Hybrid Biogas Photovoltaic System

MODELING AND PERFORMANCE ANALYSIS OF A HYBRID BIOGAS-PHOTOVOLTAIC SYSTEM Analysis of the Electrical and Thermal System in Eichhof Center, Germany - A Case Study in Jordan

By,

Rand Al-Zu'bi

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 SYSTEM Analysis of the Electrical and Thermal System in Eichhof Center, Germany - A Case Study in Jordan

By,

Rand Al-Zu'bi 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 Engineering Cairo University

Faculty of Electrical Engineering/Computer Science University of Kassel

February, 2012

DISCLAIMER To the best of my knowledge I do hereby declare that this thesis is my own work. It has not been 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 Information derived from the published or unpublished work of others has been acknowledged in the text and a list of references is given.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-Zu’bi Kassel, 22.02.2012

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ABSTRACT The integration of PV and biogas with a focus on biogas as a flexible electricity supplier is thoroughly 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 and contributes 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 more than 0.02 JD/kWh (0.021 €/kWh) for a 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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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, Ma’en 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 .......................................................................................................................... iii ABSTRACT .............................................................................................................................. iv ACKNOWLEDGMENT ............................................................................................................ v TABLE OF CONTENTS .......................................................................................................... vi LIST OF FIGURES ................................................................................................................... ix LIST OF TABLES .................................................................................................................. xiv NOMENCLATURE ................................................................................................................. xv ABBREVIATIONS AND TERMS ......................................................................................... xvi 1.

INTRODUCTION .............................................................................................................. 1 1.1.

Motivation ................................................................................................................... 1

1.2.

Research Objectives .................................................................................................... 2

1.3.

Scope of the Work ....................................................................................................... 2

2.

A BRIEF REVIEW OF THE LITERATURE .................................................................... 4

3.

TECHNICAL BACKGROUND ........................................................................................ 6 3.1.

Hybrid Systems............................................................................................................ 6

3.2.

Biogas .......................................................................................................................... 8

3.2.1.

Substrates ............................................................................................................. 8

3.2.2.

Biogas formation .................................................................................................. 9

3.2.3.

Environmental conditions .................................................................................. 10

3.3.

4.

Photovoltaics ............................................................................................................. 12

3.3.1.

Components ........................................................................................................ 12

3.3.2.

Classifications .................................................................................................... 13

3.3.3.

System sizing...................................................................................................... 14

ANALYSIS OF THE ENERGY SYSTEM OF EICHHOF AGRICULTURE CENTER 15 4.1.

System Description .................................................................................................... 15

4.1.1. 5.

The existing gas grid .......................................................................................... 15

SIMULATION ................................................................................................................. 20 5.1.

Biogas Production...................................................................................................... 22 vi

5.1.1.

Feed .................................................................................................................... 22

5.1.2.

Fermenter ........................................................................................................... 24

5.1.3.

Fermenter temperature model ............................................................................ 35

5.1.4.

Biogas storage .................................................................................................... 43

5.1.5.

Distribution model.............................................................................................. 45

5.1.6.

Biogas burner ..................................................................................................... 47

5.1.7.

Micro-gas turbine ............................................................................................... 49

5.1.8.

Combined heat and power unit........................................................................... 53

5.1.9.

Natural gas burners ............................................................................................. 54

5.2.

5.2.1.

Solar radiation on a tilted angle .......................................................................... 57

5.2.2.

PV system power output .................................................................................... 61

5.3.

System Analysis ........................................................................................................ 65

5.3.1.

Demand side analysis ......................................................................................... 65

5.3.2.

Current status...................................................................................................... 67

5.4.

6.

Photovoltaic System .................................................................................................. 56

Proposed Future Scenario .......................................................................................... 74

5.4.1.

Battery model .................................................................................................... 77

5.4.2.

Biogas control strategy ....................................................................................... 78

5.4.3.

Substrate management ........................................................................................ 80

5.4.4.

Results ................................................................................................................ 81

CASE STUDY: HAMMOUDEH DAIRY FARM IN JORDAN .................................... 85 6.1.

Site Description ......................................................................................................... 85

6.2.

Input Parameters ........................................................................................................ 86

6.2.1.

Biogas ................................................................................................................. 86

6.2.2.

PV ....................................................................................................................... 88

6.3.

Simulation .................................................................................................................. 89

6.3.1.

System design ..................................................................................................... 89

6.3.2.

Results ................................................................................................................ 91 vii

6.4.

Economical Analysis ................................................................................................. 94

6.4.1.

Total investment ................................................................................................. 94

6.4.2.

Annual costs ....................................................................................................... 96

6.4.3.

Annual revenue .................................................................................................. 96

6.4.4.

Extra costs .......................................................................................................... 97

6.4.5.

Levelized cost of energy (LCE) ......................................................................... 97

6.4.6.

Cash flow diagram ............................................................................................. 99

7.

CONCLUSIONS ............................................................................................................ 102

8.

REFERENCES ............................................................................................................... 104

APPENDIX A: GAS CONSUMERS ..................................................................................... 106 APPENDIX B: EQUIPMENTS’ SPECIFICATIONS ........................................................... 107 APPENDIX C: SOLAR RADIATION .................................................................................. 108 C.1 Apparent solar time (AST) .......................................................................................... 108 C.2 Solar declination (δ)..................................................................................................... 108 C.3 Hour angle (h) .............................................................................................................. 109 C.4 Incidence angle (θ) ...................................................................................................... 109 C.5 Solar altitude angle (α) ................................................................................................ 109 APPENDIX D: CORRELATION COEFFICIENT CALCULATIONS ................................ 110 APPENDIX E: REGMODHARZ .......................................................................................... 112

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LIST OF FIGURES Figure 3.1: Biogas-PV hybrid system. ....................................................................................... 7 Figure 3.2: Stages of anaerobic fermentation process. ............................................................... 9 Figure 3.3: Influence of the temperature on the time of fermentation. .................................... 11 Figure 3.4: Major PV system components. .............................................................................. 12 Figure 4.1: The layout of Eichhof agricultural center. ............................................................. 16 Figure 4.2: Gas grid layout. ...................................................................................................... 18 Figure 5.1: Eichhof energy system simulation. ........................................................................ 21 Figure 5.2:Biogas production blocks. ...................................................................................... 22 Figure 5.3: Substrate input flow rate. ....................................................................................... 23 Figure 5.4: Feed properties....................................................................................................... 24 Figure 5.5: Fermenter model (Jan-Dec, 2009 feed). ................................................................ 26 Figure 5.6: Kinetic parameter versus volatile solids concentration. ........................................ 27 Figure 5.7: Fermenter Temperature throughout 2009. ............................................................. 28 Figure 5.8: corrected fermenter temperatures. ......................................................................... 29 Figure 5.9: Retention time in the fermenter. ............................................................................ 29 Figure 5.10: Methan content block. ......................................................................................... 30 Figure 5.11: Methane content throughout the year. ................................................................. 30 Figure 5.12: Exponential biogas accumulation model. ............................................................ 31 Figure 5.13: Methane production for substrate entering in 2009. ............................................ 32 Figure 5.14: Biogas output from substrate which is fed in the last fifty days of 2008. ........... 32 Figure 5.15: Actual biogas production block. .......................................................................... 33 Figure 5.16: Daily biogas production in 2009 (a) calculated, (b) actual. ................................. 34 Figure 5.17: Fermenter temperature model block. ................................................................... 35 Figure 5.18: Substrate volume in fermenter ............................................................................. 35 ix

Figure 5.19: Area calculations of heating system components. ............................................... 37 Figure 5.20: Agitator block. ..................................................................................................... 37 Figure 5.21: Heat of agitation. ................................................................................................. 38 Figure 5.22: Density and specific heat capacity calculation. ................................................... 38 Figure 5.23: Overall heat transfer coefficient calculations. ..................................................... 39 Figure 5.24: Outlet temperature of water. ................................................................................ 41 Figure 5.25: Fermenter temperature model. ............................................................................. 42 Figure 5.26: Water flow rate required for heating of fermenter. .............................................. 43 Figure 5.27: Biogas storage modelling. ................................................................................... 44 Figure 5.28: Comparing biogas out to needed and available biogas in storage ....................... 44 Figure 5.29: Used storage volume for biogas storage throughout the year. ............................. 45 Figure 5.30: Biogas distribution block. .................................................................................... 46 Figure 5.31: Biogas flow rate in for biogas consumers. ........................................................... 46 Figure 5.32: Biogas burner model. ........................................................................................... 47 Figure 5.33: burner load and temperature dependency. ........................................................... 48 Figure 5.34: Biogas needed for burner ..................................................................................... 48 Figure 5.35: Gas consumption for burner. ............................................................................... 49 Figure 5.36: Heat output of burner. .......................................................................................... 49 Figure 5.37: Micro-gas turbine model...................................................................................... 50 Figure 5.38: Biogas needed for turbine. ................................................................................... 50 Figure 5.39: Efficiency and power of micro-gas turbine and their relation with temperature through 2009. ........................................................................................................................... 51 Figure 5.40: Power output from micro-gas turbine. ................................................................. 52 Figure 5.41: Actual and calculated power output from the micro-gas turbine......................... 52 Figure 5.42: CHP unit model. .................................................................................................. 53 Figure 5.43: CHP electrical power output. ............................................................................... 54 Figure 5.44: Comparison between actual and calculated power out from CHP unit. .............. 54 x

Figure 5.45: Natural gas consumers block. .............................................................................. 55 Figure 5.46: Natural gas needed for natural gas burners. ......................................................... 55 Figure 5.47: PV system model. ................................................................................................ 56 Figure 5.48: PV installations .................................................................................................... 57 Figure 5.49: Global solar radiation at Grebenau in 2009. ........................................................ 57 Figure 5.50: Equation of time block......................................................................................... 58 Figure 5.51: Apparent solar time block. ................................................................................... 58 Figure 5.52: Radiation calculations block. ............................................................................... 59 Figure 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. ...................................... 61 Figure 5.55: PV power model. ................................................................................................. 62 Figure 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. ................................................... 63 Figure 5.58: Total PV power output in 2009. .......................................................................... 64 Figure 5.59: Load profile. ........................................................................................................ 65 Figure 5.60: Simplified electrical infrastructure at Eichhof showing production and consumption loads. ................................................................................................................... 66 Figure 5.61: Estimated heat demand for 2009. ........................................................................ 66 Figure 5.62: Energy system analysis block. ............................................................................. 67 Figure 5.63: Inside energy analysis block. ............................................................................... 67 Figure 5.64: Penetration level calculations. ............................................................................. 68 Figure 5.65: Penetration level of electrical power from RE resources throughout the year 2009. ......................................................................................................................................... 68 Figure 5.66: Penetration rate for heat produced from RE resources throughout 2009. ........... 69 Figure 5.67: Load profile versus production profile in 2009. .................................................. 71 Figure 5.68: Heat produced from CHP versus heat consumed for fermenter heating. ............ 72 Figure 5.69: Biogas availability in 2009. ................................................................................. 73 xi

Figure 5.70: Future scenario block. .......................................................................................... 74 Figure 5.71: PV residual load calculations block. .................................................................... 76 Figure 5.72: Residual load from PV produced electricity. ....................................................... 76 Figure 5.73: Battery model....................................................................................................... 77 Figure 5.74: Battery utilization block. ..................................................................................... 78 Figure 5.75: Number of days for heat storage calculations sample. ........................................ 78 Figure 5.76: Biogas control strategy flow diagram. ................................................................. 79 Figure 5.77: Needed biogas calculations after applying the control strategy. ......................... 80 Figure 5.78: Calculating monthly average needed biogas (for January here). ......................... 81 Figure 5.79: Comparison between inflow and outflow and needed biogas for the future case scenario..................................................................................................................................... 81 Figure 5.80: Storage volume occupied by biogas in the future case scenario......................... 82 Figure 5.81: Production profile versus load profile in the future case scenario (excluding batteries). .................................................................................................................................. 83 Figure 5.82: Fermenter heat demand versus CHP heat generation in the future case scenario. .................................................................................................................................................. 83 Figure 5.83: Load profile versus power production with battery usage in the future case scenario..................................................................................................................................... 84 Figure 5.84: biogas availability in the proposed future scenario. ............................................ 84 Figure 6.1:Hammoudeh Food Industries Company's (a) dairy farm (b) dairy plant. ............... 86 Figure 6.2: Global solar radiation in Mafraq. ........................................................................... 88 Figure 6.3: Temperature profile throughout 2008 in Mafraq. .................................................. 88 Figure 6.4: Hybrid system model for Hammoudeh dairy farm and plant in Jordan. ............... 90 Figure 6.5: Micro-gas turbine power output changes throughout the year. ............................. 92 Figure 6.6: Solar radiation absorbed by the PV systems in its four configurations. ................ 92 Figure 6.7: PV system power output. ....................................................................................... 93 Figure 6.8: Satellite image of Hammoudeh dairy farm. ........................................................... 93 Figure 6.9: Total investment calculations. ............................................................................... 94 xii

Figure 6.10: Annual costs and revenues. .................................................................................. 97 Figure 6.11: Economical analysis block. ................................................................................. 98 Figure 6.12: Discounted cash flow diagram, P: price for selling electricity. ......................... 100 Figure A.1: Natural and biogas consumers, nominal capacity and variable output. .............. 106 Figure B.1: Burner output range. (source: manufacturer) ...................................................... 107 Figure B.2: Net power and efficiency at ambient temperature. (source: manufacturer) ........ 107 Figure C.1: Solar declination. ................................................................................................ 108 Figure 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. ...................................................................... 8 Table 5.1: Simulation parameters. ............................................................................................ 20 Table 5.2: Substrate properties. ................................................................................................ 22 Table 5.3: Ultimate methane yield of substrates. ..................................................................... 27 Table 5.4: Mean biogas yield for the substrates. ...................................................................... 33 Table 5.5: Biogas production prediction evaluation. ............................................................... 33 Table 5.6: Properties of fermenter heating system components. ............................................. 36 Table 5.7: Dimensions and details of PV installations............................................................. 56 Table 5.8: Overall efficiency for PV systems. ......................................................................... 62 Table 6.1: Location of Hammoudeh dairy farm. ...................................................................... 85 Table 6.2: Properties of dairy manure. ..................................................................................... 86 Table 6.3: Simulation results for the Jordan study case. .......................................................... 87 Table 6.4: Simulation results over one year for the Jordan study case (scenario one: more biogas). ..................................................................................................................................... 91 Table 6.5: Simulation results over one year for the Jordan study case (scenario two: more PV). .................................................................................................................................................. 91 Table 6.6: PV system pricing indices. ...................................................................................... 95 Table 6.7: Factors for annual costs calculations. ..................................................................... 96 Table 6.8: suggested scenarios for Hammoudeh plant hybrid system. .................................... 99 Table 6.9: economical indicators for scenario one: more biogas. .......................................... 100 Table 6.10: economical indicators for scenario two: more PV. ............................................. 101

