* 5* Maximum order of the numerical differentiation formulas (NDFs)

The solver ode45 (Dormand-Prince) is the default variable step size solver, it computes the

model's state at the next time step using an explicit Runge-Kutta (4,5) formula (the Dormand-

Prince pair) for numerical integration. Ode45 is a one-step solver, and therefore only needs

the solution at the preceding time point.

The default value of 1 10-3 was used as the relative tolerance which specifies the largest

acceptable solver error, relative to the size of each state during each time step. If the relative

error exceeds this tolerance, the solver reduces the time step size.

The procedure for process simulation includes three primary parts:

1. Biogas process modelling2. PV system modelling3. System analysis and suggested future scenario modelling

Figure 5.1 shows the entire model with the main blocks and relationships between different

parts.


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Figure 5.1: Eichhof energy system simulation.


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5. SIMULATION

22

5.1.Biogas Production

Figure 5.2:Biogas production blocks.

5.1.1. FeedThe biogas production model starts with the substrate input after being collected, processed

and pretreated. The substrates that were fed into the fermenter in the years 2008-2009 were

dairy manure, corn silage and wheat grain. The table below summarizes the characteristics of

the feed.

Table 5.2: Substrate properties.

Substrate Average flow rate in 2009(kg/day)

TS [2](kg/kg input)

VS [2](kg/kg input)

Density [1](kg/m

3)

Manure 9,965 0.11 0.09 990

Corn silage 2,016 0.35 0.34 720

Wheat grain 33 0.08 0.07 700

The feed is entered as time series with daily volumetric manure flow rate and daily mass flow

rate of corn and wheat.


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5. SIMULATION

23

Figure 5.3: Substrate input flow rate.

The next block input parameters is simply used to calculate the total solid and volatile

solids content of the feed based on the number showed in Table 5.2. The following figure

shows an additional parameter that is calculated for each input ST.

The volatile solids concentration (St) is found to be 89.1 kg/m3 for manure, 244.8 kg/m3 for

corn silage and 49 kg/m3 for wheat grain after running the simulation.


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5. SIMULATION

24

Figure 5.4: Feed properties.

5.1.2. FermenterSeveral studies come out with models describing the process of fermentation, however due to

the complex nature of the fermentation process it is difficult to find a simple accurate model

that fits with all conditions. Therefore it is necessary to treat each case individually and

optimize it based on experiences or trial and error methods. The actual biogas production data

obtained from the Eichhof biogas plant are kept as a reference to measure the accuracy of the

model.

Several models have been developed to describe the kinetics of anaerobic digestion and are

increasingly considered an important supporting tool for the design, operation and control of

biogas systems. There are generally three classes of mathematical kinetic model which vary in

complexity and accuracy used to describe and predict the anaerobic digestion process. These

three classes are as follows [4]:

1. The simplest class is the models that use steady-state solutions of first-order kineticequations, mainly because of the simplicity of its inputs. Steady state models are based

on the slowest process kinetic rate that governs the overall behavior of the system and

relates this process rate to the system design and operating parameters. The main


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5. SIMULATION

25

drawback of these models is their in ability in most situations to predict operational

optima or failure which is due to the assumptions made in their derivation.

2. Models that use Monod kinetic which describes biochemical as well asphysiochemical processes. These models include differential and algebraic equation

which could reach more than 30 equation describing disintegration from homogeneous

particulates to carbohydrates, proteins and lipids; hydrolysis of these particulate

substrates to sugars, amino acids, and long chain fatty acids, acidogenesis from sugars

and amino acids to volatile fatty acids and hydrogen; acetogenesis of VFAs to acetate;

and separate methanogenesis steps from acetate and hydrogen/CO2. Such parameters

and equations are very difficult to determine for complex substrates and require

extensive computer analysis. Monod models have, as their major advantage,

unquestionable accuracy in predicting process failure and optima. A well-known

example of this model is the IWA Anaerobic Digestion Model No 1 (ADM1).

3. The third model was introduced by Chen & Hashimoto (1979) as a modification of theContois model. This model can be considered to possess characteristics of both the

Monod kinetics model and the first order kinetics model. It has simplified inputs,

requiring only one kinetic parameter (K) and the ultimate microbial growth rate (J -1).

This model however has an ability to predict inhabitation; it can for example predict

process failure due to wash-out effects. This ability is, however, limited because of the

fact that the model is derived for the steady state and in the formation of its kinetic

equation; thus, it will not predict complete process failure due to inhibition of

microorganisms.

A comparison was made between the three models in order to determine which is more

suitable for the purposes of this study. Since it is not the objective of the research to design or

operate a biogas reactor but rather to predict the general behavior and output of the fermenter,

a compromise between the accuracy and complexity was made and the third model was found

to be able to accomplish the objective of the model with high accuracy and reasonable

complexity.

More accurate and detailed modeling is out of the scope of the research and can be considered

for future development procedures. The inability to predict complete process failure can be

tolerated in the stage of performance and evaluation of a system. The simulation model of theanaerobic digestion process is shown in the following figure.


