Evaluating a Power Supply System for a Small-Scale Cocoa Processing Plant

A Multi-Criteria Decision Analysis Approach

Alexander Rothoff

2018

Student thesis, Master degree (one year), 15 HE Decision, Risk and Policy Analysis Master Programme in Decision, Risk and Policy Analysis

Supervisor: Fredrik Bökman Examiner: Ulla Ahonen-Jonnarth

Evaluating a Power Supply System for a Small-Scale Cocoa Processing Plant A Multi-Criteria Decision Analysis Approach by

Alexander Rothoff

Faculty of Engineering and Sustainable Development University of Gävle

S-801 76 Gävle, Sweden

Email: [email protected]

Abstract The global supply and demand of energy is facing different challenges. On one hand an increasing energy demand, foremost in developing countries, and an increasing pressure on reaching climate goals changes the requirements on the design of power supply systems. This may be particularly relevant in terms of decentralized energy solutions and hybrid systems that incorporates renewable energy sources. This study exemplifies the use of multi-criteria decision analysis (MCDA) to evaluate different power supply alternatives for a small-scale cocoa processing plant (SSCPP), placed in Côte d´Ivoire. MCDA is an analytical approach to evaluate decision alternatives according to certain criteria, with the aim to find a preferred alternative. The function of a cocoa processing plant depends highly on its power supply. This study has been performed by first analysing the energy needs of the processing plant, which includes electricity, heating and cooling. Based on those specific energy needs different power supply alternatives have been created. In a following evaluation it has been exemplified how MCDA can be used within the energy sector to evaluate different alternatives. Within this exemplifying evaluation, following power supply solutions have been considered: power grid, power grid with back-up generator, power grid with LPG-heating (Liquified Petroleum Gas), power grid with solar energy, off-grid solar system with back-up generator and an off-grid generator with heat exchanger. The evaluation of alternatives has been made by using the three evaluation attributes: levelized cost of energy (LCOE), loss of load hours (LOLH) and carbon footprint of energy (CFOE).

Contents

Abbreviations and Terminology ...... 1 1 Introduction ...... 2 1.1 The Aim ...... 3 1.2 Content and Structure ...... 4 1.3 Terms and Expressions ...... 4 1.4 Conditions ...... 5 1.5 Assumptions and estimations ...... 5 1.6 The Author ...... 6 2 The Cocoa Industry ...... 7 2.1 The Cocoa Value Chain ...... 7 2.2 Small Scale Cocoa Processing Plant...... 8 3 Methods ...... 10 3.1 Multi-Criteria Decision Analysis ...... 10 3.1.1 MCDA Approaches ...... 10 3.1.2 Evaluation of Consequences ...... 13 3.1.3 How MCDA has been used in this thesis ...... 16 3.2 Energy Analysis ...... 17 3.2.1 LCOE - Levelized Cost of Energy ...... 17 3.2.2 CFOE - Carbon Footprint of Energy...... 19 3.2.3 LOLH – Loss of Load Hours ...... 20 3.2.4 Solar power input ...... 22 3.2.5 Energy and power demand ...... 24 3.3 Collection of information ...... 25 4 Power Supply Requirements ...... 27 4.1 Target Requirements ...... 27 4.2 Energy Requirements ...... 28 4.2.1 Drying ...... 29 4.2.2 De-hulling ...... 29 4.2.3 Roasting ...... 30 4.2.4 Grinding...... 30 4.2.5 Tank storage and piping ...... 31 4.2.6 Tempering, blocking and storage ...... 31 4.2.7 Summary of energy needs ...... 31 5 Objectives and Criteria ...... 33 5.1 Cost...... 35 5.1.1 Investment cost...... 35 5.1.2 Annual costs ...... 35 5.1.3 Lifetime ...... 35 5.2 Environmental Impact ...... 36 5.3 Reliability ...... 37 6 Power Supply Alternatives ...... 39 6.1 Alternative 1 ...... 40 6.2 Alternative 1b ...... 41 6.3 Alternative 2 ...... 42 6.4 Alternative 3 ...... 43 6.5 Alternative 4 ...... 45 6.6 Alternative 5 ...... 46 7 Consequences ...... 48 7.1 Levelized Cost of Energy (LCOE) ...... 48 7.1.1 Investment Cost ...... 48

7.1.2 Annual Cost ...... 49 7.1.3 System Lifetime ...... 50 7.1.4 LCOE values ...... 51 7.2 Carbon Footprint of Energy (CFOE) ...... 51 7.3 Loss of Load Hours (LOLH) ...... 53 7.4 Summary of Consequences ...... 54 8 Evaluation ...... 55 8.1 Utility values ...... 56 8.2 Weight assessment...... 58 8.2.1 Swing weighting ...... 58 8.3 Overall utility ...... 60 9 Production scenarios...... 62 9.1 Scenario evaluation ...... 62 9.2 Production scenario results ...... 63 10 Sources of uncertainty ...... 66 10.1 Internal uncertainties ...... 66 10.2 External uncertainties ...... 66 11 Discussion ...... 69 Acknowledgements ...... 71 References ...... 72 Appendix I - Fact sheet “Cocoa liquor mini-plant” ...... 76 Appendix II - Energy needs of machinery ...... 77 Appendix III - Hypothetical decision maker ...... 79 Appendix IV - Power supply and energy cost by alternatives ...... 82 Appendix V - Investment and annual costs of alternatives ...... 84 Appendix VI - Carbon Footprint of energy sources ...... 86 Appendix VII - Solar energy yield ...... 88 Appendix VIII - PV simulation ...... 92 Appendix IX - Production scenarios ...... 94

Abbreviations and Terminology

CFOE = Carbon Dioxide Footprint of Energy1

CO2eqv. = Carbon Dioxide Equivalents GHG = Greenhouse Gases HES = Hybrid Energy System LCA = Life Cycle Assessment LCOE = Levelized Cost Of Energy2 Li-ion = Lithium Ion LOLE = Loss of load events LOLH = Loss of load hours LPG = Liquefied Petroleum Gas MCDA = Multi-Criteria Decision Analysis MAUT = Multi-Attribute Utility Theory Nibs = Cocoa bean kernels after having removed the shell Off-grid = Electric power supply without connection to a local power grid On-grid = Electric power supply with connection to a local power grid OCC = Opportunity cost of capital PV = Photovoltaics SSCPP = Small Scale Cocoa Processing Plant

1 Also referred to as Carbon Dioxide Footprint of Electricity. Since the application here also relies on the generation of process heat, the expression energy has been used instead. 2 See remark above: Electricity vs. Energy.

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1 Introduction Two central issues in world politics are the efficient supply of energy and the worldwide distribution of wealth. This thesis tangents both these issues in that it deals with selecting the most suitable power supply out of a range of alternatives for a small, profit- bringing, processing plant in a developing country. The future supply of energy is likely to change and already today it meets many challenges due to various requirements such as energy resources and climate impact. In Germany, the decision has been made to abandon the nuclear technology for the generation of electricity.3 Parallel with this, the automobile industry and the consumers are encouraged to increase the share of electric cars on the German roads.4 The closing down of nuclear power plants with a simultaneous increase of electric mobility put new requirements on electric grids and the overall supply of electricity. Globally international initiatives like the Kyoto-protocol stipulates the reduction of Carbon- dioxide emissions in order to reduce the global warming. At the same time the world is facing an increasing demand of energy in the years to come. In Africa, the energy demand of the sub-Saharan countries is predicted to raise by about 80 % until 2040.5 In India the increase in energy demand is forecasted to double within the same period and also China is expected to reach an energy demand peak in 2040.6,7 All in all the requirements on the designing and dimensioning of a power supply system change and face new challenges. Within this thesis an analytical evaluation process according to multi-criteria decision analysis has been used for choosing a power supply system. The use of multi-criteria decision analysis assists in selecting the best solution out of a range of alternatives according to defined objectives. The processing of cocoa beans is typically made at an industrial level with high throughputs of 2 t/h of processed beans or more. While the cocoa tree is grown in a narrow area around the equator the refining process of the cocoa beans is mainly taking place in Europe and in the USA even if this trend has started to change. For the 2014/15 grinding season, the number one processing country was predicted to be Côte d´Ivoire before the Netherlands,8 indicating that local processing has been growing in importance even though this trend was interrupted in the following season 2015/16.9 Despite of this the cocoa industry is suffering from financial sustainability problems and a poorly balanced value chain. An increased refining in origin countries helps to improve local economies, but especially the farmers still suffer from low income and a very low share of the value chain of chocolate.10 To improve this situation one possibility could be to increase the value added on the cocoa farms or cooperatives. To be able to do this the cocoa beans need to be processed to a higher state of refinement, which requires processing equipment. This equipment would have to be adapted to the existing conditions and the amount of beans at such an installation site. Thus, it would require the use of a small-scale cocoa processing plant11 (SSCPP). Such a plant could either be

3 German Federal Ministry for Economic Affairs and Energy, 2017b 4 German Federal Ministry for Economic Affairs and Energy, 2017a 5 International Energy Agency, 2014, p.76 6 International Energy Agency, 2015, p.56 7 Xu et.al., 2017 8 International Cocoa Organisation, 2015, p.12 9 International Cocoa Organisation, 2017 10 Cocoa Barometer, 2015 11 The term "Small-Scale Cocoa Processing Plant" is by no means a standardized processing unit, which can be bought off the shelf. In this case the term refers to a processing unit with an output

2 installed directly on a cocoa farm that is big enough,12 or in smallholder farmer cooperatives. Farmer cooperatives exist in most cocoa producing countries such as Côte d´Ivoire.13 This report will have a look at such a processing scenario in terms of the energy supply of the plant. The question that has been examined is how different power supply alternatives for such a processing plant can be evaluated in order to associate each alternative with a score according to chosen criteria and preferences. Thereby different possible solutions need to be evaluated and compared to each other. As approximately 72 %14 of the total produced cocoa comes from Africa, with the biggest share coming from Côte d´Ivoire, this has been chosen as the reference installation country for this report.

1.1 The Aim The aim of this thesis is to exemplify how different power supply alternatives for a small-scale cocoa processing plant can be evaluated by using MCDA. The use of a SSCPP would imply a decentralized cocoa processing structure with benefits and challenges. One important topic for decentralized energy systems, especially in developing countries, is the adequate power supply.15 Secondly, an installation like a small-scale processing plant may have a lower “on-site” energy efficiency than bigger plants, which puts requirements on a well elaborated power supply to keep key-factors such as production cost and environmental impact low. Therefore, another aim has been to demonstrate how renewable energy solutions can be compared with traditional power systems in this kind of evaluation. Similar MCDA-studies that deal with the evaluation of power supply systems already exist. However, most such studies deal with the electrification of rural villages with an optimization target while including renewable energy.16,17,18 This study deals with the evaluation of a set of power supply alternatives. The evaluation has been done according to a number of objectives: to minimize the overall cost, to minimize the emission of GHG and to maximize the power supply reliability. The chosen objectives are commonly used for the evaluation of energy systems, which allow this study to be related, if not directly compared, to similar studies. To enable such a comparison the aim has been to also use common attributes to evaluate the performance of the alternatives. The resulting selection of attributes are: LCOE (Levelized Cost of Energy), CFOE (Carbon Footprint of Energy) and LOLH (Loss of Load Hours). Six relatively common power supply alternatives have been evaluated, with the idea to create an exemplifying study. The alternatives are mainly hybrid energy systems (HES), and consist of: power grid connection, power grid and LPG-heating (Liquified Petroleum Gas), power grid and solar power, power grid with back-up generator and two off-grid solutions: solar power with back-up generator and Diesel generator with heat recovery.

of 200 kg/h or less of grinded cocoa liquor. For a more detailed description of the plant see Section 2.2. 12 As in South America (Interview: Nengerman H.). 13 The size of a Côte d´Ivoire-cooperative varies considerably and may include between 200 – 2.000 members (Interview: Dr. Anga, J.-M.) 14 International Cocoa Organisation, 2015, p.6 15 In developing countries challenges such as regularly power losses or even non-existing power grids need to be dealt with. 16 Bortolini et.al., 2015 17 Gharavi et.al., 2015 18 Moharil & Kulkarni, 2010

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1.2 Content and Structure The described energy supply alternatives are not intended to represent an exhaustive range of alternatives, but rather as an exemplifying list of different on- and off-grid solutions to demonstrate how these may be evaluated. The result is intended as a reference for similar comparisons and, on one hand as a discussion basis for the decision-making process in the layout of small hybrid energy systems and small power producing units. On the other hand, it is meant as a support for possible future discussions around small scale cocoa production in origin countries. On the following pages, first the background of the cocoa value chain problem and the SSCPP will be described more in detail. In Section 3 the used methods are described, followed by a closer look at the actual energy needs of the exemplified SSCPP and chosen evaluation objectives in Section 4 and 5 respectively. In Section 6 the evaluated power supply alternatives are described. In the following sections, 7 and 8, the evaluation takes off with a description of consequences and the following analysis of the overall utility. A visual presentation of the structure can be seen in Figure 1 below. The different shades of blue in the figure are explained in Section 8.

Energy Normalized Overall Objectives Alternatives Consequences need Utility Values Utility

Criteria Utility Curve Weights

Section 4 Section 5 Section 6 Section 7 Section 8

Figure 1. Schematic representation of the structure of the thesis.

1.3 Terms and Expressions There are different terms for naming a small power producing unit. For single small power producing systems the term distributed generation might be used or, for the producer, small power producer. However, also expressions such as power supply system or energy supply system are frequently used. Within this thesis, both of the later mentioned expressions have been used. The reason for using both expressions is context depending. The term energy supply has been used when it comes to the amount of energy which is supplied to the SSCPP. The term power supply is used in a more general context to express the need and supply of power. When it comes to provide energy, the term generation has been used. The term power generation is widespread and has therefore been considered adequate also for this thesis. For power supply alternatives with combined energy sources the term hybrid energy system (HES) has been used and for non-power-grid connected alternatives the expression off-grid has been adopted even though the term autonomous can also be found in the literature. During the multi-criteria evaluation, the expressions, objectives, criteria and attribute have been used to describe and evaluate characteristics of the power supply alternatives. Hereby the term objective represents what the decision maker or committee considers important for the decision outcome. The criterion expresses a characteristic according to which an objective can be evaluated, whereas the attribute represents a measurable index or unit for a certain criterion.

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1.4 Conditions This study has been made under certain conditions, which exert a significant influence on the outcome of the multi-criteria evaluation process. To begin with the definition of the small cocoa production unit, as described closer in Section 2.2, set the overall energy need. Furthermore, this evaluation is based on a 10 h/day production rate with the night hours in standby mode. This production rate could favour solar alternatives because of the time of the energy need corresponding to the time of solar irradiation (daytime). Industrial sized cocoa processing plants normally operate around the clock. To verify this possible advantage, two further production scenarios have been analysed in Section 9. The annual production has been associated with a 5-day week or 260 productive days/year. The remaining days of the year have been considered as standby time with a corresponding standby energy need. The fictive installation site has been chosen to be located in southern Côte d´Ivoire. Apart from local prices for energy and fuels, this also has an impact on the daily, monthly and annual solar energy input.

1.5 Assumptions and estimations This thesis includes different assumptions and estimations. In some cases, exact data have not been available and an extended research for this thesis would blow up the content too much. For example, this concerns the values of Carbon Dioxide emissions during the system lifetime. The used values have been obtained from different sources and in some cases averages or estimations based on these sources have been used. In the case of installation costs of equipment, estimations based on European conditions have been used for all alternatives. This has been done since reliable information about local installations is not available. In regard to the use of different heat sources for some processing equipment, the eventual necessary adaption of the machinery has been assessed as cost neutral. On one hand, it is difficult to estimate such a cost without diving into the machinery construction and on the other hand this cost has been assumed to be unimportant in the complete financial context. The assessed lifetime of the power supply equipment is based on available approximate figures. These figures have different origins such as reports or statements from suppliers. When it comes to the reliability and energy yield of solar power an approximate daily and hourly output has been calculated based on a solar yield simulation (see Appendix VIII). The result of the simulation is a monthly energy yield from one PV-panel. Based on this average, daily energy deficits have been calculated and transformed into a generator running time or an amount of energy from a backup- system. By this the influence of temporary shadow has only been considered in the long run average irradiation level. Due to the use of a battery energy storage or a permanent power grid connection the impact of temporarily shadow has been neglected. The utility curve, used for the assessment of utility values of consequences, represents the preferences of the decision maker. Within this analysis all attributes have been assigned a linear utility curve, which is motivated in Section 8.1. This assumption of a decision maker´s preferences comes along with other assumptions that are related to the lacking decision maker during the evaluation process. Within the different sections any made estimations have been explained specifically.

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1.6 The Author I have been active within the cocoa processing industry for more than 10 years and have been able to collect very interesting and diverse experiences during this time. As an engineering manager, I have been responsible for single machinery developments as well as for the planning and execution of complete processing plant projects. Within most projects, the issue about an adequate energy supply in its various forms has played an important role, but still a wider perspective with an analytic approach for selecting a proper energy supply system has been missing. Without correct cooling the large grinding machines either get too hot and burn the cocoa liquor or get too cold and get blocked. Without proper heating of product piping and storage tanks the product builds up and eventually blocks the product line. In most cases, traditional solutions are being preferred without evaluating hybrid solutions with renewable alternatives. As many projects have taken place in origin countries, mainly West-Africa with local investors, I have had the opportunity to get to know the conditions that exist at such an installation site. This experience has enabled me to write about this topic in the first place and many notes and remarks have their origin in personal experiences. This background has also enabled me to get an idea of the possibilities and problems associated with a small local processing unit. One of the major issues concern the energy supply system due to its share of the processing costs.

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2 The Cocoa Industry Even if cocoa has been grown in large scale since the sixteenth century it was not until the twentieth century that it spread to Africa and the industry, as we know it today, started to take shape. The today’s largest cocoa bean producer, Côte d´Ivoire, is really a newcomer seen in a historical perspective and today it supplies about 40 % of the world’s cocoa.19,20

2.1 The Cocoa Value Chain The value chain of cocoa is dealing with different issues related to terms as sustainability, traceability and fair-trade.21,22 These issues have been growing more important over the last decade due to increased environmental awareness and the financial situation of cocoa farmers. On the consumer-side a growing interest in this topic can be observed by the steadily increasing demand for certified cocoa products.23,24,25 On the producer’s side, we find different international projects and reports by international organizations, national cocoa societies and governments that deal with these issues. The focus of most such projects is to ensure, stabilize and increase the income of the cocoa farmers. Of the total cocoa production, about 72 %26 comes from Africa with a typical cocoa farm being a small-scale family run farm with a size of 2-5 hectares and an annual yield of about 350 – 650 kg/ha.27,28,29,30 With the cocoa farmers getting older combined with a low income, not only the economy of millions of families is threatened, but also the cocoa supply as such.31,32 Figure 2 below shows the value share of each step within the chocolate value chain as a percentage of the total value added. With this in mind, the importance of improving the value chain of chocolate in favour of the cocoa farmers becomes more obvious.

19 International Cocoa Organisation, 2017 20 Encyclopedia, 2008 21 International Cocoa Organisation, 2007 22 Capelle, 2008 23 Felperlaan et.al., 2010, p.14 24 International Cocoa Organisation, 2006 25 Cocoa Barometer, 2015, p.19 26 International Cocoa Organisation, 2015, p.6 27 International Cocoa Organisation, 2007, p.2 28 World Cocoa Foundation, 2014, p.2 29 United Nations, 2008, p.16 30 The World Bank, 2012, p.4 31 Cocoa Barometer, 2015, p.3 32 There is an estimated no. of 14 million cocoa producers worldwide with some 90% of the world cocoa coming from family run smallholdings. Source: United Nations, 2008, p.16.