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NOMENCLATURE A B CC C cp CV C(x,x) d DOD Ds E h h i I k k K m

m n N Nu P Pr Q Re St t T ΔTm U V V

V

VHR w y Δx Z

Area (m2) methane yield of organic waste (m3 CH4/kg VS), correlation coefficient Capacity specific heat capacity (J/kg.K) calorific value (kWh/m3) variance of the data sequence Diameter (m) battery depth of discharge battery autonomy (day) Energy (Wh) the heat transfers coefficients (W/m2.C) Hour angle discount rate Radiation (W/m2) thermal conductivities (W/m.C) the first order kinetic constant (day-1) kinetic parameter which indicates the overall performance of the digester mass (kg) mass flow rate (kg/day) number of the years of a project Number of readings Nusselt number Power (kW) Prandtl number rate of heat flow (W) Reynolds number volatile solids concentration (kg/m3) time temperature log mean temperature difference overall heat transfer coefficient (W/m2.C) Volume (m3) Voltage volumetric flow rate (m3/day) volumetric heat release (W/m3) mass fraction Biogas accumulation (m3/kg) Thickness (m) azimuth angle

Greek letters  solar altitude angle  surface tilt angle from the horizontal θ θ μ μ μ

  ρ

 δ

hydraulic retention time (day) incidence angle mean value viscosity (N.s/m2) the maximum specific growth rate (day-1) volume fraction solar zenith angle Density (kg/m3) Efficiency Solar declination

Subscripts a Ambient ag Agitator B battery BG Biogas c concrete c cell cf correction factor e Equivalent el electrical f Fermenter G global i ins L loss NOCT m o p ref s s th tot w

Each substrate insulation Load losses nominal operating cell temperature (oC) Microorganisms Ultimate value Peak reference Substrate Surface thermal total water

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ABBREVIATIONS AND TERMS AC AC ADM1 BG CC CHP C/N CRF DC DER DS ET GSM HDPE ISET IWA IWES JD LCE LL LST NERC MENA SL SoDa oTS PV RE RegModHarz ROI ROR TAC TS VFA VS

Alternating Current Annual Cost Anaerobic Digestion Model no 1 Biogas Capital Cost Combined Heat and Power Carbon Nitrogen ratio Capital Recovery Factor Direct Current Distributed Energy Resources Daylight Saving Equation of Time Global System for Mobile communications High-Density Polyethylene Institut für SolareEnergieversorgungstechnik International Water Association Institute for Wind Energy and Energy systems Jordanian Dinar Levelized Cost Of Energy Local Longitude Local Standard Time National Center for Research & Development Energy Research Program Middle East and North Africa Standard Longitude Solar data organische Trockensubstanz Photovoltaic Renewable Energy Regenative Modellregion Harz Return On Investment Rate Of Return Total Annualized Cost Total Solids Volatile Fatty Acids Volatile Solids

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1. INTRODUCTION

1. INTRODUCTION As 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. Motivation This 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 bioenergy’s 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.

1

1. INTRODUCTION

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 Objectives The 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 energy resources 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 be implemented in different environmental conditions.

1.3. Scope of the Work In 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 electrical energy 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 2

1. INTRODUCTION

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 and proposing 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 the MENA 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 plant’s 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.

3

2. A BRIEF REVIEW OF THE LITERATURE

2. A BRIEF REVIEW OF THE LITERATURE Many studies are available on hybrid systems including well-established renewable energy resources 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 system as 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 Börjesson [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. 4

2. A BRIEF REVIEW OF THE LITERATURE

Axaopoulos, Panagakis, Tsavdaris and Georgakakis [26] developed a mathematical model that simulates a solar-heated anaerobic digester. The model investigates a below-ground-level digester with a flat plate solar collector roof. The results of experimental performance analysis on the system indicate that the use of solar collectors as a cover reduces the digester thermal losses; the back heat losses from the solar also affect the heat balance of the digester in a positive manner. They also concluded that the temperature of the digester is influenced by the quantity and time of the input substrate. Also their mathematical model over the heat balance of the system was able to predict the thermal behavior of the system. Diaf, Diaf, Belhamel, Haddadi and Louche [27] proposed an optimization model for sizing of an autonomous hybrid PV/wind system with battery storage. The model is based on the Loss of Power Supply Probability (LPSP) and the Levelized Cost of Energy (LCE) concepts. They concluded that in order to insure a 100% renewable energy contribution, more than 30% of the energy production is wasted or very large storage is needed. Reducing the renewable energy contribution to 85% rapidly decreases the excess energy to 5%. The use of a third intermittent energy source (diesel in this case) was the best was of reducing the energy excess while achieving the lowest LCE. The LCE values were ranging from 1.4 to 3 dollar per kWh. Finally, Shaahid and El-Amin [28] presented a techno-economic evaluation of an off-grid PV-diesel system in a rural area in Saudi Arabia, a location with high solar radiation and low fuel prices. In the paper, the solar radiation of the location is first analyzed then the hybrid system is design using HOMER simulation and then economically analyzed. The PV penetration was 27% where as the cost of energy generation was 0.17 dollars per kWh. The cost of energy increase as the battery capacity increases and the number of operational hours of diesel generators decreases as the PV capacity increases.

5

3. TECHNICAL BACKGROUND

3. TECHNICAL BACKGROUND

3.1. Hybrid Systems Through 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 that represents 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 the combination 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.

6

3. TECHNICAL BACKGROUND

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.

7

3. TECHNICAL BACKGROUND

3.2. Biogas The formation of methane is a biological process that takes place naturally when organic material (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. Substrates In general, any type of biomass can be used as a substrate for biogas production as long as it contains 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 103-105°C 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 Vegetable wastes Hay Maize straw Bio waste Leftovers (canteen kitchen) Sewage sludge Liquid dairy manure Excreta from dairy Liquid pig manure Excreta from pigs Excreta from chicken

TS (%) 5-20 86 86 40-75 9-37 6-11 25-30 3-10 20-25 10-29

VS in TS (%) 76-90 90-93 72 30-70 75-98 68-85 80 77-85 75-80 67-77

Biogas yield (m3 kg-1 TS) 0.4 0.5 0.4-1.0 0.3-1.0 0.4-1.0 0.2-0.75 0.1-0.8 0.6-0.8 0.3-0.8 0.27-0.45 0.3-0.8

Retention time (d) 8-20 27 17 8

3. TECHNICAL BACKGROUND

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:

Cc H h Oo N nSs  yH 2O  xCH 4  nNH 3  sH2S  (c  x)CO 2

‎3.1

Having,

x  1/8  (4c  h  20  3n  2s) y  1/4  (4c  h  20  3n  3s) 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 partly stand 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 9

3. TECHNICAL BACKGROUND

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 during the 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 conditions The 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. 10

3. TECHNICAL BACKGROUND

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.

11

3. TECHNICAL BACKGROUND

3.3. Photovoltaics Photovoltaic (PV) is the well known technology in which direct current (DC) electrical power is 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. Components The 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 appliances or feeds it back into grid line.



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



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

Figure ‎3.4: Major PV system components.

12

3. TECHNICAL BACKGROUND

3.3.2. Classifications The 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, their components 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 consumer unit 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 directcoupled 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.

13

3. TECHNICAL BACKGROUND

3.3.3. System sizing The 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 sun’s 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

14

4. ANALYSIS OF THE ENERGY SYSTEM OF EICHHOF AGRICULTURE CENTER

4. ANALYSIS OF THE ENERGY AGRICULTURE CENTER

SYSTEM

OF

EICHHOF

The Eichhof agricultural center is an information and training institution under the Hessian Service Center for Agriculture for rural areas. The responsibilities of the center include the experimental activities in arable farming and plant cultivation, vegetation management, forage production, nature protection, landscape conservation and biomass production. The Eichhof center is a location that includes the existing biogas plant, the agricultural experimental farm, the village-like structure, the photovoltaic system and the laboratories under the cooperation of a cluster of several research and consulting entities. A satellite image (using Google earth) of the center is shown in Figure ‎4.1 with the main buildings and systems indicated.

4.1. System Description 4.1.1. The existing gas grid The natural gas is supplied by the Stadtwerke Bad Hersfeld GmbH Company. The gas producers (of which is the biogas plant) and consumers in Eichhof create a local micro gas grid in which the different elements are independent and where operation of the grid requires adequate supply, consumption and storage management. The natural gas flows in a High-Density Polyethylene pipe (HDPE) with an outer diameter of 160 mm until the transfer point and then in a HDPE line with an outer diameter of 110 mm to the individual buildings. Only the line from the stables to the biogas system building is made out of steel with a nominal diameter of 80 mm. The total pipe length in the land of Eichhof is about 950 m. The gas net can be operated as a separate (island) net through detaching the inlet point. The total supply of Eichhof is done be a single mainline and the line branching from the main line do not lead to any other consumer outside the center. 4.1.2. Biogas plant and pipe lines The biogas system at the Eichhof agricultural center was built by the BIOGAS NORD GmbH Company and commenced operation in October, 2002. The biogas system consists of a 600 m3 fermenter with an attached secondary fermenter; the latter is only used as final storage and is not heated. The current mean retention time is 50 days. The fermenter feed has a VS value of 1.5 kg/m3.day, this value can be increased to 3 kg/m3.day, and therefore can cause significant improvement for the performance of the biogas system. 15

4. ANALYSIS OF THE ENERGY SYSTEM OF EICHHOF AGRICULTURE CENTER

Figure ‎4.1: The layout of Eichhof agricultural center.

16

4. ANALYSIS OF THE ENERGY SYSTEM OF EICHHOF AGRICULTURE CENTER

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 m3. 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 consumption Figure ‎4.2 shows the natural and biogas consumers in Eichhof. 4.1.3.1.

Natural gas consumption

The 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 manager’s house and the former workplace. A 200 kW gas burner located in the basement of the castle is used for the castle’s 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 m3 of consumption. The information read by the meter is sent and stored through a GSM-sender.

17

4. ANALYSIS OF THE ENERGY SYSTEM OF EICHHOF AGRICULTURE CENTER

Figure ‎4.2: Gas grid layout.

18

4. ANALYSIS OF THE ENERGY SYSTEM OF EICHHOF AGRICULTURE CENTER

4.1.3.2.

Micro-gas-turbine

The 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 kWel. 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 unit

The 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 kWth) 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 burner

The 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.

19

5. SIMULATION

5. SIMULATION To evaluate the performance and analyse the hybrid system in Eichhof, full process simulation 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 Solver Relative tolerance Maximum step Minimum step Initial step Maximum order*

Value Ode45 1 × 10-3 1 0.001 0.01 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 DormandPrince 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 modelling 2. PV system modelling 3. System analysis and suggested future scenario modelling

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

20

Figure ‎5.1: Eichhof energy system simulation.

5. SIMULATION

5.1. Biogas Production

Figure ‎5.2:Biogas production blocks.

5.1.1. Feed The 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 Manure Corn silage Wheat grain

Average flow rate in 2009 (kg/day) 9,965 2,016 33

TS [2] (kg/kg input) 0.11 0.35 0.08

VS [2] (kg/kg input) 0.09 0.34 0.07

Density [1] (kg/m3) 990 720 700

The feed is entered as time series with daily volumetric manure flow rate and daily mass flow rate of corn and wheat.

22

5. SIMULATION

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.

23

5. SIMULATION

Figure ‎5.4: Feed properties.

5.1.2. Fermenter Several 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 kinetic equations, 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 24

5. SIMULATION

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 as physiochemical 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 the Contois 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 the anaerobic digestion process is shown in the following figure.

25

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

5. SIMULATION

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]:

  K  B  Bo 1     1  K  m 

‎5.1

where, θ 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), μm 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. The following table lists the ultimate methane yield for manure, corn and wheat and the corresponding reference. Table ‎5.3: Ultimate methane yield of substrates.

Substrate Manure Corn Wheat

Ultimate methane yield Bo (m3 CH4 per kg VS) 0.52 0.33 0.30

Reference [3] [5] [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]

27

5. SIMULATION

For high volatile solids concentration values for anaerobic digestion of animal manure the following relation[4] is used:

K  0.6  0.0006 e(0.118St )

‎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 St 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:

K  0.6  0.0006 e(0.0012 St )

‎5.3

The maximum specific growth rate (μm) of the microorganism depends on the digester temperature. μm can be considered as a linear function of the digestion temperature in the range 30 - 60°C:

m  0.013 T  0.129

‎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 operators of 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. 28

5. SIMULATION

Figure ‎5.8: corrected fermenter temperatures.

The hydraulic retention time (θ) 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:



Vf   s m s

‎5.5

where, Vf is the volume of fermenter (m3), ρs is the density of substrate (kg/m3), m s 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.

29

5. SIMULATION

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

CH 4 %total   CH 4 % s  s

‎5.6

1

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.

30

5. SIMULATION

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]

y(t)  ymax (1  e  kt )

‎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:

y(t )   ( ymax  y(t ))  k dt

‎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.

31

5. SIMULATION

Figure ‎5.13: Methane production for substrate entering in 2009.

Figure ‎5.13 shows that the biogas accumulation in the fermenter starts from zero m3/day. This is 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. 32

5. SIMULATION

Table ‎5.4: Mean biogas yield for the substrates. [

Substrate Manure Corn Wheat

Biogas yield (m3/ton) 33 230 40

6]

Methane content 59 % 53 % 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 Manure Corn silage Wheat grain Total

Feed 3,674 (m3) 735,840 (kg) 12,045 (kg) 4,385,145 (kg)

Expected biogas (m3) 120,029 169,243 481 289,810

Calculated biogas (m3) 120,962 145,800 413.1 267,175

Actual biogas (m3) N/A 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.

33

5. SIMULATION

(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.

34

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5.1.3. Fermenter temperature model

Figure ‎5.17: Fermenter temperature model block.

Assumptions: 1. 2. 3. 4.

Steady state operating conditions exist. Heat transfer is two dimensional (no change in the axial direction). Thermal and physical properties are constant for each substance. 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.* * Volume in the first 50 days is lower than actual because the fermenter is considered empty at time 0 in the model

35

5. SIMULATION

For the mathematical analysis of the heating system of the fermenter, the substrate in the digester is assumed to be always well mixed and therefore at a uniform temperature Tf (oC) which varies only with time. The heat balance for the digester can be expressed as:

 sVs c p, s

dT f dt

 Q w  Q ag  Q s  Q loss

‎5.9

Where, Vs is the substrate volume (m3), cp,s is the substrate specific heat capacity (J/kg.K), ρs is the substrate density (kg/m3), Tf is the fermenter temperature (oC), Q w is the rate of heat delivered by the heating water (W), Q s is the rate of heat delivered to the substrate (W), Q ag is the rate of heat dissipation by the agitator (W) and, Q loss is the total heat losses from the fermenter to the surroundings (W). The dimensions of the fermenter and the heating coils and properties of fluids are listed in the following table. Table ‎5.6: Properties of fermenter heating system components.