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Figure 5.5: Fermenter model (Jan-Dec, 2009 feed).


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5. SIMULATION

27

The kinetics of methane production from organic waste under steady-state conditions can be

expressed as a function of the fermenter parameters, substrate properties and the ultimate

methane yield, this relation can be described as follows [3]:

KKBB mo

11

5.1

where,

is the hydraulic retention time expressed in days,

B is the methane yield of organic waste (m3 of CH4 per kg of VS added),

Bo is the ultimate methane yield (the methane yield at infinite retention time, m3 CH4/kg VS),

m is the maximum specific growth rate of microorganisms (day-1),

K is the kinetic parameter which indicates the overall performance of the digester.

The ultimate methane yield of biogas substrate was investigated by several researchers. Thefollowing table lists the ultimate methane yield for manure, corn and wheat and the

corresponding reference.

Table 5.3: Ultimate methane yield of substrates.

SubstrateUltimate methane yield

Bo (m3

CH4 per kg VS)Reference

Manure 0.52 [3]

Corn 0.33 [5]

Wheat 0.30 [5]

The methane yield is seen from equation (1) to increase as the dimensionless kinetic

parameter K which depends on the volatile solids decreases. In animal manure digestion of

high concentrations of volatile solids and ammonia inhibits digester performance, which is

reflected in the value of K. K, is relatively constant for low volatile solids concentration as

seen in Figure 5.6. However for the manure feed, the St value is calculated to be around 90

kg/m3 which is out of the constant value range.

Figure 5.6: Kinetic parameter versus volatile solids concentration.[4]


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5. SIMULATION

28

For high volatile solids concentration values for anaerobic digestion of animal manure the

following relation[4] is used:

)118.0(0006.06.0 t

SeK 5.2

The same relation was used for the evaluation of the kinetic parameter of the wheat grain.

However this relation, does not estimate the K value accurately for the corn silage substrate.

The reason for that is that the volatile solid concentration is very high as mentioned earlier

(244.8 kg/m3) and therefore the equation is not able to estimate the K value at such high S t

values. However, through studying and investigating the data in hand, which includes daily

input substrates and output biogas for 2008 and 2009 it was found that the same relationship

can be used with the correction of dividing the constant in the exponential term by the value

of density (720 kg/m3). Equation 2 then becomes:

)0012.0(0006.06.0 tSeK 5.3

The maximum specific growth rate (m) of the microorganism depends on the digester

temperature. m can be considered as a linear function of the digestion temperature in the

range 30 - 60C:

129.0013.0 Tm 5.4

The temperature in the fermenter is kept through an integrated heating system at 38 2 oC.

The variations in the temperature of fermenter in the year 2009, as measured by the operatorsof the biogas plant, are shown in the following figure.

Figure 5.7: Fermenter Temperature throughout 2009.

As seen from Figure 5.7 the measured fermenter temperature seems highly fluctuating, for

example changing from 39 C to 37 C one day and then back to 39 C the next day, this of

course is not realistic and is in most case caused by errors in the temperature measurement

device; therefore, in order to avoid any inaccuracies caused by error of temperature reading, a

smooth polynomial curve is fitted to the measured data and the improved readings will be

used for the model. The result of the fitting curve is shown in the figure below.


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5. SIMULATION

29

Figure 5.8: corrected fermenter temperatures.

The hydraulic retention time () is the theoretical average retention time of a volume of liquid

in a completely-mixed reactor and it is conventionally defined as the reactor volume divided

by the rate of liquid throughput. The average retention time in the fermenter is 50 days in our

case, however, since the substrate is entering the fermenter at variable flow rates and

densities, a more accurate approach is to calculate the retention time at each step especially

that the flow rate of substrate is much lower in the first months of the year which means it

would spend longer time in the fermenter. We can express the retention time as:

s

s

m

fV 5.5

where,

Vfis the volume of fermenter (m3),

s is the density of substrate (kg/m3),

sm is the mass flow rate of substrate (kg/day)

The total density of the substrates is calculated in the temperature model block that will be

presented in the following section. The calculated density is fed to the fermenter block and the

retention time is thus calculated. The retention time changes from 70 days to 40 days as the

feed changes as shown in Figure 5.9. The x-axis of the figure is the day of the year at which

the substrate enters the reactor, corresponding to the mass flow rate and density at that day.

Figure 5.9: Retention time in the fermenter.


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5. SIMULATION

30

By applying equation 5.1 we get the methane output from each kg volatile solids of substrate,

to get the total biogas volume out (VBG) we need to multiply by the VS value then divide the

methane yield (B) by the methane content of the biogas which is changing with time

depending on the feed substrate. Each substrate has a mean methane content which is listed in

Table 5.4. The calculation block is shown below.

Figure 5.10: Methan content block.

The methane content is simply calculated as follows:

s

sstotal CHCH1

44 %% 5.6

where,

s is the volume fraction of each biogas volume produced from each substrate at a given

time.

The resulting methane content throughout the year will be changing in the manner shown

below.