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6.6% 6.3% 7.6% 35.2% 44.2%

Farming Transport Processing Manufacturing Retail

Figure 2. The cocoa value chain with the value share of each step noted as a percentage of the total (Cacao Barometer 2015).

One possibility of adding more value to a certain geographical point in a value chain is to further refine the product at that point. In means of cocoa, the state of the product at the cocoa farm is a fermented and pre-dried cocoa bean. By applying the idea of further refining for adding value,33 this would imply one or more additional processing steps. For cocoa that could include, drying, de-hulling, alkalizing, roasting, de- bacterizing, grinding, pressing and powder-milling.34 A possibility to realise this is the use of an SSCPP, owned and operated by farmer cooperatives. This idea is the trigger of this investigation and as a result the starting point for this analysis is a hypothetical SSCPP for a farmer cooperative in Côte d´Ivoire. The refining end-stage for this unit is the producing of cocoa liquor, filled and blocked in 25 kg blocks. This means that out of the previously mentioned eight processing steps, five are included in the SSCPP: drying, de-hulling, roasting, de-bacterizing and grinding. By the approach of SSCPP a partly decentralised cocoa refining operation could help to add more value at the point where it is needed the most. A successful implementation could lead to an increased popularity of the cocoa farming, increased incomes for farmers and an improved social situation35 within farming areas.

2.2 Small Scale Cocoa Processing Plant The term “Small Scale Cocoa Processing Plant” in this case refers to a small unit for the processing of pre-dried cocoa beans into industrial cocoa liquor. The cocoa liquor36 is the product, which is obtained when de-hulled and roasted cocoa beans are being fine- grinded. The processed cocoa liquor is the first semi-refined product of the fermented and pre-dried cocoa beans that is traded to a larger extent.37 This is the background of the argument for using a SSCPP to increase the value share at the geographic origin of the cocoa value chain. The processing steps of the exemplified small-scale unit contains the same steps as a full-scale plant, even if the machinery may be slightly different and easier to handle.38 The different processing steps are visualized in Figure 3 below, where grinding is the final refining stage and blocking, preceded by tempering, is necessary for handling, packaging and transporting reasons. The definition “blocking” means to

33 The value chain of cocoa is a complex economic structure that depends on politics, trading market, yield etc. In this study the intention has not been to analyse the cocoa value chain. Instead the assumption has been made, that a processing of a good increase the value added at the point where this processing takes place. 34 This list does not claim to be a complete processing description but is intended to give an impression of the different refining stages. For example, a primary cleaning stage is missing since this assumedly can be carried out by hand at this throughput rate. 35 By this we refer to the access of electricity and enhanced infrastructure as a result of the local processing plant. 36 Cocoa liquor has nothing to do with alcohol but is rather the liquid state of cocoa which occurs when the high-fat cocoa kernels are being grinded. 37 Other tradeable semi-finished product may be roasted nibs (cocoa kernels). 38 Based on personal experience.

8 fill up and let the cocoa liquor solidify in cardboard boxes with plastic inlays. In Section 4.2 the different processing steps and their respective energy needs have been described more in detail. There is neither a common standardized size for an SSCPP nor is there a widespread used installation type in cocoa growing countries. Furthermore, as mentioned in the introduction, it is an experimental approach to possibly change the imbalance of the cocoa value chain. Nevertheless, the expression Small-Scale Cocoa Processing Plant is justified and known due to different trends within the cocoa and chocolate industry. Due to an increased trend of artisanal and small-scale chocolate manufacturing many machinery producers offer different small-scale production alternatives.39 However, the capacity and machinery range varies considerably between different manufacturers, which is reflected in price as well as in energy consumption. This makes the definition of the exemplified SSCPP important, and in this case the defined capacity is an output of 100 kg/h of liquid cocoa. Due to weight losses40 during the process this reflects a bean input capacity of approx. 120 kg/h. The liquid cocoa can then be traded either as cocoa blocks of 25 kg or as liquid mass. In Table 1 below the other defining characteristics of the SSCPP are listed. In Appendix I a fact sheet of one type of a small- scale cocoa processing plant is attached.

Drying De-hulling Roasting/ Grinding Storage Blocking Storage Debact.

Figure 3. The processing steps from cocoa bean to cocoa liquor (Fincke et.al. 1965)41.

Table 1. Specifications of the small-scale cocoa processing plant.

Technical Specifications –Small-Scale Cocoa Processing Plant

Mode of operation: 10 h/day; 260 days/a Output: 100 kg/h, Cocoa Liquor Energy consumption: 40-50 kWh/100 kg42 Necessary external sources: electric power / fresh water

39 Foxwell, 2015 40 The loss of weight occurs due to different reasons in different processing steps: the removal of stones and other impurities within the cleaning step, the removal of shells in the de-hulling and the removal of moisture during drying and roasting. The caused weight difference can be approximated to 20%. 41 The displayed order between de-hulling and roasting may be reversed depending on if a beans roasting or a nibs roasting process is used. 42 Average energy consumption. The consumption varies slightly between the different alternatives depending on the energy sources. For example, the use of LPG for heating applications has a different energy efficiency than electrical heaters. See also Section 4.2.

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3 Methods In this section, the used methods will be named and briefly explained. Made assumptions, for example in regard to power supply characteristics, are also mentioned even though a more detailed description and explanation will follow under the section specifically dealing with the concerned power supply alternative or evaluation criterion. In those cases, there are references to the sections where this information is available. When it comes to the energy analysis, the “background” data can be found in appendices, also referred to at the relevant place. Section 3.3 describes how and where information for the thesis has been collected.

3.1 Multi-Criteria Decision Analysis The importance of the use of MCDA (Multi-Criteria Decision Analysis) has increased since its introduction and is today used in many areas to help solving decision problems. Over the years many different MCDA methods have been developed each with its respective typical features, advantages and disadvantages.43 Important developments for the solving of multi-criteria problems are the works by Keeney and Raiffa in the mid 1970´s.44 MCDA is, as the name indicates, used to support the solving of decision problems45 containing multiple criteria. If a problem contains at least two, if not more, alternative solutions, these are evaluated according to each criterion and provided with a consequence (i.e. score, value, ranking or monetary amount) according to how a criterion is met. The consequence of an alternative solution according to a specific criterion may be of a normative or a descriptive nature depending on the criterion characteristics. In the following section, we will have a look at how a multi-criteria decision problem can be solved.

3.1.1 MCDA Approaches Within this thesis the structure has been inspired by the “PrOACT”46 (Problem, Objectives, Alternatives, Consequences, Trade-offs) method and for the evaluation of alternatives, with its utility assessment, elements of “MAUT” 47 (multi-attribute utility theory) have been used. A common systematic of most MCDA-approaches is the means of “divide and conquer”, meaning to split up the decision problem in smaller portions. In most cases a modelling process can help to identify key elements that are helpful, or even essential, to find a satisfying solution of a given decision problem. In PrOACT the initial modelling steps include to carefully consider the decision problem (is the problem correctly formulated), to formulate the objectives that are to be met, to create alternatives and to describe the consequences of the different alternatives. In Figure 4 below, these initial steps are presented in a schematic way. The subsequent text describes how MAUT and the additive utility function have been used within this study.48

43 To read more about MCDA, see, e.g., Greco et.al., 2016 44 Keeney & Raiffa, 1976 45 The “problem” could also be named “opportunity”, however the term problem is undoubtedly the most common expression in this context. Hammond et.al., 1999, p.17 46 Hammond et.al., 1999 47 Clemen & Reilly, 2014, p.717 48 Clemen & Reilly, 2014, p.720

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Problem Objectives Alternatives Consequences

Figure 4. Schematic representation of the four first modelling steps as used within PrOACT.49

To formulate the problem may seem trivial, but in some cases the initial formulation of the problem might be wrong, focusing only on a limited part of the problem or on a resulting symptom. To get this right it can sometimes be helpful to know one’s objectives before dealing with the formulation of the problem, which is why the above displayed order of the analysing steps is controversial.50 To some extent or maybe even exclusively this will depend on the decision context. In the current study, with a choice of a power supply system, we assume the problem to be known as an external requirement of energy. Based on this, the order in Figure 4 will be kept as shown and the formulation of objectives will follow that of the problem. The objectives should describe what the decision maker wants to achieve by making the decision in the first place. Consequently, the formulation of objectives is a subjective act that depends on personal fundamental values or the values of an enterprise. The expression value in this context refers to the fundamental goals and wishes of a person, organization, enterprise etc. (Later on, the term value will also be used for naming a numerical value). In the literature, the objectives are divided into fundamental and mean objectives.51 For a decision analysis, the fundamental objectives are the ones that should be kept in focus. However, in some cases an easy to measure mean objective can replace a fundamental objective.52 To distinguish fundamental from mean objectives is not always easy and sometimes it could be helpful to use a series of questions in order to reach the core of an issue.53 By asking oneself why a certain objective is important, the answer will vary from, helping to achieve something else (means objectives) or, for fundamental objectives: it is important just because it is important. While fundamental objectives are organized in hierarchies, the mean objectives are organized in networks. Figure 5 shows an example of an objectives hierarchy for evaluating different power supply systems. Depending on the circumstances and the decision maker such a hierarchy could be composed and appear differently. For example, an objective “minimizing risk of accidents” could play an important role for a big power plant.

49 Hammond et.al., 1999, p. 5 50 Clemen & Reilly, 2014, p.8 51 Clemen & Reilly, 2014, p.49 52 Clemen & Reilly, 2014, p.51 53 Clemen & Reilly, 2014, p.51

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Find the best power supply alternative

Minimize Maximize Minimize environmental reliability overall cost impact

Minimize Minimize Minimize Minimize Minimize Minimize Maximize annual emission toxic power outage investment lifetime cost of GHG emission outages time

Minimize Minimize emission emission of other of CO2 GHG

Figure 5. An objectives hierarchy for a power supply system.

Once the objectives are known, different alternatives to reach the mentioned objectives are formulated. The list of alternatives should be created with an open mind in order not to limit the possibilities and leave out potential solutions. On the other hand, too many alternatives might make the subsequent evaluation unnecessarily complex. Instead, in accordance with an iterative view on the decision analysis, missed out alternatives can be added in a second review. With the help from the previously defined objectives, suitable criteria need to be found according to which the alternatives can be expressed in terms of consequences. The nature of the consequence depends on the chosen attribute to represent a certain criterion. Depending on the objective and ultimately on the attribute, the resulting consequence might be a numeric value, a judgement or a description. Up to the point of the description of consequences, many methods for solving multi- criteria decision problems follow the same approach. At this point the intermediate outcome is a so-called consequences table.54 The consequences table includes all alternatives, the evaluation criteria and the consequences of the considered alternatives related to criteria. In Table 2 below a consequence table of a simple example of selecting a power supply system is shown. At this place, it is worth to mention that the criteria that are supposed to reflect the objectives should be chosen in a way that attributes can be found, that can be measured or otherwise validated in a way that makes sense for the decision. In this context, the attribute is the explaining scale according to which a consequence is expressed. In the consequence table below the attributes are: cost measured in Euro [€], reliability, as the availability of power over time, expressed in percent [%] and pollutions, as unwanted summarized emissions, measured in g per generated kWh [g/kWh] in a lifecycle approach. The consequences below represent descriptive values.

54 Hammond et.al., 1999

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Table 2. Consequence table for a decision problem on the choice of an energy system.

Alternative Price Reliability Pollutions [€] [% availability] [g/kWh] Diesel generator 25,000.- 95 1,000 Wind turbine 50,000.- 65 150 Solar panels 150,000.- 75 100

In this example, we have kept it simple and assumed that the criteria can be clearly measured for all alternatives. In regard to “Pollutions” for example, a clearly defined list of substances that belong in this category are presumed to exist.55 Within this thesis slightly different criteria have been used as is explained in Section 3.2.

3.1.2 Evaluation of Consequences As the consequences of different decision alternatives have been described, the alternatives can be evaluated related to each other. As mentioned earlier, this is where many MCDA-approaches differ. The PrOACT-method begins the evaluation of consequences by looking for dominated alternatives, where an alternative A is dominated when another alternative B is better than A in at least one consequence and at least as good as A in the other. In the example of Table 2 there is no alternative being dominated by another. Instead we are facing three different alternatives that have different advantages and disadvantages. The PrOACT method deals with this by making Trade-offs, which is done by applying the approach of even swaps.56 Within this paper, elements of MAUT and more specifically an approach with an additive utility function will be used to evaluate the alternatives and their consequences. By recalling the consequences in Table 2, we will have a look at the example of a power supply system choice by using an additive utility function. For an evaluation based on an additive utility function we start by assessing the different consequences as utility values. A utility value should reflect how well a consequence meets an objective in the eyes of the decision maker. This step could therefore be called utility assessment as the consequences are being reformulated onto utility scales. Mostly, a utility scale is ranging from 0 – 1. How this assessment is done depends on the decision maker´s utility function for the different consequences. The utility function describes the preferences of a decision maker and could be seen as a translating tool to turn a descriptive consequence into a normative utility value according to a specific decision maker and decision context.57 The utility value denotes how “good” a consequence is, or how well a consequence meets the preferred decision target. The shape of the utility curve, therefore (linear, exponential etc.) reflects how the utility value changes as the consequence changes. In the case of a linear utility curve the worst consequence is represented by a utility value of zero and the best consequence by a utility value of 1. In between these end points the utility value is increasing linear from 0 to 1.58

55 Such a listing would also need to include a definition of how hazardous the different substances are. Typically, this could be defined by a recommended critical value or concentration. 56 Hammond et.al., 1998 57 Apart from descriptive numerical consequences also other types of consequences, such as colours or a describing text can be turned into utility values. 58 This does not always have to be the case. In the case of pollution, for example, it could be that a change from 100 to 490 g/kWh is relatively uncritical for the decision maker, whereas a regulation law makes the step from 490 to 500 g/kWh highly relevant. In that case the range from 100 to 490 could be linear, followed by a great drop in utility value as the 500 mark is

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To return to the example in Table 2 we assume that the decision maker has considered all attribute values, with the given range of consequences, to have a linear utility function. In such a case, with numerical values, the consequences can be assessed as utility values by using Equation 1 below.

(,, ) 59 푈, = Eq. 1 (,, )

X1,A is the numeric consequence for alternative A and criteria 1 and U1,A is the resulting utility value. This will result in a utility scale ranging from 0 to 1, where 0 is the worst utility value and 1 the best. After applying this formula, the consequences in Table 2 can be expressed with the utility values as shown in Table 3 below.

Table 3. Utility values of the consequences caused by different power supply alternatives.

Alternative Price Reliability Pollutions Diesel generator 1 1 0 Wind turbine 0.8 0 0.94 Solar panels 0 0.33 1

At this point the consequences are all expressed on utility scales with a range from 0 to 1. The next step to evaluate the alternatives is to create an overall utility score for each decision alternative. As this analysis uses the additive utility function to obtain the overall utility, this overall score is obtained by summarizing the individual utility values. This is done by applying weight coefficients for the different utility functions according to the preferences of the decision maker. For a utility scale ranging from 0 to 1 the weight coefficients are often assessed in a way that they sum up to one. The assessed weight coefficients are incorporated in the overall utility according to Equation 2 below.

60 푈 = 푘 ∗ 푈, + 푘 ∗ 푈, + 푘 ∗ 푈, Eq. 2 where k1, k2 and k3 represent the weight coefficients for the utility functions of the different attributes. Ux,A is the normalized utility value of consequence x for alternative A. As mentioned, the assigning of weight coefficients is a subjective process done by the decision maker according to his or her preferences. Concluding, the weight assessment may very well differ significantly between different decision makers. Furthermore, when assessing the weights, it is important to not only focus on the criteria and their attributes, but to also consider the actual consequence values and their respective ranges between the worst and the best consequence. This is important since a small difference between the worst and the best consequence of a specific attribute may make this irrelevant for the further evaluation. At the same time a seemingly irrelevant attribute should not be removed from the evaluation, since possible later inclusions of alternatives may drastically change consequence ranges and thus their importance. The weight assessment is a tricky procedure and there are different methods

exceeded due to the consequences (penalty payments or similar) of exceeding a regulating limit. After the 500 g/kWh the curve might return to linearity up to the next regulation value depending on the regulation circumstances. 59 Clemen & Reilly, 2014, p. 722 60 Clemen & Reilly, 2014, p.721

14 available in order to assist the decision maker to do this.61 Different methods have different qualities when it comes to biases and to incorporate the consequence range. This means that also the method, which is being used to assess the weight coefficients, has an impact on the result. In this study the weight coefficients will be assessed by applying the swing weighting method.62

3.1.2.1 Swing weighting method To describe the swing weighting method, an imaginary decision maker is used for the example in Table 2 for choosing a power supply alternative. The first step of the swing weighting method is to create an “all-worst” benchmark, which is a hypothetical worst- case alternative, incorporating the worst consequences for all criteria. This will be the reference point from which the consequences of one attribute at a time “swings” from the worst to the best by considering hypothetical alternatives that reflect the different best consequences. Table 4 below shows the resulting compilation of swung attributes in the first column. The next step is to rank the hypothetical swings from the best to the worst, according to the personal preferences of the decision maker. In the example, we assume the reliability to be valued as the most important, followed by pollution and price. As mentioned previously, it is important that the priorities are not set only by valuing the criteria as such, but also to consider the range of the consequences for the different criteria, i.e. the swing amplitude. Having filled out the ranking numbers, the rates of the best and the worst swings are set to 100 and 0 respectively.

Table 4. Swing-weight assessment table for choosing a power supply system.

Swing attribute Consequences Rank Rate Weight

Benchmark 150,000,- € / 65 % / 1,000 g/kWh 4 0 Price 25,000,- € / 65 % / 1,000 g/kWh 3 Reliability 150,000,- € / 95 % / 1,000 g/kWh 1 100 Pollution 150,000,- € / 65 % / 100 g/kWh 2

To assess the other rates, we use swings. Principally this is done by relating the imaginary satisfaction of swinging a consequence from the worst to the best related to the satisfaction of swinging the top-ranked attribute from the worst to the best. In the example above we could compare to swing the price from 150,000.- to 25,000.- with the swing of the reliability from 65 % to 95 %. Hereby the task is to value how much less satisfaction the swinging of price would yield compared to the swinging of the reliability. Or, how many percent of the satisfaction achieved by swinging the reliability from worst to best would the swinging of price achieve? In the example we assume the imaginary decision maker to rate the swinging of price to be worth 40 % and the swinging of the pollution level to be worth 80 % of the swinging of reliability. Even if the percentage rating is presented very abruptly here, it is important to point out that it should not be an intuitive rating. In an actual example with a power supply system, surrounding conditions, such as legal regulations or cost factors, influence how the decision maker value these swings. A legal pollution limit may prohibit production if this is to be exceeded, or a power outage might result in lowered profitability due to production losses. Such known data can be helpful for the swing weighting process as

61 Such as: pricing out, lottery weights and swing weighting method. Clemen & Reilly, 2014, p.730 ff. 62 Clemen & Reilly, 2014, p.731

15 it can support the valuation of consequences against each other in an analytical way. To continue with the example, the assessed percentage rates for price and pollutions of 40 % respectively 80 % are filled out as shown in Table 5 below. To obtain the corresponding weights for the consequences, these are calculated by using Equation 3, where kx is the weight factor, rx is the rating value for attribute x and ri is the rating value of attribute i with a total of n attributes. In Table 5 the resulting weights have been noted.