Property Volume of fermenter Radius of fermenter Wall thickness Coil radius (DN 125) Number of coils Insulation thickness Density of concrete Specific heat capacity of water @ 60oC Specific heat capacity of man ure Specific heat capacity of corn silage Specific heat capacity of wheat Thermal conductivity of the soil Thermal conductivity of concrete Thermal conductivity of insulation Thermal conductivity of coils (steel) Thermal conductivity of water @ 60oC Viscosity of water @ 60oC

Symbol Vf rf t rcoil ncoil tins ρc cp,w cp,c cp,c cp,c ksoil ksoil kins kcoil kw μw

Value 600 14 0.18 0.07 2 0.10 2400 4.18 4.2 1.41 1.63 0.9 0.8 0.033 43 0.654 4.467 × 10-4

Unit m3 m m m m kg/m3 kJ/kg.C kJ/kg.C kJ/kg.C kJ/kg.C W/m.C W/m.C W/m.C W/m.C W/m.C N.s/m2

36

5. SIMULATION

The surface area of the fermenter is the summation of the top area (πr2) and the cylindrical side area (2πrh), therefore the area of the jacket would be:

A j  2r f h f  r f 2 

2V f rf

 r f 2

‎5.10

And the heating pipes surface area would be: Acoils  2 rcoil  2 r f  ncoil

‎5.11

Those calculations are done in the block shown in the following figure.

Figure ‎5.19: Area calculations of heating system components.

5.1.3.1.

Rate of heat dissipation

The energy dissipated from the agitator can be expressed by the Volumetric Heat Release (VHR) and the time of agitation per day fed into the model. The agitator is turned on for a number of intervals, with an average value of 8 minutes per interval. The average number of intervals throughout the year was 10 per day. The hour per day agitation time is calculated in the block represented below.

Figure ‎5.20: Agitator block.

Q ag can be calculated using the following relation:

 

 h   W   J/s  3600s Q ag  VHR  3       operation time   V f m3  h m   W   day 

‎5.12

where, VHR is the volumetric heat release of the agitator = 10 W/m3. 37

5. SIMULATION

Figure ‎5.21: Heat of agitation.

5.1.3.2.

Substrate heating

The rate of which heat is delivered to the incoming substrate is calculated as follows:

 s c p, s T f  Ts  Q s  m

‎5.13

Where, m s is the substrate flow rate (kg/s), Ts is the Temperature of the incoming substrate (oC) = ambient temperature, The substrate specific heat capacity is a function of its components and is changing with time:

c p, s   wi c p, i

‎5.14

Where, wi is the substrate mass fraction (kg substrate /kg total), calculated as shown in the following figure.

Figure ‎5.22: Density and specific heat capacity calculation.

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5. SIMULATION

5.1.3.3.

Heat losses

To begin with, the heat loss by natural convection from the vertical wall and roof is calculated. The overall heat transfer coefficient is estimated as follows:

x x 1 1 1    ins  c U w hs hair kins kc

‎5.15

where, Uw is the overall heat transfer coefficient between substrate and air (W/m2.C), hs and hair are the heat transfers coefficients of substrate and air respectively (W/m2.C), kins and kc are the thermal conductivities of insulation and concrete respectively (W/m.C), Δxins and Δxc are the thickness of insulation and concrete respectively (m)

The exact estimation of heat transfer coefficients for air and substrate can be a complicated trial and error process, however the low value of the insulation conductivity and its thickness makes it the dominant thermal resistance; the mean values of heat transfer coefficients will be taken as reported in the literature since they would not change the value of the overall heat transfer coefficient greatly. The heat transfer coefficient of the substrate is taken 8.2[8] W/m2.C and for air 11.6 [9] W/m2.C. Therefore,

1 1 1 0.10 0.18      U  0.29 W/m 2 .C as seen in the figure below. U w 8.2 11.6 0.033 0.8

Figure ‎5.23: Overall heat transfer coefficient calculations.

And

Q loss, wall  U w A j T f  Ta 

‎5.16

where, Ta is ambient temperature in oC. Secondly, we calculate the floor heat losses; for a plate at temperature Tf (fermenter temperature) placed on a semi-infinite solid, the heat loss can be calculated as:

Q loss, floor  4r f k soil T f  Ta  5.1.3.4.

‎5.17

Heating-water heat transfer

We can calculate the heat transferred from water using two relations: 39

5. SIMULATION



 w c p,w Tw,in  Tw,out Q w  m



‎5.18

where, Tw is the Temperature of the inlet water (oC),

And the well known formula for sizing heat exchangers presented below.

Q w  U c Acoil LMTD

‎5.19

where, Uc is the overall heat transfer coefficient between water and substrate, LMTD is the log mean temperature difference between water and substrate.

Since the water flow rate is yet unknown, equation 15 will be used to find out the temperature at which the water is leaving the fermenter. To calculate the Overall heat transfer coefficient Uc between water and substrate:

1 1 x 1   coil  U c hs kcoil hwater

‎5.20

where, Uw is the overall heat transfer coefficient between substrate and water (W/m2.C),

Heat transfer coefficient of water can be calculated by the following formula;

Nu  C Re

0.8

Pr

     w 

0.33 

0.14

‎5.21

where, Nu is Nusselt number = hw d e ,

kw

4m w , d coil ncoils  w c p, w  w

Re is Reynolds number = Pr is Prandtl number =

,

kw hw is inside coefficient, W/m2.C, de is equivalent diameter, m = di for tubes, kw is water thermal conductivity, W/m.C, μ, μw are water viscosity at bulk temperature at the wall, respectively N.s/m2, Cp,w is water heat capacity, J/kg.C, C = 0.023 for non-viscous liquids. The log mean temperature difference is:

LMTD 

Tw,in  Ts,in   Tw,out  T f   Tw,in  Ts ,in    ln   T    T w , out f  

‎5.22

Into equation 18 we get: 40

5. SIMULATION

(Tw,in  Ts ,in )  (Tw,out  T f )  Tw,in  T f    ln   T    T w , out f  



Q w 0 U c Acoil

‎5.23

Equation 5.23 has one unknown (Tw,out) and can be solved by feeding into the algebraic solver block Simulink as seen in Figure ‎5.25. Q w will be calculated using the energy balance that is presented in the next section and the water flow rate is also fed after giving the initial value of Tw,out as 50 °C. The change in outlet temperature can be seen in the following figure.

Figure ‎5.24: Outlet temperature of water.

5.1.3.5.

Heat balance

By applying equations 5.12, 5.16, 5.17 and 5.19 into equation 5.9, we get the following ordinary first order differential equation:

 sV f c p,s

dT f dt

 Q ag  m s c p ,s T f  Ts   UA j T f  Ta  4r f k soil T f  Ta   U c Acoil LMTD ‎5.24

To calculate Q w needed to maintain the fermenter temperature, we can assume that the temperature is constant at any given day. Thus equation 16 becomes:

 s c p,s T f  Ts   UA j T f  Ta  4r f k soil T f  Ta   U c Acoil LMTD 0  Q ag  m

 s c p, s T f  Ts   UA j T f  Ta   4r f k soil T f  Ta   Q ag Q w  m

‎5.25

Energy balance and Q w calculations are illustrated in Figure ‎5.25.

41

Figure ‎5.25: Fermenter temperature model.

5. SIMULATION

The amount of energy required to heat the water to keep the desired digester temperature will be:

m w 

Q w c p , w Tw, in  Tw, out 

‎5.26

Figure ‎5.26: Water flow rate required for heating of fermenter.

The total heat consumption for fermenter in 2009 was around 2500 MWh. The daily heating requirements will be later used to evaluate the CHP unit performance. 5.1.4. Biogas storage The biogas storage block serves the purpose of ensuring that the biogas used by the next units is not more than the available biogas in storage following the next relation and as shown in Figure ‎5.27:

VBG , out  Vneeded VBG , out  VBG , stored

for VBG , stored for VBG , stored

 Vneeded  Vneeded

‎5.27 ‎5.28

Vneeded is a feedback from the different units that consume biogas. A comparison between the three values (Biogas in, out and needed) is illustrated in Figure ‎5.28.

43

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Figure ‎5.27: Biogas storage modelling.

Figure ‎5.28: Comparing biogas out to needed and available biogas in storage

Figure ‎5.28 shows that the required biogas volume is not always available (assuming that more than 1000 m3 biogas is already in the storage tank) and for the first the days of the year the biogas used is equal to biogas in and less than two thirds of the required. The required or used storage volume throughout the year can be calculated by performing a mass balance over the storage volume: 44

5. SIMULATION

mass input –mass output + mass accumulated = 0

‎5.29

therefore,

dV VBG , in  VBG , out  acc  0 dt By integration we get

Vstorage   VBG , in  VBG , out  dt

‎5.30

The calculated volume that is staying in the storage is shown in the following figure.

Figure ‎5.29: Used storage volume for biogas storage throughout the year.

The actual available volume for biogas storage in Eichhof is less than 400 m3. The figure shows that this storage volume is sufficient and that the biogas storage is empty most of the days of the year 5.1.5. Distribution model The biogas goes from the storage to the consumers which are divided into three main systems: biogas burner, Micro-gas turbine and CHP unit. And as seen before, since the needed amount of biogas is not always available, this block serves as a distribution point where each unit gets a fraction proportional to its need in the case of shortage of biogas in storage, this is illustrated in the following figure.

45

5. SIMULATION

Figure ‎5.30: Biogas distribution block.

The resulting biogas out of storage and into each of the consumers is shown below.

Figure ‎5.31: Biogas flow rate in for biogas consumers.

46

5. SIMULATION

5.1.6. Biogas burner The biogas flow rate needed to supply the heat demand of the burner is calculated according to the following relation:

 BG,needed  V

P  24 hours η burner  CVCH4  %CH4

‎5.31

where,

 BG,needed is the volumetric flow rate of biogas needed (m3/day) V

P is the output of burner (kW), 3 CVCH is the calorific value of methane (= 10 kWh/m ), 4

%CH4 is the percentage of methane in biogas (%). The biogas consuming burner was identified in chapter 4 with a capacity of 50 kW and a variable output. The actual consumption requirement of the burner was approximately estimated by giving a monthly average value of gas consumption in previous years in Eichhof and by normalizing this value and scaling the maximum output of the burners through multiplying it by the normalized value as seen in Figure ‎5.32.

Figure ‎5.32: Biogas burner model.

The expected gas consumption was calculated through a roughly estimated relationship with temperature which was established through similar trends that were found in previous years (2005 – 2008), the next graph was the result of this relation. 47

5. SIMULATION

Figure ‎5.33: burner load and temperature dependency.

The maximum value experienced in the burner load in Eichhof was determined to be (246.6.44 kW), and then the load profile was divided by this value to get a factor between 0 and 1. This value is multiplied by the burner rated maximum output to get the approximate gas consumption for the burner. However, the logs of Eichhof show that natural gas was used as feed for this burner and therefore, the actual values of natural gas used were entered into the model. After subtracting the natural gas consumption we get the biogas needed in 2009 seen in Figure ‎5.34.

Figure ‎5.34: Biogas needed for burner

The rate of heat output for the burner has a nominal value 50 kW with a variable output between 60 and 300 kW as reported and seen in appendix A. The maximum heat output (300 kW) was assumed at the highest load (factor =1). The biogas and natural gas consumption is shown in Figure ‎5.35 and the total heat output of the burner is shown in Figure ‎5.36. 48

5. SIMULATION

Figure ‎5.35: Gas consumption for burner.

The heat rate out of the burner is calculated by the summation of the heat produced from the natural gas and from the biogas as follows:

Qthermal  Qthermal , BG  Qthermal , NG

Qthermal 

VBG ,in   %CH 4  CVCH4 24



‎5.32

VNG,in   CVNG 24

‎5.33

Figure ‎5.36: Heat output of burner.

5.1.7. Micro-gas turbine The second biogas consumer is the micro-gas turbine in the laboratories building. The model describing the biogas electrical consumers is a simplified cogeneration model taking into account the thermal and electrical efficiencies only. The biogas needed for the turbine was calculated in the same approach as the biogas burner.

49

5. SIMULATION

Figure ‎5.37: Micro-gas turbine model.

The efficiency and power of the turbine depend highly on ambient temperature; therefore the efficiency-temperature and power-temperature curves that are provided by the manufacturer were fed into the model to get an accurate power and heat output. Those curves are show in appendix B. The resulting change in power and efficiency throughout the year are shown in Figure ‎5.39. Accordingly, we calculate the needed biogas flow rate to reach this maximum power with a specific temperature and efficiency using equation 5.31 with changing the terms for the turbine. We get the following flow rate of biogas needed as shown below.

Figure ‎5.38: Biogas needed for turbine.

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5. SIMULATION

Figure ‎5.39: Efficiency and power of micro-gas turbine and their relation with temperature through 2009.

The outside temperature is then fed into the model with the corresponding power and efficiency and with the actual input biogas we calculate the power output using an equation similar to equation 5.33 with removing the natural gas components of the equation. We get the power output shown below.

Qelectrical 

VBG ,in   el  %CH 4  CVCH 4

‎5.34

24

The effect of the main two parameters that influence the output electrical power is apparent in the graph where in the last fifty days in the year enough biogas is available and the 51

5. SIMULATION

temperature is below 15 oC. This conclusion has the exception of low power in few days in December which is because of the high biogas consumption from the burners which are located at the pig stables at the extremely low temperature in the given days.

Figure ‎5.40: Power output from micro-gas turbine.

The combustion air enters the generator of the micro-gas turbine and cools it down, causing high temperature exhaust air. This air can be utilized by passing it in a heat exchanger, and extracting the heat in the air. The turbine specifications show an effluent air with a temperature of 275 oC and mass flow of 0.31 kg/s and total energy of 327,000 kJ/hr (90.83 kW). This value, however, represents the maximum heat produced from the turbine at optimum conditions. The actual heat produced by the turbine can be estimated by introducing a heat efficiency term, this term is determined from actual heat produced from the turbine throughout the years. This value was reported to be 50%. Thus using equation 5.34 with the thermal efficiency given, an energy time series similar to the energy shown in Figure ‎5.40 scaled to reach a maximum of 55.56 kWth is generated. Logs for electrical power produced from the turbine were available from august 2009 until the end of the year and if it was compared to the calculated value to the actual power we find that the results are accurate. A sample period comparing the actual and calculated electrical power output from the turbine is show in Figure ‎5.41.

Figure ‎5.41: Actual and calculated power output from the micro-gas turbine.

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5. SIMULATION

5.1.8. Combined heat and power unit The third biogas consumer is the combined heat power unit which consists of a dual fuel engine and a heat exchanger. As explained previously the same simplified model for the micro-gas turbine will be used to describe the CHP unit. The efficiency of the dual fuel engine decreased throughout the years of operation, the efficiency of this type of engine is reported to range between 30% and 35% after few years. The efficiency is taken as 32.5% in this simulation. Similar to the procedure in the previous units, the needed biogas flow rate is first calculated and as the temperature does not affect the performance of the engine, the needed flow rate depends only on the methane content of the available biogas. Therefore, the needed biogas volume is fluctuating around a value of 390 m3/day. Similar to equation 5.34 we calculate the output electrical power as shown in Figure ‎5.42.

