final report review of ad financial modelling/planning tools economic modelling report.pdf ·...

Final report

Review of AD financial

modelling/planning tools

A review of free and fee-paying anaerobic digestion (AD) financial models/tools covering a wide range of tools and AD technologies for businesses and organisations who might be interested in developing AD capacity in the UK.

Project code: OIN002-015 Research date: February to May 2013 Date: September 2013

WRAP‟s vision is a world without waste, where resources are used sustainably. We work with businesses, individuals and communities to help them reap the benefits of reducing waste, developing sustainable products and using resources in an efficient way. Find out more at www.wrap.org.uk Document reference: [e.g. WRAP, 2006, Report Name (WRAP Project TYR009-19. Report prepared by…..Banbury, WRAP]

Written by: Alexander Henderson MPhys, Organic Resource Agency Ltd

Front cover photography: Anaerobic digestion plant

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Review of AD financial modelling/planning tools 3

Executive summary

The Organic Resource Agency Ltd (ORA) has been commissioned by WRAP (Waste and Resources Action Programme) to undertake a review of anaerobic digestion (AD) financial models/tools which are available to businesses and organisations who might be interested in developing AD capacity in the UK. The aim of this work is to provide an understanding of these models/ tools, their usefulness and assess:

what the models are designed to do;

what questions they are able to answer; and

what, if any, are the gaps.

58 consultancies, membership organisations and universities were contacted, including those cited as consultant members of the Anaerobic Digestion and Biogas Association (ADBA) and asked if they have an AD finance model that they would like to have included in the review. Ten organisations responded positively which allowed a wide range of models covering a range of applications of AD technologies to be reviewed. This included both free and fee-paid modelling services. The reviews report against a set series of questions to allow all models to be compared on an equal basis, with a focus on which of the questions above they can answer and what limitations there are to the approach used by each model. Most of the models reviewed had explicit financial outputs. Sensitivity analyses were run on these models, but not on the single model which did not include financial outputs. Key input parameters for the sensitivity analysis were provided by ORA, with less fundamental inputs generally being provided by model owners, or standard values in published online models. The aim of the sensitivity analysis was to provide an understanding of the potential impact that individual input parameters could have on financial performance. Model owners were given a “right of reply” to the reviews to improve the accuracy of the work and as an opportunity to reply to the opinions expressed in the review. This report concludes that there are a wide variety of models available, including some which are available without charge covering all of the inputs, outputs, target users, processes and purposes expected to be seen in such models. However, there may be a gap in the availability of models which combine finance with specific mitigation in relation to environmental performance, for example odour, noise and emissions to water. The following table provides a summary of the information obtained during the review.


Figure 1 Summary of information obtained

AM

EC

Bio

meth

ane

Regio

ns

Bio

watt

s

E&

J Solu

tions

Gold

er

Ass

oci

ate

s

KTBL

Laure

nce

Gould

Part

ners

hip

OR

A

Ram

boll

UK

NN

FCC

Purpose Engineering X X X X X X

Economic X X X X X X X X X

Environmental

Education X

Carbon planning X

Project management X

Processes covered

Waste collections X

Pre-treatment X X X X

Anaerobic digestion X X X X X X X X X

Biogas upgrading to

biomethane

X X X X X

Biogas use in CHP X X X X X X X X

Biogas other uses X X X X

Post digester treatment X

Emission control X X

Digestate use X X X X

Intended users

Developers X X X X X X X

Potential owners X X X X X X X X

Operators X X X X X X

Consultants X X X X

Financiers X X X X X X X X

Entrepreneurs X X X X X

Landowners/agents X X X X X X X

Farmers X X X X X X X X

Educators/students X X X

Other X X

Knowledge required

No specialist knowledge

required – consultant runs model

X X X X X

Basic biogas X X X

Advanced biogas X X

Basic economics X

Intermediate economics X Basic waste

management

X

Basic farming

X X

Financial outputs

Capex X X X X X X

Opex X X X X X X X X

Life cycle replacement X X X X X X

Revenues X X X X X X X X

IRR X X X X X X

NPV X X X X

EBITDA X X X X

Other X X X X X X


Contents

1.0 Introduction ................................................................................................. 7 2.0 Method ......................................................................................................... 7

2.1 Identification of financial models/tools ......................................................... 7 2.2 Review of financial models/tools .................................................................. 7 2.3 Sensitivity analysis ..................................................................................... 8

3.0 Overview of information obtained during the review ................................... 8 4.0 Reviews ........................................................................................................ 9

4.1 How to use the reviews .............................................................................. 9 5.0 Model reviews............................................................................................. 10

5.1 AMEC Environment & Infrastructure UK Limited .......................................... 10 5.2 Biomethane regions ................................................................................. 14 5.3 Biowatts online ........................................................................................ 17 5.4 E&J Solutions Ltd ..................................................................................... 21 5.5 Golder Associates (UK) Ltd ........................................................................ 26 5.6 Kuratorium für Technik und Bauwesen in der Landwirtschaft (KTBL) ............ 29 5.7 Laurence Gould Partnership Ltd ................................................................. 34 5.8 Organic Resource Agency Ltd .................................................................... 37 5.9 Ramboll UK Ltd ........................................................................................ 42 5.10 The National Non-Food Crops Centre (NNFCC) ........................................... 46

6.0 Conclusions ................................................................................................ 54 6.1 Advice on selecting models ....................................................................... 54 6.2 Key findings ............................................................................................. 55

Appendix 1 - Feedstock translation for KTBL model ............................................. 57


Glossary

AD Anaerobic digestion

ADBA Anaerobic Digestion and Biogas Association

Capex Capital expenditure

CHP Combined heat and power (engine)

DM Dry Matter (content of a material remaining after 105oC exposure)

EBITDA Earnings before interest, taxation, depreciation and amortisation

FiT Feed in Tariff

FYM Farm yard manure

IRR Internal rate of return

NPV Net present value

Opex Operating expenditure

Parasitic load The amount of energy required to run a facility (usually taken from the facility‟s energy output, hence parasitic)

RHI Renewable heat incentive

ROC Renewables obligation certificate

RTFO Renewable transport fuel obligation

TM Read as DM (q.v.)

Acknowledgements

ORA and WRAP would like to thank all of the companies and individuals who took time to provide their models for the review and for their cooperation during the review process.


1.0 Introduction The Organic Resource Agency Ltd (ORA) has been commissioned by the WRAP (Waste and Resources Action Programme) to undertake a review of the various existing anaerobic digestion (AD) financial business modelling/planning tools that are available (in the public domain or otherwise and/or accessible free of charge or by fee/subscription) and to provide a report summarising this information for businesses and/or other communities who might be interested in developing AD capacity in the UK. The aim of this work is to provide an understanding of these modelling/planning tools and their usefulness from the perspective of those with an interest in developing AD capacity in the UK. The purpose was to review a sample of those models available in the industry to provide information for DEFRA‟s Anaerobic Digestion Strategy and Action Plan in terms of whether the models available provide for the needs of the industry in terms of what the models do, what questions they answer, what gaps are present and what limitations models have. 2.0 Method 2.1 Identification of financial models/tools 58 organisations were contacted to ask if they have AD finance models that they would like to have included in the review. The contact list included all relevant consultancies listed by the Anaerobic Digestion and Biogas Association (ADBA), and searches were carried out to ensure that the sample taken of the industry included a membership organisation, a university and a collaborative project. Models which can accommodate waste feedstocks and purpose grown agricultural feedstocks are included in the review. All of the companies and organisations that replied to the request positively are included. However, it should be noted that the models reviewed are only a sample of those available to the industry. 2.2 Review of financial models/tools Some models are available freely online, these are: Biomethane Regions, Biowatts and KTBL. All other models included in the review are provided via consultants. Some of these models were made directly available for ORA to review: E&J Solutions Ltd, ORA and NNFCC. The remaining models were not directly available to the report‟s author but were reviewed through contact with the models‟ owners using telephone and written questionnaires. These included models from AMEC Environment and Infrastructure Ltd, Laurence Gould Partnership Ltd and Golder Associates and Ramboll (UK) Ltd. Sensitivity analyses for these models were completed by the model owner using where possible, basic standard conditions which were provided to them. The exception was Golder Associates, whose model did not include financial outputs and so no sensitivity analysis was carried out on this model. The results of the review are reported against a set series of questions to allow all models to be compared on an equal basis. The focus was on what the models do, what questions they can answer (or not), and what limitations there are to the approach used by each model. The models were reviewed in terms of the following financial outputs:

capex;

opex;

life cycle replacement costs;

revenues;

internal rate of return (IRR);

net present value (NPV); and

earnings before interest, taxation, depreciation and amortisation (EBITDA).


The above outputs were chosen because they include the most fundamental and some of the most useful measures commonly used to evaluate an investment opportunity. Life cycle replacement costs (also known as annual capex) was also selected as a financial measure to target as it is often a considerable cost and needs to be appropriately considered in financial forecasts. 2.3 Sensitivity analysis The aim of the sensitivity analysis is to provide an understanding of the potential impact that individual input parameters may have on financial performance. This is achieved through looking at the changes to outputs when each input to the model is changed in turn. The sensitivity analyses on the models which were accessible were undertaken by the report‟s author. The remaining models had sensitivity analysis conducted by the model‟s owner, based on key standard parameters provided by the report‟s author. Additional model-specific input parameters were also specified by the model owners. For models which could accommodate source segregated household food waste the standard values were based on mesophilic, wet AD with a throughput of 25,000 tonnes. The remaining models used a mixture of cattle slurry and maize as the input for the sensitivity analysis. The model specific standard parameters provided by model owners are generally marked as “confidential”, or “not available in test” in the analyses to protect the model owners‟ intellectual property. Test ranges describe by how much each parameter is changed in the sensitivity analysis, and were selected based on a fixed plus and minus percentage. Once the test ranges had been decided, the input parameters were altered, one at a time and the percentage change in the outputs was noted. Thus the larger the percentage change recorded the more sensitive the model is to changes to the corresponding input parameter. The sensitivity analysis is to allow potential model users to direct their resources to the most sensitive parameters. The choice of gas utilisation technology was also included in the sensitivity analyses where applicable. It should be noted that the test range for this parameter was a list of technology options rather than a numerical change.