푟푥 푘푥 = Eq. 3 ∑()

Table 5. Completed swing-weight assessment table for the choice of a power supply system.

Swung attribute Consequences Rank Rate Weight

Benchmark 150,000.- € / 65 % / 1,000 g/kWh 4 0 0 Price 25,000.- € / 65 % / 1,000 g/kWh 3 40 0.18 Reliability 150,000.- € / 95 % / 1,000 g/kWh 1 100 0.46 Pollution 150,000.- € / 65 % / 100 g/kWh 2 80 0.36

With the weights set, the overall utility for the different alternatives can be calculated according to Equation 2. The resulting overall utility of the alternatives are as listed in Table 6. As can be seen, the assessment of weight coefficients according to the preferences of the hypothetical decision maker results in the diesel generator being the recommended first choice.

Table 6. Overall utility for energy systems.

Alternative Overall utility

Diesel generator 0.63 Wind turbine 0.48 Solar panels 0.51

3.1.3 How MCDA has been used in this thesis In this paper, MCDA has been used to demonstrate how different power supply alternatives can be evaluated according to a number of objectives. When assessing the overall utility, the additive utility function has been used. To be able to perform the evaluation with utility values, the preferences of a hypothetical decision maker have been created. With help from these preferences the utility functions of the attributes and the weight coefficients have been assessed. The weight coefficients have been assessed by using the swing weighting method. To simplify the reasoning around priorities and assumptions made by the hypothetical decision maker, the preferences have been given a strict financial character. The economic reasoning by the hypothetical decision maker during the weight assessment can be found in Appendix III. The resulting overall utility may subsequently be analysed by means of a sensitivity analysis. Even if there are different computer-based tools available for the evaluation of decision alternatives, this part has been done manually within this study. The reason for this is to obtain a high level of transparency and to provide an easy access to the different stages of the evaluation process for possible further analyses.

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3.2 Energy Analysis The following section describes how the energy analysis has been performed in terms of the energy demand of the SSCPP as well as the energy supply alternatives and the description of their consequences. It begins by describing how the consequences, according to certain attributes, have been assessed. For this, the initial sub-sections describe the chosen attributes and how they have been calculated for the different power supply alternatives. Section 3.2.4 explains how the energy yield of solar based systems has been assessed and Section 3.2.5, deals with the energy demand of the SSCPP. The attributes, that have been used are:

Levelized cost of energy (LCOE) Carbon footprint of energy production (CFOE) Loss of load hours (LOLH)

More of the background for choosing these attributes is described in the following subsections and in Section 5. For common, single modular power supply systems, such as the power grid option or the stand-alone generator, information on the above- mentioned indicators have either been available directly or have easily been possible to calculate based on available information. For combined, hybrid systems and especially for the solar alternatives these values have had to be calculated, partly based on assumptions.63 For all alternatives the energy need of the SSCPP has been considered instead of the energy generated where this applies. The reason for using the energy need of the SSCPP instead of the energy generated is discussed further in Section 3.2.1, but principally it is based on the approach of considering the useful energy. For the index LCOE, where a lifetime is included, the lifetime of the power supply system has been used. The used approach is described in the following sub-sections.

3.2.1 LCOE - Levelized Cost of Energy The Levelized Cost of Energy (LCOE) is an economic index for power supply systems that considers the initial investment, the annual cost, the system lifetime and the generated energy. It also integrates the inflation rate and the opportunity cost of capital (OCC).64 The resulting unit of the LCOE is the cost per generated energy unit expressed in €/kWh. In this study, a small exception to the standard procedure for calculating the LCOE has been made. As already mentioned in Section 3.2, the amount of energy, used in the calculations, is based on the energy need of the consumer, in this case the SSCPP, instead of the energy generated by the power supply system. This approach has been used due to the solar alternatives that might generate more energy than can be used. When it comes to power supply systems which include renewable energy it can cause a remarkable difference if the total generated energy or the total needed energy is considered... Due to power reducing effects such as dust and climate conditions, the off- grid solar plant is highly over-dimensioned when considering the peak power of the solar plant related to the peak power of the SSCPP. By that the solar plant can generate more power than the SSCPP can use during sunny weather. This is partly compensated for by the battery storage, but only to an extent which gains the energy need of the SSCPP. In this particular case there has been no consideration of a possible feed-in tariff or storage of surplus energy. Therefor any generated surplus energy is of no use. To make the LCOE index valid for this case, the annual energy need, instead of energy generated, has been used. In regard to the considered time frame for the index, the system lifetime of the power supply system has been applied. To calculate the LCOE index of a single power source the Equation 4 has been used, where Cinv is the initial

63 As for example the monthly average solar irradiation, which is based on climate statistics. 64 The expected return from an investment that could have been done instead of the current one.

17 investment, cj the annual cost, Et the average annual energy demand of the SSCPP, n the expected lifetime of the power supply system in years, g the inflation rate and OCC the opportunity cost of capital.

∗() ∗() 퐿퐶푂퐸 = 퐶 + ∑ / ∑ Eq. 465 () ( )

For hybrid systems, with multiple energy sources, the combination of the single LCOE values has been made by applying their respective percentage rate of the total generated energy. See Equation 5, with a total of p energy sources and an energy supply share of fi percent.

퐿퐶푂퐸 = ∑(푓 ∗ 퐿퐶푂퐸) Eq. 5

Whereas the investment cost for the different alternatives is relatively indisputable, the annual cost is not. This is due to the approach to only consider useful energy. Since no further trade of energy to external nets or consumers has been considered only the energy that can be used by the SSCPP is accounted for. Due to HES alternatives, solar irradiation differences, power failures and different backup systems the average energy need of the processing plant alone does not include enough information to define the annual cost. To make a comparison between LCOE values relevant, the annual cost must consider the expected split of energy on the different energy sub-systems.66 Consequently, the considered useful annual energy generation is based on results from the calculated hourly solar energy (for alternative 3 and 4) and the expected downtime of sub-systems. In line with this the generated useful energy, thereby reflecting the energy need of the SSCPP, of a sub-system is calculated according to Equation 6. For the back-up generator, the relevant amount of supplied energy is based on the LOLH of the main system as shown in Equation 7. Since the backup generator is not expected to fail when it is not operating, its own LOLH value has been extracted from the expected active running time. An exception concerns the alternative with an LPG-burner for heat applications. In this case, a system efficiency rate for the heating system has been used, with the result that the annual cost accounts for the gross amount of energy, necessary to deliver a useable net amount of energy according to Equation 8. Apart from this, the LPG-based energy supply has also been considered with the same downtime as the main power supply system. This is due to the fact that the processing plant cannot operate without electric power, which makes heating energy useless during this downtime.

퐸 = 푃 ∗ 푇 − 퐿푂퐿퐻 + 푃 ∗ 푇 − 퐿푂퐿퐻 Eq. 6

In Equation 6, Esu is the annually generated useful energy, Psu the average generated useful power during production time, Tp the annual production time and LOLHp the annual power downtime during production time. The index stby denotes the responding standby specifications.

퐸 = 푃 ∗ 퐿푂퐿퐻, + 푃 ∗ 퐿푂퐿퐻, − 푃 ∗ 퐿푂퐿퐻 Eq. 7

65 Bortolini et.al., 2015, p.1029 66 Note: the installed percentage of different energy sources may differ from the annual average percentage. This is the case with the solar powered alternative with a back-up generator, where the estimated generator running time has been considered.

18 where Ebup is the annually generated back-up energy, LOLHm,p the annual downtime of the main system during production time, LOLHm,stby the annual downtime during standby time and LOLHbup the back-up generator downtime.

퐸 = Eq. 8 where Esu is the generated useful energy, ELPG the required input energy and ɳ the efficiency rate of the burner system.

3.2.2 CFOE - Carbon Footprint of Energy The carbon footprint is widely used as an index to describe the environmental impact of a service, a means of transportation, a good, a type of food or other. Within this report, the expression Carbon Footprint of Energy (CFOE) has been used, which refers to the carbon footprint of a specific energy generation technology with an LCA approach. The denomination CFOE has previously been used to evaluate a photovoltaic-battery-diesel generator hybrid system.67 In LCA studies the most common term is simply to refer to the carbon footprint. The CFOE value expresses the emission of greenhouse gases (GHG) under consideration of manufacturing, transportation, operation and recycling. The index includes all GHG, but expresses these as carbon dioxide equivalents: kgCO2, eqv./kWh. This study does not have the ambition of conducting its own LCA-analysis for the examined energy supply alternatives, but instead relies on various existing sources. Finding the right carbon dioxide emission for energy sources that rely on fuels, that are also commonly used for the means of transportation or heating, has not been uncomplicated. This is due to the fact that a widespread use of the carbon footprint index is to compare different means of transportation with each other. In these cases, given values often refer only to the combustion of the fuel, whereas for the energy generation the total carbon footprint is of interest.68 In the case of the diesel generator, this has been less of a problem, since the diesel generator is a very common off-grid power supplying solution. For an LPG (Liquefied Petroleum Gas)-installation, however, it is a different story. Carbon dioxide emission values for LPG mostly refer to heating applications for private homes. In those cases, the value is used to compare different heating fuels with each other. That means that the surrounding equipment is excluded from the given carbon dioxide footprint value, since this is assumed to be the same for all heating fuels.69 To compensate for this in this report, the surrounding equipment of a relevant LPG installation has been assessed separately. This assessed value is based on a defined size of a steel LPG-storage tank and on the information about the release of CO2 during steel production. See also Table AVII-1 in Appendix VI. The resulting total value for the LPG installation has been calculated by applying the Equation 9 below:

70 퐶퐹푂퐸 = 푦 + Eq. 9 ∗ where Et is the average annual useful energy, Yinst the life-cycle emission as kgCO2,eqv. 71 for the equipment, yLPG the CO2, eqv. emission per generated kWh and n the system lifetime in years.

67 Bortolini et.al., 2015. 68 Including exploitation, refining, transports etc. 69 Johnson, n.d., p.14 70 Bortolini et.al., 2015, p.1030 71 Related to the combustion, transports and extraction of LPG.

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For HES alternatives with multiple energy sources, the percentage share of the respective source and its energy generation has been used for calculating the added CFOE value. The relevant percentage of the shares that have been used is the energy split based on an annual average energy generation. As with the LCOE index above, also in this case the theoretical expected energy supply of the different power supply sub-systems has been used. That means that also back-up systems are included with an expected running time based on the expected downtime of the main system. The CO2,eqv.-value for a specific energy source has been multiplied with its percentage share of the total supplied energy and then it has been summarized to form a united value. For the alternatives with a power grid connection, the percentage shares of the power mix, as used in Côte d´Ivoire, have been considered.72 For the off-grid solar powered alternative the energy storage capability of the battery bank is included. The considered alternative with an off-grid diesel generator, combined with heat recovery for the exhaust fumes, has gained a better CO2,eqv.-value than for a generator without heat recovery. This comes from the recovered waste heat energy, which has been assessed with a CO2,eqv.-value of zero. The calculations for the breakdown of the energy supply shares can be found in Table AIV-1 in Appendix IV. According to the above-mentioned approach, a system’s total CFOE value has been calculated according Equation 10, with a total of p different energy sources, with a share of f percent.

퐶퐹푂퐸 = ∑(푓 ∗ 퐶퐹푂퐸) Eq. 10

3.2.3 LOLH – Loss of Load Hours The Loss of Load Hours (LOLH) index is one of multiple indicators to denote the reliability of an energy system. For more extensive energy source reliability assessments, the LOLH index can be used together with other indexes, such as the failure rate or LOLE (Loss of Load Events), MTTR (Mean Time To Repair) or EUE (Expected Unserved Energy) to better reflect the overall performance.73,74 However, when evaluating smaller energy supply systems often only one index is used as a comparing value.75 In this evaluation the LOLH has been used, which denotes the time where the energy system is not available to deliver the power that is needed by the consumer. Consequently, the LOLH is measured in system downtime hours during a year. Where the required information is available, an existing LOLH-value has either been used directly or been calculated by using the Equation 11, where LOLE = loss of load events, and MTTR = mean time to repair. For the Ivorian power grid, different values can be found for the annual outage time in the context of a business. As an interpretation of the different outage values, the expressions annual outage time, LOLHa, and annual production outage time, LOLHpt, have been introduced. This has been done as an attempt to handle the great differences of the values as long as these cannot simply be explained by historical developments. For the analysis, the values from the internet portal “Trading Economics” have been used, where the number of outages per month coincides with data from the World Bank. These values are referred 76 to as outages experienced by a typical firm and has thus been used as LOLHpt.

퐿푂퐿퐻 = 퐿푂퐿퐸 ∗ 푀푇푇푅 Eq. 11

72 See Table AVI-3 in Appendix VI. 73 Ford & Heath, 2012 74 Bartos et.al., 1990 75 Gharavi et.al., 2015 76 Trading economics, 2017

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For the solar powered alternatives, the approach has been slightly different due to the dependence on the weather conditions and the back-up power system. For the PV- system with a power grid connection (Alternative 3), the LOLHpt value of the power grid has been used. The logic behind this is that the power grid represents the most stable power connection within this HES. Due to power decreasing effects such as clouds, dust and conversion losses the PV-system is not able to feed the plant alone but depends on the power grid. Thus, the power grid is assumed to be able to back up PV-system downtimes, whereas the PV-system is not able to back up power grid downtimes. For a continuous or a back-up generator the expressions “annual and production time outage” have not been considered. This is explained by the fact that the generator does not fail when it is not operating and so the LOLH value always relates to production time. For the PV-system with a back-up diesel generator (Alternative 4) a similar approach as for the solar-power grid-Alternative has been used. In this case, the LOLH- value of the diesel generator has been used with an additional “start-up”-delay time. This delay time addition is a subjective estimation and correlates to a preparation time for any unexpected issues (for example emptying or preparing machinery) that is likely to occur during a power outage.77 The actual time used in the basic calculation setting is 30 min/outage. The annual back-up running time for Alternative 4 has been calculated based on two components: the months where the daily solar power will statistically78 not be able to deliver enough power due to weather conditions and due to the expected failure rate of components that cause a significant power drop.79 For months with statistically enough sun irradiation, the back-up battery system has been expected to be able to bridge temporary irradiation drops. An important remark in the case of low irradiation months is that this approach assumes either that the power loss of the PV-system does not exceed the storing capacity of the battery bank, or more realistic, that the generator is left running until the battery back-up has been fully recharged after an interruption. In the LOLH scoring evaluation the latter approach has been used by applying the statistically energy shortfall as a necessary back-up energy. The mentioned start-up delay time has also been used for the hybrid reference alternative (Alternative 1b), with a combination of power grid and a back-up generator, and for the diesel generator alternative (Alternative 5). In these cases, statistical values on loss of load events (LOLE) for distributed generators have been used for assessing the number of annual “start-up”-delays.80 The data on the failure rate of the diesel generator has been used in three different cases: as a back-up for a power grid, as a back-up for a solar plant and as an autonomous power source. In order to reflect these differences, the failure rate has been assessed out of three different perspectives: firstly, as a permanent back-up system for the whole year, LOLHbu, secondly, as a back-up system during the rainy season and low irradiation months, LOLHbu´, and thirdly as a continuous operating system, LOLHc. For all cases the annual failure rate refers to production time and has been re-calculated for the “active” days of the generator: 260 days for cases where the power supply depends on the backup during all year and 200 days for alternative 4. The background of the 200

77 The occurrence of a delay time for a restart is based on personal experience. The chosen value is just a reference value, which could be very different. Due to this, the delay time has been applied as a variable in the Excel©-calculations. 78 Based on weather statistics for Abidjan with noted sun radiation as W/m². The radiation value has then been applied on the design size of the PV-system with related transformation efficiency rates. 79 This includes components such as inverters, connection boxes or connecting strings. 80 Bartos et.al., 1990

21 days (which is longer than the rainy season) is that this reflects the annual period where the PV-system cannot bear any power loss caused by a component failure. The statistical failure rate of the PV-system is higher than that of the generator, with the most common and influential occurrences being inverter, distribution lines or connection failures.81,82 Due to the layout of the system within this analysis, such a failure causes an approximate power reduction of 10 %.83 Thus, the need of the diesel back-up has been reduced by the days where, despite a 10 % power loss, the PV-system can still deliver enough energy for the processing plant. For the power grid alternative with LPG heating, the same reliability index as for only a power grid connection has been used. The background of this is that, in case of a failure of the LPG heating system, back-up electrical heaters can cover up for the LPG- burner.

3.2.4 Solar power input Among the evaluated alternatives there are two HES that rely on solar power (Alternative 3 and 4). Both these systems have three different solar powered sub- systems: air heating, water heating and photovoltaics (PV). Generally, the dimensioning of the systems has been done in correspondence to the average load of the installed machinery. As far as the heating requirements are concerned the power demand is relatively stable (drying of beans and standby water heating). Thus, in these cases the nominal power reflects the average energy need, without sharp peaks and downs. The water heating is required foremost during night-time and the system is based on a hot water reservoir that is heated during day-time and that releases the heat during night- time. This has allowed a straight forward dimensioning based on the statistical mean solar irradiation (GHI) for the hypothetical installation site. 84 For both solar alternatives (3 and 4) the same system size for the solar heating has been applied. For the PV-system the dimensioning has been made differently depending on the fact if it is an on- or an off-grid system. This is due to the different back-up systems, which have different characteristics when it comes to cover up for decreased PV-power. The on-grid system has a permanent connected power grid back-up, which can cover up for any PV shortcoming without processing still stand. Therefore, this alternative has been dimensioned according to the peak load demand of the processing plant as shown in Equation 12. During normal operation that practically means that the on-grid PV- system will represent a supporting source, which (due to power losses caused by dust, clouds etc.) will not be able to feed the plant alone.

푃 = Eq. 12 ɳ where Pload. is the load demand by the installed machinery and ɳPV is the efficiency of the solar system. The off-grid alternative on the other hand has a diesel generator as a standby back-up source. This kind of back-up system requires a starting time85 and has a characterizing minimum fuel consumption threshold. Due to this, the PV-system of this alternative is dimensioned to be able to feed the plant solely during the non-rainy

81 Miller et.al., 2012 82 Collins et.al., 2007 83 The solar plant layout is based on 10 strings with 10 inverters. In the case of a defect in a connection box, string or inverter a 10 % power loss will occur. This approach has been used for both alternatives with a solar PV-plant. 84 Taken from the NASA Surface meteorology and Solar Energy database. 85 In this analysis a manual generator start-up system has been chosen. Alternatively, the back- up generator could be equipped with an automatic power loss detection and start up.