Figure ‎5.42: CHP unit model.

The resulting electrical power through the year 2009 is fluctuating with the available biogas as shown in the following figure. The thermal output is calculated with an efficiency of 36% according to the actual performance of the engine. The output thermal energy changes in a trend similar to the electrical output throughout the year.

53

5. SIMULATION

Figure ‎5.43: CHP electrical power output.

Actual CHP electrical power output is available over the last three months of 2009, the mean value of power generated was around 31.5 kW which indicates that the efficiency might be higher than the used value of 32.5%, however the readings also show several off (zero kW) periods and that would lower the total mean power out. Therefore we will keep the efficiency and nominal capacity as specified in order not to augment the output more than reality. A comparison between actual and calculated power output from CHP unit is shown in the following graph.

Figure ‎5.44: Comparison between actual and calculated power out from CHP unit.

5.1.9. Natural gas burners The rest of the burners in Eichhof (thirteen heaters and burners) are currently operating with natural gas and in order to understand the entire situation of the heat consumption we need to take into account this consumption (heat consumption will be discussed in more details in chapter six). Since the natural gas is available upon demand we can simply find out the needed natural gas by using the dependency relationship between temperature and heat 54

5. SIMULATION

consumption shown in Figure ‎5.33 and the known nominal capacity of each of the burners. The natural gas burners’ maximum power outputs are shown in Figure ‎5.45, more details about the burners can be found in Appendix B. The required flow rate is shown in Figure ‎5.46.

Figure ‎5.45: Natural gas consumers block.

Figure ‎5.46: Natural gas needed for natural gas burners.

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5.2. Photovoltaic System

Figure ‎5.47: PV system model.

There are three photovoltaic installations in Eichhof agricultural center. The center is located at latitude 50.84 and longitude 9.68. We start the analysis by defining the three PV installations on site; the details of area, tilt and orientation are presented in the following table and shown in Figure ‎5.48. Table ‎5.7: Dimensions and details of PV installations.

System number (1) (2) (3)

Dimensions (a) 1.6 × 0.8 27 ×3.8 (b) 38 × 3.8 33 × 12 10 × 6.01

Number 144 modules On 2 roof tops 1 panel 2 panels

Tilt angle 15o

Orientation angle 15o

20o

45o

30o

5o Tracking

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(1)

(2) Figure ‎5.48: PV installations

(3)

The solar radiation data are obtained from the meteorological data stations of the Hessian Agency for Environment and Geology [12], Half-hourly average values of global radiation and temperature are obtained from the station Grebenau located 20 km south west of Eichhof. The measured solar radiation profile from 01.01.2009 to 31.12.2009 is shown in Figure ‎5.49.

Figure ‎5.49: Global solar radiation at Grebenau in 2009.

The most noticeable thing seen in the solar radiation profile is that there is frequent fluctuation in radiation even in January and June, where a peak with high solar radiation is appearing in the first few days of the year and also very low values of radiation for some days in June. Those fluctuations will have apparent effects on the resulting PV production. 5.2.1. Solar radiation on a tilted angle The global solar radiation is measured for a horizontal surface and the PV collectors are not installed horizontally but as shown in Table ‎5.7 have several different configurations. The tilt 57

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and orientation of the PV panel increase the amount of radiation intercepted and reduce reflection and cosine losses. Consequently we need to convert the horizontal global radiation data to radiation on tilted surfaces. The global radiation takes into account the two components of radiation, direct and diffused radiation; therefore we will not calculate a separate value for each component but they will be treated collectively. The solar radiation on a tilted angle (I) can be calculated as follows:

I  IG

cos( ) cos( )

B1

where,  is the incidence angle, the angle between the sun’s ray and the normal on surface,  is the solar zenith angle, the angle between the sun’s ray and the vertical, IG is the global radiation (including direct and diffused radiation) W/m2

  90 o  

B2

where,  is the solar altitude angle The details of the solar angle calculation are out of the scope of this research and are described in Appendix C. The blocks that represent those equations are shown in the figures below in a consequent manner.

Figure ‎5.50: Equation of time block.

Figure ‎5.51: Apparent solar time block.

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Figure ‎5.52: Radiation calculations block.

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Since there are four different configurations of PV panels, the effect of different orientation and tilt angles on the solar radiation reaching the panel can be easily seen. Two example days are taken to demonstrate the variation, one day in winter and one day in summer as shown in Figure ‎5.53 and Figure ‎5.54.

Figure ‎5.53: Solar radiation on PV panels in the 15th of January, 2009.

From Figure ‎5.53 we notice that the highest solar radiation which is captured by the tracking PV system, it is very apparent especially at peak hours in this winter day, the rest three configurations seem to have relatively close captured radiation where the orientation of 5° has the highest of three and then comes the orientation of 45° then 15° which interchange the position of having the least radiation between the first half and second half of the day. The additional radiation of the tracking system is 170% - 210% compared to the fixed-mount installation in this specific day. Also from the figure, we see that the highest radiation is around 630 W/m2 and that the sun is shining for almost 30% of the day (approximately 7 hours and 12 minutes). As for a summer day, we can see the radiation profile throughout the day in Figure ‎5.53. It is observed that the shining hours are much higher (approximately 14 hours and 30 minutes) which is twice the duration in winter. Also the peak radiation is around 1100 W/m2. Figure ‎5.54 shows that unlike the case in winter, the radiation captured by the PV panels reaches almost the peak value for all configurations however the main difference is that for the tracking system the peak value is captured at a wider range collecting more energy throughout the day.

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Figure ‎5.54: Solar radiation on PV panels in the 27th of July, 2009.

5.2.2. PV system power output Photovoltaic panel electrical performance depends on environmental conditions such as the temperature, solar radiation, incidence angle, solar spectral (air mass), and the type of PV cell. There are several models and equations and models that are used to predict the performance of a PV panel, we will use a model that takes into account the outside temperature and compare the calculated output to the actual PV produced in 2009 in order to validate the model. The power output (Pout) can be estimated by the following relation [13]:

Pout  Tref A I [1  0.0045 (Tc  25)]

‎5.35

where,

Tref = 0.14, A: module/panel area m2, Tc: cell operating temperature (oC). and,

Tc  Tambient 

I (TNCOT  20) 800

‎5.36

where, TNOCT is the nominal operating cell temperature (oC), Tambient is the ambient temperature (oC) = 43°C [14]

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Figure ‎5.55: PV power model.

The overall efficiency of each system was calculated from the manufacturers’ guides and by comparing the output to the actual produced power that is measured in Eichhof, as listed in the following table: Table ‎5.8: Overall efficiency for PV systems.

System PV1a PV1b PV2 PV3

Overall efficiency 0,112 0,133 0,113 0,137

The output power throughout 2009 is shown in the following figures and also compared to the actual produced in some days for two of the systems. 62

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(a)

(b)

(c)

(d)

Figure ‎5.56: Power output from PV systems in 2009, (a) PV1a, (b) PV1b, (c) PV2, (d) PV3.

(a)

(b) 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.

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The variation between actual and calculated PV output as seen Figure ‎5.57 is expected because the measuring station for the solar radiation is not exactly in the position of the PV panels and any cloud in the sky or any other small variation reasons will affect the output. This however is acceptable for the purposes of our model where the calculated power is close and accurate enough to represent the performance of the system. The total output from the entire PV system is shown in the figure below.

Figure ‎5.58: Total PV power output in 2009.

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5.3. System Analysis As clarified throughout the previous chapters, there are currently three sources of electrical energy: PV, biogas and from the electrical grid and two sources of thermal energy: biogas and natural gas. 5.3.1. Demand side analysis 5.3.1.1.

Electrical demand

The first step of an energy system analysis and evaluation is to carry out a simple energy demand analysis so as to understand the electrical consumption to be supplied. The load profile over 2009 is shown below and it follows approximately the same trend when compared to the consumption of 2007 and 2008, this makes it more accurate and simple to reflect the findings on the future.

Figure ‎5.59: Load profile.

From the load profile we can notice that electrical energy system should be able to handle a peak load of around 190 kW and evidently with a fluctuating load changing according to the season and throughout the day. The maximum load however is as seen occurring at one moment throughout the year and the following peak is around 170 kW, this implies, from the first glimpse, that demand side management could reduce the peak and consequently could reduce the required producers’ capacity.

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Demand side management studies and measures are being continuously conducted as explained in chapter two and the importance of such measures will be further solidified in the remaining of this chapter. The electrical system of Eichhof is connected through four 3-phase transmission lines. A concise load and power production plan is shown in the following figure.

Figure ‎5.60: Simplified electrical infrastructure at Eichhof showing production and consumption loads.

5.3.1.2.

Heat demand

Records of heat consumption in 2009 were not accessible; however biogas and natural gas consumption in the years 2005, 2006 and 2007 and partly in 2009 were available and were used as a reference to estimate the consumption in 2009 through simple relationships between outside temperature and gas consumption similar to the relations deduces in Figure ‎5.33 and depending on the nominal heating capacity of heaters, burners and heat exchangers. The maximum heat consumption (heat demand) was in the first part of the simulation, it includes natural gas burners and heaters, the biogas/natural gas burner, the heat exchanger heater. The resulting heat demand is shown in the figure below.

Figure ‎5.61: Estimated heat demand for 2009.

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5.3.2. Current status

Figure ‎5.62: Energy system analysis block.

Bearing in mind that the long-term goal in Eichhof is to realize a 100% renewable energy system that utilizes the available potential, we start by evaluating the performance of the current status of the electrical energy system measuring the penetration rate of renewables in both the electrical and thermal energy systems.

Figure ‎5.63: Inside energy analysis block.

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5.3.2.1. 

Penetration level

Electricity

The penetration of electricity produced from PV, micro-biogas turbine and biogas engine can be calculated every 15 minutes and in total for the whole year. This is simply done by dividing the power produced by all of the units over the load at the time, and by dividing the total production over the load over the whole year as done in the penetration block which is shown in Figure ‎5.64.

Figure ‎5.64: Penetration level calculations.

We begin with examining the 15-minutes penetration percentage; the results are highly fluctuating ranging from 0.016 to 900 (1% and 80000%) the high values occur only at two times and it is cause by very low consumption at the time (146.2 kW produced power and 0.16 load at this case), however such high values occurred only few times due to this match of very low load with high production and most values fall in the region between 0.01 to 5 and therefore the very high values will be left out of Figure ‎5.65 which shows how the penetration level changes with time in order to have a clearer picture of the fluctuation around a penetration of 100% .

Figure ‎5.65: Penetration level of electrical power from RE resources throughout the year 2009.

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The penetration level calculations demonstrate that the availability of power produced from RE sources is highly fluctuation and does not at all match the load profile resulting in excess of energy at times and shortage in many other times. To complete the picture we examine now the yearly penetration which was already shown in Figure ‎5.64. Surprisingly the value of penetration was 1.011 (101.1%). This value gives a very positive indication that with only renewable energy sources the electrical consumption of Eichhof center can be completely achieved. We need to keep in mind however that this number was based on calculations and is variable depending on the solar and bio resources available at that year and on the changing load profile and therefore a higher value of penetration level could ensure a complete dependency on renewable energy resources, this can be achieved by increasing the total production by either adding more PV panels or increasing the amount of substrate fed to the fermenter or by applying demand side management measures. 

Heat

The penetration level of heat produced from renewable energy resources is then calculated in the same manner as done for the electrical value. The situation however for the heat consumption is different because there are several natural gas consumers on the site that do not rely on renewables. This is expected to lower the penetration level. The penetration level including all heat consumers is shown in Figure ‎5.66.

Figure ‎5.66: Penetration rate for heat produced from RE resources throughout 2009.

It is apparent that the penetration levels are much lower than the electrical values and reach a minimum value of 0.044. The penetration rate for the complete year was found to be 0.178 (17.86%). This value implies that the heat produced in Eichhof from renewable energies is at this moment far from the actual consumption. More intensive demand side management measures, adjustments to the burners and heaters to be suitable for biogas usage and new sources of heat should be sought in order to achieve a 69

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higher level of penetration. The main consumers of heat are the laboratories (≈57%) and the castle (≈24%). Nevertheless, in order to properly evaluate the current situation we need to examine the penetration of renewable energies in the heat consumer designed for renewables which are here the natural gas burner, the fermenter heat exchanger and the hot water produced from the micro-gas turbine. After repeating the previous procedure while excluding the natural gas burners; the penetration level rises to a high value of 1.4 (141.7%). 5.3.2.2.

Correlation coefficient

The correlation coefficient is briefly used to understand the relations between the load profile and production profile. The correlation coefficient is a statistical tool that quantifies the similarities of two time series and it has a value between -1 and 1 where 1 means a complete positive (increasing) linear correlation (relation), -1 means a complete negative (decreasing) linear correlation and 0 indicates that there is no correlation between the two sets of data. More details about how the calculations were made are found in Appendix D. The correlation coefficient is an important tool that can be investigated more in depth however it will be considered as just an indication in this work. 

Electricity

The value of the correlation coefficient was fond to be 0.0324 for the current situation in Eichhof. This shows that there is almost no relationship between production and consumption and that electricity from renewable energies does not have and similarity to how the consumption is. The production profile versus load profile in the year 2009 is shown in the following figure whilst magnifying four random days as samples to better inspect the curves. Figure ‎5.67 supports the conclusion that the correlation coefficient gave, that there is no relation between production and consumption and the electricity is being produced randomly regardless of the consumption at the given time. The four samples showed four different cases of supply-demand relationships. In case number (1) the 147th day of the year (27th of May) shows a situation where the production fits the load curve to a good degree, both on the quantity and pattern levels. The second case (occurring on the 15th of December) shows a summer day with a lower consumption than case (1) but a lower correlation between the two curves. Cases number (3) (10th of January) and (4) (17th of August) show the opposite scenarios of relatively higher load as in case (3) to excess in production as in case (4). The fourth case also shows almost completely negative correlations, where the production behaves in an opposite manner to the load.

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Figure ‎5.67: Load profile versus production profile in 2009.



Heat

Such analysis cannot be performed over the heat demand of Eichhof because heat cannot be easily accurately measured at desired time intervals for supply and demand and therefore the correlation coefficient will not be calculated. However similar analysis like the one made in Figure ‎5.67 can be done to compare the heat consumption of the fermenter and the heat produced from the CHP unit. Figure ‎5.68 shows that similar to the case in electricity there is no relation between the heat demand of the fermenter and the generated heat from the CHP unit. Only in few days of the year the two values do match. Zooming into four weeks of the year we see the differences more clearly, it is apparent in winter at the beginning of the year that there is a large shortage which is substituted by using 71

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natural gas heaters. The same is noticed in the following winter at the end of the year (days 350 to 365). Between April and October a slightly better correlation is observed. The reason for that is that the demand is lower in summer than in winter and has nothing to do with the generated heat.

Figure ‎5.68: Heat produced from CHP versus heat consumed for fermenter heating.

5.3.2.3.

PV to biogas ratio

The PV participation in the electrical power production is calculated and found to equal 34.3% in the year 2009.

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5.3.2.4.