Figure 5.11: Methane content throughout the year.


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5. SIMULATION

31

The value of methane yield (B), that determines the total volume of biogas produced from a

specific mass of substrate entering at a specific time, does not take into account the rate in

which the biogas is being generated throughout the time the substrate spends in the fermenter.

The biogas accumulation was simulated using an exponential rise to maximum function

which is used for continuous fermentation operations [1]. The course of biogas production thus

can be described using the following relation: [16]

)1(maxkteyy(t) 5.7

where,

y(t) is the accumulation at a given time t (m3/kg),

ymax is the maximum biogas yield (m3/kg),

t is time throughout the retention time (day)

k is the first order kinetic constant (day-1).

Equation 5.7 can be rearranged, differentiated and then integrated to get the following form:

dtktyyty ))(()( max 5.8

This equation is simulated as shown in the following figure for each of the substrates. The

values of k are taken as 0.06 for manure, 0.13 for corn and for wheat. [17]

Figure 5.12: Exponential biogas accumulation model.

The calculated biogas production is shown in the following figure.


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5. SIMULATION

32

Figure 5.13: Methane production for substrate entering in 2009.

Figure 5.13 shows that the biogas accumulation in the fermenter starts from zero m

3

/day. Thisis only true if the fermenter was empty at the beginning of the process, therefore in order to

simulate the actual situation more accurately, we need to take into account the substrate that

entered the fermenter before the first of January of 2009. For this purpose the substrate that

was fed in the last 50 days (average retention time) of 2008 will be also simulated to

determine the remaining volumes of biogas that are produced in 2009 from these substrates.

This is simply done by re-running the simulation with substrate feed from 2008. The resulting

biogas out is shown in the following figure.

Figure 5.14: Biogas output from substrate which is fed in the last fifty days of 2008.

The traces that are seen from day 0 need to be added to the first days of 2009. Therefore, the

biogas originated from substrate fed in 2008 is pre-calculated and saved and the values of

biogas produced in 2009 (days 0 to 50 in the figure) are then entered to the model as an input.

The total calculated biogas output from each substrate from the digestion process over the

year 2009 can be compared to both the total biogas output for the 2009 and the expected

biogas that results from each substrate separately. This can be estimated through the mean

biogas yields that are reported in many references as in the following table.


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5. SIMULATION

33

Table 5.4: Mean biogas yield for the substrates.[6]

SubstrateBiogas yield

(m3/ton)

Methane content

Manure 33 59 %

Corn 230 53 %

Wheat 40 60 %

The actual biogas readings were taken from meters connected to the three biogas consumers

(burner, CHP and micro-gas turbine), those values however were taken on daily basis and at

different times for each device. Therefore, taking an average daily consumption for each

month (by dividing the total monthly consumption over the number of days of the month) was

the most appropriate approach to deal with the meter readings and get a clear idea of the

actual biogas production. The calculation of the actual biogas production is shown in the

figure below.

Figure 5.15: Actual biogas production block.

The comparison between actual, calculated and expected biogas volume is shown in Table

5.5.

Table 5.5: Biogas production prediction evaluation.

Substrate FeedExpected

biogas (m3)

Calculated

biogas (m3)

Actual

biogas (m3)

Manure 3,674 (m ) 120,029 120,962

N/ACorn silage 735,840 (kg) 169,243 145,800

Wheat grain 12,045 (kg) 481 413.1

Total 4,385,145 (kg) 289,810 267,175 264,600

The previous data show that the model used is highly accurate in representing the complex

anaerobic digestion process for each substrate and for the whole scenario. Figure 5.16 shows

the actual daily biogas production in contrast with the daily calculated values.


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(a)

(b)Figure 5.16: Daily biogas production in 2009 (a) calculated, (b) actual.

The actual production curve does not reflect the actual daily production trend as explained

earlier and therefore it is safe to ignore the sharp edges in the actual production figure and

look at the entire graph in order to compare correctly. We see from the previous figure that

the calculated biogas production matches, to a high level, the trend and quantities of the actual

production.


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* Volume in the first 50 days is lower than actual because the fermenter is considered empty at time 0 in the model 35

5.1.3. Fermenter temperature model

Figure 5.17: Fermenter temperature model block.

Assumptions:

1. Steady state operating conditions exist.2. Heat transfer is two dimensional (no change in the axial direction).3. Thermal and physical properties are constant for each substance.4. The fermenter is occupied mostly by substrate at any given time.

The fourth statement can be safely assumed by calculating the expected volume of substrate at

any time with the mean retention time being 55 days and assuming that the minimum time to

start producing biogas is 10 days[7]. As a rough estimate, volumes of input substrates are

subtracted from the fermenter after 40 days, the result is seen in the following graph. The

produced biogas goes directly to the storage tank which is not heated.

Figure 5.18: Substrate volume in fermenter.*


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The surface area of the fermenter is the summation of the top area (r2) and the cylindrical

side area (2rh), therefore the area of the jacket would be:

22 22 f

f

ffffj r

r

Hybrid Biogas Photovoltaic System

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