22 season and on statistical non-rainy days. This means that the system in this case is over dimensioned in regard to the installed PV peak power. The base for the dimensioning of the system is a PV-simulation of the annual energy output (kWh) of one solar panel. This simulation has been done by using the program Polysun-online®86 (see Appendix VII for the report) and the resulting figures have subsequently been evaluated by using Excel©. The evaluation has been done with the target to find a minimum PV peak power related to the energy demand. This has been done by relating the monthly simulated solar energy yield with the monthly energy demand of the SSCPP. This includes the concept of a battery storage that is dimensioned to be able to bridge shorter PV power drops, cover standby energy needs and to allow the generator to run at nominal power and store the excessive energy. In order to balance the investment with an efficient energy use a certain energy shortfall during the rainy season has been accepted. Thus, the installed size is a compromise between energy deficit during cloudy (rainy) months and energy surplus during sunny months. This compromise reflects a decision, which would make sense for an actual case to optimize according to project circumstances. At this place, it is worth mentioning that in this analysis no optimization for the different applications has been performed. It could very well be that there are better system sizes that could deliver a more optimal relation between investment, annual costs and reliability. Such an optimization requires a more detailed dynamic study of the hourly power demand as well as the hourly solar energy yield as can be found in other studies.87 The size of the battery storage for the off-grid system is related to the PV-installation size, daily expected solar irradiation and the standby energy need. Thus, the designing parameter for the battery bank is a combination of the expected surplus of solar energy and a timeframe in which the battery storage can feed the processing plant with a certain amount of energy. The minimum back-up capacity that has been used within this evaluation is the 14 hours of standby time outside the daily production time. This means that the minimum back-up energy represents the standby energy need. The maximum (installed) battery size has been dimensioned in accordance with the excessive solar power during the non-production daytime in low solar irradiation periods. This approach has been used to avoid an expensive over-dimensioning of the battery bank. For example: during high irradiation periods the PV-system will be able to deliver sufficient energy for the production plant. There will also be a greater amount of excess energy produced during the non-production daytime. However, for a 24-hour timeframe, the only back-up energy need will be represented by the night time standby functions. During low irradiation periods, on the contrary, there will occur a back-up energy need also during the daily production time. The degree, to which it is possible to cover this need, depends mainly on the size of the installed PV-system rather than the battery size. As a conclusion, the size of the battery bank has been calculated according to Equation 13 below, which represents available surplus energy during a certain day. The used “reference” day is an average day during a low energy yield month with the lowest PV-energy deficit. This maximizes the effective use of the solar energy during low irradiation periods. Practically, this means that during those months with an average daily irradiation lower than the reference day, the battery bank will be over-dimensioned and be able to store more energy than what is available. On the contrary, during months with an average daily irradiation which is higher than the reference day, the battery bank is under-dimensioned and will not be able to store all excessive energy of the PV-system. The idea behind this relies on the approach of monthly averages when it comes to solar

86 Vela Solaris, 2017 87 Bortolini et.al., 2015

23 irradiation and daily needs when it comes to energy consumption. The excessive solar energy during non-production daytime in high yield months will not be useful during production due to sufficient yield from the PV-system. Within this study, the production time is set to 10 h/day, with the non-productive daytime hours divided equally between before and after production time. For the off-grid solar powered alternative this allows the use of a smaller battery capacity that can be re-charged before the nightly standby time and then again in the morning, before the production starts. Also, in this case Excel© has been used for the necessary calculations. See also Table AVII-2 in Appendix VII for the used spreadsheet layout.

퐸 = 푃 ∗( ) Eq. 13 . where Ebatt. is the energy capacity of the battery bank, PPV is the power delivered by the PV-system in kW and tnp represents the daylight hours, where the plant is not in operation. When calculating the daily surplus or deficit of solar energy, for example to estimate the necessary running time of a back-up generator, the battery storage has been included as useable energy. This has been done by relating the hourly solar yield with the average power demand of the plant. The battery storage has been integrated as a storage for the daily solar energy yield outside the production time. Consequently, the total daily energy surplus or deficit of the PV-system is calculated as: PV energy yield during production time plus stored solar energy outside production time minus the demand of the production and necessary standby energy. See Equation 14 below, where ΔEPV is the solar energy surplus or deficit, tp is the daylight hours during production, PPV the average daily solar power in kW, Eload the daily energy need of the production plant, Ebatt. the available energy in the battery storage and Estby the required energy for the standby night-time. Ebatt is calculated according to Equation 13. The negative outcomes (energy deficits) are used for calculating the annual running time of the back-up system by multiplying the result with the number of productive days in that month and adding up deficit months.

∆퐸 = 푃 ∗ 푡 + 퐸. − 퐸 − 퐸 Eq. 14

3.2.5 Energy and power demand The energy demand of the processing plant can be defined on one hand by a maximum installed power in kW and on the other hand by an average hourly energy demand expressed as kWh/h. For the calculations related to the annual energy consumption, such as the LCOE and CFOE indexes, the average energy demand has been the deciding value, whereas the maximum installed power has been used for the dimensioning of the on-grid PV-system and the back-up generator. As explained in Section 4.2.4, the required amount of fresh water to feed the evaporation cooling for the milling has not been included in the analysis. Due to the consideration of different energy types, as is explained in Section 4.2, the maximum installed power varies slightly depending on the considered power supply alternative. This comes from the fact that differently powered components may have different power ratings and efficiency rates, such as an LPG-burner compared to an electrical heater. The necessary standby energy need during night time is the same for all alternatives. The electrical standby energy demand depends on the consumption of the compressor type air coolers, lights, tank stirrers and water circulation pump. Apart from an electrical standby power need there is also a standby heat energy need for piping and tank heating. The average daily energy demand of the plant has been calculated based on the above mentioned average hourly energy demand. This average demand has been obtained by

24 applying a load coefficient on the rated power of the electric drives. The load coefficient denotes a percentage of the nominal power and should reflect the expected part load of the various drives.88 Consequently a motor which is running at full power at all times would have a load coefficient of 100 %.89 The lower average power comes from shifting load cycles and over-dimensioned drives. The drives are generally designed to be able to handle maximum filling level of the machinery, which many times does not reflect a continuous state during normal processing. The size of the load coefficient has been approximated by available amperage measurements90 in some cases and on informal design parameters from machinery suppliers in other cases.91 For the dryer, theoretical calculations based on the evaporation of moisture have been used to calculate the energy demand. Applied values for the load coefficient of different drives and components are presented in Table AII-2 in Appendix II. Thus, the daily average energy consumption, based on a part load, depends on the use of Equation 15, where Ēload is the average daily energy need of the processing plant, Pnom the installed power, cload the machinery load coefficient in percent and tp the daily production time in hours. The part loads for the different machinery are listed in Table 8 in Section 4.2.7.

퐸 =(푃 ∗ 푐 ∗ 푡)/100 % Eq. 15

3.3 Collection of information The necessary machinery and processing related information and data for the various calculations, assessments and evaluations have been collected by reviewing scientific papers, machinery data sheets, machinery quotes, books and other written sources. In some cases, non-existent written data, such as average load coefficients of machinery have been replaced by information from informal correspondence with suppliers and consultants or through personal experience. In regard to some heat applications also thermodynamic calculations have been performed based on a given product throughput and temperature. For the solar irradiation statistics, the program Polysun-online®92 has been used, which is an online PV-simulation program that uses NASA meteorology statistics. For energy-related statistics on emissions of GHG, fuel consumption, prices and reliability figures various internet sources and reports have been reviewed. In terms of equipment and installation costs foremost quotes have been used as data source. Efficiency rates have also in most cases been accessible through quotes or, where not available, in reports concerning similar applications. To examine the attitude and possible problematic issues related to the installation of an SSCPP in a cocoa growing country, a few interviews have been performed. The interviews have been held by phone and skype in a semi-structured way by using a set of questions. The questions have concerned whether and which issues could be expected to cause problems and/or possibilities. For example, in a production-related context such

88 U.S. Department of Energy, 1997 89 The lower efficiency rate of an electric drive that operate under part load has not been considered. According to: See also following note on energy consumption approximation. 90 U.S. Department of Energy, 1997, p. 4. 91 In this study such an approximation has been considered to be sufficient. Due to different cocoa processing possibilities, production rates and different suppliers of machinery, a detailed energy consumption analysis of each drive would only gain one specific line-up from one specific supplier. Therefore, idle currents and motor internal efficiency rates have not been considered. However, partly considerable differences between the rated power and the average load (foremost in cases of shifting load cycles and heat pump applications) made the approach of applying a lower average load seem reasonable. 92 Vela Solaris, 2017

25 as storage, product quality and infrastructure, but also in a social context such as education. In total, there have been four of these more formal interviews. The interviewed persons possess extensive experiences from the cocoa industry in general and the conditions in cocoa growing countries specifically. The results from the interviews have not been presented separately but have been integrated in the text and at some places explicitly referred to by footnotes.

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4 Power Supply Requirements Within this section, the requirements of the power supply and the different energy needs of the SSCPP will be discussed. To begin with the target requirements are explained followed by a presentation of the energy needs and types required for the different processing steps. In Table 8 in Section 4.2.7 an overview of the energy needs is presented. Due to the characteristics of different power supply alternatives the actual installed power and energy consumption of the SSCPP may vary. This comes from the fact that some machinery is flexible when it comes to the type of energy input, which may lead to different energy efficiencies. As an example, the cocoa bean dryer may use either electrical heaters or an LPG-fired burner to supply heat energy. In such a case an LPG-burner has different heating characteristics than an electrical heater when it comes to energy transformation efficiency, heat transfer and energy density. The correct supply of energy for the SSCPP is an essential issue due to different reasons. On one hand the small production unit has a higher per ton energy consumption than large industrial processing plants and on the other hand different power supply systems may be unequally suitable depending on the conditions for a certain installation site. The higher energy consumption needs to be addressed by optimizing the energy sources in order to reduce costs and the environmental impact. The different installation conditions at the same time puts requirements on a certain flexibility in the choice of the system. This makes this report an example of how different power supply systems can be evaluated by using MCDA, rather than a definite choice of an “all best” alternative. In Table 1 in Section 2.2 the main characteristics of the described SSCPP have been listed. Due to the widespread distribution of cocoa farmer cooperatives, there are different power supply conditions depending on the actual cooperative site. This applies to the availability of a stabile local power grid, the suitability of wind power and the possible local emissions regulations. In addition to that, a stand-alone processing plant with a higher “on spot”93 energy consumption brings new challenges. The de-centralized concept of SSCPP, as described here, at the same time could bring new possibilities of a more significant use of renewable energy within the cocoa industry. The relatively low per-unit power requirement of a small processing plant could make it suitable for the use of renewable energy sources. In Section 6 different possible power supply systems will be reviewed in regard to supplying a SSCPP with energy. An important note at this place is that we assume that the machinery can partly be delivered for different energy inputs for the generation of heat, such as a burner instead of an electrical heater, with no or negligible cost variation.

4.1 Target Requirements The target requirements on the power supply have been formulated subjectively by the author based on personal experience from cocoa processing and developing countries. The aim of this requirement-list is to use it as a “pre-selection” sieve to avoid generating a too extensive list of alternatives and to eliminate non-mature alternatives already from the very beginning.94 At the same time the requirements have been kept simple with the idea to not limit the scope too much. When formulating the requirements, the focus has been set on the cocoa process itself which has led to the following arguments:

93 The term on spot refers to the energy consumption which is amended at the processing site. It may be the case that the overall energy balance looks different due to shorter/different transport routes. 94 Hammond et.al., 1999, p.47

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- A continuous power supply is important since black-outs interrupt important processes such as roasting or tempering. A restart of such a process is in some cases not possible without a complete reset which could be afflicted with complications. - A low or no need of manpower allows the main focus to stay on the cocoa processing. The power supply should hereby be able to operate without too much attention.95 - The power supply should be able to deliver all kinds of energy in order to feed the complete process. - A requirement on “off the shelf”-technology is intended to ease maintenance, accessibility to spare parts and qualified technicians. The above arguments have led to the requirements as listed in Table 7 below. Due to their character, as “background” information, the requirements will not be discussed further during the evaluation.

Table 7. Requirements on power supply systems. Requirements on power supply - Continuous power supply - Operation without permanent need of manpower - Able to deliver all types of energy needed for the process - “off the shelf” standard solutions

4.2 Energy Requirements Within the processing plant there are different needs of energy with different requirements on “energy quality” or “energy density”. The term quality in this case refers to the level of exergy96 that is required for different applications. The level of exergy will not be reviewed at a great depth within this study but is rather intended to keep in mind while creating alternatives. A complete energy/exergy analysis of the system would exceed the scope of this paper. As mentioned previously there has not been any intention of creating an exhaustive list of power supply alternatives in this report. Therefore, a basic knowledge of the exergy requirements helps to create further future alternatives that are not included here, but that might be suitable for a certain installation site. The use of the knowledge on exergy could bring great advantages in the result that “cheaper” energy may be used where it is possible. For example, it enables a differentiated view on heat sources (such as waste heat or solar heat) for heating applications, instead of only using electricity to heat. The aim therefore is to focus on the difference between lower quality heat demands in contrast to higher quality electricity demands. To be able to formulate different possible alternatives it is essential to know the amount and kind of energy that is actually needed. To enable such a description, we

95 Too much is a highly subjective expression that in this case would normally refer to a need of attention more often than every-eight-hours. This would imply one check up at the beginning of each operating shift. Since this study relies on a strict daytime production, with 10 operating hours per day, the situation is slightly different. In this case an inspection interval of each 10 hours would be better suitable. As a practical example of the effect of this inspection requirement, the need of a certain volume of fuel storage for fuel consuming power units can be mentioned. 96 Exergy is defined as the maximum useful work until an equilibrium of the system has been achieved. See also Gong et.al., 2012.

28 take a look at the cocoa process during which the cocoa beans are being treated in different manners in order to reach a final97 state as a liquid cocoa mass. The different steps in this processing chain are: drying, de-hulling, roasting, debacterizing, grinding, storage, tempering and blocking. The brief descriptions of the processing steps below deliberately exclude any numbers on power and energy requirement. Instead they are intended to enable a broader understanding of what kind of energy is actually needed. For a decision maker or committee, it seems essential to know more than just a number in order to find and evaluate viable and feasible power supply alternatives. In Section 4.2.7 the processing steps are finally presented with their respective energy demand.

4.2.1 Drying The sundried cocoa beans coming from the farmers normally have a moisture content of about 5-7 %.98 In order to be able to grind the beans effectively however, the moisture content should not exceed 2 % since levels above this figure have a great impact on the cocoa mass viscosity.99 During the subsequent roasting process, which is also a heat treatment process, a certain amount of moisture will be removed, however, a too high input moisture will influence the roasting time and capacity negatively. In order to avoid this the cocoa beans are often artificially dried at the entering stage of the processing chain. The most common way to reduce the moisture content of cocoa beans is the so-called warm air drying,100 where pre-heated air passes through the product. The hot air heat the beans, to accelerate the evaporation, as well as it removes the moisture by absorbing the moist vapours. The product temperature should ideally be around 70°C101, which implies an air temperature of 75-80°C.102 The amount of air streaming through the product depends on the relative moisture content of the input air, the amount of moisture to be removed in l/h and the structure of the product by means of the influence on the air velocity. The largest share of the energy requirement is the heating demand. As the drying step is represented by a continuous process in this machinery setup, the heating demand has been assessed constant at 100 % of the theoretical calculated demand. Thanks to relatively low hot air temperatures, the drying is suitable for solar heat. The average electrical load lies slightly lower than the installed power due to dosing elements not running at full power during all time.

4.2.2 De-hulling The de-hulling or winnowing stage contains the breaking of the beans and the removal of the shell fragments from the cocoa nibs (cocoa kernels). The breaking of the beans is achieved by applying a mechanical impact on the beans. This can be achieved by using different technologies such as centrifugal breaker, roller crusher or reflection breaker.103 The rotating speed of the centrifugal breaker and the breaking gap distance of the bean crusher are important adjustment settings to reach a required particle size. To remove the shell fragments from the mixture of nibs and shells, this mixture passes by a stream of air. By adjusting the airflow correctly, the lighter shell particles are carried away by the air and the nibs pass through. For a well-functioning separation of shells and nibs

97 Final, in the sense of the reviewed SSCPP. 98 Fincke et.al., 1965, p.59 99 Fincke et.al., 1965, p.363 100 Mühlbauer, 2009, p.94 101 Jacquet et.al., 1980, p.55 102 Mühlbauer, 2009, p.110 103 Fincke et.al., 1965, p.148

29 the airflow needs to be at a constant level. The de-hulling step depends only on electric power and is running constantly at 100 %.

4.2.3 Roasting In the SSCPP, as described here, a batch nibs roasting process based on a drum roaster is being employed. The batch process in this case means that two roasting cycles are absolved every hour. During this process, the product is filled into a rotating drum, which is being heated by hot air on the outside. The nibs are being heated by the heat transfer from the drum wall. Typical roasting product temperatures lie between 110°C and 120°C,104 whereas the heating air, streaming around the drum, has a temperature of 350°C to 450°C.105 The roasting process is essential for the output quality, since during the nibs roasting also a de-bacterizing step is taking place. Further the roasting process influences the cocoa flavour. Depending on bean origin and the destined end-use the roasting parameters can be adjusted differently. Therefore, for a good quality roasting the heating air and product temperatures need to be adjustable and controlled with a good precision. This in turn puts requirements on the supply of hot air to have constant temperature and airflow regulation. The most significant energy demand of the roasting process is the heating of the roasting air. Due to the discontinued character of the batch process, the heating demand has a recurring pattern, where phases of full load are followed by phases of zero heat load. This explains the relatively low load coefficient for the average energy demand. The high temperature requirement of the heating air makes a solar air heating system less suitable for roasting.

4.2.4 Grinding The grinding of the cocoa nibs is realized using different grinding stages. The grinding effect is thereby achieved by mechanical impact as shearing and pressure forces caused by the moving grinding elements. The pre-grinding stage can be a fast turning impact grinder or a slower turning friction grinder that turn the high fat solid nibs into a coarse liquid paste. In the processing plant used as a reference within this paper a slower turning stone mill is used for the pre-grinding. The subsequent grinding stages; medium- and fine- grinding, are represented by ball mills106 that use an additional grinding media (grinding balls) to reduce the particle size. The ball mills have a slower shaft speed than the pre- grinder and are constructed for a liquid input. Thus, this grinding technology may sometimes be referred to as wet-milling. The grinding process includes the largest electrical drives within the plant and represents the biggest part of the electric load. As far as the pre-grinding stone mill is concerned, this has been assessed with a 100 % load coefficient for the average energy consumption. The ball mills have a slightly lower average load due to installed power reserves to handle the grinding media and product weight during re-starts with full grinding tank. To keep the product temperature stabile, the grinding process needs heating as well as cooling energy. This energy is supplied by heating and cooling water that circulates in the jacketed piping and milling vessels. The cooling water in this context, is supplied by evaporation cooling towers. This means, that the cooling effect comes from the consumed heat energy during the vaporisation of water. The corresponding consumption of water to feed this cooling process has not been considered within this evaluation.

104 Fincke et.al., 1965, p.137, 140 105 BEAR Mühlen & Behälter GmbH, 2015 106 Fincke et.al., 1965, p.161

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4.2.5 Tank storage and piping To store and to transport the liquid cocoa mass, jacketed piping and stirring tanks are used. Due to the characteristics of the product to solidify if it gets too cold (less than approx. 35°C) it needs to be kept warm at all times. To prevent the product from cooling down during stops and thus getting solid, the jacket is filled with circulating hot water. To prevent the product temperature from falling below 40°C the heating water should not be colder than 50°C. The storage tank is further equipped with a slow turning stirrer, which should operate continuously, to prevent a separation of solid particles and the fat content within the cocoa liquor. Consequently, these functions; heating and stirring are the main standby operations that need to be active around the clock. The lower average load of these components is explained by the idling stirrer, with power reserves to handle different product viscosities.107

4.2.6 Tempering, blocking and storage The tempering technology used in this setup is a batch type temperer based on a jacketed stirring tank with temperature controlling. During this tempering process, the cocoa liquor is being cooled under constant stirring down to a temperature below the melting point in order to start the forming of crystals. After this initial cooling the product is being slightly heated to prevent an uncontrolled crystallization within the tempering tank. Thus, the tempering unit needs cooling as well as heating to enable the blocking of the liquid cocoa. The blocking stage itself is not more than a controlled dosing procedure, where the cocoa is filled into cardboard boxes with a plastic inlay. After the boxes have been filled they need to rest and solidify in a tempered room with a temperature around 20°C. Similar, as for the roasting process, the batch character of the tempering process causes a changing energy demand that switches between cooling and heating. Thereby the cooling is achieved by a water cooler based on heat pump technology and the heating either by an electrical heater or solar heat. Thus, the average energy demand is lower than the installed power.