Biogas availability

The biogas availability (biogas out of the storage / biogas needed) fluctuates from 0.34 to 1 throughout 2009. For the whole year it is found to equal 0.76 (76.33%). Figure shows how this availability changes throughout the year.

Figure ‎5.69: Biogas availability in 2009.

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5.4. Proposed Future Scenario

Figure ‎5.70: Future scenario block.

After examining the current electrical and thermal energy situation in Eichhof we can present a future plan for the energy production. Since a high electrical penetration level and a low heat penetration level was found, the basis of the future scenario model will be to deploy the electrical production capabilities of the system in order to achieve the highest possible coverage of the load, with 100 % renewable energy sources for electricity production if possible. To begin with, the following assumptions and considerations are made in order to guide the procedure and specify the target of the scenario to be proposed: 1. The PV power output will be considered unchanged in the proposed scenario in order to focus on exploring the potential of the biogas components. There is always room for further improvement through increasing the number of PV panels, adjusting the slope and declination angles or using higher efficiency technologies. 74

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2. Fermentation process failure, deficiencies in the PV or biogas systems or any other externally caused phenomenon will not be taken in consideration, based on the assumption that the processes are efficiently controlled and devices are monitored and periodically maintained. 3. The thermal energy output will be taken in a secondary level for decision making in the strategy for controlling the system in the case of CHP heat output. However, for the burner and turbine heat output, it will be dealt with throughout addressing the changes on the thermal output that were caused by the changes in input parameters. 4. Any additions or improvements will be provoked and taken from the system itself, the reason for that is to be able to discover the potential of the current configurations and their ability to reach the goal with moderate modifications. Other renewable energy sources could be suggested at other phases of the project such as small wind turbines. 5. The control strategy governing the biogas withdrawal from the storage into the electrical devices is assumed to be available and fits the intended purposes. The comprehensive and dynamic controller design is however not addressed and is out of the scope of the study The next step is to determine whether the electrical generators are able to cover the peak load when operating at their highest capacity or not. By looking at the load profile (Figure ‎5.59) we can instantly realize that the system can cover around 36% of the peak even at full capacity. This is done by comparing the peak load (≈190 kW) with the summation of nominal capacities of biogas electrical generators (CHP and turbine) and the PV output at that day (28 kW +30 kW + 12 kW = 158 kW) this means that in order to fulfill this peak, new electricity generators should be added. Consequently, two additional CHP unit and micro-gas turbine will be added to the system, which means there will be three engines and three turbines operating for electricity production. The new device will have the same characteristics as the old ones and therefore the nominal capacity will be simply multiplied by two in the simulation. At the peak load day, this alteration makes the maximum possible production at the day of the peak equal 186 kW. Thus the system is able to cover 97 % of the peak load when needed. Additional alterations in order to make the system suitable for the demand are storage devices for the electrical and heat excess energy. Electrical storage for the biogas system is naturally not needed because there will be theoretically no excess or lack of energy. The following step is to evaluate the potential of the PV system. The evaluation is done based the goal of reducing the PV storage size, accordingly, Whenever there is consumption and there is day light, whatever electricity produced from the PV system will be directly sent to the connected loads. Excess electricity is stored in a battery and when the produced electricity is not sufficient biogas is used to produce the remaining demand. 75

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In order to do this assessment, the residual load (difference between production and consumption) for the PV production will be calculated. Figure ‎5.71 shows the block for the residual load calculations.

Figure ‎5.71: PV residual load calculations block.

Positive residual load mean excess energy and is then sent to the battery for sizing purposes. The negative residual load means the PV output is out sufficient, this is communicated to the biogas control system and consequently the production of electricity from the biogas system should cover this negative residual load, in addition to using the energy stored in the battery if needed. The PV residual load is shown below.

Figure ‎5.72: Residual load from PV produced electricity.

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As seen in Figure ‎5.72, most of the residual values are negative indicating that the PV load cannot provide enough electricity at most times of the year. Moreover the residual load values are far-away from zero in both positive and negative direction for most of the time. This means that either there is too much or too less energy produced. The positive residual load equals the excess energy and thus the quantity of surplus power is sent to the battery block to calculate for battery sizing. The negative residual load is used to calculate the needed power to cover this load. The resulting m3/day biogas needed is the sent to the biogas storage and distribution blocks and continue the loop. This process is done in as shown in the following figure. 5.4.1. Battery model [15] Battery storage is sized to meet the load demand during non-availability periods of renewable energy source, commonly referred to as days of autonomy. Normally the number of days of autonomy is taken to be 2 or 3 days. The main purpose for adding the battery to the system is in order to store the excess power from the PV system and using this energy when the biogas storage is empty or in the case of emergencies. Battery sizing depends on factors such as maximum depth of discharge, temperature correction, rated battery capacity and battery life. The total capacity of the battery bank (CB) that is to be employed to meet the produced power is determined using the following expression (model shown in Figure ‎5.73):

CB 

E L .Ds VB DODmaxTcf

‎5.37

where, EL is the battery load in Wh, Ds is the battery autonomy or storage days, VB is the battery bank voltage in Volt, DODmax is the maximum battery depth of discharge, Tcf is the temperature correction factor.

Figure ‎5.73: Battery model.

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With the battery back voltage equalling 12 V, a maximum battery depth of discharge of 0.65 and temperature correction factor of 0.9, required battery capacity is found to be 33.2 Ah. The stored energy is used when there is a lack in energy, state of charge and discharge and detailed battery modelling are not taken into consideration. The stored energy is simply accumulated when not needed and withdrawn in the case of shortage. This is done as shown in the following figure.

Figure ‎5.74: Battery utilization block.

5.4.2. Biogas control strategy The biogas control strategy was based on several trials and runs of the simulation testing more than an option. The first option was to control the biogas withdrawal from the biogas storage tank without taking the energy consumption for heating the fermenter. This means that the heat generated from the CHP unit should be stored in order to meet the fermenter demand. If the option is to be applied, at least 40 days of thermal storage are needed in order to ensure that heat demand is met at all times. This is calculated by summing the entire heat generated in intervals of 1, 10, 20 and 40 days and the corresponding consumption and comparing them until the generation is more or equal to the consumption at all times. The simple procedure is shown in the following figure and is repeated for all the specified time intervals.

Figure ‎5.75: Number of days for heat storage calculations sample.

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Therefore it is neither practical nor feasible to consider this option with such large thermal storage requirements. The other option is to put the fermenter heating requirements into consideration in the control strategy as explained hereinafter. The negative residual load from the PV production is used as load for the biogas units. The required biogas volumetric flow rate needed to suffice the load is calculated with the fermenter heat requirements in mind. Since the CHP unit should generate heat enough for the requirements of the fermenter heating. We should make sure first that the required heat is produce then what is left of the load is divided between the CHP and the turbine by using the same relations used in the first loop of the model. The logical strategy flow diagram for control is illustrated below.

Figure ‎5.76: Biogas control strategy flow diagram.

It is important to note that the signals sent to the CHP or the turbine can be positive, negative or zero. Positive signals represent the actual needed biogas flow rate; negative signals mean that an excess energy would be produced when the engine or turbine is started; this is the result of adding the fermenter requirements to the CHP load, if the addition makes the total output at that specific time larger than the total load system, it will result in negative values after subtracting it from the turbine. Therefore negative and zero value means shutting down the engine at these times. 79

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Figure ‎5.77: Needed biogas calculations after applying the control strategy.

The CHP thermal output after applying the control strategy is also analyzed in order to determine whether thermal storage is needed also in this case and for how many days. It is found that almost no thermal storage is needed under this control scheme and that one day emergency heat storage should be sufficient. The biogas continues through the distribution unit where the priority is given to the micro-gas turbine and the CHP unit needs. The remaining biogas goes to the burner.

5.4.3. Substrate management As mentioned earlier, the biogas output can be controlled in two methods; by controlling the withdrawal of the biogas storage or by controlling the amount of biogas to be produced in the first place. The control of the biogas produced prevents excess biogas production and shortage. Without such measurements high shortage is experienced in winter and higher excess in summer. When entering a constant volume of biogas to the storage (the actual average value which equals 720 m3/day) resulting in almost 5 × 105 m3 needed biogas storage. 80

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This means deciding 40 – 70 days ahead how much substrate should be fed into the fermenter to produce the required amount of biogas. This however is not realistic to manage on hourly, daily or even weekly basis; therefore, an average value of the required biogas volumetric inflow (m3/day) is calculated as done in Figure ‎5.78. The calculated monthly averages of biogas are the new input for into the storage block instead of the actual biogas production rate.

Figure ‎5.78: Calculating monthly average needed biogas (for January here).

5.4.4. Results The simulation is run for a second loop after applying the previous changes. The main results are explained below. The first change can be seen in the biogas output of the fermenter, which is fed into the storage and compared to the needed biogas. The resulting time series of the three values is shown in the following figure. The biogas inflow to the storage is apparently higher in winter and decreases as it gets warmer. The needed substrate to produce such average for each month is out of the scope of this research and can be approximated in future work. Nevertheless the figure shows that a the biogas needed is available for most of the year (98%)

Figure ‎5.79: Comparison between inflow and outflow and needed biogas for the future case scenario.

An important observation is that the peak biogas demand (corresponding to the peak load) is not achieved, as seen in Figure ‎5.79. 81

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The volume of storage capable of handling the system is found to equal 5000 m3. With such volume the previous availability was achieved. The volume of the storage occupied by biogas throughout the year is shown in the following figure.

Figure ‎5.80: Storage volume occupied by biogas in the future case scenario.

5.4.4.1.

Correlation

After going through the rest of the stages, the power produced which includes electricity produced from the biogas units and the from the PV panels excluding the energy stored in batteries is calculated and compared to the load. An analysis similar to the one carried out for the current scenario which was made in Figure ‎5.67 is done, the same days are used for comparison. It is clearly noticed that the power produced is in almost perfect positive correlation with the load. The three other main observations are; firstly at most times the production is slightly lower than the consumption, secondly at other times the production is relatively much higher than the load, finally that the peak load is now partially covered unlike it was with the biogas withdrawal from the storage. The first observation is expected due to the large number of variables, iterations and operations that take place to calculate the load. This difference could be higher or lower in real life due to more reasons. The second observation is caused by the addition of the fermenter heat demand to the control scheme of the biogas units, resulting in higher power production in sometimes to satisfy the heater demand. The third observation is obviously caused by the addition of the PV output to the biogas one. The peak load however is yet not completely covered.

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Figure ‎5.81: Production profile versus load profile in the future case scenario (excluding batteries).

A comparison between the fermenter heating demand and the thermal energy generated from the CHP unit is shown in the following figure. The fermenter demand is met at all times, excess heat however is produced as expected.

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

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After adding the electricity that was saved in the battery bank in to the whole picture, we get the following.

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

We notice that the peak is now completely covered after the usage of the energy stored in the battery. The small left uncovered part of the load can be simply covered by adding a PV panel or increasing the biogas storage. The correlation coefficient for the proposed future scenario jumped to a high value of 0.9515. 5.4.4.2.

Biogas availability

The biogas availability also increased highly after controlling the input to the storage and the needed biogas. The availability is 100% for most of the year with few drops that reach 0.3 in some days of the year (figure). The availability is 0.98 (97.84%) for the whole year.

Figure ‎5.84: biogas availability in the proposed future scenario.

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6. STUDY CASE: HAMMOUDEH DAIRY FARM IN JORDAN

6. CASE STUDY: HAMMOUDEH DAIRY FARM IN JORDAN The last part of this thesis investigates the feasibility of the previously explained hybrid system in Jordan. For this purpose the developed model will be used after making some changes to fit the new inputs and then some economical indicators will be calculated to determine whether it would be feasible or not. Changes to the model will take place primarily because of the lack of detailed data due to the fact that no hourly measurements are available; consequently, all calculations are done in reference to one year. Also, some units are omitted from the model such as the biogas and natural gas burners because there is no natural gas grid in Jordan, the heat generated will be calculated none the less.

6.1. Site Description The site chosen for this study is Hammoudeh Food Industries Company including its dairy farm and dairy plant. Hammoudeh Food Industries Company depends primarily on fresh milk, sourced from its own farm, in the production of a wide range of dairy products. The factory is located in Marka district of the capital Amman. The Hammoudeh dairy farm is located in Al-Khaldieh in the Al-Mafraq city (see Table ‎6.1) and is considered one of the largest farms in Jordan, with a herd of 3000 Holstein dairy cows. Table ‎6.1: Location of Hammoudeh dairy farm.

Longitude

36°17'22"E

Latitude

32°9'58"N

The farm utilizes the latest technology and machinery during all stages of milk production and in feeding systems, in addition to animal care and monitoring systems. It also uses a computer controlled, fully automated milking parlor. This causes a high electrical demand. This farm is considered as an optimum location for a hybrid renewable energy project based on PV and biogas because of the availability of both. Photos from the farm and the plant are shown in Figure ‎6.1.

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(a)

(b) Figure ‎6.1:Hammoudeh Food Industries Company's (a) dairy farm (b) dairy plant.

The yearly electrical consumption reaches 4,000,000 kWh per year as reported by Hammoudeh dairy plant.

6.2. Input Parameters 6.2.1. Biogas The properties of the biogas substrate which is only dairy manure in this case study are as reported by Hammoudeh as shown in the following table. Table ‎6.2: Properties of dairy manure.

Property Amount Moisture Organic matter Ash C/N Density

Value 14600 m /yr (40 m3/day) 23.9 % 31.8 % 44.3 % 12:1 900 kg/m3 3

The moisture content of the dairy excreta is very high and needs high dilution in order to be pumped and digested. The goal is to get a TS value of 15% maximum which means the moisture content should be increased to at least 75% (25% TS). 86

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moistsure  For 40 m3 of manure: V  40 m 3  900

mtotal  mdry mtotal

‎6.1

kg  36000 kg , of which 23.9% is water, thus; m3

mwater = 8604 kg and mmanure = 27396 kg In order to increase the moisture content to 75% we need to add 73656 kg water, thus adding a volume of almost 73.65 m3/day water. Making the new volume of manure = 113,656 m3/day (2.84 times the original volume) with a density of 967.17 kg/m3. The volatile solids value (organic matter) was 31.8% which equals 11448 kg/day. This mass would make 10.41% of the new diluted manure mass. The parameters needed for kinetic modeling and other design parameters are presented in Table ‎6.3. The volume of the fermenter must be larger than 4800 m3 in order to ensure a minimum retention time of 40 days. The retention time might of course change if the inflow rate is changed. Therefore, the existing value of 600 m3 of fermenter volume is changed to match the new settings. Table ‎6.3: Simulation results for the Jordan study case.

Parameter TS VS St K θ

μm Vf (minimum) Tf

Value 15 % 10.41% 102 kg/m3 101.3 42 days 0.365 day-1 4800 m3 38 °C ± 2 °C

Hammoudeh group has other projects also with more organic waste such as the poultry industry which produces large amounts of poultry that presents another option for substrates that can be more investigated in the future because poultry manure has a low C/N ratio which is 7:1 and therefore requires pretreatment stages, the amount of poultry waste is currently 50 m3/day.