3.0 Overview of information obtained during the review The following information was obtained for each model:

its purpose, covering:

- financial;

- engineering;

- environmental;

- education;

- carbon planning; and

- project management.

elements of the digestion process covered;

intended users;

knowledge required to use the model; and

financial outputs covered.

Recognising that the review was targeted at models for financial and planning purposes and was not a review of environmental impact models, the only topic not specifically covered by all of the reviewed models was under “Purpose: Environmental”. However it should be noted that some models do provide technical information that relates to environmental


performance such as the impact or emissions of carbon (equivalent) to atmosphere and the nutrient content of digestate. It would appear that none of the models automatically enable environmental controls to be accounted for in terms of modifying the associated capital and operating costs. Nor do they allow for any specific environmental monitoring to automatically input into the models. However, it is possible that the models which included capital and operating costs assumed the equipment can achieve the required controls to manage and mitigate the risk associated with environmental issues under typical circumstances. Therefore a separate opinion may be necessary on the level of environmental impact that needs to be mitigated for specific feedstock in a particular location. The assumed level of capex or opex would then need to be adjusted to allow for sufficient controls to be accounted for financially in the model.

4.0 Reviews 4.1 How to use the reviews Each review is set out in the same format, with contact details and availability at the top. Each model has been checked against set options for:

main purpose of the model;

processes covered financially;

intended users; and

level of knowledge required to use the model.

Model inputs are listed along with an indication of which inputs are compulsory and which inputs have standard suggested values to help with the exercise. Outputs are checked against set options for important financial measures. Other financial outputs are listed in full with an indication of any other more technical model outputs. Technical outputs have not been listed in full as the focus is on modelling to inform financial decisions. Users of the models should bear in mind how each of the financial outputs should be used and the limitations of the technique. For example, EBITDA excludes the effect of depreciating capital assets and debts. „Limitations of the model‟ is an important section of each review which gives a guide as to whether a model is likely to be suitable for any given project, given limitations to composition and tonnage of feedstocks, gas utilisation technology and the modelling approach used. Under „Data quality‟ an indication from where information is taken is given, if it is not taken from industry experience. This section also includes a guide to the frequency of updates and the date of underlying data. Model owners were given a “right of reply” to the reviews to improve the accuracy of the work and as an opportunity to reply to the opinions expressed in the review. Their comments have been included at the end of each review.


5.0 Model reviews 5.1 AMEC Environment & Infrastructure UK Limited

AMEC Environment & Infrastructure UK Limited

Developed by:


http://www.amec.com/ukenvironment Contact: Dave Auty

Availability: Model outputs available through fee-paid consultancy only

File type: Excel (outputs available only)

Purpose: Engineering X

Economic X

Environmental

Education

Carbon planning

Project management

Processes covered: Waste collections

Pre-treatment X

Anaerobic digestion X

Biogas upgrading to biomethane X

Biogas use in CHP X

Biogas other uses (District heating and transport)

X

Post digester treatment X

Emission control X

Digestate use

Intended users: Developers X

Potential owners/purchasers X

Operators X

Consultants

Financiers X

Entrepreneurs

Landowners/land agents

Farmers

Educators/students

Other (Local authorities, community groups, water companies)

X

Knowledge required: Client requires no specialist knowledge – consultant operates model on their behalf

http://www.amec.com/ukenvironment



Model inputs: (c=compulsory, o=optional, s=standard values are provided)

Feedstock type (c) Feedstock tonnage (c) Feedstock percentage dry solids (c)(s) Feedstock percentage volatile solids (c)(s) Percentage volatile solids destroyed (o)(s) Biogas yield (o)(s) Volatile solids specific gravity (o)(s) Inert solids specific gravity (o)(s) Feedstock gross calorific value (o)(s) Preferred retention time (o)(s) Digester aspect ratio (o)(s) Type of gas holder (o)(s) Biogas composition (c)(s) Gate fee (c) Feed in tariff, FiT (o)(s) ROC value (o)(s) Electricity grid export price (c)(s) Electricity grid import price (c)(s) LEC value (o)(s) Biomethane export price (o)(s) Digestate export price (o)(s) Gas utilisation technology (c) Option: wet digestion / dry digestion (c) Option: standard grade / top grade infrastructure (c) Option: Food waste only / mixed feedstock (c) Option: depackaged waste only / depackaging equipment required (c)

Model outputs:

Economic Capex X Internal rate of return X

Opex X Net present value X

Life cycle replacement costs X EBITDA

Revenues X

Other: Cash flow

Notes: Capex is itemised and includes: all processing equipment civils

installation and connection to utilities labour site works Remediation of site is included as an additional cost.

Opex is itemised and includes: electricity consumption consumables (dewatering flocculent, water) staffing


AMEC Environment & Infrastructure UK Limited land leasing effluent disposal

mobile plant non-domestic business rates licence fees professional/legal fees insurances technical assistance health & safety (PPE, signage) landfill disposal of rejects indirect costs (books, office, telephone) Transport is excluded.

Life cycle replacement is expressed as maintenance cost including:

capital maintenance operational maintenance

Revenues includes: heat sales electricity sales

gate fees digestate sales

Other outputs The model is used by AMEC for the calculation of numerous technical outputs.

Model limits: As the model is based on volatile solids destruction and/or biomethane potential, it is not limited to a strict feedstock range. Any feedstock including mixed residual waste and sewage sludge can be accommodated. The permitted feedstock tonnage is validated to a maximum of 40,000 tonnes per annum of food waste and 10,000 tonnes dry solids per annum of sewage sludge.

Gas utilisation technology CHP Gas to grid (based on pressure swing adsorption, water washing, membrane separation) Transport fuel District heating

Data quality The model was originally developed in 2009/10 and is continuously reviewed. Sensitivity analysis: The results of the sensitivity analysis showed that in terms of internal rate of return (IRR), the most sensitive parameters are: gas utilisation technology, gate fees, feed in tariff and throughput, with gas utilisation technology being the single most important parameter for all of the financial outputs. It should be noted that the AMEC model deliberately rounds the input to the IRR calculation (to avoid over reliance on the value) – for example an increase in the digestate output price of 10% did increase the IRR, but it is shown as zero percent change because the change was not statistically significant.


AMEC Environment & Infrastructure UK Limited Parameter Standard

value Test range Capex (%

increase) Annual life cycle costs (% increase)

Annual opex (% increase)

Throughput (ktpa)

25 -10% +10%

-0.3 +0.1

-0.3 +0.1

-2.2 +2.2

Preferred retention time

Confidential

-10% +10%

-0.9 +0.9

-0.9 +0.9

0 0

Digester aspect ratio

Confidential

-10% +10%

0 0

0 0

0 0

Gate fee Confidential

-10% +10%

0 0

0 0

0 0

FiT value Confidential

-10% +10%

0 0

0 0

0 0

Energy prices (input and output)

Confidential

-10% +10%

0 0

0 0

-1.0 +1.0

Digestate output price

Confidential

-10% +10%

0 0

0 0

0 0

Gas utilisation technology

CHP Gas to grid Transport fuel

+21.2 +21.2

+21.2 +21.2

+4.3 +17.6

Parameter Standard value

Test range Annual revenue (% increase)

Net present value (% increase)

Internal rate of return (% increase)

Throughput (ktpa)

25 -10% +10%

-4.6 +4.6

-1.2 +1.2

-10.2 +10.4

Preferred retention time

Confidential

-10% +10%

0 0

-0.4 +0.4

+1.4 -1.4

Digester aspect ratio

Confidential

-10% +10%

0 0

0 0

0 0

Gate fee Confidential

-10% +10%

-4.6 +4.1

0 0

-13.6 +12.2

FiT value Confidential

-10% +10%

-3.6 +3.1

0 0

-10.6 +9.1

Energy prices (input and output)

Confidential

-10% +10%

-2.0 +1.5

-0.5 +0.5

-4.6 +3.0

Digestate output price

Confidential

-10% +10%

-0.5 0

0 0

-1.5 0


CHP Gas to grid Transport fuel

+31.1 +51.5

+13.0 +19.5

+14.9 +26.9

Comment from AMEC: “AMEC have developed this model as far as it needs to go because it already provides all the information we normally need to provide consultancy for feasibility studies, shadow bids and due diligence. However, we recognise that it is a complicated, detailed model and are currently making it easier to use by providing some new graphical user interfaces to reduce the time needed for dealing with simple enquiries.”


5.2 Biomethane regions

Biomethane Calculator V20

Developed by:

Technische Universität Wien Institut für Verfahrenstechnik, Umwelttechnik und Technische Biowissenschaften http://www.bio-methaneregions.eu/

Availability: Downloadable (no fee)

File type: Executable file (programme)


Economic X

Environmental

Education

Carbon planning

Project management


Pre-treatment

Anaerobic digestion


Biogas use in CHP

Biogas other uses

Post digester treatment

Emission control

Digestate use



Operators X

Consultants X

Financiers

Entrepreneurs


Farmers

Educators/students

Other

Knowledge required: Advanced biogas

http://www.bio-methaneregions.eu/



Biogas flow rate from AD (c)(s) Biogas methane content (c)(s) Biogas oxygen content (c)(s) Biogas nitrogen content (c)(s) Biogas hydrogen sulphide content (c)(s) Cost of biogas (o)(s) Type of biogas upgrading equipment (c) Biomethane desired methane content (c)(s) Inclusion of desulphurisation (o) Inclusion of biomethane pipeline (o – requires length) Inclusion of biomethane transfer station to gas grid (o) Inclusion of biomethane pressurisation (o – requires pressure)(s) Inclusion of biomethane odour addition (o) Inclusion of propane addition (o – requires % propane)(s)

Model outputs:

Economic Capex X Internal rate of return

Opex X Net present value


Revenues

Other: In addition, costs are present on a specific basis in terms of energy and volume of biogas and biomethane.

Notes:

Other outputs Technical outputs include biomethane and waste gas characteristics.

Model limits: The model is limited to the costs associated with equipment for biogas upgrading to biomethane only.