4.2.7 Summary of energy needs The specific energy needs of the different processing steps that are listed in Table 8 are defined as installed (peak) power. Depending on the application the average need of power (load coefficient) will be lower than the peak power. See also Section 3.2.5. Due to this the average electric power has been indicated in Table 8. In line with previous mentioned considerations of exergy, the energy needs have been divided in the respective type of energy (electricity, cooling, heating). The listed electric power therefore does not include all heating power demands, since these can be replaced by other energy sources, such as solar heating or fuel burner. For the cooling demands, however, there are no known available standard options for these applications. Due to this, the cooling demands are covered by electric power in all alternatives and are consequently included in the electric power demand in Table 8. The values that have been used are based on available machinery specifications and, in some cases, theoretical calculations. In Appendix II the origin of the different values is explained more precisely. Interconnecting product feeding elements (such as conveyors and pumps) have been integrated in one of the processing steps that it connects. Under the title “blocking” the air cooling of a small crystallization storage has been integrated. The listed average power consumption has been calculated with the help from the peak power needs and a load coefficient based on machinery characteristics. The lower figure of the average power consumption is due to discontinuous batch processes, non- maximal load of components and heat pump applications (such as compressor chiller

107 Due to the natural origin of the cocoa beans, the specifications of the beans can differ when it comes to bean size, moisture and fat content. As changing input specifications require changing processing parameters, there may occur intermediate change batches with deviating parameters.

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for tempering). See Section 3.2.5 for an explanation of this average power approach. As mentioned in Section 3.2.5 the installed power of different power supply alternatives might vary due to their different characteristics and possibilities to cover up different energy needs. In Section 6 the different considered alternatives, their components and their respective installed power are described in detail.

Table 8: The different cocoa processing steps with peak and average power requirements, expressed in kW. Processing Hot Hot Average step air water Electrically supplied power [kW] Electric 80°C 55°C power

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5 Objectives and Criteria For the evaluation of the power supply alternatives, a number of objectives have been formulated with a number of criteria associated with these objectives. The criteria have been chosen in a way that the consequence of an alternative according to any objective can somehow be measured. The definition of objectives would normally be shaped by a decision maker or a decision committee, however since a decision maker is not existent for this study, the selection of objectives has been made by relating it to similar studies.108 Thus, commonly used objectives and criteria for the evaluation of power supply systems have been used. The thereby used fundamental objectives hierarchy is displayed in Figure 6 below. Depending on the conditions and the circumstances of a specific project and the preferences of the decision maker or committee, this hierarchy could look differently. There could be more, other or less objectives playing an important role. As for example, a risk analysis of the alternatives could in some cases play an important role. In this study, this has been excluded due to different reasons. On one hand the small size of the system reduces the potential of catastrophic events and on the other hand the absence of site related information eliminates the possibility to describe relevant consequences. Instead the focus has been set on the objectives minimizing the overall cost, minimizing the emission of greenhouse gases (GHG) and maximizing the reliability of the system. As can be seen in Figure 6 below, this objective’s hierarchy looks different than the objective’s hierarchy displayed in Figure 5 during the example in Section 3.1.2. In this study, the leaking of possible toxics has been excluded as a means of environmental objective. This has limited the environmental aspect to the emission of GHG.

Find the best power supply alternative

Minimize Minimize Maximize environmental overall reliability cost impact

Minimize Minimize Minimize Minimize Minimize Maximize annual emission power outage investment lifetime cost of GHG outages time

Figure 6. The used fundamental objectives hierarchy for the evaluation of power supply alternatives.

In order to be able to evaluate different alternatives according to mentioned objectives, it must be possible to describe the alternatives as resulting consequences related to these objectives. To do this, suitable and measurable criteria need to be applied. The objectives that have been used are, as mentioned, commonly used for power generating systems, but there are still different possibilities to formulate criteria with appropriate attributes to use for the evaluation of consequences. Apparently, there are different attributes available to make the criteria measurable in a meaningful way.

108 For energy supply systems common objectives are: to minimize costs, minimize environmental impact and maximize reliability. Resulting evaluation criteria could be: cost (for example as a life-time cost), environmental impact and reliability. See also: Alanne et.al., 2007 and Gharavi et.al., 2015

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In this study, the chosen criteria and their attributes to reflect the mentioned objectives are: - Cost, measured as: Levelized Cost of Energy (LCOE) - Environmental impact, measured as: Carbon Footprint of Energy (CFOE) - Reliability, measured as: Loss of Load Hours (LOLH)

The background for choosing these attributes is partly explained in the following sub-sections. Generally, apart from being commonly used, these attributes provide a good basis for further profitability evaluations due to their ability to be expressed as financial values: the LCOE is directly linked with energy costs, the value of the LOLH can easily be used to calculate the financial value of production losses and the value of the CFOE may be related to costs or profits due to the trading of GHG emissions.109,110 As can be seen the attributes are of a descriptive character and in all cases the consequences are expressed as numeric values. In some cases, subjective assumptions have had to be made during the evaluation of the alternatives, which have influenced the resulting values.111 This background is described in the following sub-sections and in Section 3.2. In Table 9 below, the used criteria, their related attributes and, in the case of the criterion “cost”, the included components, investment cost, annual cost and lifetime have been listed. In the subsequent sections a more thorough description of the different criteria and their attributes follows.

Table 9. Criteria and attributes that have been used for the evaluation of power supply alternatives. Criterion Attribute Description Cost Levelized Cost of The cost of energy, based on the total Energy (LCOE) produced energy during the expected lifetime [€/kWh] of the system. Investment Start-up investment for cost power supply system including installation Annual cost Operating cost incl. fuel, maintenance etc. Lifetime Expected lifetime Environmental Carbon Footprint Amount of released GHG related to the amount impact of Energy (CFOE) of produced energy during the system lifetime. [kgCO2,Eq./kWh] Reliability Loss of Load Total power supply downtime within one year. Hours (LOLH)

[h]

109 United Nations, 1998, Article 6 110 Tenenbaum et.al., 2014, p.141 111 This is for example the case for the assigning of a reliability index for the solar power or for the indication of the lifetime. These are values that are based on different descriptive background information that in the end have been concluded to a total value.

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5.1 Cost When comparing different power supply systems from a financial point of view, there are different indicators to do this. Mostly, they combine occurring costs with a system lifetime, such as a lifetime cost index or the LCOE (Levelized Cost of Energy). As a financial criterion within this analysis, the later: LCOE has been used. The LCOE incorporates investment cost, annual cost and the system lifetime. This makes it a powerful index as a means of comparing the overall energy cost between different power generating alternatives. At the same time, it is important to point out that such an index hides background information that can be important to consider, such as the composition of annual cost and initial cost or sub-system lifetimes. Due to this, the following sections will describe the included sub-criteria and their background. The mathematical method for calculating the LCOE for different power supply alternatives is explained in Section 3.2.1. Another important remark and a difference, related to other studies, is that here the total energy demand of the processing plant is of interest when calculating the LCOE. In other cases, the LCOE is related to the total energy generated by a specific power supply system. This different approach is related to the solar powered alternatives, where the total amount of energy generated might be higher than the actual amount that can be used by the processing plant. This depends on varying weather conditions and varying energy demands at different times. For the off-grid solar system the excessive generated energy during full sun irradiation days cannot be fully stored and thus not be fully used.

5.1.1 Investment cost The investment cost includes the cost for components, installation materials and the installation. In some cases, the values for installation have had to be estimated based on values for comparable works, since exact values could not be found. In this study installation cost estimations have been made based on an installation in Europe, due to available figures for this location. Transport costs have been eliminated for all alternatives due to lacking or too uncertain information sources. A similar approach can also be found in other studies of HES, where the initial investment is focused on the plant installation excluding transport cost.112 Furthermore, it is to assume that a transport would be coordinated with the processing machinery, which would put the specific transport cost for the power producing unit in a different relation.

5.1.2 Annual costs The annual costs include all known costs for maintenance and means of production. For grid connected alternatives, this is the cost per generated kWh and for alternatives with a generator it is the cost for fuel (diesel) and maintenance. For the fuel costs in general (diesel and LPG) the price per litre or per kg has been considered. Since this study deals with stationary fuel consumers that require any kind of fuel deliveries, an additional 10 % has been included in the fuel price to compensate for such deliveries.

5.1.3 Lifetime The system lifetime denotes the time frame, which is used to define the total amount of generated energy and the accumulated cost of the power supply system. To use the system lifetime as the time frame to calculate the total amount of energy to be considered within the LCOE value is a standard approach. Therefore, the system lifetime has been used as the time frame also within this evaluation. An alternative approach could have been to consider a different time frame (a decision planning horizon) instead, where the expected lifetime of the SSCPP could have represented the time frame within which the generated useful energy and accumulated cost would be considered. An approach with a fixed timeframe is common (also for power supply

112 Bortolini et.al., 2015

35 systems) when the net present value of different projects or investments are to be compared with each other.113 With such an approach an economic criterion could have been “the total energy cost during the lifetime of the SSCPP”. To calculate a combined expected lifetime of an HES, reliable and specific data for the included components and sub-systems must be present.114 This study we will not get that deep, but rather use indicating values that enable the use of the lifetime related index LCOE. The lifetime figures are based on different information sources and could be considered as qualified estimations. Thus, they can be considered accurate enough to differentiate a relatively long-lasting power grid solution from a shorter lasting diesel generator with a limited number of operating hours.

5.2 Environmental Impact The title Environmental impact is admittedly slightly misguiding since the environmental evaluation within this study is based only on the CFOE (Carbon Footprint of Energy). Despite this, the title Environmental impact has been kept as to symbolize the possibility of expanding this objective with further criteria. The CFOE value denotes the amount of GHG (Greenhouse gases), expressed as an equivalent amount of CO2 in kg, which is caused per produced kWh. The CFOE value is based on a system’s lifetime, meaning that also the production of components is included as GHG-causing phases. As mentioned, impacts caused by other possible environmental risks or effects, such as fuel leakages, noise level, hazard of fire etc. at the local installation site have not been considered here. Due to its dominating influence as a greenhouse gas, CO2 is often used as an equivalence indicator for comparing various energy carriers with each other.115 Likewise, the CFOE index is commonly used for analysing hybrid energy systems.116 Another aspect of the carbon emissions is the possibility of transforming these into financial values through the trading of emission credits.117 The common view of the dominating impact of GHG, environmentally seen, is furthermore strengthened by the content of the Kyoto protocol.118 Due to this the CFOE has been considered as an appropriate attribute to use also within this study. An important remark is that the carbon footprint, as found in published lists, relates the amount of released GHG to the total amount of generated energy during the system´s lifetime.119 In this study, this approach is a bit complicated when it comes to the off-grid solar power system, since the total amount of generated energy is not the same as the effectively used energy (see also the comments in Section 3.2.1). Due to the high degree of over-dimensioning of the off- grid PV-system, some amount of energy will not be useful for the SSCPP. A possibility to compensate for the difference between generated and useful energy when using listed CFOE values, could be to divide this value by the theoretical efficiency of the use. This would generate a higher CFOE value to reflect the reduced amount of used energy. In this study this has not been done. The reason for this is that a multitude of different CFOE values can be found for the use of solar energy. The differences between different values come from technical as well as installation related issues.120 Depending on the

113 For example, as in Clement, 2012 114 Rausand & Höyland, 2004 115 Bondesson, 2010, p.22 116 Bortolini et.al, 2015 117 Tenenbaum et.al., 2014, p.141 118 United Nations, 1998 119 Lübbert, 2007, p.5 120 Lübbert, 2007, p.17,18

36 choice of material for modules and installation frames the manufacturing carbon footprint is different. Depending on the installation angle, location and supply conditions (on-grid or off-grid) the solar energy yield is different. This makes the CFOE a plant-specific value, which means that this study lacks a well-defined reference value for applying the previously mentioned efficiency compensation. Instead this study relies on average values without an efficiency-related compensation. To still differentiate between the smaller on-grid PV-system, with less un-useable energy, from the over- dimensioned off-grid PV-system, a “higher” average carbon footprint value has been used for the off-grid system. The large variety of carbon footprint values for PV-system allow some interpretation freedom. For this study a “min” average value has been used for the on-grid system and a “medium-high” value has been used for the off-grid system. The use of a higher average CFOE value for the off-grid PV system, with a higher degree of energy over-production, has been accepted as a good enough compromise.

5.3 Reliability The reliability of a power supply is here referred to as the avoidance of failure during the supply of energy. Due to the wide variance of power sources, with completely different characteristics, it has been difficult to find a specific attribute to make such a compare reasonable. For commercial power distribution systems, there are highly developed reliability methodologies available, whereas the system reliability includes the system adequacy as well as the system security.121 Within this paper only the latter, the system security, will be considered in regard to the power supply stability. To describe a systems reliability, there are different indicators that can be used depending on the type of system, and also depending on the type of energy consumer. Common values for power distribution nets are SAIDI (System Average Interruption Duration Index) and CAIDI (Customer Average Interruption Duration Index).122 For single power unit’s common indexes are LOLE (Loss of Load Events), MTTR (Mean Time To Repair) and LOLH (Loss of Load Hours),123 where 퐿푂퐿퐻 = 퐿푂퐿퐸 ∗ 푀푇푇푅. In this study, the LOLH value, which indicates the total time of power loss within one year, has been used. The LOLH index has been chosen due to a few specific reasons, even though it is not self-evident that this is the most suitable index for this application. The LOLE index, as an indicator for the number of power failures during a year, could also be assigned a high relevance, since each power loss bears the potential to stop important processes with possible problematic chain effects as a result. What speaks in favour of LOLH is the direct link to production losses caused by an interrupted power supply. In such a case the total outage time is of relevance, since this denotes the production outage time. This enables a very straight forward possibility to convert the estimated power reliability into a financial value. However, some caution is called for during this conversion, since certain processes may need additional start-up time after power is back on. This has been accounted for by a “start-up delay time”. Another aspect is that of the risk of the liquid product to solidify, due to the absence of heating and movement, which increases with time. The LOLH value also has the advantage of allowing a simpler approach for solar powered systems, by enabling the use of widely available weather statistics, such as sunshine hours and a cloudy weather probability. To assign other reliability values, such as the LOLE for solar powered systems, more sophisticated methods and simulations124 are required. A disadvantage of using only the LOLH index

121 Zhu, 2003, p.5 122 IEEE Standard 1366, 2012, p.5 123 Ford & Heath, 2012 124 Moharil & Kulkarni, 2010

37 is that the differentiating information on the number of outages and their duration gets lost. There are different ways, in which a future reliability can be assessed. A common method for assessing the reliability of a power system is a probabilistic approach, which incorporates the characteristics of each specific component.125 In this method the actual system, with its actual components and specifications, is simulated in order to obtain a reliability value based on the probability of a failure within the system. The advantage of this approach is that the simulated reliability adapts as components are replaced by others, with different characteristics. A disadvantage can be seen in the amount of effort needed to create an appropriate simulation model. Another method to assess a system reliability value is the deterministic approach, which delivers a value based on former interruption statistics. In order to use this method, the availability of statistical data is a requirement. The advantage of this method is its simplicity, as long as data are available, whereas the disadvantage is that the resulting reliability value is static and does not adapt to changes within the system. Within this report, the approach to assign the different power supplying sources a reliability value is diverse. For the electric power grid and the diesel generator a traditional deterministic assignment has been used by applying statistical values. For the solar powered alternatives, a deterministic approach is not possible. Therefore, a probabilistic approach, based on weather statistics and the simulation of one PV-panel, has been used. With this, the expected solar yield for each month has been calculated. By breaking down the solar energy yield in day by day periods and by considering the battery storage (for the off-grid system) the daily shortfall can be calculated. In Section 3.2.5 the relevant equation, as used during this assessment, is explained.

125 Meliopoulos et.al., 2005

38

6 Power Supply Alternatives The selection of power supply alternatives is intended to give a base for comparing hybrid energy systems (HES), including renewable energy sources with traditional power generation solutions, such as electricity from a power grid. Similar off-grid comparisons have already been performed for less energy intensive applications, such as electricity generation for a village in rural areas in developing countries126 or for off- grid tele-communication stations.127 The major difference in this study is the amount of energy needed and the different types of energy requirements (heat, motion, cooling) included in the system. The variation in energy demand provides a basis to also consider the energy quality required. This can be an important factor, especially in the context of using renewable energy sources.128 Due to this reason, the expression exergy has been introduced in Section 4.2. As mentioned in that section, a detailed exergy analysis is not included in this study, but instead the knowledge of the different exergy needs has been used to formulate power supply alternatives. By considering exergy needs, a more specific formulation of alternatives can take place in that it opens up for heat generating sub-systems. Accordingly, the power supply alternatives have not only been dimensioned according to peak power, but also by considering the energy quality required and the average energy consumption. The approach of an average power need most of all concerns off- grid solar power and relies on the fact that the machinery of the processing plant is switched on in a sequence rather than all at the same time. Furthermore, some processing steps rely on batch processing or cycles, which result in a non-uniform power demand. In the case of an all-electric power supply, the installed heating power represents a peak burning power, which does not reflect the continuous energy consumption. In the process of formulating and evaluating power supply alternatives this has been considered and presented in Appendix II. For a diesel generator, for example, that means to choose a size that can deliver the required amount of energy at the lowest possible fuel consumption and for the solar plant it means to limit the relatively costly investment of PV-modules related to the average energy demand. When it comes to possible power peaks, these are absorbed either by a back-up power grid connection or by a battery storage for a solar off-grid solution. As far as the off-grid diesel generator is concerned, this can absorb power peaks itself during shorter intervals.129 Based on the available information on energy amount and type for the different processing steps, as described in Section 4.2, suitable power generating solutions have been drafted. This has been done in accordance with the listed energy needs in Table 8 and the target requirements in Table 7, both in Section 4, and by considering the hypothetical installation site in Côte d´Ivoire.130 The chosen alternatives are listed in Table 10 below. The different alternatives have been chosen as to give an overview of traditional solutions, compared to solar powered alternatives for off-grid as well as on- grid scenarios. Other possible renewable HES alternatives could include for example

126 Gmünder et.al., 2010 127 Bondesson, 2010 128 By considering the required energy quality, the use of renewal energy sources can be optimized in regards of resources. For example: to use solar heat, with an efficiency rate of up to 40 %, for low temperature heat applications instead of using PV-arrays, with an efficiency rate of only 10-15 %, See also: Gong & Wall, 2014. 129 Price quote from Feeser GmbH, 2015 130 There are certainly more different possibilities, but the listed alternatives have been considered as being realistic and feasible under current conditions. A solar powered cooling would, as an example, be an interesting solution, but has in this case been excluded due to lacking standard “off-the-shelf” units.

39 wind power or biogas fuelled solutions based on agricultural waste. The latter has been excluded due to a higher complexity, required amount of biological waste and necessary handling associated with the feeding of the gas generator. Wind power has been excluded due to the requirement of relatively open surrounding spaces in order to operate this efficiently, which makes it highly site-dependant.131 Despite of the non- consideration of these alternatives in this case, options like these may become relevant under certain circumstances.