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6. STUDY CASE: HAMMOUDEH DAIRY FARM IN JORDAN

6.2.2. PV The solar radiation data were taken from the SoDa database [29] and temperature hourly data were provided by National Center for Research & Development Energy Research Program (NERC)[30], in Jordan. Changes of both throughout the year 2005 are shown in the following figures.

Figure ‎6.2: Global solar radiation in Mafraq.

Comparing the global solar radiation annual profile of the location in Jordan and the global radiation profile of Eichhof, it is noticed that in both sites, a maximum global normal radiation of about 1000 kW/m2 is reached, the difference however stems from the consistency of the profile. Taking the average irradiance (daily, weekly or monthly) for each of the location would show the difference more clearly. Such high and intense solar potential can be greatly utilized by providing a flexible base load and minimizing the battery size. The temperature profile throughout the year is shown in the following figure. The temperature is considered moderate for more a large part of the year which reduces the heating requirements of the system.

Figure ‎6.3: Temperature profile throughout 2008 in Mafraq.

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6. STUDY CASE: HAMMOUDEH DAIRY FARM IN JORDAN

6.3. Simulation 6.3.1. System design In order to adopt the simulation to the new settings and conditions, few changes are made. As explained earlier, the size of the fermenter is adjusted in order to handle the daily inflow of biogas and still can keep the substrates for a reasonable retention time. Hence, dimensions of the fermenter are modified. In a moderately warm region, the fermenter heating system needs to be adapted to the new environmental conditions. As seen from Figure ‎6.3, the temperature does not exceed 40 oC and is not expected to in the future, therefore cooling is not needed. Nonetheless, at the hours when temperature is between 36 °C and 40 °C (the desirable temperature of the fermenter), the heating system should be turned off, and in order not to raise the temperature of the fermenter than the desired value, the heating water pumps must be shutdown. The fermenter model is adjusted to realize this criterion by adding an if-condition block to run only if the temperature is out of the range specified earlier. Finally, in order to evaluate the full potential of the system, one biogas consumer is considered, the micro-gas turbine. The micro-gas turbine has a higher efficiency than the engine used in the CHP unit in Eichhof and it depends on temperature in its efficiency which gives more realistic results. Also, the needed biogas volume is not calculated because the nominal capacity (or number of turbines) is unknown yet. Thus, all produced biogas is directly entered to the turbine and the output power will accordingly determine the required turbine size. Since the produced biogas goes directly to the turbine, the storage and distribution model are removed. If a time series of electrical consumption is available, a controlled biogas system can be suggested and the storage needed can be then calculated. The PV system will be left out without changes in order to study the effect of orientation and tilt on the output energy and in order to assess how much PV is actually needed to supply the required load, therefore, the sizing of the PV system will be done after calculating the penetration level of the current arrangements. The design is made for a grid-connected system, the reason for that is that there is no information about the hourly electrical consumption of the plant; therefore, unless the price of kWh of electricity is much lower than the purchased price, there will be no economical benefit to make the system off-grid. An economical block is also added for feasibility analysis. The operations and calculations in this block will be discussed in more details next. The adjusted model is shown in Figure ‎6.4. 89

Figure ‎6.4: Hybrid system model for Hammoudeh dairy farm and plant in Jordan.

6. STUDY CASE: HAMMOUDEH DAIRY FARM IN JORDAN

6.3.2. Results The most significant results after running the simulation over a period of one year are summarized in the following table. Table ‎6.4: Simulation results over one year for the Jordan study case (scenario one: more biogas).

Parameter Daily biogas production Total biogas produced Maximum turbine electrical power output Maximum turbine thermal output Maximum PV power output Peak of supply Total electrical energy production Total thermal energy generation Penetration level of RE PV share (in total)

Value 1276.5 4.659 × 105 82 154 109.7 188.4 905 1350 22.64 35.09

Unit m3/day m3/year kWel kWth kW kW MWh MWh % %

We can see that the penetration level of both biogas and PV compared to the load is only 22.64%. This means that in order to fulfill the load, more production is required. There are two options available, adding more PV panels or treating the poultry manure that was mentioned earlier and using it as biogas substrate. Since high PV potential is available and the poultry manure needs pretreatment and is in another location, requiring transportation, more PV will be added to substitute the shortage. Nonetheless, Hammoudeh groups showed a high interest in utilizing the poultry manure for biogas production, therefore it can be considered as a future expansion step and add to the attractiveness of the project. In order to raise the penetration level to higher than 100% we need to increase the PV installed capacity to 15 times more than the previous value. This of course, represents the extreme ideal scenario where the entire load is provided by renewable energy resources; in the economical analysis both scenarios will be represented. After making the changes we get the following results. Table ‎6.5: Simulation results over one year for the Jordan study case (scenario two: more PV).

Parameter Daily biogas production Total biogas produced Maximum turbine electrical power output Maximum turbine thermal output Maximum PV power output Peak of supply Total electrical energy production Total thermal energy generation Penetration level of RE Biogas share (in total)

Value 1276.5 4.659 × 105 82 154 1645 1724 4.2 1350 105 19

Unit m3/day m3/year kWel kWth kW kW GWh MWh % % 91

6. STUDY CASE: HAMMOUDEH DAIRY FARM IN JORDAN

The electrical output of the micro-gas turbine depends on the outside temperature which reaches values higher than 15 °C, and therefore the turbine efficiency decrease. The change in turbine output is shown in the following figure.

Figure ‎6.5: Micro-gas turbine power output changes throughout the year.

The nominal power of the micro-gas turbine can be taken as the maximum power output of 82 kWel. This value however changes radically if the withdrawal of biogas from the storage is controlled. The direct radiation falling on each PV configuration is shown in the following figure. The systems are abbreviated in the same manner as before.

Figure ‎6.6: Solar radiation absorbed by the PV systems in its four configurations.

The much higher radiation that the tracking system catches in summer is apparent in Figure ‎6.6. The next best configuration is a tilt of 30° and orientation of 5°. 92

6. STUDY CASE: HAMMOUDEH DAIRY FARM IN JORDAN

The following figure shows the power output from the PV system after increasing it 15 times.

Figure ‎6.7: PV system power output.

Adding this power to the one produced from the micro-gas turbine we get a peak of 1.7 MW that the hybrid system can meet without any control of the biogas part. For this ideal case, an area of 14,200 m2 (≈0.014 km2) is required, which is a reasonable area to install in the region of the dairy farm as seen from the satellite image shown in Figure ‎6.8. The total are of the farm is 1.28 km2 approximately, with a large unused space.

Figure ‎6.8: Satellite image of Hammoudeh dairy farm.

93

6. STUDY CASE: HAMMOUDEH DAIRY FARM IN JORDAN

6.4. Economical Analysis As mentioned earlier, two scenarios will be investigated, scenario one having more biogas and scenario two having more PV. Nonetheless, the results for the second scenario will be represented in the economical analysis. Since, as shown earlier, it is the ideal or extreme scenario of 100% renewable energy penetration. There are several economical indicators for hybrid energy systems, which can help in assessing its feasibility. Some of them are determined and described briefly in this section. The main factors that govern the feasibility of this project are: 

Capital cost including the entire biogas plant, the PV installations and micro-gas turbine.



Materials which only include water for substrate dilution.



Operating costs



The changes that the new system brings which include: buying manure or fertilizers for the farm's needs. 6.4.1. Total investment

The total investment of the project is calculated in block “installed cost” in the model which is shown in the following figure. All values are converted to Jordanian Dinars (JD) at the end of each step.

Figure ‎6.9: Total investment calculations.

94

6. STUDY CASE: HAMMOUDEH DAIRY FARM IN JORDAN

Biogas plant The manure in Hammoudeh dairy plant is already collected and does not require further pretreatment. The capital costs of the plant were estimated in reference to the actual costs for building the biogas plant in Eichhof and on values mentioned in the literature. [2] That capital cost includes mainly the following equipment among others: 1. Fermenter tank, including: reinforced concrete tanks, reactor base, thermal insulation, leak detection, man hole, agitator, pipes and sample valves and measurement devices for temperature and pressure. 2. Fermenter heating system, including: heating pipes, heating distribution outlets, pumps and flow meters. 3. Substrate management, including: substrate storage tank, pumps and inlet lines. 4. System control, including required devices and software. 5. Installation and commissioning of the system. These costs are summed in one factor that depends on the size of the plant represented by the volume of the fermenter. This factor is equal 200 €/m3. With the volume of around 4700 m3, the capital cost of the biogas plant equals 950,000 €. This value is to be converted to JD later on. Micro-gas turbine The investment cost of the micro-gas turbine depends on its nominal capacity. Including the heat exchanger and the other attachments, a factor of 550 €/kW [2] can be used for the cost calculations. For an 85 kWel micro-gas turbine, the capital cost equals 46,750 €. Added to with the biogas plant capital cost, the total biogas system capital would equal 934,000 JD. PV system Retail solar price indices[18] for the module, the inverter and the battery were used to determine the installed cost of the PV system. The following table summarizes the price indices for the three components in January, 2012. Table ‎6.6: PV system pricing indices.

Unit Module (€/Wp) Inverter (€/continuous Watt) Battery (€/output Wh)

Pricing 2.31 0.548 0.164 95

6. STUDY CASE: HAMMOUDEH DAIRY FARM IN JORDAN

Since the time series of the electrical consumption and the system is grid connected, the size of the battery will be taken as the previously calculated size for Eichhof ≈ 33 Ah multiplied by the voltage (12 V) and by 15 times for the increment in PV size, we get a value of around 5100 Wh. The maximum continuous Watt for inverter should be about 10% higher than the PV array size to allow safe and efficient operation of PV power system. [19] Thus we get,

CC PV  1645 kW  2.31

€ € €  1809 .5 kW  0.548  5100  0.164 W kW kW

 4,792,062 €  4,470,000 JD where, CCPV is the capital cost of the entire PV system The total investment for the entire project is 5,405,000 JD.

6.4.2. Annual costs The annual costs include the operating costs of the biogas plant and the PV system. Those are taken as factors from the capital cost and are summarized in the table below. Table ‎6.7: Factors for annual costs calculations.

PV Biogas

Costs related to Operating and maintenance Buildings Technical

Factor 0.01 0.01 0.02

The annual costs for operating the biogas plant also include the price of water that is used for dilution. The price of a meter cube of water for industrial uses in Jordan is around 1.8 JD/m 3. This makes the summation of water costs in one year equal 48,300 JD. 6.4.3. Annual revenue According to the Renewable Energy and Energy Efficiency law in Jordan, issued in 2010 [20], any person or institute can produce electricity from renewable energy resources and sell the energy through a contract with the government. Thus, yearly produced energy can be sold to the grid and bring income to the owner. The maximum price at which the electricity is bought from the grid for industrial consumers equals (0.046 JD/ kWh). To know whether it is feasible to sell the electricity at this price, several cases will be studied; the first case by assuming that 96

6. STUDY CASE: HAMMOUDEH DAIRY FARM IN JORDAN

the electricity price is at least equal to the upper limit price at which the electricity is bought, following cases with higher price for selling electricity. The calculations are shown in Figure ‎6.10 among other annuity calculations for the project. 6.4.4. Extra costs The manure that is used as substrate is currently used in the farms of Hammoudeh for fertilization. A large part of the fertilizers must be bought to substitute the volume that is used for biogas production. The fermentation by-product can be used for fertilization as well but would not be enough. Price for a meter cube of manure in Jordan is around 4 JD/m3. Calculations are shown in Figure ‎6.10.

Figure ‎6.10: Annual costs and revenues.

6.4.5. Levelized cost of energy (LCE) The levelized cost of energy can be defined as the price per kWh (or MWh) at which electricity must be generated causing the investment to just break even. Typically, LCEs are calculated over 20 to 40 year life. It is a methodology used as a tool to assess the costeffectiveness of energy production technologies and compare between them. LCE can be calculated using the following expression: 97

6. STUDY CASE: HAMMOUDEH DAIRY FARM IN JORDAN

LCE 

TAC E tot

‎6.2

where, LCE is the levelized cost of energy (€/kWh), TAC is the total annualized cost (€), and Etot is the annual total energy produced (kWh) TAC is calculated as following:

TAC  CC  CRF  AC

‎6.3

where, CC is the capital cost at time =0 (€), AC is the annual cost (€/year), and CRF is the capital recovery factor and it equals:

CRF 

i (1  i ) n (1  i ) n  1

‎6.4

where, i is the discount rate which will be taken 6% in this study, n is the number of the years of the project which will be taken as 20 years. The LCE are made as in the block shown below.

Figure ‎6.11: Economical analysis block.

The hybrid system (second scenario) has an LCE value of 0.136 JD/kWh (= 0.146 €/kWh). 98

6. STUDY CASE: HAMMOUDEH DAIRY FARM IN JORDAN

6.4.6. Cash flow diagram Since no feed-in-tariff schemes are established in Jordan until this moment, further investigation aiming to determine the minimum price to make the project profitable is required. In this section, the previously explained scenarios are studied through changing the price at which the electricity is sold to the utility. The summary of each scenario is presented in Table ‎6.8. The total installed capacity was calculated assuming a capacity factor of 25% for PV and 90% for biogas. [22] Table ‎6.8: suggested scenarios for Hammoudeh plant hybrid system.

Dominating renewable energy source Share of the other source Penetration level of both sources Total investment (JD) Total installed capacity (MW) LCE (JD/kWh)

Scenario one Biogas 35% PV 22.64% 1,232,780 0.22 0.183

Scenario two PV 19% biogas 105% 5,404,940 1.65 0.136

For each of the two scenarios, the cumulative discounted cash flow diagram is constructed and the payback period is from the diagram. The return on investment and the rate of return are also calculated to facilitate the comparison between the different options, the following relations are used:

ROR 

annual profit annual revenue - annual cost   100 % total investment total investment

‎6.5

where, ROR is the rate of return.

ROI 

total profit by the end of project life  100 % total investment

‎6.6

where, ROI is the return on investment.

The discounted cash flow diagram is shown in Figure ‎6.12 and the differences between the two scenarios are very clear from the first glance.

99

6. STUDY CASE: HAMMOUDEH DAIRY FARM IN JORDAN

Figure ‎6.12: Discounted cash flow diagram, P: price for selling electricity.

The numerical results for each scenario are presented in Table ‎6.9and Table ‎6.10 . Table ‎6.9: economical indicators for scenario one: more biogas.

Price of electricity Annual revenue (JD) Payback period (years) Return of investment (%) Rate of return

0.046 -74,640 > 20 N/A N/A

0.01 -25,740 > 20 N/A N/A

0.02 0.03 0.04 0.05 64,810 155,400 245,900 336,500 > 20 10 5.5 3.8 N/A 53.22 142.56 231.81 0.05 0.13 0.21 0.29

For the first scenario, it is noticed that there is no revenue if the tariff at which the electricity is sold is less than 0.02 JD/kWh, which means that the cost of producing energy is much higher than the price of buying it which causes the cumulative cash flow diagram to decrease with time and never reach zero, as seen in Figure ‎6.12. Also, it is concluded that in order to 100

6. STUDY CASE: HAMMOUDEH DAIRY FARM IN JORDAN

get a practical payback period (less than project life), the tariff should be more than 0.02 JD/kWh. Table ‎6.10: economical indicators for scenario two: more PV.