Input biogas flow rate from AD: No limits

Type of biogas upgrading equipment: Gaspermeation (membrane separation) Pressurised water scrubbing Pressure swing adsorption Amine scrubbing

Data quality: ORA has requested the date of data used in the model and the frequency of updates, but no reply has been received at time of publication.


Sensitivity analysis: The results of the sensitivity analysis showed that the most sensitive parameter in the model is the choice of gas clean up technology and that the input biogas flow rate is also important in all of the financial measures. A lesser factor affecting all costs for gas permeation technology only is the input and desired output contents of methane. Increasing the desired methane content in the output biomethane appears to decrease investment costs, annual capex and annual opex. This seems illogical but no clarification could be obtained. It should be noted that although hydrogen sulphide content only affected annual opex slightly in the test, in reality it may alter this cost hugely. For example if a concentration of 1,000 ppm is input to the model, the change in annual opex is 103% which outweighs even the choice of clean up technology.

Investment (€)

Annual capex (€)

Annual opex (€)

Using the standard values the outputs were: 704,604 72,548 96,195

Parameter Standard value Test range Investment (% increase)

Annual capex (% increase)


Input biogas flow rate (m3/h)

250 225 (-10%) 275 (+10%)

-4.8 +4.7

-4.8 +4.7

-6.9 +6.8

Biogas clean up technology

Gaspermeation (low recovery)

1.Gaspermeation (medium rec) 2.Gaspermeation (high rec) 3.Pressurised water scrubbing 4.Pressure swing adsorption 5.Amine scrubbing

+14.7 +21.9 +55.8 +59.1 +41.8

+14.7 +21.9 +55.8 +59.1 +41.8

+37.2 +62.8 +40.8 +31.0 +75.2

Biogas methane content (%)

50

45 (-10%) 55 (+10%)

-1.0 +0.9

-1.0 +0.9

-4.5 +4.5

Biogas oxygen content (%)

0.1

0.09 (-10%) 0.11 (+10%)

0 0

0 0

0 0

Nitrogen content (%)

0.4 0.36 (-10%) 0.44 (+10%)

0 0

0 0

0 0

Hydrogen sulphide content (ppm)

50 45 (-10%) 55 (+10%)

0 0

0 0

-0.5 +0.5

Desired methane content (%)

97

87.3 (-10%) 99.1 (+2.2%)

+1.0 -0.2

+1.0 -0.2

+5.0 -1.0


5.3 Biowatts online

Biowatts beta

Developed by:

Biowatts.org

http://www.biowatts.org

Availability: Online programme

File type: N/A (online with user interface)


Economic X

Environmental

Education X

Carbon planning

Project management


Pre-treatment


Biogas upgrading to biomethane

Biogas use in CHP

Biogas other uses


Emission control

Digestate use



Operators X

Consultants

Financiers

Entrepreneurs X

Landowners/land agents X

Farmers X

Educators/students X

Other

Knowledge required: Basic biogas Basic farming

http://www.biowatts.org/



Feedstocks (c) Annual input tonnage (c) Livestock type (o)(s) Number of animals (o) Crop type (o)(s) Land area of crop (o) Digestion efficiency (c)(s) % of biogas use in CHP (c)(s) % of biogas use as heat (c)(s) % of biogas use as fuel (c)(s) CHP electrical efficiency (c)(s) CHP thermal efficiency (c)(s) Feed in tariff rate (c)(s) To add new feedstocks to the Biowatts database all of the following inputs are required: % dry matter (o) % organic dry matter (o) Biogas yield (per unit of organic dry matter) (o) Biogas yield (per unit of fresh matter) (o) Biogas % methane content (o)

Model outputs:

Economic Capex Internal rate of return

Opex Net present value

Life cycle replacement costs EBITDA

Revenues X

Other:

Notes: Revenue includes feed in tariff from electricity generation only.

Other outputs Technical outputs include biomethane (in biogas) production, and electrical and thermal power outputs.

Model limits: The model is limited to AD with CHP only. Revenue (for UK projects) is limited to feed in

tariff only.

To avoid unrealistic values being entered, the model is limited to CHP electrical and thermal efficiencies of 45% and 50% respectively.

Throughput is not limited.

Feedstock is limited to a lengthy list, of both pre-set options and options entered by other users. Any user can add their own feedstock providing they have knowledge of the parameters required (see model inputs above).

Currently parasitic load is not included in the calculation.

Data quality:


The programme shows which feedstock parameters have been estimated, and which have been measured. Feedstock data can be entered by users and made public on the Biowatts database. Users are encouraged to reference the source of their data. Be aware that as users can name feedstocks they are not necessarily named in the same way globally. For example, MSW in the context of AD in the UK usually refers to mixed residual waste, but in the Biowatts database it has the same characteristics as food waste. Feed-in-tariff rates are not automatically updated and should be checked by the user before using the model – they can be changed as required by the user.

Sensitivity analysis: The results of the sensitivity analysis showed that for a given tonnage of feedstock, the nature of the material has the largest effect on the model output revenue. This indicates the importance of knowing feedstock characteristics. Other than CHP thermal efficiency all other outputs varied in line with the inputs demonstrating a simple linear relationship for these parameters.

Revenue from feed-in-tariff (£/annum)

Using the standard values the outputs were: 890,838


Test range Revenue (% increase)

Throughput (ktpa)

25 22.5 (-10%) 27.5 (+10%)

-10.0 +10.0

Feedstock Food waste Pig Slurry Cattle manure (dairy) Waste water primary sludge Biowaste

-83.9 -98.2 -88.3 -29.9

Digester efficiency (%)

100 90 (-10%) N/A

-10.0 N/A

Biogas use in CHP (%)(remainder to flare)

90 81 (-10%) 99 (+10%)

-10.0 +10.0

CHP electrical efficiency (%)

40 36 (-10%) 44 (+10%)

-10.0 +10.0

CHP thermal efficiency (%)

50 45 (-10%) N/A

0 N/A

Feed-in-tariff (£/kWhel)

0.094 0.0846 (-10%) 0.1034 (+10%)

-10.0 +10.0


Comment from Biowatts: “Our vision is to develop a unified set of tools to help you in taking the best decision for your project by connecting you with a community of biogas experts and enthusiasts. Biowatts users can comment on „public‟ projects and results and eventually guide you to better estimates. Today Biowatts provides biogas yield estimates based on scientific literature with feed-in tariffs rules for a few countries as a pilot (Germany, Italy). Biowatts financial model is still on its first phase of development. FUTURES: We aim to cover most countries in Europe/Worldwide and include energy overheads for running the plant and feedstock transport. We also plan to include a GHG emissions and reductions calculator (following UNFCCC objectives) to our model in order to take in account carbon credits. In a nutshell, Biowatts is an open platform with the ambition to:

- make existing science (AD Model) available to the community so that technology can develop at faster pace;

- take away organic waste and energy crops barriers; - connect stakeholders and generate more information exchange; - implement a unified and complete financial model adapted to each country.”

Joël Maranhão, Biowatts, April 2013


5.4 E&J Solutions Ltd

E&J Solutions AD Feasibility Calculator v1

Developed by:

E & J Solutions Ltd

http://www.eandjsolutions.co.uk/



Purpose: Engineering

Economic X

Environmental

Education

Carbon planning

Project management


Pre-treatment



Biogas use in CHP X

Biogas other uses


Emission control

Digestate use

Intended users: Developers

Potential owners/purchasers

Operators

Consultants

Financiers X

Entrepreneurs


Farmers X

Educators/students

Other


http://www.eandjsolutions.co.uk/


Model inputs: (c=compulsory, o=optional, s=standard values are provided) CHP size, kWh (c)(s) – although essentially fixed at 499kW (see model limits) AD size, kWh (c)(s) – although essentially fixed at 499kW (see model limits) Feedstock types (c)(s) Feedstock yields, t/ha (c)(s) [has no dependent cells] Feedstock DM, kg/t (c)(s) Feedstock ODM, % (c)(s) Feedstock biogas yields, m3/t (c)(s) PlanET input tonnes (c) PlanET running hours (c) Feed storage capacity (c) Electricity export price (c) Electricity purchase price (c) LEC + embedded electricity change (c) CHP downtime, % (c) Digester downtime, % (c) Daily AD attendance, hrs (c) Daily telehandler attendance, hrs (c) Cost of degritting (c)(s) Annual permitting costs (c)(s) FIT for biogas cost (c)(s) Land area owned (c) Interest on borrowing (c) Repayment period (c) Annual escalation on O&M (c)(s) Annual escalation on labour (c)(s) Annual escalation on electricity/heat (c)(s) Escalation on feedstock costs (c)(s) Escalation on gate receipts (c)(s) Escalation on land rental (c)(s) Tax on profit (c)(s) FIT ROR (c)(s) Financing cost (c) Project sundries (c) Escalation on consultancy (c)(s) Escalation on permitting (c)(s) Escalation on insurance (c)(s) Fertiliser application rate data (c)(s)

Model outputs:

Economic Capex Internal rate of return X


Life cycle replacement costs EBITDA X

Revenues X

Other: Annual profit, startup capex & opex calculator for the first year by month, cost calculator for growing the feed materials


Notes: Opex includes:

Labour Manager Telehandler Maintenance (AD plant) Maintenance (CHP) 4 yearly degritting

Revenues are itemised:

Feed in Tariff Electricity sales

LEC NPV is measured over 20 years.

Other outputs

Electricity price / downtime sensitivity calculator Clamp size calculator for feedstock storage

Model limits: The model is designed for farm AD where feed materials will be grown by the same enterprise therefore gate fees are omitted. The model is essentially fixed at 499 kW output, although there are no input tonnage limitations as the model contains capex information for a facility of this size and capex does not automatically recalculate for different size inputs. It is assumed therefore that the model is operated by adding feedstock which is appropriate for a 499 kW size plant. Heat use is excluded therefore the Renewable Heat Incentive is omitted too. CHP size is CHP electrical output i.e. after any efficiency losses. Methane percentage in biogas is set to 50%.

Feedstock Grass Maize Poultry Horse manure Sugar beet Whole crop wheat FYM Slurry Dirty water


Data quality: The model is populated with agricultural data taken in 2011 and inflation has been applied. FiT data for agricultural plants up to date to 2012/13.