Table 10: Power supply alternatives and their installed power [kW]. Power supply alternative Electric Heat power power* (max/aver.) (max/aver.) [kW] [kW] 1 100% electricity from local power grid 56.1 / 48.6 0 / 0 1b Electricity from local power grid + diesel back-up 56.1 / 48.6 0 / 0 generator 2 Electricity from local power grid + gas (LPG) for 29.0 / 24.8 34.6 / 30.4

On-Grid On-Grid drying and roasting 3 Electricity from local power grid + PV-panels + solar 36.6 / 30.6 19.5 / 18.0 heat for drying and hot water 4 PV-panels w. batteries + solar heat for drying and 36.6 / 30.6 19.5 / 18.0 hot water + back-up with generator Generator (diesel) + heat recovery for drying 40.0 / 31,.6 17.0 / 17.0 Off-Grid Off-Grid 5

*not including electrically generated heat power.

6.1 Alternative 1 Where available, the local power grid can serve as a continuous power supply even if an estimated 15 million people live without access to electricity in Côte d´Ivoire.132 Non-connected households are mainly situated in rural areas, whereas the electrification rate in urban areas is considerably higher. Consequently, for installations in urban areas a power grid supply could be a good solution, whereas an installation in rural areas may lack this possibility. Figure 7 shows the layout of a power supply system relying on a local power grid. This solution is based on a 100 % electric energy in contrast to the LPG alternative where heating applications are supplied by LPG-fired burners. For a connection to a local power grid the necessary auxiliary equipment consists of a transformer, a low-tension station and compensation unit. On top of that the main connecting cable might be a crucial financial factor as this depends on the distance between power grid and consumer.

131 Feuk, 2008 132 International Energy Agency, 2014, p.31

40

Figure 7. Power supply system layout, alternative 1.

The power mix for electricity generation within Côte d´Ivoire is highly depending on natural gas, which is estimated to further increase within the future133 even though there are efforts to extend the capacity of hydro-electric power generation.134 The current power mix135 in Côte d´Ivoire is divided in the primal energy sources: natural gas, hydro-power and renewables as displayed in Figure 8.

18% 15%

hydro-power natural gas 67% bio-mass

Figure 8. Energy mix for electricity generation in Côte d´Ivoire. Source: www.proparco.fr, globalriskinsights.com

6.2 Alternative 1b Due to considerable power distribution problems and an underdeveloped electricity distribution net, an HES (Hybrid Energy System) solution with a grid connection combined with a back-up generator is a very common solution in many African countries.136 This makes such a system a realistic reference for installations in developing countries such as the Côte d´Ivoire. Figure 9 below shows the basic system layout with a back-up generator. The composition of the distributed electric energy is the same as in alternative 1. The back-up generator is diesel powered and of the same type as in alternative 5. The annual overall energy balance between power grid and

133 Tolchinsky, 2015 134 Traoré, 2013, p.8 135 The displayed electric power mix is an interpretation based on different sources with slightly different information. Whereas the share of natural gas and hydro-power is relatively reliable the exact composition of the 18 % “bio-mass” is uncertain. 136 Castellano et.al., 2015

41 back-up generator is displayed in Figure 10 below, where the running time of the generator is calculated based on the LOLH-value of the power grid.137

Figure 9. Power supply system layout, alternative 1b.

2,3%

electricity from grid 97,7% backup

Figure 10. Average annual energy split between power grid and back-up for alternative 1b.

6.3 Alternative 2 In this alternative, a local power grid connection is combined with LPG-fired burners for heating applications. Even though the share of LPG is fairly low related to the total national energy consumption in Côte d´Ivoire and the majority of the consumed gas is sold in bottles to private households, the gas is also available as bulk.138 This, and the commonly attractive price makes LPG-based energy interesting as a possible alternative. In many countries, the LPG fuel represents a cheap alternative139 to other fuels and is therefore a popular fuel for cars in some countries. For the evaluation, it has been difficult to get access to up to date local price information. Thus, the price that has been used is an estimation based on older price

137 See also: Section 3.2.1 138 The World Bank, 2001, p.55 139 GlobalPetrolPrices, 2015

42 information,140 current world market prices for propane141 and the current price in Ghana.142 The installation effort for this layout is slightly higher than for just the power grid connection. The gas supply system consists of the gas storage tank, valves, evaporator and a switch box. The storage tank is dimensioned to last three months at the foreseen production rate. Figure 11 shows the system and its components in a schematic way. Due to practical reasons, all needs for heating energy cannot be supplied efficiently by LPG. Therefore, in this alternative LPG covers the need for the dryer and for the roaster. The resulting share between electricity from grid and LPG is displayed in Figure 12.

Figure 11. Power supply system layout, alternative 2.

39,5%

electricity from grid 60,5% LPG

Figure 12. Annual energy split between electricity from power grid and LPG.

6.4 Alternative 3 Alternative 3 is an HES alternative that relies on a theoretical 100 % solar energy during sunny days. During rainy days and times of low sun irradiation a power grid connection delivers the necessary back-up energy. In reality the solar power will only rarely be able to feed the plant solely, since the PV-plant is dimensioned according to the installed peak power (electric) of the processing plant without any oversizing. Due to dust, dirt and clouds the PV-power will regularly be partly reduced and thus fall short of supplying the machinery. With the solar thermic plant, the situation is different because this system can be expected to deliver enough energy during most situations. The system

140 The World Bank, 2001, p.61 141 LPG consist of propane and butane in a variety of mixes ranging from mostly propane to mostly butane (see: Wikipedia, 2017). World trade prices of propane have been taken from indexmundi, 2017. 142 AIT/FIA, 2014

43 is divided in the four main parts: PV-system, solar air-heating system, solar water- heating system and a power grid connection as presented in Figure 13. Due to the high temperatures needed for the roasting process, a standard solar heating system that can supply this heat has not been found. Consequently, the roaster is heated by electricity in this alternative. The solar electric installation consists of roughly 300 m² of high efficiency polycrystalline PV-arrays with a conversion performance of 16.7 %. The solar air heating is a solar process-heating system with 40 m² of absorbing surface, which heats an airflow passing through the system. For water heating a traditional solar system with 6 m² of flat thermic solar panels has been considered. Since warm water is primarily consumed during the standby night time, a buffer tank is used to store the energy. All together the acquired installation surface for the solar energy system makes out approx. 350 m². At some installation sites this surface might be a problem, but due to the uncertainty if, where and to which extent this could be the case, the aspect “surface requirement” has not been included as an evaluating criterion.

Figure 13. Power supply system layout, alternative 3.

The power split during full sun irradiation and with “clean” solar panels is displayed in Figure 14 below. The overall annual energy balance looks different due to the necessary running time of the back-up system (power grid). The annual energy split can be seen in Figure 15.

5%

21%

Solar electricity

Solar air heating

74% Solar water heating Figure 14. The shares of the different power generating systems during full sun irradiation and clean solar panels.

44

32,8% 39,3% Solar electricity

Solar air heating

Solar water heating Powergrid 24,2% 3,7%

Figure 15. The annual energy share of the different sub-systems of alternative 3.

6.5 Alternative 4 This is an HES alternative that relies, as alternative 3, on 100 % solar power during sunny days. The difference however, is that this alternative is an off-grid solution, which lacks a power grid connection. This puts extra requirements on the system to cover up for power drops due to cloudy weather, dirt and dust. This issue has been handled by three measures: over-dimensioned PV-surface, a battery storage and a backup diesel generator. Thus, the plant layout is divided in the five main parts: PV-system, solar air- heating system, solar water-heating system, battery storage and back-up generator as displayed in Figure 16.

Figure 16. Power supply system layout, alternative 4.

The size of the battery back-up has been dimensioned to be able to supply the necessary standby functions during non-productive night time. Further, the Li-ion battery storage is assumed to be enough to cover up for shadow interruptions on “sunny” days and thus being able to supply the plant without the back-up generator on these days. For the statistical “rainy” days the duration of shadow interruptions due to clouds are that dominating that a battery storage alone cannot bridge these power losses.143 On those days the system relies on the back-up diesel generator, which is designed to be

143 See Appendix VII and VIII for more information on the sun-time statistics, assumptions and calculations.

45 able to deliver sufficient power to feed the complete plant. Still, even under cloudy weather conditions, the solar plant will have a supporting effect. During a system optimization, it could be interesting to study this effect closer in order to optimize diesel generator running time and battery storage size. As mentioned, the off-grid solar power solution has been over-dimensioned in order to cover up for losses due to dust, aging and light cloud cover. The solar PV-system thus consists of roughly 600 m² of the same type of PV-arrays as in alternative 3. With a conversion performance of 16.7 %, the peak PV-power is over-dimensioned with a factor of roughly 3 related to the average demand. The air heating is a solar heating system with 40 m² of absorbing surface. As for alternative 3, the roaster is heated by electricity due to the temperature limitations of the solar air heating. For the water heating, the same system as in alternative 3, with 6 m² of flat thermic solar panels, has been used. The solar thermic systems have been dimensioned in a way that the average energy yield is enough to cover also statistical low-irradiation days. Both heating systems furthermore possess the possibility to use electrical heating for temporary power losses, although these systems are relatively insensitive towards such events. The water heating for example, stores the energy during the day-time to release it during night-time. The overall installation surface for the solar systems sum up to approx. 650 m². The supplied energy split between the different energy sources during full sun irradiation is displayed in Figure 14, whereas the annual overall energy balance is showed in Figure 17 below.

3,7% 2,6%

24,1%

Solar electricity Solar heat Solar water heating Backup 69,6%

Figure 17. The annual overall energy share of the different power supply systems of alternative 4, while considering the necessary back-up running time.

6.6 Alternative 5 The use of a continuous operating diesel generator is still a very common solution for off-grid applications with higher energy demands.144,145,146 Diesel is a worldwide well distributed fuel type and the diesel engine technology is robust and well known by mechanics all over the world. Furthermore, the layout and installation are relatively simple. Apart from the diesel generator itself this option also includes the extra fuel tank, to prolong the running time between re-filling intervals, and a battery storage. The battery storage is dimensioned to supply standby functions147 during non-productive

144 The expression “higher energy demands” is very vague and in this case, refers to off-grid applications with an installed power of more than 200 W. This could be remote tele- communication stations, power supply units for rural areas in developing countries or for secluded households. 145 Fleck & Huot, 2009 146 Gmünder et.al., 2010 147 Hot water circulation pump, lights etc. See Table AII-2 in Appendix II.

46 time (night-time) in order to eliminate the need of having the generator running around the clock. Figure 18 below shows the system components of alternative 5.

Figure 18. Power supply system layout, alternative 5.

In this scenario, we assume the use of standard fossil diesel fuel when it comes to pricing and environmental impact. The use of bio-diesel or straight vegetable oil (SVO) is possible but will not be considered since many generator suppliers withdraw the warranty under such operating conditions. Nevertheless, such an alternative fuel could be interesting under the right conditions and there are many private and organisational initiatives that promote the use of SVO as a fuel for diesel engines.148 In regard to the environmental impact of SVO, this relies heavily on the source and the process of extracting the oil itself.149 Thanks to a relatively high demand of heating energy within the process, the generator has been combined with an air-to-air heat exchanger. This helps to reduce the generator size and to improve the fairly low efficiency of the diesel generator.150 The resulting amount of energy from such a heat recovery under these circumstances can be seen in Figure 19.

23,4%

Diesel generator 76,6% Heat recovery

Figure 19. The share of heat recovery of the total power supply for alternative 5.

148 See also: Addision, n.d. 149 Gmünder et.al., 2010 150 The electric efficiency factor is around 40% at rated power output (DLG e.V., Prüfbericht 5664 F, 2007)

47

7 Consequences The power supply alternatives have been evaluated by describing their resulting consequences according to chosen attributes. As mentioned earlier, foremost in Section 3.2, the way to obtain the consequence values is not uncomplicated and includes assessments that depend on the decision context and the decision maker. Among others this concerns the reliability value of the off-grid solar powered alternative. Other values that incorporate assessments are the use of a start-up delay time in combination with power interruptions, the value of the OCC for the LCOE and the carbon footprint values etc. The use of a diesel back-up generator for the off-grid solar powered alternative does on one hand improve the reliability of this alternative but on the other hand degrades the carbon footprint consequence. Such two-sided options are helpful to keep in mind, since they may be sources of potential optimization. For the power grid + LPG alternative, the carbon footprint value is a combination of different values from different sources, which could possibly be assessed differently by a different decision maker. When it comes to the pricing of alternatives this partly relies on prices with fluctuations, such as fuel prices and the cost of grid-electricity. These and other issues influence the consequence values by introducing uncertainties. Such uncertainties may be examined in a sensitivity analysis in order to avoid a too narrow result. In this study such a sensitivity analysis has not been included. In the following sub-sections, the consequences of the power supply alternatives are presented according to attributes instead of according to alternatives. This allows a direct comparison of the alternatives in relation to each criterion. In the end of the section a summarizing table lists all alternatives with all their consequences.

7.1 Levelized Cost of Energy (LCOE) The consequence in regard to the attribute Levelized Cost of Energy, LCOE, depends on the amount of energy that an alternative is able to supply at a certain cost. In this study, the amount of energy is given by the energy demand by the SSCPP. This is valid also for solar powered alternatives even if they might generate more energy from which, however, not all can be used by the plant. The absence of a feed-in tariff for surplus solar PV-energy leaves this aspect aside in this study. The issue with such a feed-in tariff could change the outcome of a decision recommendation in the case that this would be available at a certain installation site. The LCOE is calculated based on the multiple inputs for: investment cost, annual cost and expected system lifetime. As mentioned in earlier sections there are certain estimations within these values. In the following sub-sections, the background values and assessment of these are explained more specifically for each alternative. In the end of the section the resulting LCOE of each alternative is presented.

7.1.1 Investment Cost The investment cost of the power supply alternatives describes a relatively wide span, with a roughly 10-fold difference between the cheapest and the most expensive alternative. As this power supply is dedicated for a defined purpose, namely a small- scale processing plant, the estimated cost of the processing plant151 itself would surely be interesting to know in relation to the cost of a power supply. For a real project the complete financial situation would be treated in a business plan, where the cost of

151 The total cost of the processing plant varies considerably depending on supplier, throughput and plant layout. For the mentioned throughput of 100 kg/h, the price ranges from roughly 350,000.- to 700,000.- Euro, with the lower valued being more dependent on manual operation. Manufacturer such as H-D-M or Bühler aims at extensive processing automation, which has an impact on price as well as on installed power. Source: BEAR Mühlen & Behälter, Bühler, CacaoCucina, Jafinox (DuyvisWiener), LADCO (H-D-M), PackInt, Tecno3.

48 energy would be one component. As this thesis deals with the initial selection of a power supply system we will not involve machinery and production costs at this stage. Also, since the investment cost is only one component within the LCOE value, the relation to the cost of the processing plant is less interesting than it would have been if investment cost would have been a criterion itself. Still, the investment size might have a greater impact when considering the initial financing of a project in terms of credit conditions. In Table 11 below, the investment cost for the different power supply alternatives have been listed. See also Table AV-1 in Appendix V.

Table 11. Investment cost for the power supply alternatives. Power supply alternative Investment [€] 1) 100% electricity from local power grid 25,200.00 1b) Power grid + back-up generator 43,403.00 2) Electricity from local power grid + gas (LPG) for drying and roasting 37,200.00 3) Electricity from local power grid + PV-panels + solar heat for drying 137,885.00 and hot water 4) PV-panels w. batteries + solar heat for drying and hot water + back- 254,888.00 up with generator 5) Generator (diesel) + heat recovery for drying 51,753.00

7.1.2 Annual Cost The annual cost is the second component of the LCOE value and consists of operating cost and maintenance cost. One exception from this concerns the solar PV-plants, where the cost of an inverter-change every 10 year has been integrated in the annual cost. For the off-grid solar system also a battery-change every 10 year has been spread out as an annual cost. This depends on the fact that this component alone would otherwise limit the system lifetime considerably. In regard to the range of the annual costs for the different power supply alternatives, the difference between the highest and the lowest cost is about 4-fold, with the solar powered off-grid solution being the most economical and the off-grid diesel generator being the most expensive one. Additionally, this difference depends on future fuel prices. The solar powered alternatives have been listed with a fixed annual rate for cleaning materials and maintenance work. Apart from cleaning the solar panel surfaces and in the case of the air heating system, the changing of filter, the solar units do not have any regularly operating costs.152 The operational cost for the back-up generator in alternative 4 is based on the total annual energy demand of the processing plant less the useful solar energy under consideration of the solar system failure rate. The resulting energy difference has then been used to calculate a generator running time, associated with an hourly fuel consumption, based on the nominal power of the generator. For the combination of solar power and a power grid connection, the energy demand of the processing plant has been balanced against the solar energy yield, with the power grid connection covering up for the difference. Also, for this case the failure rate of the PV-system has been incorporated. In Table 12 below the resulting values of the annual costs are presented for each alternative. Thus, the total annual cost of an alternative is simply an addition of the cost for the sub-systems in respect of their gross153 energy generation under consideration of their respective failure rate, LOLH. Due to the 1-shift operation mode, with a certain number

152 E-mail correspondence with Grammer Solar GmbH. 153 In the case of the LPG-powered sub-system a burner efficiency rate has been considered.

49 of standby days per year, the LOLH-value refers to the annual power supply downtime during production time. See also Section 3.2.3.

Table 12. Annual cost for the power supply alternatives. Power supply alternative Annual cost [€] 1) 100% electricity from local power grid 16,608.44 1b) Power grid + back-up generator 18,197.89 2) Electricity from local power grid + gas (LPG) for drying and roasting 12,192.36 3) Electricity from local power grid + PV-panels + solar heat for drying 6,841.46 and hot water 4) PV-panels w. batteries + solar heat for drying and hot water + back- 6,050.63 up with generator 5) Generator (diesel) + heat recovery for drying 21,328.44

7.1.3 System Lifetime As mentioned previously in Section 5.1.3, the used system lifetime for calculating the LCOE-value decides the accumulated amount of generated energy and costs. When considering the system lifetime, the power supply alternatives are relatively close to each other, with the exception of the diesel generator. This has an assessed lifespan of ten years during continuous operation. When it comes to the PV-based systems the weakest links are the Li-Ion batteries and the inverters.154 For these components, a lifespan of ten years has been applied, which with one replacement results in a total system lifetime of twenty years. This still reduces the overall system lifetime from some expected twenty-five years,155 which would be the expected lifetime of the other components. The cost for the battery-back-up and inverter renewal has been spread out over their expected lifetime of ten years and been included in the annual cost. In Table 13 below the assigned lifetimes are listed. The “plus”-sign indicates that a longer than indicated lifetime is more probable than a shorter.

154 Gevorkian, 2012 155 Castellano et.al., 2015, p.54

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Table 13. Lifetimes of different power supply systems. Energy source Lifetime [yrs] Electrical transformation station156 35+ Diesel generator157 10 LPG-installation158 20+ Solar heating159 20+ PV (grid connected w. inverter renewal) 20+ PV w. battery back-up (off-grid) 20+

7.1.4 LCOE values The resulting values for the LCOE of each alternative have been presented graphically in Figure 20 below (see also Table 15 in Section 7.4 for a summary of all consequences). The resulting consequence values range from 0.13 €/kWh to 0.24 €/kWh, where the combination of power grid electricity and an LPG-fired heating system turn out to deliver the cheapest energy over the system lifetime. The most expensive energy comes from the off-grid diesel generator with a heat exchanger.

LCOE €/kWh 0,25

0,20

0,15

0,10

0,05

0,00 10 h/260 days

1) 100% Power grid 1b) Power grid / back-up 2) Power grid / LPG 3) Power grid / Solar 4) Solar, off-grid 5) Diesel generator / Heat-exch.

Figure 20. LCOE values of alternatives.