Price of electricity Annual revenue (JD) Payback period (years) Return of investment (%) Rate of return

0.046 35,110 > 20 N/A 0.006

0.01 261,800 > 20 N/A 0.05

0.02 0.03 0.04 0.05 681,600 1,101,000 1,521,000 1,941,000 10 5.5 3.8 2.9 53.32 147.77 242.20 336.73 0.13 0.22 0.3 0.38

For the second scenario, the project brings profit even if the electricity is sold at the same price it is bought (0.046 JD/kWh), the payback period is, however, very long. If we increase the tariff, the Payback period starts decreasing and it starts reaching a reasonable value when the tariff is more than 0.01 JD/kWh. When comparing the two scenarios at the same tariff, the second scenario has a higher return on investment and rate of return. To be more exact, in order for scenario one to get the same ROR and ROI values, as scenario two, the tariff should be 0.01 JD/kWh more in each case. Points 1, 2 and 3 in Figure ‎6.12, show how each cash flow diagram of scenario one intersects at cumulative discounted cash flow of zero with the 0.01 JD/kWh lower cash flow diagram of the second scenario. The total profit at the end of the project is always much higher for the second scenario. In other words, selling the electricity to the utility for 0.05 JD/kWh in the first scenario brings a total profit for the project which equals the profit that the second scenario brings by selling the electricity for only 0.02 JD/kWh (Point 4 in Figure ‎6.12). It can be concluded after examining the two scenarios, that in order for the project to be fairly feasible, a tariff of more than 0.03 JD/kWh for the first scenario must be agreed on with the government. For the second scenario, the tariff can decrease to 0.02 JD/kWh, with the same payback period. The second scenario of 100% renewable energy and more PV share is more profitable in all cases.

101

7. CONCLUSIONS

7. CONCLUSIONS The model developed using Simulink was capable of predicting the outcome of the fermenter using simplified kinetics with a high level of accuracy. The simulation also can calculate the output of the electrical and thermal devices and of the PV panel and the results are found to match the actual values on site. Currently, the electricity produced in Eichhof is sold to the grid and is generated with the no correlation to the load, with a correlation coefficient as low as 0.03. The penetration level of renewable energy with respect to the load is calculated; it was found that the electricity produced from renewable energy resources in one year can cover slightly more than 100% of the entire electrical consumption of that year. That does not apply to the thermal consumption, where the main source of heat in Eichhof is found to be natural gas, the thermal penetration level of RE is currently only 17%. Nevertheless, the thermal energy generated by biogas is capable of covering the entire yearly heating demand of the fermenter and the other current biogas consumers. The biogas produced from the fermenter has an availability of only 76%. This means that almost 25% of the times, biogas is not available upon demand. The Share of PV is about 35% In order to determine the capabilities of the system and to what level in can reach, a future scenario is proposed. The scenario consists of adding a new control strategy that treats biogas as flexible electricity supplier with the aim of covering the load completely through renewable energy resources. The biogas cogeneration devices (CHP and micro-gas turbine) are to be tripled (in nominal capacity) in order to cover the peak load. The peak load occurs only one time, and the following peak occurs 20 kW lower than the first one, thus demand side management measures are highly recommended. Most of the PV output is directly consumed as it is generated and the excess energy is stored in batteries until needed, the size of the battery bank equals 33 Ah. The control of biogas system can be accomplished in reference to the either the electrical or thermal demands, or both. If only the electrical load is considered as a reference while neglecting the fermenter heating demand, thermal storage for over 40 days would be required. Therefore, it was found that the best possible control is achieved through considering both electrical and thermal demand. The control of biogas is done on two levels: controlling the substrate feed to the fermenter and controlling the biogas withdrawal from the biogas storage tank. A monthly average of substrate inflow is assumed and biogas output from the fermenter (into the biogas storage) is controlled to vary from a minimum of 480 m3/day in summer to a maximum of 1200 m3/day in winter. The biogas availability equals 98% and the required volume for biogas storage would be 5000 m3. In the proposed scenario, the system is capable of covering 99% of the year's load. The peak load (190 kW) is completely covered using biogas, PV and electricity stored in the battery 102

7. CONCLUSIONS

(also from PV). The correlation coefficient increases to a high value of 0.952 in the proposed scenario. In the case study of Hammoudeh dairy farm and plant in Jordan, the same system as in Eichhof was able to cover about 22% of the load, with a total investment of 1.2 million JD and a LCE that equals 0.183 JD/kWh. However, the system was only found feasible if the electricity is sold to the grid with a tariff higher than 0.03 JD/kWh. The ROR and ROI remain low and do not anticipate a profitable project. Increasing the installed capacity, from 0.22 MW to 1.65 MW through adding more PV panels, showed a much more feasible option for the dairy farm. The project is found to bring profit even if the electricity is sold at the same tariff it is purchased (0.046 JD/kWh), nonetheless it would have a very long payback period. The feed-in-tariff should be more than 0.01 JD/kWh for the project to start being a practical option. If the electricity is sold at 0.02 JD/kWh to the utility, the ROI would be 53.32% with a ROR of 13%. To sum up, the hybrid biogas/PV system in Eichhof shows very high prospects with suitable control systems and biogas is able to provide a flexible supply based on demand. Together, PV and biogas are able to almost completely cover the load. In Jordan, three factors render the project feasible: a relatively high installed capacity, a high share of PV and a feed-in-tariff higher than 0.02 JD/kWh. This, however, only applies to consumers with a very high load and might not apply to other cases in Jordan.

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8. REFERENCES

8. REFERENCES 1 2 3 4 5 6 7 8 9

10 11 12 13

14 15 16

17 18 19

20 21

Dieter Deublein and Angelika Steinhauser, “Biogas from Waste and Renewable Resources”, Wiley-VCH, 2008. Institut für Energetik und Umwelt gGmbH, Bundesforschungsanstalt für Landwirtschaft, ‗‗Handreichung Biogasgewinnung und –nutzung‖, Gülzow, 2006. Y. R. Chen, ―Kinetic Analysis of Anaerobic Digestion of Pig Manure and its Design Implications‖, 1983. D. T. Hill, ―Simplified Monod Kinetics of Methane Fermentation of Animal Wastes‖, 1983. Andrew G. Hashimoto and Steven A. Robinson, ―Pilot-Scale Operation and Economic Assessment of a Two Stage, Straw-Manure Fermentation System‖, 1984. Institut für Energetik und Umwelt gGmbH, Bundesforschungsanstalt für Landwirtschaft, ―Leitfaden Biogas Von der Gewinnung zur Nutzung‖, Gülzow, 2010. B.T Nijaguna, ―Biogas Technology‖, 2006 Ashrae, ―Handbook of Fundamentals‖ , American McGraw-Hill, New York, 1989. P. Axaopoulos, P. Panagakis, A. Tsavdaris and D. Georgakakis, ―Simulation and Experimental Performance of a Solar Heated Anaerobic Digester‖, Solar Energy Vol. 70, No. 2, pg. 155–164, 2001. Martin Malenshek, Daniel B. Olsen, ―Methane number testing of alternative gaseous fuels‖, Fuel Vol. 88 (pg650–pg656), 2009 Dirk Kirchner, ―Die Wirkung von Speichern auf die Einspeisedynamik aus dem Biogaspfad―, Kassel University, 2008. http://www.hlug.de/popups/luftmessdaten.html, The Hessian Agency for Environment and Geology, retrieved in January, 2012. E. Skoplaki, J.A. Palyvos, ―On the Temperature Dependence of the Photovoltaic Module Electrical Performance: A Review of Efficiency/Power Correlation‖, Solar Energy Vol. 83, pg. 614-624, 2009. A. Fanney, B. Dougherty, M. Davis, “Evaluating Building Integrated Photovoltaic Performance Models”, NIST, 2002. M. Deshmukha, S. Deshmukh, “Modeling of hybrid renewable energy systems”, Renewable and Sustainable Energy Reviews Vol. 12, pg. 235–249, 2008. H. Lo, T. Kurniawan, M. Sillanpää, T. Pai, C. Chiang, K. Chao, M. Liu, S. Chuang, C. Banks, S. Wang, K. Lin, C. Lin, W. Liu, P. Cheng, C. Chen, H. Chiu,H. Wu, “Modeling biogas production from organic fraction of MSW co-digested with MSWI ashes in anaerobic bioreactors”, Bioresource Technology Vol. 101, pg 6329-6335, 2010. Pia Mähnert, “Kinetik der Biogasproduktion aus nachwachsenden Rohstoffen und Gülle“, 2007. Retail Price Environment, http://solarbuzz.com, retrieved in January, 2012. A. Chel , G. Tiwari, A. Chandra, “Simplified Method of Sizing and Life Cycle Cost Assessment of Building Integrated Photovoltaic System” Energy and Buildings Vol. 41, pg 1172–1180, 2009. Official newspaper in Jordan, January, 2010 R. Luna-Rubio, M. Trejo-Perea, D. Vargas-Vazquez, G.J. Rıos-Moreno, “Optimal sizing of renewable hybrids energy systems”, Solar Energy, 2011. 104

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22

23

24 25

26

27

28

29 30

R. Tidball, J. Bluestein, N. Rodriguez, and S. Knoke, “Cost and Performance Assumptions for Modeling Electricity Generation Technologies”, NREL, U.S. Department of Energy, Office of Energy Efficiency & Renewable Energy, 2010. Ajai Gupta, R P Saini, M P Sharma "Computerized modelling of hybrid energy system— Part I: Problem formulation and model development" International Conference on Electrical and Computer Engineering, pg 7-12, 2008. Maria Berglund and Pål Börjesson, "Assessment of energy performance in the lifecycle of biogas production", Biomass and Bioenergy Vol. 30, pg 254-266, 2006. Andrew L. Rosenthal, Steven J. Durand, Andrew L. Rosenthal and Steven J. Durand, "Economics and Performance of PV Hybrid Power Systems: Three Case Studies", Sandia National Laboratories, 2012. P. Axaopoulos, P. Panagakis, A. Tsavdaris And D. Georgakakis, "Simulation and Experimental Performance of a Solarheated Anaerobic Digester", Solar Energy Vol. 70, pg. 155–164, 2001. S. Diaf, D. Diaf, M. Belhamel, M. Haddadi and A. Louche, "A methodology for optimal sizing of autonomous hybrid PV/wind system", Energy Policy Vol. 35, pg 5708-5718, 2007. S.M. Shaahid and I. El-Amin, "Techno-economic evaluation of off-grid hybrid photovoltaic–diesel–battery power systems for rural electrification in Saudi Arabia— A way forward for sustainable development", Renewable and Sustainable Energy Reviews Vol. 13, pg 625-633, 2009. www.soda-is.com, The project SoDa "SoDa. Integration and exploitation of networked Solar radiation Databases for environment monitoring", retrieved in December, 2011. National Center for Research & Development Energy Research Program (NERC), Amman, Jordan. Contact person: Eng. Firas Alawneh.

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APPENDIX A: GAS BURNERS AND HEATERS

APPENDIX A: GAS CONSUMERS Figure A.1: Natural and biogas consumers, nominal capacity and variable output.

Location

Nominal capacity (kW)

Span of the variable output (kW)

Burner (BG)

Biogas plant

50

60 to 300

Burner

Laboratory

400

90 to 680

Burner

Laboratory

200

60 to 300

Burner

Eichhof castle

200

60 to 300

10.89

--

10.89

--

10.89

--

10.89

--

12

8 to 24

3× 12

10 to 24

3× 12

10 to 24

3× 12

10 to 24

12

10 to 24

Workshops

46.52

--

Eichhof castle

31

--

Type

Radiator Radiator Radiator Radiator Water heater Water heater Water heater Water heater Water heater Heating system Oven

Equipment stores Equipment stores Stables Residential building Residential building Residential building Residential building Residential building Residential building

106

APPENDIX B: EQUIPMENTS’ SPECIFICATIONS

APPENDIX B: EQUIPMENTS’ SPECIFICATIONS Gas burner: *

Figure B.1: Burner output range. (source: manufacturer)

Micro-gas turbine: **

Figure B.2: Net power and efficiency at ambient temperature. (source: manufacturer)

* Source: www.weishaupt.de ** Source: www.microturbine.com

107

APPENDIX C: SOLAR RADIATION

APPENDIX C: SOLAR RADIATION * C.1 Apparent solar time (AST)

AST  LST  ET  4(SL  LL)  DS

C.3

where, LST: local standard time, ET: equation of time, SL: standard longitude, LL: local longitude, DS: daylight saving (either 0 or 60 mins) The dates of time change in Germany were in 29, March 2009 and 25, October 2009. And Since the location is east of the standard meridian, the correction is added to the clock time.

ET  9.87sin(2B )  7.53cos(B)  1.5sin(B)

C.4

and

B  (N  81)

360 364

C.5

where, N is the day number

C.2 Solar declination (δ)

 360  (284  N )  365 

  23.45 sin 

C.6

Figure C.1: Solar declination.

* Reference: S. Kalogirou, Solar Energy Engineering Processes and Systems, Elsevier, 2009.

108

APPENDIX C: SOLAR RADIATION

C.3 Hour angle (h)

h  (AST  12)15

C.7

C.4 Incidence angle (θ) cos( )  sin( L) sin(  ) cos( )  cos( L) sin(  ) sin(  ) cos( Z s )  cos( L) cos( ) cos(h) cos( )  sin( L) cos( ) cos(h) sin(  ) cos( Z s )

C.8

 cos( ) sin( h) sin(  ) sin( Z s ) where,  is the surface tilt angle from the horizontal Zs is is the surface azimuth angle, the angle between the normal to the surface from the true south, westward is designated as positive

C.5 Solar altitude angle (α)

sin(  )  cos( )  sin( L) sin(  )  cos( L) cos( ) cos(h)

* Reference: S. Kalogirou, Solar Energy Engineering Processes and Systems, Elsevier, 2009.

C.9

109

APPENDIX D: CORRELATION COEFFICIENT CALCULATIONS

APPENDIX D: CORRELATION COEFFICIENT CALCULATIONS * The correlation coefficient between two sets of data sequences (t,x) and (t,y) is defined as:

CC 

C ( x, y ) C ( x, x).C ( y, y )

D.1

where, N

C ( x, y )   ( xn   x ).( yn   y )

D.2

n 1

N

x 

 xn

n 1

N

D.3

N

 yn

 y  n 1

N

D.4

where, CC is the correlation coefficient C(x,x) is the variance of the data sequence N is the number of readings μ is the mean value The following figure shows how these calculations were implemented in Simulink.