Sensitivity analysis: The scenario input to the model was based on a 12% maize, 88% cattle slurry feedstock composition. The results of the sensitivity analysis showed that it could be considered that the model was most sensitive to the value of the feed in tariff for electricity production and that electricity value and CHP downtime are also important. Throughput and biogas yield sensitivity results at first look counter-intuitive with biogas yields apparently showing no effect on facility finance and lower throughputs apparently increasing the worth of the operation measured through EBITDA, NPV and IRR. The reason the results look like this is due to the way in which the model is designed to be operated and run. The tonnage entered to the model should be the appropriate amount to produce biogas to run a 499 kW CHP engine. The capex is fixed for a facility of this output and so, for example, if the throughput of feedstock is increased, the opex increases due to an increase in feedstock cost, thus leading to an apparent decrease in NPV. This serves to reiterate the point to understand how a model is designed to work – in this agricultural case the CHP output in relation to feed in tariff boundaries is more important than the throughput to the plant.

Parameter Standard value Test range Opex (%

increase) Income (% increase)

Throughput 25 ktpa -10% +10%

-6.1 +6.1

0 0

Biogas yields Not available in test

-10% +10%

0 0

0 0

FIT Not available in test

-10% +10%

0 0

-7.2 +7.7

Electricity value

Not available in test

-10% +10%

0 0

-2.8 +2.8

CHP downtime


-10% +10%

+0.7 -0.7

+1.1 -1.1

Capital borrowing cost


-10% +10%

0 0

0 0

Capital repayment period


-10% +10%

0 0

0 0

Financial escalators


-10% +10%

-0.2 +0.2

0 0

Fertiliser costs


-10% +10%

-0.1 +0.1

0 0


Parameter Standard value Test range EBITDA (% increase)

20yr NPV (% increase)

IRR (% increase)

Throughput 25ktpa, 12% maize, 88% slurry

-10% +10%

+12.0 -12.0

+62.9 -62.9

+24.5 -25.5

Biogas yields Not available in test

-10% +10%

0 0

0 0

0 0

FIT Not available in test

-10% +10%

-21.4 +23.0

-129.8 +139.4

-57.0 +53.6

Electricity value


-10% +10%

-8.2 +8.2

-40.4 +40.1

-16.4 +16.1

CHP downtime


-10% +10%

+2.0 -2.0

+12.1 -12.1

+5.0 -5.0



-10% +10%

0 0

+17.8 -18.0

0 0

Capital repayment period


-10% +10%

0 0

-4.9 +4.7

0 0

Financial escalators


-10% +10%

+0.4 -0.4

-10.2 +10.4

-4.5 +4.8

Fertiliser costs


-10% +10%

+0.2 -0.2

+1.5 -1.5

+0.7 -0.5

Comment from E&J Solutions: “Whilst the example examined was from a notional 500kW plant, it is possible to see the estimated plant size based upon technology gas yields. If necessary the feedstock rates can then be refined to reach a chosen size. Care should be taken doing this as different technologies will give different outputs per tonne of any given feedstock.”


5.5 Golder Associates (UK) Ltd

AD-Sim version 1.0

Developed by:

Golder Associates (UK) Ltd and Cranfield University

http://www.golder.co.uk

Availability: The model is a beta version used by Golder Associates currently available through fee-paid consultancy. It will eventually be sold as software.

File type: Executable file (programme)


Economic

Environmental

Education

Carbon planning

Project management X


Pre-treatment



Biogas use in CHP X

Biogas other uses


Emission control

Digestate use



Operators X

Consultants X

Financiers X

Entrepreneurs X


Farmers X


Other (regulators, policy advisers) X

Knowledge required: Advanced biogas Basic waste management

http://www.golder.co.uk/


Model inputs: (c=compulsory, o=optional, s=standard values are provided) Feedstock (c)(s) Feedstock tonnage (c) Feedstock parameters: Organic content (o)(s) Water content (o)(s) Inorganic content (o)(s) Volatile solids fraction (o)(s) Carbohydrate content (o)(s) Cellulose content (o)(s) Lignin content (o)(s) Lipid content (o)(s) Protein content (o)(s) Total carbon (o)(s) Fixed carbon content (o)(s) Total nitrogen (o)(s) Total hydrogen (o)(s) Total phosphorous (o)(s) Chemical oxygen demand (o)(s) Calorific value (o)(s) Methane yield (o)(s) K-values (degradation rates of feedstock and intermediate products)(o)(s) Planned/unplanned maintenance (o)(s) Parasitic load (o)(s) Temperature (standard values for mesophilic and thermophilic AD, also the option to specify user defined temperature)(o)(s)

Model outputs: Golder Associates has chosen not to include direct financial outputs in their model at this stage, but to concentrate on providing technical outputs that can then be used to inform financial opinion. The primary technical outputs are as follows:

Other (technical) outputs

Volume of digesters Volume of gas storage Water addition rate Organic loading rate Biogas production rate Digestate production rate Electricity production rate Heat consumption Energy balance


Model limits: The model is limited to providing technical outputs which can then be used to inform financial decisions. The model is currently limited to a list of feedstocks which the model has data for, however many feedstock parameters (such as water content, methane yield etc.) can be overwritten as required by the user. The model is currently limited to use of the gas through CHP to provide heat and electricity. Gas utilisation methods such as using the biogas to produce hot water for example are excluded.

Comment from Golder Associates: “Golder Associates and Cranfield University have worked together to develop AD-Sim, a technical model which can be used to feed into or inform financial decision making. AD-Sim can give an objective view of plant sizing and a quantified auditable assessment of potential plant operational performance. Scenario modelling can be used to understand the impact on the microbial population of different feedstocks, and ultimately which feedstocks will generate better net energy revenues, without upsetting the biological system established in the digester. Such a feature is attractive to Merchant plant operators. The decisions they make on gate fee needs to be done ahead of feedstock acceptance, but the timeframe for this decision process to take place may be quite short. ADSim forecasts the time dependency of biogas production, energy and heat production, and digestate production from AD plant with either a simple single feedstock or a range of different feedstock types. The model is modular. The AD design module allows simulation of a mesophilic or thermophilic system. The AD biochemistry module is a simplified version of the anaerobic digestion model ADM1, created by the International Water Association. This has been combined with the AD design module to allow investigation of:

Degradation through initial hydrolysis and intermediate acidogenic and acetogenic degradation processes through to methanogenesis;

Simulation of the growth and decay of microbial populations using Monod kinetics, allowing the impact of inhibition processes on the operation of the reactor to be investigated;

Biogas production rates and biogas composition and calorific value; and Impact of mixed and variable feedstock compositions which can be fed into the

digester, and which feedstock compositions can cause inhibition and could cause eventual digester failure.

AD-Sim is currently in its pilot stage as a beta version. It has been calibrated against ADM1 and validated on a small number of AD plants in the UK.”


5.6 Kuratorium für Technik und Bauwesen in der Landwirtschaft (KTBL)

KTBL Wirtschaftlichkeitsrechner Biogas

Developed by:

Kuratorium für Technik und Bauwesen in der Landwirtschaft e.V.; Association for Technology and Structures in Agriculture (KTBL) http://www.ktbl.de

Availability: Online programme

File type: Online with user interface Outputs through Adobe Acrobat


Economic X

Environmental

Education

Carbon planning

Project management


Pre-treatment



Biogas use in CHP X

Biogas other uses


Emission control

Digestate use X



Operators

Consultants

Financiers X

Entrepreneurs


Farmers X


Other

Knowledge required: Intermediate economics Basic biogas Basic farming

http://www.ktbl.de/


Model inputs: The model consists of three parts, the choice of feedstock, the definition of key data for plant layout, application of digestate and the EEG payment (from German government to renewable energy producers) and the calculation of profitability: Choice of feedstock:

Feedstock (c) consisting of: - energy crops (“Pflanzen und Pflanzenteile”) - manure and slurry (“Wirtschaftsdünger”) - miscellaneous (“Sonstiges”)

Feedstock DM (c)(s)

Feedstock costs (c)(s)

Definition of key data: Type of CHP (c)(s) Aspired hours for CHP running at maximum throughput (“angestrebte

Vollbenutzungsstunden”) (c)(s)

Ensured heat use (“Wärmenutzung”) (c)(s) Digestate storage period (“angestrebte Lagerzeit”) (c)(s) Application of digestate: Nutrient costs for N, P, K (“Preis für Reinnährstoff”) (c)(s)

Additional costs of digestate application (“Mehrkosten der Gärrestausbringung”) (c)(s)

EEG payment:

Start-up year (“Jahr der Inbetriebnahme”) (c)(s) Electricity price based on monthly average of stock exchange (“Monatsmittelwert EPEX

Spot”) (c)(s) Installed additional CHP power (“installierte Zusatzleistung”) (c)(s) Electricity sale price (“Stromverkauf”) (c)(s)

Calculation of profitability: Type and size of biogas facility (“Auswahl einer Biogasanlage”) (c)(s) Location of heat use (“externe Wärmenutzung”) (c)(s)

Amount of external used heat (“Wärmeanfall, extern genutzt”) (c)(s) Heat sale price (“Verkaufpreis ab Anlage”) (c)(s) Choice of electricity payment (“Wahl der Stromvergütung”, either according to EEG or

direct marketing (“Direktvermarktung”)) (c)(s) Biogas plant, revenues and costs:

Costs for design, approval, miscellaneous as % of investment (“Zuschlag für Planung, Genehmigung, Sonstiges”)(c)(s)

Subsidies (“Fördermittelanteil”) (c)(s) Interest subsidy loan (“zinsverbilligter Betrag”)(c)(s) Lowering of interest (“Zinsverbillgung”)(c)(s) Interest rate (“Zinssatz”)(c)(s) Costs of operating supplies (fuel, engine oil, electricity) (”Betriebsstoffpreise (Diesel,

Motoröl, Strom)”)(c)(s) Analysis costs (quantity and overall costs per analysis (“Laboranalysen (Anzahl und

Gesamtkosten)”)(c)(s) Insurance as percentage of investment (“Versicherung”)(c)(s) Labour costs (“Lohnkosten”)(c)(s)

Operational hours for supervision, maintenance and troubleshooting as well as feeding (“Arbeitszeitbedarf”) (c)(s)


Model outputs:

Economic Capex (“Investitionen”)

X Internal rate of return

Opex (“fixe und variable Kosten und Gemeinkosten”)

X Net present value

Life cycle replacement costs (“Abschreibung”)

X EBITDA (“kalkulatorischer Gewinnbeitrag”)

X

Revenues (“Leistungen”)

X

Other: Return on investment (“Gesamtkapitalrentabilität”), contribution margin (“Deckungsbeitrag”)

Notes: Capex includes:

feeding system and mobile equipment feeding system for liquids digester digestate storage

pump work biogas and energy technology addition for design, approval, miscellaneous

Opex includes:

capital consumption interest costs costs for maintenance and repair operating supplies feedstock costs

analysis interest of circulating assets labour costs insurance general expenses

Life cycle replacement includes: plant equipment without costs for design, approval, miscellaneous etc.