7.2 Carbon Footprint of Energy (CFOE) The used values within this study originates from previous studies and published reports. As mentioned in Section 5.2, this approach includes the generation of energy as well as the manufacturing of system components. Even though the therefore

156 U.S. Department of Energy, 2012, p.20 157 Gmünder et.al., 2009 158 LPG UK, 2012, p.9, 72 159 Grammer Solar GmbH

51 necessary LCA approach is regulated by standards160,161 this depends strongly on the existence and quality of data and on made assumptions. Due to this, results may vary considerably.162,163 Consequently, the aim has been to use indicating values from one and the same study for all analysed energy sources. Unfortunately, this has not been possible and because of this, available values from different studies have been used. As an effort to compensate for this, used values have been related to each other by comparing values of the same energy source between different studies. Especially for LPG such a comparison has been substantial, since a common approach for this fuel is to only consider the release of CO2 during combustion. To deal with this issue, different information sources, with and without an LCA approach, for heating fuels have been related to each other. In order to adapt164 existing non-LCA values of LPG, the difference between LCA and non-LCA values for other fuels have been considered.165,166 At the same time, it should be noticed that the view on the environmental impact of LPG is controversial, not at least depending on the application,167 but also since it is partly a by-product during oil refinery.168 For HES alternatives with multiple fuels or sources, the respective CFOE values have been combined as described in Section 3.2.2. For the alternatives with a grid connection, a value based on the power generation mix, as this exist in Côte d´Ivoire, has been used. For the solar based energy alternatives: 3 and 4, the calculated expected back-up running time has been integrated in the combined value. In Table 14 the used values for different single energy sources have been listed (see also Appendix VI for the calculations of the different values). To calculate the combined value of a power supply alternative, the values in Table 14 have been used according to the shares of different energy sources. For example, if an alternative would consist of a 50/50-split between power grid and a diesel generator the combined CFOE value would also be a 50/50-split of the single CFOE-values. In Figure 21 the respectively combined values for the different power supply alternatives can be seen graphically. The numerical values can be seen in Table 15 in Section 7.4.

160 International Organization for Standardization, 2006a 161 International Organization for Standardization, 2006b 162 Ardente et.al., 2005, p.129 163 Lübbert, 2007, p.20 164 The adaption is not exclusively mathematical in this case, but rather estimated by referring to the differences between LCA- and non-LCA-values of other fuels. 165 See Table AVI-2 in Appendix VI. 166 Due to the similarity between the heating installation (burner) for the application within this study and a household heating, this conversion has been accepted as plausible. The final value is the median value of three corrections based on four different sources. 167 Johnson, n.d., p.4 168 Environmental Protection Agency, 1995, ch.1.5.1 p.1

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Table 14. The used CFOE value for different energy sources.

Energy source CFOE [kgCO2eqv./kWh] Electric power mix, Côte d´Ivoire 0.479 Diesel generator 1.584 LPG 0.322 Solar heating (air)169 0.020 Solar heating (water) 0.020 PV (grid connected) 0.064 PV (off-grid w. battery back-up) 0.090

CFOE kgCO2eqv./kWh 1,20

1,00

0,80

0,60

0,40

0,20

0,00 10 h/260 days

1) 100% Power grid 1b) Power grid / back-up 2) Power grid / LPG 3) Power grid / Solar 4) Solar, off-grid 5) Diesel generator / Heat-exch.

Figure 21. CFOE values of alternatives.

7.3 Loss of Load Hours (LOLH) To obtain a system LOLH-value for the HES alternatives, the expected solar system downtime170 has been assigned in slightly different ways depending on the back-up alternative. How this has been done for the different power supply alternatives is explained in Section 3.2.3. Generally, the approach with the strongest link covering up for weaker links has been used. For the power grid connection and generator listed values have been used. When it comes to the power grid LOLH, this has been adopted to the production time as explained in Section 3.2.3. Especially regarding the power grid connection, this value should be kept in focus during future evaluations, since there are various projects that work on improving the power supply reliability of African countries.171 It is to assume that this value will see a positive change within the next few

169 Since there are no values available for solar air heating the value for solar water heating has been used. The solar collectors are similar between the two systems and the absence of a glycol mixed fluid is assumed to rather improve than deteriorate the CO2-bilance. 170 “Downtime“ in this context refers to a lowered power generation of the PV-panels due to cloudy weather or component failure. 171 International Energy Agency, 2014

53 years. In Figure 22 below the assessed and calculated values for the LOLH of the different alternatives are displayed.

h/year LOLH 200,00 180,00 160,00 140,00 120,00 100,00 80,00 60,00 40,00 20,00 0,00 10 h/260 days

1) 100% Power grid 1b) Power grid / back-up 2) Power grid / LPG 3) Power grid / Solar 4) Solar, off-grid 5) Diesel generator / Heat-exch.

Figure 22. LOLH values for the alternatives.

7.4 Summary of Consequences As a summary, Table 15 represents a consequences table for the evaluated power supply alternatives. In Table 15 we can see that the alternatives 2 and 3 dominate alternative 1 and that alternative 4 dominates alternative 5. We will not remove any alternatives from the list at this place, since possible changes could change the domination situation. The evaluation is an iterating process where changing conditions can be considered by going back to previous steps. Furthermore, it might be of interest to obtain a complete order of the alternatives as an evaluation output instead of only one preferred alternative. However, in this very example the domination practically means that the alternatives 1 and 5 cannot become a best choice.

Table 15. Summarizing consequence table for the power supply alternatives.

Power supply alternative LCOE CFOE LOLH [kgCO2eqv./ [h] [€/kWh] kWh] 1) 100% electricity from local power 0.16 0.48 189 grid 1b) Power grid + back-up generator 0.17 0.53 65 2) Electricity from local power grid + 0.13 0.42 189 gas (LPG) for drying and roasting 3) Electricity from local power grid + PV-panels + solar heat for drying and 0.14 0.20 189 hot water 4) PV-panels w. batteries + solar heat for drying and hot water + back-up with 0.19 0.12 33 generator 5) Generator (diesel) + heat recovery 0.24 1.22 58 for drying

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8 Evaluation The evaluation process is influenced by the decision context,172 the chosen criteria173, the evaluation method and the decision maker´s preferences. Furthermore, we have to consider the planning horizon as an important factor, which could be implemented in the decision context formulation. The evaluation as a whole undergoes steps with normative influences, which are shaped by the decision maker´s preferences and useful to keep in mind in combination with a decision recommendation. As we start off (ideally) with descriptive data and information, these are being judged, evaluated and interpreted to finally end up as a decision recommendation for one specific case. In Figure 23 below, the way from a decision problem to a decision recommendation has been displayed schematically. In the figure, also a navigation guide for this thesis is included as already displayed in Figure 1 in Section 1. The decision background (decision problem) is visualized by the first dark blue box, in this case being a certain need of energy. The other dark blue boxes in the top row symbolizes the alternatives’ evaluation process, which are influenced by the method. The brighter coloured boxes account for steps that are influenced by the decision maker. In the scheme in Figure 23 the decision maker has an influence on the formulation of objectives, criteria and alternatives, which does not always have to be the case depending on the decision context.174 Other than the figure seem to suggest, the evaluation process is an iterative process where, for example, alternatives or criteria might later have to be added or changed and thus cause a retake.

Energy Normalized Overall Objectives Alternatives Consequences need Utility Values Utility

Criteria Utility Curve Weights

Section 4 Section 5 Section 6 Section 7 Section 8

Figure 23. Schematic representation of the evaluation process.

The method used within this thesis is a multi-criteria decision analysis approach, specifically an additive utility approach with swing weights.175 How this method works and has been used within this study is described in Section 3.1.2. The decision context is to provide a power supply system for a small production unit, in this case a small- scale cocoa processing plant (SSCPP), which is hypothetically situated in Côte d´Ivoire. The presented power supply alternatives are evaluated according to the objective’s hierarchy, as this has been visualized in Figure 6 in Section 5. This objective’s hierarchy, related to other power supply system evaluations, has led to the choice of the

172 Clemen & Reilly, 2014, p.54 173 Clemen & Reilly, 2014, p.53 174 In some decision problems, the decision maker might face pre-defined alternatives that cannot be replaced or extended by new ones, for example to turn right or left (or to turn around) at a Y- junction. It might also be that other decision conditions, for example legal regulations, influence and set boundaries for utility curves and weight setting. 175 Clemen & Reilly, 2014, p.720

55 evaluation criteria and attributes as described in Section 5. Due to the non-existing lifetime expectancy values for relevant processing equipment under the presumed conditions,176 the planning horizon has been oriented around the system lifetime of the power supply alternatives. This means that values that rely on the lifetime depend on different time periods depending on the power supply alternative. For example, a diesel generator that is running continuously has a significantly shorter expected lifetime than a power grid connection. This is a weakness, which has deliberately been accepted due to the mentioned circumstances and in order to enable a comparison of the obtained values with other studies.177 With the values linked to a lifespan of an SSCPP, they would be only valid for this very application. However, as mentioned in Section 5.1.3, an assessed SSCPP lifetime could make sense to apply, at least for a few preferred alternatives. The system lifetime (or the decision planning horizon) concerns the LCOE index. Depending on the HES in some alternatives, the description of the consequences for these alternatives have to reflect the energy split appropriately. Depending on the attribute and the alternative, the combination of consequences has been done in slightly different ways. To obtain a total consequence for the criteria LCOE and CFOE the respective values for the specific energy source have been multiplied with their percentage share of the total annual energy generation as has been explained in Section 3.2. This addition is meant to reflect the relative operating time of the multiple parallel systems. For the criteria LOLH the method of obtaining a common consequence depends on the energy sources, their respective stability and ability to cover up for a power loss caused by another system. See Section 3.2.3 for an explanation of this.

8.1 Utility values To evaluate and to compare the alternatives and their respective consequences, the consequence values have first been assessed a utility values as described in Section 3.1.2. This means that the consequence values are expressed on a utility scale, mostly ranging from 0 – 1. This helps when different alternatives with multiple characteristics are to be compared with each other. At the same time the assessment of utility values indicates a step from a descriptive consequence to a value with a normative distinction. Thus, the first step for the decision maker is to define what is good and what is bad. In this analysis, all three criteria attributes: LCOE, CFOE and LOLH have in common that a lower value is preferred to a higher value. During the assigning of utility values, a linear decreasing utility function has been used for all three attributes. Thus, the utility value decreases in proportion to an increase of the consequence value for all three criteria. This is motivated by the following arguments:

LCOE: An increase of the levelized cost of energy causes a proportional increase of the production cost of the SSCPP. An increase of the production cost causes a proportional decrease of the production profit. Thus, the utility value decreases linearly with an increase of the LCOE value.

176 It does exist recommended depreciation periods for similar machinery. However, the size and the installation conditions vary considerably from ”normal” operating conditions of such machinery. On one hand the wear of the machinery can be expected to be higher per produced kg at operating conditions as these are found in cocoa growing regions. On the other hand, this study presumes a one shift operation, which deviates from the traditional 3-shift operation. Furthermore, it can be expected that more extensive repairs are accepted before the machinery is considered to be expended. 177 A direct compare is still difficult and should be done with caution. Due to the applied principle of considering the amount of generated useful energy instead of total energy generation the conditions are slightly different than in many other studies.

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CFOE: A higher carbon footprint value denotes a worse environmental effect. As no emission limits or thresholds are known for this case, the utility value is considered to decrease linearly with a higher CFOE value.

LOLH: The loss of load hours can directly be translated into a loss of production, which generally causes a linear loss of profit. One exception from this, which is worth to mention, is in the case of a delay related to a delivery contract. If this would occur due to a power blackout, the financial loss would be considerably higher than just the value of the shortage in product. Consequently, the assessed utility curve of the LOLH in such a case would be different. However, this cannot be generalized according to a certain loss of load time. Instead this is related to when a power shortage occurs in relation with a certain contract. This cannot be accounted for with the utility curve without detailed knowledge of reoccurring contracts. Therefore, the hypothetical decision maker is assumed to be equally concerned about equal differences at different intervals. This means, that each increase of the loss of load time causes the same proportional decrease of the utility value.

Since the descriptive background data is being re-scaled during the transformation into utility values, it is important to keep the original values in mind in order to avoid misinterpretations and mistakes during the following weight assessment.178 As argued above, a linear utility function has been used for all criteria consequences. Thus, the utility values have been calculated by using Equation 1 (see Section 3.1.2). In combination with the above statements concerning the utility functions, the attitude of the hypothetical decision maker, towards the attributes and consequences can be understood. However, a strictly limited budget, CO2-emission regulations, production cost subventions or similar could influence his or her attitude towards the consequences and thus result in other weight coefficients which would affect the outcome of the evaluation. The outcome of the transformation into utility values is represented by the utilities; LCOE´, CFOE´ and LOLH´. The resulting values for the different alternatives can be seen in Table 16. Note, that in Table 16, a high utility value is preferred to a low value, which is the opposite to the actual consequence values in Table 15, where a low value is preferred to a high.

178 See also: Keeney, 1996, p.147

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Table 16. Utility values of the power supply alternatives.

Power supply alternative LCOE´ CFOE´ LOLH´ 1) 100% electricity from local power 0.69 0.67 0.00 grid 1b) Power grid + back-up generator 0.58 0.62 0.79 2) Electricity from local power grid + 1.00 0.73 0.00 gas (LPG) for drying and roasting 3) Electricity from local power grid + PV-panels + solar heat for drying and 0.90 0.92 0.00 hot water 4) PV-panels w. batteries + solar heat for drying and hot water + back-up with 0.43 1.00 1.00 generator 5) Generator (diesel) + heat recovery 0.00 0.00 0.84 for drying

For the further evaluation, the utility values are put together according to the additive utility function, as explained in Section 3.1.2, to form an overall utility for each alternative. To obtain this total score the utility values are multiplied with a weight factor and summarized according to Equation 2 (see Section 3.1.2).

8.2 Weight assessment As mentioned in Section 3.1.2, when assessing the weight coefficients of the consequences it is important to consider the actual consequence values and the difference between the best and the worst outcome. The weight assessment is one of the subjective actions within the evaluation that is performed according to the decision maker´s preferences. Depending on the attitude of the decision maker and the conditions, the weight assessment and following results may vary considerably. Due to the fact that a real decision maker is missing in this particular case, the assessment of the utilized weight coefficients has been performed with hypothetical preferences according to the swing weighting method. The used preferences have a relatively single- track character, which is intended to enable a transparent and consequent motivation for made assessments. See also Appendix III. As a summarizing reflection of the existing background conditions, Table 17 lists the used attributes and the range between the maximum and the minimum consequence.

Table 17. Consequences value-ranges. Attribute Range LCOE 0.13 – 0.24 €/kWh CFOE 0.12 – 1.22 kgCO2eqv./kWh LOLH 33 – 189 h/a

8.2.1 Swing weighting When assessing the weight factors according to the swing weighting method179 the value ranges of the consequences, as listed in Table 17, must be considered. In this case, it turns out that the attribute with the smallest relative consequence difference is the LCOE, with the other two having a difference of more than 300 % between the worst and the best consequence value. The CFOE index is the one with the biggest span,

179 Clemen & Reilly, 2014, p.731

58 ranging from 0.11 to 1.22 kgCO2Eq./kWh. In this listing the consequence ranges have only been related to the power supply system without considering the production unit, which could be seen as a weakness. It could make sense to put these values in relation to the complete SSCPP as well, since this could influence the importance of a specific attribute in the overall view. As an example, the LCOE value could become more or less important depending on the overall production cost. As mentioned in Section 7.1.1, such considerations are being left out in this study, but would find their place in a real project´s business plan. Before the actual swing weighting can begin, the hypothetical swing alternatives are ordered according to a priority list by the decision maker. Since this study is based on an example without a real case and a real decision maker, the decision maker´s preferences have been shaped hypothetically. To do this in a way that it can be discussed rationally and transparently the profit maximization hypothesis, when it comes to the goals of a Firm, has been applied.180 This has resulted in a rather financially oriented decision approach. See also Appendix III for an explanation of the used hypothetical preferences and the motivation behind the priority setting and following rating. The resulting priority list looks as follows:

Cost (LCOE) Reliability (LOLH) Environmental impact (CFOE)

The further process is performed as described in Section 3.1.2. The ranking order of the attributes is set as shown in column “Rank” in Table 18. After having filled out the ranking column, the rate values are assessed.

Table 18. Swing weighting assessment table. Swinging Consequences Rank Rate attribute Benchmark 0.24 €/kWh / 1.22 kgCO2eqv./kWh / 189 h 4 0 (all worst) LCOE 0.13 €/kWh / 1.22 kgCO2eqv./kWh / 189 h 1 100 CFOE 0.24 €/kWh / 0.12 kgCO2eqv./kWh / 189 h 3 20 LOLH 0.24 €/kWh / 1.22 kgCO2eqv./kWh / 33 h 2 45

See also Appendix III for a motivation of the assessed rating values. How the swing weighting method is performed is explained in Section 3.1.2.1. The assessment of rates according to the swing weighting method is explained in Section 3.1.2. The LOLH has been given a slightly higher value than what a strict financial comparison would represent. This is defended by the circumstance that each power loss contains the risk of unexpected events that could cause more problems than just a production loss related to downtime. Furthermore, as mentioned previously, the selling value of not processed beans is very insecure. Concerning the environmental impact, which is very hard to value, this has been valued relatively low. This can be explained by the used hypothetical preferences, which has a strong financial focus. An environmental index like the CFOE is difficult to replace by a financial value. In this case the emissions trading value181 on the international stock market has been used to value the CFOE differences, which has resulted in a relatively low impact of this index despite the great

180 Keat et.al., 2014, p.49 181 Finanzen.net, 2017

59 differences between consequences. However, the possible positive effect of a “greener” image with lower CO2-emissions justifies the index to be included in the evaluation. Even if moral aspects have been left out in this evaluation it is likely that a real case decision maker would prioritize and rate the CFOE index differently for a similar range of consequences. In this study the hypothetical preferences reflect an idea to compare renewable energy sources with traditional power generation technologies with a “business as usual” approach.182 The resulting assessed ratings can be seen in the column “Rate” in Table 18. To convert the rating values and calculate the resulting weights, Equation 14 has been used:

푘 = Eq. 3 ∑() where kx is the weight factor for criteria x and rx is the rated value for criteria x, with a total of n criteria. The resulting weights are as follows:

kLCOE =0.61; kCFOE =0.12; kLOLH = 0.27

8.3 Overall utility The overall utility represents the combined utility of all consequences for an alternative. To calculate the overall utility, the utilities have been combined as described in Equation 2 in Section 3.1.2. As mentioned in the introduction of Section 8, this study uses the swing weighting method to assess the weight coefficients. By applying the consequence utility values in Table 16 and Equation 2, the overall utility of the evaluated alternatives has been calculated. The resulting values can be seen in Table 19. The result is that a power grid connection with LPG-fired heating applications is favoured. The second- best alternative is almost a tie between the alternatives, 1b and 3; power grid connection with a backup generator and power grid connection with solar-power. For off-grid conditions, consequently the solar powered alternative is to prefer to a diesel generator. The displayed results in Table 19 confirm the mentioned dominance in relation with Table 15 in Section 7.4. In Figure 24 the overall utility is presented as a graph.

Table 19. The overall utility for the alternatives with weight coefficients assessed according to the swing weighting method. Power supply alternative Overall utility 1) 100% power grid 0.51 1b) Power grid + back-up generator 0.64 2) Power grid + LPG 0.70 3) Solar + power grid 0.63 4) Solar + Battery + Generator 0.59 5) Diesel Generator 0.23

182 Even though this “business as usual” is likely to include more emissions-regulations in the coming future.

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Overall utility 1,00 0,90 0,80 0,70 0,60 0,50 0,40 0,30 0,20 0,10 0,00 10 h/260 days 1) 100% Power grid 1b) Power grid / back-up 2) Power grid / LPG 3) Power grid / Solar 4) Solar, off-grid 5) Diesel generator / Heat-exch.