* Reference: Y. Li, V. Agelidis, “Wind-Solar Resource Complementarity and its Combined Correlation with Electricity Load Demand”, 2006

110

APPENDIX D: CORRELATION COEFFICIENT CALCULATIONS

Figure D.1: Correlation coefficient block for current situation and proposed future scenario.

* Reference: Y. Li, V. Agelidis, “Wind-Solar Resource Complementarity and its Combined Correlation with Electricity Load Demand”, 2006

111

APPENDIX E: REGMODHARZ

APPENDIX E: REGMODHARZ The project "Regenerative Modellregion Harz" (RegModHarz) is one of the six projects from the eEnergy-initiative of the Federal Ministry of Economics and Technology (BMWi) and the Federal Ministry for the Environment, Nuclear Safety and Climate Protection (BMU). Harz, a region in Germany, is taken as a model region where the integration of renewable electricity producers, electricity networks, consumption, business models and communication technology solutions are investigated. The Harz region has a population of 240,000 and had an energy consumption of 1,300 GWh in 2008. The electrical generation in the region of Harz comes from wind, photovoltaic systems, hydro, biomass, and natural gas-powered plants. In 2008 a total of 467 GWh of electricity were generated in the Harz region. This corresponds to 36% of the total electricity consumption, the share of wind and solar energy was 69% (321 GWh). In the RegModHarz project, the flexible power generation from biogas plants is investigated.

* Source: www.regmodharz.de

112

MODELLIERUNG UND LEISTUNGSBEWERTUNG EINES HYBRIDEN PVBIOGAS ENERGIESYSTEMS Analyse des elektrischen und thermischen Systems am Eichhof Zentrum in Deutschland - Einer Fallstudie in Jordanien

Masterarbeit im Master-Studiengang für Erneuerbare Energien und Energieeffizienz (REMENA)

By, Rand Al-Zu'bi

Zusammenfassung: In dieser Arbeit wird die Integration von PV und Biogas mit einem Fokus auf Biogas als flexiblem Stromlieferanten untersucht. Hierzu wird die Leistung der Biogas/PV-Hybridanlage im Landwirtschaftszentrum Eichhof, Deutschland ausgewertet. Dessen komplettes elektrisches und thermisches Energiesystem wird analysiert und mit Simulink modelliert. Es wird ein Szenario für ein nachhaltiges, zuverlässiges und autarkes System vorgeschlagen. Abschließend wird eine Fallstudie für einen Milchviehbetrieb in Jordanien angestellt, in der die Machbarkeit dieser Art von Hybridsystemen überprüft. Im Gesamtsystem beträgt der Anteil der PV ca. 36% mit einer Spitzenleistung von 130 kWp. Als restlicher Anteil werden täglich im Durchschnitt 720 m3 Biogas erzeugt, welches mit Hilfe eines Blockheizkraftwerks (30 kWel/ 48 kWth), einer Mikro-Gasturbine (28 kWel/ 60 kWth) und eines Biogasbrenners (50 kWth) für die thermische und elektrische Energieerzeugung verwendet wird. Am Standort Eichhof können PV und Biogas 101% der gesamten elektrischen Last erneuerbar bereitstellen. Derzeitig ist die Erzeugung nicht an die Last gekoppelt und somit unabhängig von der Nachfrage (Korrelationskoeffizient zwischen Last und Erzeugung beträgt 0,03). Über das Jahr betrachtet kann Biogas den benötigten Bedarf im Mittel nur zu 76% gänzlich decken und mehr als 80% der thermischen Nachfrage wird durch Erdgas bereitgestellt. In dem vorgeschlagenen Zukunftsszenario ist das System in der Lage, nahezu die gesamte Last (99%) während des gesamten Jahres zu decken. Dies wird durch eine geeignete Steuerungsstrategie erreicht, die die thermische und elektrische Nachfrage berücksichtig und die Anlage entsprechend betreibt. Das Biogas wird anhand eines monatlichen Durchschnittswerts zwischen einem Minimum von 480 m3/Tag im Sommer und einem Maximum von 1200 m3/Tag im Winter erzeugt. Diese Steuerung hat eine Begrenzung des Biogas-Speichervolumens bis 5000m3 und eine Erhöhung der Verfügbarkeit des Biogases auf 98% zur Folge. Zusätzlich wird der Abruf des gespeicherten Biogases durch Änderungen der Nachfrage gesteuert. Der größte Teil des aus PV erzeugten Stroms wird bei bestehender Nachfrage direkt eingespeist, während überschüssige Energie in Batterien (33 Ah) gespeichert wird. Als Konsequenz verbessert sich der Korrelationskoeffizient zwischen Last und Erzeugung auf einen Wert von 0,95. Als Ergebnis des letzten Teils stellte sich heraus, dass zur Sicherstellung der Realisierbarkeit eines ähnlichen Hybridsystems in Jordanien eine Einspeisevergütung von mehr als 0,02 JD/kWh (0,021 €/kWh) für ein Großprojekt (1,65 MW installierte Leistung) und mehr als 0,03 JD/kWh (0,032 €/kWh) für ein Kleinprojekt (0,22 MW installierte Leistung). Das groß angelegte System mit einem Investitionsvolumen von 5,4 Mio. JD (5,78 Mio. €) besitzt Stromgestehungskosten von 0,136 JD/kWh (0,146 €/kWh) und ist im Vergleich insgesamt ökonomischer.

‫نمذجة و تقٌم أداء نظام طاقة هجٌن ٌتكون من الغاز الطبٌعً و الخالٌا الشمسٌة‬ ‫ـــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــ‬ ‫تحلٌل النظام الكهربائً والحراري لمركز "اٌشهوف" فً ألمانٌا ‪ -‬حالة دراسٌة فً األردن‬

‫رسالة ماجستٌر لبرنامج الطاقة الجدٌدة والمتجددة وكفاءة استخدام الطاقة )‪(REMENA‬‬

‫إعداد‪ :‬رند الزعبً‬

‫ملخص‪:‬‬ ‫تقدم هذه الرسالة دراسة تفصٌلٌّة حول نظم الطاقة الهجٌنة " ‪ "Hybrid Energy Systems‬التً تعمل بالغاز‬ ‫الطبٌعً (‪ )Biogas‬والخالٌا الشمسٌة (‪ )PV‬مع التركٌز على الغاز الطبٌعً كمصدر مرن لتولٌد الطاقة‪ .‬وقد قامت‬ ‫هذه الدراسة بتقٌٌم أداء نظام الطاقة الهجٌن فً مركز اٌشهوف "‪ "Eichhof‬الزراعً فً ألمانٌا من خالل محاكاة‬ ‫النظام باستخدام برنامج الـ "‪ ،"Simulink‬باإلضافة إلى تقٌٌم األداء الحراري والكهربائً للمنظومة ك ُكل‪ .‬كما تم‬ ‫ًا‬ ‫استدامة واستقاللٌ ًاّة عن المصادر غٌر المتجددة للطاقة‪ ،‬وت َّمم فً‬ ‫أٌضا ًا اقتراح سٌنارٌو مستقبلً ٌجعل النظام أكثر‬ ‫النهاٌة فحص جدوى تطبٌق هذا النوع من األنظمة الهجٌنة فً مزرعة ومصنع ألبان فً األردن‪.‬‬ ‫شكلت الخالٌا الشمسٌة ما مجموعه ‪ %36‬من الطاقة المنتجة وبلغت ذروة إنتاج الطاقة منها إلى ‪ 130‬كٌلو واط‪.‬‬ ‫أما باقً االطاقة فٌتم إنتاجها من خالل الغاز الطبٌعً بمعدل إنتاج ٌومً مقداره ‪ 720‬م‪ٌ/3‬وم وٌتم استهالمه فً‬ ‫توربٌن الغاز الصغٌر " ‪ 28 ( "Micro-gas turbine‬كٌلوواط كهربائً و ‪ 60‬كٌلوواط حراري) ومحرك‬ ‫كهربائً " ‪ 30 ( "Engine‬كٌلوواط كهربائً و ‪ 48‬كٌلوواط حراري) وحارقة " ‪ 50 ( "Burner‬كٌلوواط‬ ‫المولَّمدة من الغاز الطبٌعً والخالٌا الشمسٌة قادرة على تلبٌة‬ ‫حراري)‪ .‬تم االستنتاج بعد فحص النظام أن الطاقة َ‬ ‫كافة احتٌاجٌات المركز الكهربائٌة‪ ،‬ولكنّ إنتاج الكهرباء مستمر بغض النظر عن االستهالك ما ٌؤدي إلى معامل‬ ‫ارتباط "‪ "Correlation coefficient‬قرٌب إلى الصفر ( ‪ )0.03‬أي أنه ال توجد عالقة بٌن سالسل االستهالك‬ ‫الزمنٌة " ‪ "Time-series‬والسالسل اإلنتاجٌة‪ .‬كما وُ جد انّ الغاز الطبٌعً ٌوفر حاجة األجهزة المستهلكة بمعدل‬ ‫‪ %76‬خالل العام‪.‬‬ ‫بنا ًاء على معطٌات النظام ونتائج التقٌٌم فً الجزء األول من الرسالة‪ ،‬تم تقدٌم سٌنارٌو قادرعلى تغطٌة الحمل‬ ‫الكهربائً على نحو شبه كامل (بنسبة ‪ )%99‬على مدار العام وٌتم تحقٌق ذلك من خالل إستراتٌجٌة تح ُّكم تعمل‬ ‫وفقا ًا لتغٌر الحمل الكهربائً والحراري‪ .‬كما تم تحدٌد إنتاج الغاز الطبٌعً بقٌمة شهرٌة تقع ما بٌن قٌمة كبرى‬ ‫تساوي ‪ 1200‬م‪ٌ/3‬وم شتا ًاء وقٌمة صغرى تساوي ‪ 480‬م‪ٌ/3‬وم صٌفٌا ًا‪ .‬أدت عملٌة التحكم إلى تحدٌد حجم خزان‬ ‫ّ‬ ‫الخزان‬ ‫الغاز الطبٌعً بـ ‪ 5000‬م‪ 3‬وزٌادة تو ّفر الغاز الطبٌعً إلى ‪ %98‬كما أنّ عملٌة سحب الغاز الطبٌعً من‬ ‫ٌتم التحكم بها حسب الطلب‪ .‬أما بالنسبة للطاقة المولدة من الخالٌا الشمسٌة فٌتم استهالك معظمها مباشرة عند‬ ‫اإلنتاج والطاقة الزائدة ٌتم تخزٌنها فً بطارٌة (بحجم ‪ 33‬أمبٌر‪.‬ساعة) الستخدامها عند الحاجة‪ .‬تم ّكن النظام فً‬ ‫السٌنارٌو المقترح من رفع معامل االرتباط إلى ‪.0.95‬‬ ‫ص الجزء األخٌر من الرسالة إلى أ ّنه من أجل ضمان جدوى نظام هجٌن مماثل للنظام المطروح مسبقا ًا فً‬ ‫َخل ُ َ‬ ‫األردن فال بد من أن ٌتم تحدٌد سعر بٌع الكهرباء للشبكة الكهربائٌة "‪ "Feed-in-tariff‬بسعر بٌع اكبر من ‪0.02‬‬ ‫دٌنار‪/‬كٌلوواط‪.‬ساعة (‪ٌ 0.021‬ورو‪/‬كٌلوواط‪.‬ساعة) للمشارٌع كبٌرة الحجم (بسعة إنتاجٌة ‪ 1.65‬مٌغاواط) وأكثر‬ ‫من ‪ 0.03‬دٌنار‪/‬كٌلوواط‪.‬ساعة (‪ٌ 0.032‬ورو‪/‬كٌلوواط‪.‬ساعة) للمشارٌع األصغر (بسعة إنتاجٌة ‪ 0.22‬مٌغاواط)‪.‬‬ ‫ثبت أن المشروع كبٌر الحجم أكثر جدوى أقتصادٌا ًا بشكل عام وكانت تكلفة إنتاج الكهرباء " ‪Levelized cost of‬‬ ‫‪ 0.136 "electricity‬دٌنار‪/‬كٌلوواط‪.‬ساعة (‪ٌ 0.146‬ورو‪/‬كٌلوواط‪.‬ساعة)‪ .‬قٌمة االستثمار الكلً لهذا المشروع‬ ‫هً ‪ 5.4‬ملٌون دٌنار (‪ 5.78‬ملٌون ٌورو)‪.‬‬

‫نمذجة و تقيم أداء نظام طاقة هجين يتكون من الغاز الطبيعي و الخاليا الشمسية‬ ‫تحهٍم انُظاو انكهربائً وانحراري نًركز "اٌٌشهىف" فً أنًاٍَا ‪ -‬حانت دراسٍت فً األردٌ‬

‫إعذاد‬

‫رَذ انزعبً‬

‫رسانت يقذيت إنى كهٍت انهُذست فً جايعت انقاهرة و كهٍت انهُذست انكهربائٍت‪/‬عهى انحاسىب فً جايعت كاسم كجزء يٍ‬ ‫يتطهباث انحصىل عهى درجت انًاجستٍر فً انطاقت انجذٌذة وانًتجذدة وكفاءة استخذاو انطاقت‬

‫ٌعتًذ يٍ نجُت انًًتحٍٍُ‪:‬‬

‫_______________________________________‬ ‫جايعت كاسم‬ ‫األستار انذكتىر أنبرث كالودي‬ ‫_______________________________________‬ ‫جايعت انقاهرة‬ ‫األستار انذكتىر أحًذ انكىسً‬ ‫_______________________________________‬ ‫جايعت انقاهرة‬ ‫األستار انذكتىر يحًذ انسبكً‬

‫كهٍت انهُذست انكهربائٍت‪/‬عهى انحاسىب‬ ‫جايعت كاسم‬

‫كهٍت انهُذست‬

‫جايعت انقاهرة‬ ‫شباط‪2012 ،‬‬

‫نمذجة و تقيم أداء نظام طاقة هجين يتكون من الغاز الطبيعي و الخاليا الشمسية‬ ‫تحهٍم انُظاو انكهربائً وانحراري نًركز "اٌٌشهىف" فً أنًاٍَا ‪ -‬حانت دراسٍت فً األردٌ‬

‫إعذاد‪:‬‬

‫رَذ انزعبً‬

‫إشراف‪:‬‬ ‫دٌرك كٍرشُر‬ ‫فراوَهىفر‬

‫د‪ .‬بٍرَذ كراوتكرًٌٍر‬ ‫جايعت كاسم ‪ /‬فراوَهىفر‬ ‫يراجعت‪:‬‬

‫األستار انذكتىر أنبرث كالودي‬ ‫جايعت كاسم‬

‫األستار انذكتىر أحًذ انكىسً‬ ‫جايعت انقاهرة‬

‫رسانت يقذيت إنى كهٍت انهُذست فً جايعت انقاهرة و كهٍت انهُذست انكهربائٍت‪/‬عهى انحاسىب فً جايعت كاسم كجزء يٍ‬ ‫يتطهباث انحصىل عهى درجت انًاجستٍر فً انطاقت انجذٌذة وانًتجذدة وكفاءة استخذاو انطاقت‬ ‫شباط‪2012 ،‬‬