Revenues includes:

power export (includes EEG) heat sale digestate sale

Other outputs


Model limits: The model is designed for those who wish to plan to build an agricultural style biogas plant, and is hence limited to use as a “pre-feasibility” study. Also it includes the German system of payments from the government for renewable energy production (EEG) and this limits the model‟s use in the UK. “Direct marketing” corresponds to the German Renewable Energy Sources Act and is therefore not transferable to other countries. The model calculates CHP size based on biogas production combined with CHP running time. Thus if CHP running time is decreased, the CHP size is increased by the model. Therefore, the model assumes that the annual biogas produced can be utilised within the annual running time specified. In reality biogas produced during periods of CHP downtime is more likely to be flared than stored, thus, the lower the CHP running time input to the model, the more this assumption will affect the results.

Feedstock: Limited to: Energy crops (“Pflanzen und Pflanzenteile”, 28 options) Manure and slurry (“Wirtschaftsdünger”, 5 options) Miscellaneous (“Sonstiges”, 27 options) For a translation of feedstock names please refer to Appendix 1.

Type of CHP Gas CHP (“Gas-Otto-Motor”) Dual fuel CHP (“Zündstrahlmotor”)

Type and size of biogas facility

Limited to a choice of 67 model plants with installed electrical capacity ranging from 50 to 1,000 kW. Depending on the required electrical capacity, one or two choices of model facility are offered. The design of these choices cannot be altered in the current model version.

Choice of how heat is used

Choice of either a general estimation or detailed calculation for greenhouses (“Gewächshaus”), stables (“Stall”), drying plants (“Trocknungsanlage”) or dwellings (“Wohnhaus”)

Choice of electricity payment

According to EEG or Direct marketing (“Direktvermarktung”)

Data quality: The KTBL provides the calculations and agricultural information for the model. The model is based on KTBL‟s own data, which has been compiled in expert workgroups or collected otherwise including through co-operation with other organisations such as the Fachagentur Nachwachsende Rohstoffe e.V. (FNR).

Sensitivity analysis: The sensitivity analysis was conducted within the current model limits including limiting the input to a pre-determined model plant – it should be noted that changing the first three parameters (throughput, dry matter content and CHP running time) can lead to the choice of another model plant. The sensitivity analysis showed that the most sensitive input parameters were the assumed dry matter content of feedstock and the throughput of feedstock. CHP running time is also significant. The financial output from the model which was most sensitive to input changes was EBITDA.


Capex (€)

Opex (€) Revenues (€)

EBITDA (€)

Using the standard values the outputs were:

1 471 098

497 157 536 142 38 985


Test range Capex (% increase)

Opex (% increase)

Revenues (% increase)

EBITDA (% increase)

Throughput 9.5 ktpa; 60 % maize, 40 % slurry

-10% +10%

0 +6.4

-4.2 +8.4

-10.1 +10.2

-85.2 +33.0

DM-content maize 35 %, slurry 10 %

-10% +10%

0 +6.4

0 +4.2

-10.1 +10.2

-139.4 +87.1

CHP running time

8,000 h/y

-5% +3.75%

+6.4 0

+4.2 0

+0.3 -0.2

-48.6 -3.2


4 % -10% +10%

0 0

-0.7 +0.7

0 0

+9.2 -9.2

Fertiliser costs

1 €/kg N 0.9 €/kg P 0.7 €/kg K

-10% +10%

0 0

0 0

-1.7 +1.6

-7.9 +7.2

Labour costs 15 €/h

-10% +10%

0 0

-0.3 +0.3

0 0

+3.9 -3.9

Operating hours

1,011 hours/y without feeding

-10% +10%

0 0

-0.3 +0.3

0 0

+3.9 -4.3

Comment from KTBL: ORA understand that KTBL intend to release a new version of the model by the end of 2013. This will include biogas upgrading to biomethane. It will not be based on pre-determined model plants; the potential plant components will be selected depending on the parameters generated by the feedstock choice.


5.7 Laurence Gould Partnership Ltd

Laurence Gould Partnership (500 kW 280213)

Developed by:

Laurence Gould Partnership Ltd

http://www.laurencegould.com/




Economic X

Environmental

Education

Carbon planning

Project management


Pre-treatment



Biogas use in CHP X

Biogas other uses


Emission control X

Digestate use X


Potential owners/purchasers

Operators

Consultants

Financiers X

Entrepreneurs

Landowners X

Farmers X

Educators/students

Other


http://www.google.co.uk/url?sa=i&rct=j&q=laurence+gould+partnership&source=images&cd=&cad=rja&docid=2dvvLaAuNZaiTM&tbnid=Yerx5KjrjM21KM:&ved=0CAUQjRw&url=http://cometoburgesshill.co.uk/shopsandservices/Businesses/30-Laurence Gould Partnership Ltd/&ei=b0JAUd_UPMSo0AXH5YHIBw&bvm=bv.43287494,d.d2k&psig=AFQjCNGbB9ldnntedporSt4QQkLRlhmPhQ&ust=1363252188388747

http://www.laurencegould.com/

http://www.google.co.uk/url?sa=i&rct=j&q=laurence+gould+partnership&source=images&cd=&cad=rja&docid=2dvvLaAuNZaiTM&tbnid=Yerx5KjrjM21KM:&ved=0CAUQjRw&url=http://cometoburgesshill.co.uk/shopsandservices/Businesses/30-Laurence Gould Partnership Ltd/&ei=b0JAUd_UPMSo0AXH5YHIBw&bvm=bv.43287494,d.d2k&psig=AFQjCNGbB9ldnntedporSt4QQkLRlhmPhQ&ust=1363252188388747


Model inputs: (c=compulsory, o=optional, s=standard values are provided) Feedstock composition (up to 10) (c) Feedstock volume (o) – this is generally calculated by Laurence Gould to provide an appropriate input for a given CHP size and downtime estimate Biogas yield (c)(s) CHP running time (o) Parasitic load (c)(s) Feedstock cost (o) Gate fee (o) Capex (pre-build development (c)(s) Capex (plant) (c)(s) Capex (working capital during build phase)(c)(s) Capex (CHP engines)(c)(s) Site rental cost (o) Self-financed capital (c) Borrowed capital (c) Interest rate (c) Retail Price Index (c) Energy index (20 years) (c) Heat sales price (o) Volume of digestate Value of digestate RHI rate (o) Feed in tariff rate (c)(s)

Model outputs: Economic

Capex Internal rate of return X


Life cycle replacement costs X EBITDA X

Revenues X

Other: Profit & loss (20 years), Pay-back period

Notes: Opex includes

Feedstock cost Staff –manager Staff –operator Staff –emergency operator cover Staff –admin Staff –other Site rental Staff accommodation Heating building costs

Telephone Office Equipment Machinery for tractor Fuel for tractor Parasitic electricity costs DNO metering cost Health & safety Legal costs


Insurances Biological support Accounting advice

Consultancy Maintenance (plant) Maintenance (farm machinery) Maintenance (CHP) Contingency Decommissioning

Other outputs None – the model is purely financial

Model limits: The model is limited to agricultural feedstocks and is designed to be used once a quotation for an AD system has been obtained.

CHP size Up to 1.5MW

Biogas utilisation CHP only

Data quality: The model was written in 2011 and is updated continuously.

Sensitivity analysis: The sensitivity analysis was run with an input of 28% maize and 72% cattle slurry which was kept constant throughout. This composition was required to reach 500kW using 25 tonnes of material. It is based on a CHP system with no heat use. The results of the sensitivity analysis showed that the most sensitive input parameter for facility finance could be considered to be FiT given its effect on IRR. Biogas yields, feedstock costs and CHP running time are also significant.


Test range

Opex (% increase)

Revenue (% increase)

IRR (% increase)

EBITDA (% increase)

Biogas yields

not available in test

-10% +10%

-5.1 +5.1

-0.5 +0.5

-5.7 +5.7

-5.7 +5.7

FiT rate not available in test

-10% +10%

0 0

-6.5 +6.5

-17.2 +16.4

-15.4 +15.4

Feedstock costs


-10% +10%

-5.1 +5.1

-0.5 +0.5

+5.7 -5.7

+5.7 -5.7

Energy index


-10% +10%

0 0

-1.5 +1.5

-0.6 +0.7

-1.1 +1.1

CHP running time


-5% +5%

-2.7 -27.6

-5.0 +5.0

-8.9 +8.9

0 +8.2

Interest rate


-10% +10%

-1.6 +1.6

0 0

0 0

0 0


5.8 Organic Resource Agency Ltd

ORA anaerobic digestion finance model v14

Developed by:

Organic Resource Agency Ltd

http://www.o-r-a.co.uk




Economic X

Environmental

Education

Carbon planning

Project management


Pre-treatment X



Biogas use in CHP X

Biogas other uses X


Emission control

Digestate use



Operators X

Consultants

Financiers X

Entrepreneurs X


Farmers X

Educators/students

Other


http://www.o-r-a.co.uk/



Feedstocks (c)(s) Annual input tonnage (c) Biogas utilisation technology (c)(s) Distance travelled by feedstock (o) Distance travelled by solid outputs (o) Distance travelled by liquid outputs (o) Biogas yield (o)(s) Transport cost (o)(s) Gate fee (o)(s) Avoided landfill fee (o)(s) ROC value (o)(s) RHI value (o)(s) RTFC value (o)(s) FiT value (o)(s) Energy prices (o)(s) Cost of grid connections (o) Value of non-biogas outputs (o)(s) No. of years over which to depreciate capital (o)(s) Financial discount rate (o)(s) Calculate NPV after X no. of years (o)(s) CHP efficiency (o)(s) CHP availability (o)(s)

Model outputs:



Life cycle replacement costs X EBITDA X

Revenues X

Other: Transport opex

Notes: Capex is split into:

AD process Gas utilisation

Opex is split into:

AD process Gas utilisation

Transport costs are stated separately. Net present value excludes all transport costs. Revenues are itemised and include:

Gate fees Biogas revenues (electricity, heat, biomethane, transport fuel) Avoided landfill costs Non-biogas output revenues


Other outputs Energy outputs including parasitic load

Model limits: The model does not include a detailed breakdown of capex and opex due to the inherently variable nature of these parameters in reality. Although the model does output single figures for these parameters, they are designed to be used as a guide before tenders are sought. The model does not include:

cost of design, rents and rates on land nor remediation of site.

