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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 Information derived from the published or unpublished work of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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    vi

    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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    viii

    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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    ix

    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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    x

    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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    xii

    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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    xv

    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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    1. INTRODUCTION

    2

    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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    6

    3. TECHNICAL BACKGROUND

    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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    3. TECHNICAL BACKGROUND

    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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    3. TECHNICAL BACKGROUND

    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. TECHNICAL BACKGROUND

    9

    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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    3. TECHNICAL BACKGROUND

    10

    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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    3. TECHNICAL BACKGROUND

    11

    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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    3. TECHNICAL BACKGROUND

    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. TECHNICAL BACKGROUND

    13

    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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    3. TECHNICAL BACKGROUND

    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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    4. ANALYSIS OF THE ENERGY SYSTEM OF EICHHOF AGRICULTURE CENTER

    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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    4. ANALYSIS OF THE ENERGY SYSTEM OF EICHHOF AGRICULTURE CENTER

    18

    Figure 4.2: Gas grid layout.

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    4. ANALYSIS OF THE ENERGY SYSTEM OF EICHHOF AGRICULTURE CENTER

    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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    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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    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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    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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    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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    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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    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