The model assumes utilities are available at the edge of the site and that additional costs will not be required to obtain access to utilities.

Feedstock input: Cattle manure (2-30 ktpa)

Cattle slurry (2-30 ktpa) Poultry manure (2-30 ktpa) Pig slurry (2-30 ktpa) Agricultural crop residues (2-30 ktpa) Energy crops (maize silage) (2-30 ktpa) Source segregated commercial food and abattoir waste (5-50 ktpa) Source segregated household food waste (5-50 ktpa) Co-mingled food and garden waste (5-50 ktpa) Residual municipal solid waste (MSW) (100-200 ktpa)

Biogas utilisation: CHP Gas to grid Transport fuel

Data quality: ORA is not in a position to provide an unbiased view on the data quality in the model, although we can state that the capex, opex and life cycle replacement costs are based on cost curves developed from ORA‟s experience working on a wide variety of AD projects.

Sensitivity analysis: The sensitivity analysis was carried out using source segregated household food waste as the feedstock. The most sensitive parameter across all financial measures was gas utilisation technology, with throughput also having a major impact and gate fees having a more minor role ahead of the other parameters tested. There are some very large apparent changes to net present value (NPV); the reason for this is that an NPV output can be close to zero. When a small figure is compared to larger NPVs then the percentage change, as seen in the sensitivity analysis, can be very large. The other financial outputs measured are rarely close to zero and hence show less sensitivity to changes to inputs.

Parameter Standard Test range Capex (% Annual Life Annual opex


value increase) cycle (% increase)

(% increase)

Throughput (ktpa)

25 -10% +10%

-8.2 +7.7

-8.1 +7.7

-8.6 +8.5

Dist. travelled by outputs (miles)

10

-10% +10%

0 0

0 0

-2.0 +1.9

Biogas yield Confidential -10% +10%

-0.9 +1.0

-1.0 +1.0

-1.1 +1.0


CHP (without heat use)

Gas to grid Transport fuel

+11.6 +13.9

+13.6 +16.3

+49.8 +54.8

Gate fee Confidential -10% +10%

0 0

0 0

0 0

ROC value Confidential -10% +10%

0 0

0 0

0 0

ROC, FiT, RHI, RTFO values

Confidential -10% +10%

0 0

0 0

0 0

Energy prices Confidential -10% +10%

0 0

0 0

0 0

Value of non-energy outputs


0 0

0 0

0 0

CHP efficiency Confidential -10% +10%

-0.9 +1.0

-1.0 +1.1

-1.1 +1.0

CHP availability

Confidential

-10% +5.3%

0 0

0 0

0 0


Test range Net present value (% increase)

Internal rate of return (% increase)


Throughput (ktpa)

25 -10% +10%

-207 +244

-4.0 +3.4

-10.0 +10.0


10

-10% +10%

+70 -30

+0.7 -1.4

0 0


-441 +442

-7.4 +6.7

-4.5 +4.5




-1671 -3643

-24.4 -51.4

+9.7 -1.5


-651 +651

-10.9 +10.1

-5.3 +5.2


-58 +411

-1.4 +2.7

-0.5 +3.3

FiT, RHI, RTFO values


0 +309

0 +4.7

0 +2.6


-280 +127

-2 +2

-1.3 +1.0



-488 +480

-8.1 +7.4

-0.2 +0.2

CHP efficiency Confidential -10% -607 -9.1 -5.0


+10% +319 +4.7 +2.5

CHP availability

Confidential -10% +5.3%

-388 +2204

-9.7 +4.5

-4.9 +2.6


Test range EBITDA (% increase)

Throughput (ktpa)

25 -10% +10%

-10.6 +10.0


10

-10% +10%

+0.8 -0.9


-6.0 +6.0




-7.4 -25.5


-7.6 +7.5


-0.7 +4.7



0 +3.6


-1.5 +1.5



-0.3 +0.2


-6.5 +6.4

CHP availability

Confidential -10% +5.3%

-7.0 +3.7


5.9 Ramboll UK Ltd

Ramboll AD Economic Model

Developed by:

Ramboll UK Ltd

http://www.ramboll.co.uk




Economic X

Environmental

Education

Carbon planning X

Project management

Processes covered: Waste collections X

Pre-treatment X



Biogas use in CHP X

Biogas other uses X


Emission control

Digestate use X



Operators X

Consultants X

Financiers X

Entrepreneurs X


Farmers X

Educators/students

Other


http://www.ramboll.co.uk/


Ramboll AD Economic Model


Feedstock composition inputs:

Volumes (c)

Seasonality (c) Dry Solids in feed to digester (c)

Plant layout and data inputs:

Number of digesters (c) Storage requirements (c) Digester volume (c) Digester dimensions (c) Minimum hydraulic retention time (c) No. days biogas storage (c)

Digester plant conditions inputs:

Average raw sludge temperature (c) Hygenisation temperature (c) Digester operating temperature (c) Digester heat transfer coefficients (c)

CHP inputs: Rated Output (o) Electrical efficiency (o)

Thermal efficiency (o) Availability (o)

Biogas upgrade inputs:

Upgrade plant availability (o) Methane slip (o) Clean up technology choice (o)

Gas to grid inputs: Target Wobbe number (o)

Biomethane for vehicles inputs:

Vehicle selection ‐ fuel economy (o)

Annual mileage of vehicle (o) Biomethane supply pressure (o)

Economic inputs:

Capital Cost (c) Electricity consumption (c) Heat consumption (c) Water consumption (c) Internal labour (c)


Ramboll AD Economic Model External labour (c) Gas wholesale value (c) Retail price of propane (c) Fuel retail price (c) RTFO value (c) RHI value (c) FIT value (c) ROC value (c) Gate fee (c)

Model outputs:




Revenues X

Other:

Notes: Capex is not automatically generated and some input is required. Capex and opex may be broken down to an extent on request. Pre-treatment and parasitic load are included in all calculations.

Other outputs Energy balance

Model limits: Feedstocks modelled are limited to a list but bespoke inputs can be used where Ramboll have access to sufficient data regarding the feedstock characteristics. The process is limited by biogas yields being based on typical values; they are not adjusted for different technologies. Capex is not automatically generated and some input is required.

Data quality: The model was developed in 2011, and is updated annually as a minimum, and as required in response to industry changes. Sensitivity analysis: The results of the sensitivity analysis showed that the model is most sensitive to the choice of gas utilisation technology across all financial outputs, and that feedstock volume is important, particularly with regard to revenues and internal rate of return. CHP electrical efficiency could be considered to be the next most sensitive parameter, also with regard to internal rate of return.



Test range Capex (% increase)


Feedstock volume (ktpa)

25 -10% +10%

-2.2 +0.8

0 0




+33.6 +60.1

+100.7 +110.4



0 0

0 0

Energy prices (i.e. inc. gas wholesale value, retail price of propane, fuel retail price)


0 0

0 0

CHP electrical efficiency


0 0

0 0


Test range Annual revenue (% increase)

NPV (% increase)

IRR (% increase)

Feedstock volume (ktpa)

25 -10% +10%

-10.5 +10.5

0 -1.8

-12.3 +14.1




-2.4 -21.9

+43.3 +82.3

-41.8 -84.6



-1.9 +1.9

+0.5 -0.5

-2.8 +2.8

Energy prices (i.e. inc. gas wholesale value, retail price of propane, fuel retail price)


-1.2 +1.2

+0.3 -0.3

-1.7 +1.7

CHP electrical efficiency


-2.2 +4.1

+0.6 -1.1

-3.2 +6.1


5.10 The National Non-Food Crops Centre (NNFCC)

NNFCC AD cost calculator (standard v2.4/business v2.5)

Developed by:

The National Non-Food Crops Centre

http://www.nnfcc.co.uk/

Availability: Free to members (standard edition), Free to level 2 and 3 members (business edition)

File type: Excel


Economic X

Environmental

Education

Carbon planning

Project management


Pre-treatment X



Biogas use in CHP X

Biogas other uses (as transport fuel) X


Emission control

Digestate use X



Operators

Consultants X

Financiers X

Entrepreneurs X


Farmers X

Educators/students

Other

Knowledge required: Basic economics Basic biogas

http://www.nnfcc.co.uk/



Model inputs: (c=compulsory, o=optional, s=standard values are provided) Feedstock and gas tab: Feedstocks (c)(s) Annual input tonnages (c) Feedstock dry matter percentages (o)(s) Feedstock biogas yields (o)(s) Gate fees (o) Cost of feedstock (c) Retention period (c) – an estimation of minimum required retention time is included in the business edition to help fill in this field Monthly allocations (o) – this allows seasonal effects in feedstock to be accounted for. Digestate value tab: Cost of fertiliser (c)(s) Nutrient value of undigested manures/slurries (c)(s) Availability of nutrients to crops from undigested manures/slurries (c)(s) Availability of nutrients to crops from digested manures/slurries (c)(s) Nutrient value of total digestate (c)(s) Available nutrients crop can take up after digestate spreading (c)(s) Revenues tab: Methane content of biogas (c) (s) Losses (inefficiency) in process (c)(s) Electrical efficiency of CHP (c)(s) Thermal efficiency of CHP (c)(s) Electrical parasitic load (c)(s) Thermal parasitic load (c)(s) Option to restrict CHP size (o)(s) – business edition only Choice of ROCs/FiT (c) Feed in tariff rates (o)(s) ROC value (o)(s) Price paid for electricity used on site (o) Amount of electricity used on site (excluding parasitic load)(o) Price received of electricity sold to private users (o) Amount of electricity sold to private users (o) Price received of electricity sold to grid (o)(s) Percentage of heat used other than in parasitic load (c)(s) Price received for heat used other than in parasitic load (c)(s) For revenue comparison with CHP: RHI tariff (o) – business edition only Upgraded biomethane gas sales (o) – business edition only Biogas upgrading opex (o) – business edition only Capital tab: Front end capital (c) – business edition only Digester capital (c) – business edition only Methane use capital (c) – business edition only Digestate use and storage capital (c) – business edition only Grant assistance for Front end capital (c) – business edition only


NNFCC AD cost calculator (standard v2.4/business v2.5) Grant assistance for Digester capital (c) – business edition only Grant assistance for Methane use capital (c) – business edition only Grant assistance for Digestate use and storage capital (c) – business edition only Write off period for Front end capital (c)(s) – business edition only Write off period for Digester capital (c)(s) – business edition only Write off period for Methane use capital (c)(s) – business edition only Write off period for Digestate use and storage capital (c)(s) – business edition only Buildings and infrastructure capital (c) – standard model only Machinery capital (c) – standard model only Grant assistance for Buildings and infrastructure capital (c) – standard model only Grant assistance for Machinery capital (c) – standard model only Write off period for Buildings and infrastructure capital (c)(s) – standard model only Write off period for Machinery capital (c)(s) – standard model only Finance (base rate) (c)(s) Finance (over base) (c)(s) Proportion bank funded (c)(s) Finance term (c)(s) Overhead costs tab: Labour costs – regular and casual (o) Labour costs – management (o) Vehicle licencing costs (o) Insurance costs (o) Transport (in/out) (o) Water addition costs (o) Accreditation costs (o) – business model only Office and telephone costs (o) Assurances (o) – standard model only Professional fees (o) – standard model only Testing fees (o) – standard model only EA fees (o) – standard model only Spreading licences (o) – standard model only Misc/other costs (o) Rent and rates (o) Machinery maintenance costs – AD (o)(s) – standard model only Machinery maintenance costs – CHP (o)(s) – standard model only Machinery maintenance costs – Front end (o)(s) – business model only Machinery maintenance costs – Digester (o)(s) – business model only Machinery maintenance costs – Methane use (o)(s) – business model only Machinery maintenance costs – Digestate use and storage (o)(s) – business model only Financial summary page tab: Other income (including LECs and Triads etc) (o)



Model outputs:



Life cycle replacement costs EBITDA

Revenues X

Other: A full annual profit/loss calculation is provided including graphical outputs for:

Capital value of plant Cash at bank Loan outstanding Profit/loss Depreciation Net assets Loan interest

Return on capital is calculated, and is also expressed graphically. Capex, opex and revenues are also expressed on a per tonne basis to highlight the costs and benefits of increasing facility capacity. The business edition includes a monthly cash flow forecast for project management. It is designed to make the costs of construction, delays and timeliness clear including VAT.

Notes: The model produces a capex range described as “thumb in the air” figures. However the model will then run from the input capex provided by the user. Division of capital across different parts of the facility has a graphical output in the business edition. Opex (costs) are itemised and include:

Energy Feedstock Labour & Management Power including Depreciation General overheads Land and Building Interest Payment

Life cycle replacement costs are not an explicit output from the model but they are included automatically through the write-off periods of the equipment. For example if the CHP engine capital asset is depreciated over a period of 10 years then the model automatically replaces the asset in year 10 in the Annual Financials tab. At the point of replacing parts of the facility, all costs of equipment are assumed to be the same as in year 1, this is in line with all other costs and revenues which also do not include any escalators.


NNFCC AD cost calculator (standard v2.4/business v2.5) Revenues are itemised and include:

Electricity Heat Fertiliser Value ROCs – itemised in standard edition only Gate Fees Other Income

An income comparison is provided as a graph in the business edition for comparison of ROCs, FiT and biomethane to grid, split into electricity and heat components. Internal rate of return is expressed numerically and also graphically on an annual basis across the lifetime of the facility.

Other outputs

The required digester capacity is calculated on the feedstock and gas tab. The required engine size is calculated on the Revenue tab The “technical dash board” includes the following technical outputs:

Biogas production and yields grouped by broad feedstock group (livestock manures, energy crops, other)

Minimum land requirement for spreading Total volume of digestate Application rate of digestate

Number of houses to be supplied (heat and power) Fuel or Power comparison Feedstock value summary Sensitivity analysis (incl. capex, opex, revenue, feedstock volume and gas yield)

Model limits: The model is dominated by agricultural feedstocks (livestock feedstock and crop feedstock), although a list of „Other‟ feedstocks which includes specific food wastes, processing wastes and crop residues is included. New feedstocks can be added by typing into the boxes and entering own data for critical parameters such as dry matter and biogas yield. More specific feedstocks such as municipal food waste, commercial food wastes, residual municipal solid waste and sewage sludge are excluded if feedstocks are not listed, „Other‟ feedstocks refers the user to the Cropgen database to source their own data. Gas utilisation is limited to CHP although a stand-alone calculator is included in the business edition for comparing the revenue after costs but before depreciation and finance for biomethane to grid against CHP. Below is the full list of feedstocks for which default values (dry matter and biogas yield) are included:


Feedstock (livestock manures): Cattle muck; fresh Horse excrement Dairy cow slurry Dairy slurry including fodder remains FYM Pig slurry Pig muck Poultry excrement Sheep muck

Feedstock (energy crops): Grass silage Maize Grain Silage (Crimped seed) Maize silage Barley straw Barley straw ammonia treated Barley straw NaOH treated Clover Hay first cut Meadow hay Oat straw Oat straw NH3 treated Wholecrop wheat Wheat straw Wheat straw NaOH treated Wheat straw NH3 treated

Feedstock (other): Cauliflower Fodder beet Fodder carrot Molasses (sugar beet) Potato flakes Potato peeling wastes roughly Sugar beet; fresh Sugar beet; dried & shredded Sugar beet tops; clean Vegetable wastes Clover; 1st cut in flower Green maize; milk-ripe Green Mustard before flower Green oats in flower Meadow grass green Sunflowers green start of flowering Barley grain Maize grain; dry Field bean grain Grain peas Mixed grain Oat grain Rye grain Soy beans seeds steam-heated Sunflower seed Wheat grain Corn gluten Oats fodder flour Oat bran Rolled oats Wheat Bran


Wheat chaff Wheat Flakes Wheat germs Wheat grain Wheat semolina bran Butter milk; fresh Whole cows milk; fresh Skimmed milk dry Skimmed milk fresh Sour whey fresh Glycerin Linseed oil Rapeseed oil Sunflower oil Soya oil Palm Kernel Pellet Sunflower cake cold pressed Baking wastes Cheese wastes Leftovers; Average fat Leftovers; fat rich Leftovers; low fat and wet Brewers Waste Old bread

Data quality: The feedstock parameters are referenced to the Cropgen database. The reviewed business edition (v2.5) included updated standard values including fertiliser costs, CHP efficiencies, parasitic load, feed in tariff values and ROC value. Now that both the FIT and RHI official RPI-linked tariffs for 2013/14 have been published by Ofgem, NNFCC have stated both versions of the tool were updated with new tariffs in April and the tool visibly dated accordingly. Sensitivity analysis: The sensitivity analysis was carried out using the business edition (v2.5). The feedstock was set to 12% maize silage and 88% dairy cow slurry. The feed in tariff was used throughout, although some of the test ranges pushed the theoretical modelled facility into different FiT bands. This can be seen from the lower throughput figures, where revenues were only slightly decreased and thus the resulting IRR was increased more for a smaller facility (+10.1%) than a larger one (+3.6%). The results of the sensitivity analysis showed that in terms of IRR, the most sensitive parameters in the model are feed in tariff, CHP efficiency and biogas yield. Feedstock cost and throughput are also significant. The sensitivity analysis shows that CHP efficiency affects the facility capex. This at first seems unintuitive, however the reason is that as the model is designed to be used at feasibility stage, the expected CHP efficiency, and hence electricity production is used to calculate the capex estimates. The result is that for a model such as this to be used to calculate how on-going operational parameters such as CHP affect the facility‟s finances, then the capex must be fixed. This model allows the user to input and fix their own capex and so this can be achieved if desired. There is a positive correlation between fertiliser cost and revenue. This is because the model puts an emphasis on gaining value for digestate and its ability to save money due to reduced fertiliser cost. Fertiliser costs will also affect the cost of producing feedstock in some circumstances, although in this model the cost of producing feedstock is left as a user input.


Parameter Standard

value Test range Capex

(% increase)

Opex (% increase)


IRR (% increase)

Throughput (ktpa)

25 22.5 (-10%) 27.5 (+10%)

-10.0 +10.0

-8.4 +8.4

-5.1 +10.1

+10.1 +3.6

Biogas yields Confidential -10% +10%

-6.2 +6.3

-3.2 +3.2

-5.1 +9.6

-3.6 +14.2

Feed in Tariff Confidential -10% +10%

0 0

0 0

-6.6 +6.6

-17.1 +17.2

Electricity value


0 0

0 0

-1.5 +1.5

-3.6 +4.1


-6.2 +6.2

-2.2 +3.2

-5.1 +9.6

-7.1 +14.2

Losses and inefficiencies


0 0

+0.1 -0.1

+0.7 -1.4

+1.8 -3.6

Fertiliser costs Confidential -10% +10%

0 0

0 0

-1.1 +2.3

-3.0 +5.9

Feedstock costs


0 0

-3.5 +3.5

0 0

+7.7 -7.1


CHP Biomethane to grid

N/A N/A +12.6 N/A

Comment from NNFCC: “In 2010, in response to user demand, the „Standard Edition‟ of the tool was updated (funded directly by NNFCC) to include a broader range of feedstocks, including non-agricultural materials, and to provide further detail to support business and technical decision making. To ensure the tool remained both simple enough for users without vast prior knowledge, yet detailed enough to support business planning activities, a parallel „Business Edition‟ was also launched. Both versions of the tool are now updated at least annually by NNFCC, to update relevant default values, for example the RPI-linked FIT and RHI rates. Updates are also made to provide additional user support, in the form of „Help‟ boxes and comments on particular cells. Where it becomes apparent a particular data requirement or function is unclear, or where further guidance or a link to further information would be helpful this is also added accordingly. Although the tools remain dominated by agricultural feedstocks, the framework provides the flexibility to be used for any feedstock. The user needs to add biogas yield and DM data, and subsequent calculations are then carried out in the same way as for listed feedstocks. The review expressed concerns about the capex and opex then not reflecting the more complex technical requirements; however this aspect is reliant on user data and the higher end of the cost range quoted in the „thumb in the air‟ costings reflects likely costs associated with more complex systems, feedstocks and projects. The low end of the range reflects simple projects accepting „simple‟ feedstocks, where some of the infrastructure may already be in place or the developer may carry out some of the work themselves. This is perhaps not made explicitly clear in the guidance, but will be addressed at the next update.” Lucy Hopwood Head of Biomass & Biogas, NNFCC


6.0 Conclusions The objective of this report was not to rank models, but to show what is included within them and also to undertake sensitivity analyses on them. ORA has summarised some advice for model users based on these reviews below, followed by key findings in terms of the content of models and sensitivity analysis. 6.1 Advice on selecting models The models reviewed have been designed for a variety of purposes and for a variety of users. It is important therefore, to choose an appropriate model for the needs of any given project. This can be aided by using the information contained at the beginning of each review above and summarised at the beginning of the report. Some of the models are available directly from websites whilst others can only be used through consultancies. In general more knowledge regarding biogas, farming and finance is required by the user for models that are not run by a consultant. It is important to choose a model which uses terms you are comfortable with in your business or organisation. As the models are based on different methods, for example inflating or deflating costs and revenues going into the future, the data needs to be presented in a way you can interpret accurately, in a way that you are familiar with for comparable investments. Some models are more flexible than others with respect to their ability to give a wide range of financial forecasts and performance indicators. During the course of preparing this work it became apparent that those model owners offering access to models via consultancy are open to working with their clients to give what their clients require. Therefore, it may to be worth asking about adaptation of models where it may be relevant. The KTBL model was included to give an example of a model from Germany where AD operates in a different financial and regulatory climate to the UK. This can be seen in the model through the “EEG” payment which is rather different to the UK‟s current system of FiT and ROCs which is separate to the electricity market. However, the KTBL model is likely to be of use to some parties in the UK through its estimates of capex and opex, so German to English translations have been included in Appendix 1. Modelling the expected performance of an investment is important, but there are some limitations to the use of models in general which should be borne in mind:

Accurately forecasting capital and operational expenditure is difficult. When a project goes

out to tender, even when the required inputs and outputs of a proposed facility are clearly

specified and the conditions on site are known, the resulting tenders may vary widely in

terms of both capex and opex; factors of more than 50% variation in this respect have

been seen. Some of the models reviewed will provide an estimate of capex based on the

technical inputs to the model, whilst in others a capex has to be inserted by the user.

Be clear when using any model what is included and excluded in terms of technical

aspects e.g. parasitic load and any required pre-treatment, on-going expenditure such as

life cycle replacement (annual capex) costs and transport costs, and financial planning

aspects such as inflation and depreciation.

Pay attention to the limits of the model specified by model designers. In some cases

models will automatically warn users when input values make the outputs invalid. Pushing

a model beyond specified limits is likely to result in inaccurate outputs.


Models can be designed to be used at an early, conceptual, feasibility or options appraisal stage, or they can be designed to be used during on-going operation. The models reviewed here have been developed primarily for feasibility purposes but extra care needs to be taken when using financial models during on-going operation. When modelling on-going operations some parameters need to be fixed that would not be fixed if the model was being used at the feasibility stage. An example is that in some models the sensitivity analysis shows the estimated capex and hence the IRR are affected by the efficiency of a CHP engine. This is due to the capex estimate being based on a given expectation of biogas production, which in turn is based on CHP efficiency. If a model of this nature was going to be used to predict how CHP efficiency affects on-going performance, then capex and other parameters would need to be fixed. Users and model designers should be aware of which inputs affect which outputs when deciding if a model is suitable for modelling for a design or for a scenario involving an existing facility. The effect of model inputs on the outputs can be seen in the sensitivity analysis included at the end of each review. Sensitivity analysis can help to understand not only which inputs affect which outputs, but also by how much. This quantitative aspect can be used to identify which parameters have the greatest effect on the model outputs, based on an equal change to the inputs where possible. This method of sensitivity analysis has been included in this review. When users know which input parameters have the greatest effect on outputs then subsequent effort and resources can be utilised to determine the detail surrounding the most important inputs.

However it should be noted that this method is not a substitute for using the models to

forecast specific expected changes in the future. This needs to be done by each model user

when they have drawn up their baseline inputs, and should be conducted by varying the

inputs by realistic amounts to observe how the outputs are affected. This can be carried out

for two main reasons:

Risk management, such as predicting whether a facility would still be viable if a particular

model input was severely affected. An example is the concentration of hydrogen sulphide

in biogas and its connection with biogas clean-up costs. Varying an input hydrogen

sulphide content of 1000 ppm by +/- 10% will not reveal the full extent to which this

parameter can vary. Inputting 5000 ppm (or even greater) would give a more reasonable

upper end for some AD projects.

Managing change in an on-going operation such as changing a facility‟s feedstock

composition.

6.2 Key findings

This report has found that there are a wide variety of models available. This includes some

which are available without charge, which cover all of the inputs, outputs, target users,

processes and purposes expected. One potential exception is for financial/business models

which listed “environmental” as the tool‟s purpose. None of them automatically enabled

environmental controls to be accounted for in terms of modifying the associated capital and

operating costs. Modelling environmental issues and their connection with finance is complex

and is discussed in section 3 above.

The sensitivity analyses undertaken showed that where the models included a choice of gas

utilisation technology, this was the most sensitive parameter to change. In models where

other parameters could be varied, the most sensitive parameters were feedstock throughput,


CHP running time and efficiency and the value of the feed in tariff, especially for facilities

utilising agricultural feedstocks.

Whilst this report took measures to include a variety of AD models, it is only a selection of

the models available to the AD industry.


Appendix 1 - Feedstock translation for

KTBL model

Pflanzen und Pflanzenteile Crops and Crop Materials

Durchwachsene Silphie-Silage, 28 % TM cut plant silage

Feldfutter, frisch, 19,3 % TM field fodder, fresh

Feldfutter, Heu, 37,6 % TM field fodder, hay

Futterrübensilage, 16 % TM fodder beet silage

Getreide-GPS, 35 % TM wheat silage (whole plant)

Getreidekörner, 87 % TM grain

Gras, frisch, 18 % TM grass, fresh

Gras, Heu, 83,9 % TM grass, hay

Gras, Landschaftspflege, 50 % TM material from landscape maintenance

Grassilage, 35 % TM grass silage

Kartoffeln, gemust, 22 % TM chopped potatoes

Kleegras, Heu, 89,5 % TM clover grass, hay

Kleegrassilage, 30 % TM clover grass silage

Maiskörner, gequetscht oder gemahlen, 87 % TM

crushed maize grain

Maissilage CCM, 65 % TM maize silage

Maissilage, 35 % TM maize silage

Ölrettichsilage, 13 % TM radish silage

Raps-GPS, 31,2 % TM rape silage (whole plant)

Roggen-GPS, 29,4 % TM rye silage (whole plant)

Roggensilage, 25 % TM rye silage

Rübenblatt, frisch, 18,1 % TM beet leaf, fresh

Rübenblattsilage, 18 % TM beet leaf silage

Sorghumsilage, 28 % TM sorghum silage

Stroh, kurzgehäckselt, 86 % TM straw, short chopped

Sudangrassilage, Hybrid, 27,3 % TM Sudan grass silage, hybrid

Weißkohlblatt, frisch, 13 % TM white cabbage leaf, fresh

Zuckerrübe, frisch, 23 % TM sugar beet, fresh

Zuckerrübensilage, 23 % TM sugar beet silage

Wirtschaftsdünger Farm Fertilisers

Geflügelmist, 40 % TM poultry manure

Pferdemist, 27,2 % TM horse manure

Rinder-Festmist, 25 % TM cattle manure

Rindergülle mit Futterresten, 10 % TM cattle slurry with fodder residues

Schweinegülle, 6 % TM pig slurry


Sonstiges Others

Altbrot, 65 % TM stale bread

Apfeltrester, 22,7 % TM apple pomace

Biertreber, 24 % TM spent grains

Bioabfall, 40 % TM biowaste

Fettabscheiderfett, 5 % TM grease from grease separation

Getreideabfall, 85 % TM grain waste

Getreideausputz, 89,2 % TM grain strip waste

Getreideschlempe, 6 % TM brewers grains

Getreidestaub, 87 % TM grain dust

Glycerin, 100 % TM glycerin

Hanf, Presskuchen, 98,4 % TM hemp, press cake

Kartoffel, Fruchtwasser, 3,5 % TM potatoes, waste water

Kartoffel, Prozesswasser, 1,6 % TM potatoes, process water

Kartoffelpülpe, 25 % TM potato pulp

Kartoffelschlempe, 6 % TM potato draff

Malzkaffeetreber, 20 % TM barley malt waste

Molke, frisch, 5 % TM whey, fresh

Obsttrester, 22 % TM fruit pomace

Panseninhalt, 15 % TM cattle rumen content

Quark, frisch, 22 % TM curd, fresh

Raps, Presskuchen, 92 % TM rape, press cake

Silosickersaft, 1,4 % TM silage liquor

Speisereste, 16 % TM food waste

Traubentrester, zermahlen, 28,2 % TM grape pomace

Wasser water

Weizen-/Roggenkleie, 88,8 % TM wheat or rye bran

Zuckerrübenmelasse, 4,6 % TM sugar beet molasses

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