Figure 24. The resulting overall utility.

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9 Production scenarios In the evaluation in Section 8 the annual production time is set to 10 h per day during a 5-day working week. This results in 260 working days per year or 2,600 operating hours per year. As large-scale cocoa processing plants generally operate at a 24 h/day-pace with a 7-day week, the above mentioned operating hours appear modest. The used, shorter production time can therefore be seen as an example rather than as a strict production rate setting. For an SSCPP, an around-the-clock production may not be the most suitable production rate, but it certainly makes sense to verify how the evaluation results changes as the annual operating time increases. In order to get an impression of how an increasing operating time influences the analysis outcome, two further production scenarios have been evaluated. The two scenarios are based on a 6-day week with 12 h resp. 24 h of daily operating time.

9.1 Scenario evaluation The evaluation of the two additional production scenarios has been made with the same approach as the “original” scenario with a production time of 10 h per day. Two minor exceptions from this concerns the calculations of the solar energy yield for the alternatives 3 and 4, and for the assessment of the LOLH value. With a daily production rate of 10 h and the constant daytime of 12 h in Côte d´Ivoire183, the two extra daytime hours have been used as battery charging-time in alternative 4.184 For the additional production scenarios, with longer production days, this battery charging opportunity does not persist since there is no daylight left beyond the production time.185 This leads to an increase of the energy share from the backup system. For a 24 h/day production rate, the night-time production is completely supplied by the backup power system. In regard to the LOLH, the difference concerns all alternatives. For the original 10 h/day-scenario, the LOLH for the off-grid solar alternative (alternative 4) is based on a statistical failure rate of the backup generator for a “backup operating mode”. The logic behind this is that the PV-system, in alternative 4, covers up most of the energy need and the generator really functions as a backup with possible longer still-stands and short running times. As the production time increases, the diesel generator needs to supply more energy. For the 12 h/day-scenario the calculated average daily energy yield of the PV-system is enough to supply the production completely during 4 months of the year. During these 4 months, the failure rate of the generator has been based on values for the mentioned backup operating mode. During the rest 8 months of the year, the failure rate is based on values for a continuous operating generator due to the higher demand of “backup”-energy and thus, longer monthly running time. For the 24 h/day-production scenario the solar energy yield of alternative 4 cannot cover up the energy need of the night-time production, which means that this is supplied by the backup generator. This leads to longer and regular running times of the generator and thus the failure rate is based on a continuous operating mode of the backup generator during the complete year. When it comes to the Ivorian power grid the LOLH is based on statistics related to the number of power outages per year. For the evaluation of the additional production scenarios this has led to the same LOLH for the 12 h/day scenario as well as for the 24 h/day scenario. This seems unfortunate at the first glance, but may not be that misleading, since power outages occur due to overloads of the power distribution grid. During night-time the electric demand is lower, which could be assumed to lower the

183 Weather Atlas, 2018 184 In Côte d´Ivoire, close to the equator, the daytime is more or less constant with 12 h around the year. 185 The production time has been arranged symmetric in relation to high noon.

62 risk for overload. Another difference, related to the original scenario, is the reduction of standby days due to the increase of the annual production days to 300 days/year. As the new scenarios introduce new conditions, the consequences and their ranges change. Apart from the need to make new utility assessments, this also means that the previously assessed weight coefficients cannot be used. The assessment of the new weight coefficients for the additional production scenarios have been made with the swing weighting method by applying the same hypothetical preferences as has been used in Section 8.2.1. See also appendix IX.

9.2 Production scenario results The impact of a change in the annual production time can be observed for all evaluation attributes. In Figure 30, the effect on the LCOE-value has been visualized with a graph. For example, we can see that solar based alternatives are favoured by the longer day- time production. Also, beyond the day-time production, the combined LCOE for the solar-based alternatives remains lower than for the 10 h/day scenario. This, despite that the more expensive energy from the backup systems are needed to a greater extent. This can be explained by the greater capacity utilization of the solar energy due to fewer standby days and a full energy demand during the entire day-time.

Impact on LCOE by annual production time €/kWh 0,25

0,20

0,15

0,10

0,05

0,00 10 h/260 days 12 h/300 days 24 h/300 days 1) 100% Power grid 1b) Power grid / back-up 2) Power grid / LPG 3) Power grid / Solar 4) Solar, off-grid 5) Diesel generator / Heat-exch.

Figure 25. The impact on the LCOE-values by the production time.

As mentioned above, the resulting overall utilities of the alternatives, given the additional scenarios, have been calculated with different, scenario-specific weight coefficients. The assigned weight coefficients are noted within parenthesis above the utility columns. The swing weighting procedure for the two scenarios is presented in appendix IX. The resulting utilities for the 12 h/day production time can be seen in Table 20, whereas Table 21 shows the results for a 24 h/day production time.

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Table 20. Utilities for a 6-day production week with 12 operating hours per day. Utility value Power supply alternative LCOE´ CFOE´ LOLH´ Overall Utility (0.66) (0.07) (0.27) (Swing weighting) 1) 100% power grid 0.62 0.70 0.00 0.46 1b) Power grid + 0.53 0.65 0.88 0.63 back-up generator 2) Power grid + LPG 0.96 0.76 0.00 0.69 3) Solar + power 1.00 0.97 0.00 0.73 grid 4) Solar + Battery + 0.63 1.00 1.00 0.76 Generator 5) Diesel Generator 0.00 0.00 0.91 0.24

Table 21. Utilities for a 6-day production week with 24 operating hours per day. Utility-value Power supply alternative LCOE´ CFOE´ LOLH´ Overall Utility (0.80) (0.04) (0.16) (Swing weighting) 1) 100% power grid 0.67 0.83 0.00 0.57 1b) Power grid + 0.63 0.79 0.96 0.69 back-up generator 2) Power grid + 1.00 0.91 0.00 0.84 LPG 3) Solar + power 0.85 1.00 0.00 0.72 grid 4) Solar + Battery + 0.50 0.42 1.00 0.58 Generator 5) Diesel Generator 0.00 0.00 1.00 0.12

As can be seen in Table 20, the increased daily operating time favours the off-grid solar powered HES. As the production time is extended beyond the daylight hours, the best choice turns out to be the same as for the previous evaluation with a 10 h/day production time. Common for both cases is the second placed alternative, which is the power grid and solar combination. The biggest single difference can be found in the environmental utility-value of the solar off-grid alternative. During a 24 h/day production, the utility value of the environmental consequence drops considerably. This can be explained by the long annual running time of the diesel generator, which has to cover the energy demand for the night-time production. In Figure 26 the variation of the overall utility in regard to the annual production time can be seen graphically. Note, that the displayed overall utilities are based on different weight coefficients, but on the same preferences.

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Overall utility related to annual production time 1,00 0,90 0,80 0,70 0,60 0,50 0,40 0,30 0,20 0,10 0,00 10 h/260 days 12 h/300 days 24 h/300 days 1) 100% Power grid 1b) Power grid / back-up 2) Power grid / LPG 3) Power grid / Solar 4) Solar, off-grid 5) Diesel generator / Heat-exch.

Figure 26. The overall utility for different annual production rates.

The results of the evaluation of different production scenarios show that the choice of power supply is sensitive towards the annual production time. This sensitivity most of all concerns the solar powered alternatives. As can be seen in Figure 26, the solar powered alternatives 3 and 4, both gain in the overall rating for the 12 h/day production scenario. This can be related to the increased amount of useful energy since a 12 h/day (daytime) production assures that the generated solar energy is consumed during all daytime hours. As the daily production time increases to become 24 h/day, a bigger share of the needed energy, in alternative 3 and 4, must be supplied by other means than solar energy, which causes an overall utility rating similar to the 10 h/day production scenario.

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10 Sources of uncertainty The evaluation results, as presented in Table 19, include different uncertainties. Values such as diesel cost, the cost of LPG or electricity are typical inputs that are exposed to price changes that can have a deciding impact on the decision recommendation. But, the variation of numerical values is not the only uncertainty influencing the analysis results. There are different ways to categorize and describe uncertainties. French et.al. have described uncertainties related to three different origins: 1) the decision maker´s uncertainty about which of a selection of known events that will happen, 2) the decision maker´s uncertainty about what factors are important and 3) the uncertainty in numerical input values.186 Here we have chosen to use the expressions: internal uncertainty and external uncertainty, as used by Durbach and Stewart.187 The internal uncertainty refers to analysis-related factors, such as the method, made assumptions and decision maker preferences when modelling the decision problem. The external uncertainty refers to the input data, which may contain statistical data, assumed values or continuously changing values. To deal with mentioned uncertainties, a sensitivity analysis can be performed in order to analyse how certain changes affect the results. Here we settle with describing the existing uncertainties and leave the sensitivity analysis for future works.

10.1 Internal uncertainties In this particular study, internal uncertainties exist in made assumptions and method- related issues. Apart from assumptions in the context of numerical inputs, there are also assumptions related to the evaluation. The start-up delay time affects HES with a back- up generator in that each start is delayed by a defined start-up time. This time consequently influences the LOLH of the concerned alternatives. The use of an imaginary decision maker introduces “artificial” preferences, that may oversee important priorities. For example, if the decision maker would represent a development aid organization, the environmental consequences could be rated higher during the swing weighting process. But, also in general the decision maker introduces human- related uncertainties when it comes to knowledge and experience of the decision context. Such uncertainties might concern the decision modelling, made assumptions and preferences. Differences between MCDA methods can cause differences in the analysis results. In the study at hand, MAUT with an additive utility function has been used to describe the evaluated alternatives with an overall utility.

10.2 External uncertainties Within the processed input data, which represents the basis for this multi-criteria analysis, there are a number of uncertain values. These uncertain values have different characters and can either be dynamic, in the way that they continuously change over time (for example fuel prices), or they might be uncertain fixed values, with something turning out differently than expected (for example the assessed failure rate of the Ivorian power grid). There are different ways to analyse the impact of such uncertainties, all with the common goal to show how changes in the numerical inputs cause changes in the consequences, and ultimately in the decision recommendation. One option to analyse this impact is to run Monte Carlo simulations, where the input data can be assigned a probability distribution. In this study, there are more or less reliable statistical data mixed with less exact numerical values, partly based on assumptions. The numerical inputs that have been used either directly or indirectly to calculate the consequences according to the used

186 French et.al., 2009, p.220 187 Durbach & Stewart, 2012

66 criteria are presented in Table 22 with a brief description of their background and characteristics. An explanation of the criteria can be found in Table 9 in Section 5.

Table 22. Numerical input characteristics.

Input value Characteristics Investment Fixed value, partly based on hard data (quotation), partly on cost assumptions (installation). Annual cost Dynamic value, which depends more or less strongly on energy prices for electricity or fuel cost. Carbon Fixed value, which is based on hard data and assumptions. Footprint Assumptions occur as well within the assessment (LCA analysis) as through the selection of data to be used (selection among multiple sources). Power Fixed value that partly depends on statistics (back-up generator, outage time power grid) and partly on assumptions and interpretations on how to use the statistics (solar power, generator failure rate). Lifetime Fixed value relying on statistics/experience-based assumptions.

When looking at the above listings there is seemingly only one of the inputs that has a dynamic character: annual cost, which is an input value of the LCOE-index. This however, does not mean that this is the only value, that incorporates uncertainty. For example, the solar energy yield depends on the actual weather, whereas the value has its origin in statistical weather data. Due to HES layouts, all attributes for solar based alternatives are influenced by the solar energy yield. This derives from the fact that a lowered solar energy supply, leads to an increased energy supply from other energy sub- systems. Another dynamic value is the Euro to US Dollar exchange rate. Due to the use of prices that are partly expressed in US Dollar, combined with calculations based on Euro, the exchange rate influences the outcome of the LCOE value. Uncertainties of a fixed or static kind, where something might turn out differently than expected but without a continuous changing, can be found in the system lifetime or the installation cost. The information lying behind the system lifetime, as part of the LCOE calculation, comes from different sources, and the resulting value is expressed as a “best guess” rather than as a hard-statistical fact. The lack of statistical data, combined with information referring to components that do not exactly match the investigated type, makes the used lifetime value uncertain. For example, the power transformation unit for a power grid connection is indicated with a lifetime of 35 years, based on information from the US Department of Energy.188 However, in the same report, transformation units are mentioned that have an age of 50 years or more and that are still active. Similarly, the assessed installation costs of the power supply equipment contain uncertainties related to contractors and installation site conditions. Another, less obvious uncertain input is the OCC-value (opportunity cost of capital), which is used to calculate the LCOE. The OCC depends on factors such as business sector and region. In this context, also the inflation rate has an impact on the calculations. When it comes to the LOLH particularly the used value for the Ivorian power grid is associated with uncertainties. This is due to development plans that aim on improving and expanding the national power grid within the coming years, mixed with a growing energy demand.189 Additionally, the used underlying information to assess the LOLH of the power grid is an undifferentiated value, which indicate a number of power outages per

188 U.S. Department of Energy, 2012 189 International Energy Agency, 2014, p.83

67 year. This value is undifferentiated in the sense that information about when, during the day, these power outages occur is lacking. This makes it difficult to apply this information for the evaluation of different production scenarios that might have the same number of production days during the year, but during different times of the day. Concluding, external uncertainties can be found in:  Cost of electricity.  Cost of diesel.  Cost of LPG.  Weather (average monthly solar irradiation).  Euro to US Dollar exchange rate.  Lifetime.  Installation cost.  OCC.  Inflation rate.  LOLH of the Ivorian power grid. For a sensitivity analysis these input values would be the ones of interest to variate to analyse their respective impact on the evaluation results.

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11 Discussion

Even if a real decision maker is missing in this study, the structure that has been used exemplifies how MCDA could be used for the selection of a power supply system. Depending on what kind of decision making process a MCDA approach would replace, the advantages of the proposed method differ. If the power supply would otherwise simply be chosen by intuition or by using an existent power grid, without considering other options, the MCDA first of all brings the advantage of a wider perspective. The advantage starts already in the handling of the decision problem by acquiring a more engaged consideration of the power supply issue. The need of relevant data for the evaluation and the necessary work to get these data increases the awareness of the decision problem. The result of a more extensive research might be that new alternatives are created or that machinery is purchased or constructed to use more suitable energy sources. As has been exemplified in this study, MCDA encourages a systematic review and evaluation of different alternatives. This process could be even more powerful if it is combined with an optimization step to find an optimum combination under a wide variety of possibilities, which could be especially important when it comes to the use of renewable energy concepts. The iterative approach furthermore enables to include new alternatives or combinations and re-evaluate the consequences after a preceding iteration. When it comes to the results of this present study, there are different issues to reflect upon. For example, when a lifetime cost of a power supply system is acquired, there are proposals of integrating the loss of revenue, due to power failures, in the calculation of this lifetime cost.190 For national power grids such figures are available through the World Bank as national averages.191 Within this thesis, the loss of revenue due to power outage has not been included as a cost, but it has influenced the swing weighting assessment through the hypothetical preferences. This comes from the fact that the difference between the highest and lowest LOLH consequences have been re-calculated as a relative annual production loss, which has affected the weight assessment. As the used hypothetical preferences have been highly financially oriented the revenue loss due to power outages has been related to the total cost of energy during the weight assessment. An integration of the revenue loss in the lifetime cost would bring the advantage that all power supply LCOE´s would suffer to the same extent by increasing LOLH values. Within this analysis this is not the case, since not all alternatives have a backup system. For alternatives with a backup, the LOLH means backup running time, which means more expensive energy.192 In those cases, an increased LOLH-value, leads to an increased LCOE. However, in the case of the power grid connection without a backup generator, the LCOE is insensitive against the LOLH-value. Consequently, the combination of a power grid connection and a backup generator is “punished” for higher reliability, seen out of the LCOE-perspective. For a future analysis, it would be interesting to reduce this dependence between LOLH and LCOE. A change to apply the LOLE instead of LOLH could be a possibility even though the LOLH remains important to calculate the backup system running time. The cost of the backup-power is one further aspect, which could be valuable to consider under the aspect of the total plant feasibility. Does the backup power payoff or is it under certain circumstances too expensive? The cost of the backup power could pose another perspective to value the LOLH of main power supply alternatives without an “integrated” backup. In the study at hand, the power grid connection combined with

190 Pregelj et.al., 2001, p.1 191 The World Bank, 2016 192 This, because the backup-energy supply is commonly more expensive than the primal energy supply.

69 a backup generator (alternative 1b) beats the sole power grid connection (alternative 1) in all scenarios. But, the power grid connection combined with LPG-heating and no backup (alternative 2) beats the power grid with backup (alternative 1b) in all scenarios. In the comparison between alternative 1 and 1b, we can note that the LCOE increase of 1 cent and the slightly higher CFOE is compensated for by the higher reliability (Table 15 in Section 7.4). The latter example, of alternative 2 and 1b, tells us that an LCOE increase of 4 cents, combined with a higher CFOE, cannot be compensated by the higher reliability that the backup generator offers (Table 15 in Section 7.4). This is only valid for this case and the used preferences, but such an analyse of the results can assist in developing new alternatives for an iteration process. As mentioned in Section 3.2.4 an optimization iteration is missing within this thesis. This would be an interesting extension of the preceding analysis. The combination of an analytic selection process and a following optimization could help to create tailor- made solutions for a vast variation of applications. Especially for the use of renewable energy sources, such an approach could help to use valuable resources in a more efficient manner, such as differentiating between heating and electrical energy. This would possibly require a more accurate approach for solar power by using hourly mean solar radiation. In the evaluation of different production scenarios, we can notice that the evaluated solar powered alternatives are sensitive towards daily production time. An interesting remark is that the combination of solar power and a power grid connection (alternative 3) is favoured by longer production days. This, despite the fact that the PV- plant is under-dimensioned already for the original scenario, in terms of the average amount of supplied energy. The reason can be traced down to the LCOE, which decreases as the annual (daily) energy consumption of the plant increases. This is an issue that could be clarified in the context of an optimization analysis. For future works, also a sensitivity analysis should be included to discover further critical issues that may affect the ranking order of alternatives. The evaluation of different production scenarios is a first step in this direction, but it lacks the influences of other uncertainties. In the case of power installations in rural areas, the local knowledge and surrounding conditions play a great role. These circumstances call for an evaluation objective that deal with the handling of a system. In this context, the installation conditions may be integrated, for example in regard to the required “shadow-free” surface for solar panels or the distance to the closest electric power line. Furthermore, in times of climate changes, a risk analysis in terms of resistance against climate influences will grow more important in the future to come.

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Acknowledgements

This Master thesis has been composed within the Master Program in Decision, Risk and Policy Analysis at the Faculty of Engineering and Sustainable Development at the University of Gävle, Sweden. Initially I would like to thank my supervisor at the University of Gävle, Fredrik Bökman, for the feedback and assistance during the subject formulation and the invaluable support during the finalization phase. My appreciation and thanks also go to those in the cocoa industry that have supported me with feedback and a differentiated real-life view on the theory of small-scale cocoa processing in cocoa growing countries. Especially I would like to thank Dr. Jean-Marc Anga at ICCO for a helpful telephone interview and Sona Ebai at Tree Global for an informative talk about possible issues with a local small-scale cocoa processing. The same goes to Alan Beales at ForestFinest and Henk Nengerman at PUM. Last but not least I would like to thank my wife for the patient support during my work on this thesis, my mother in-law for correction reading and for the supportive child-care and my “uncle in-law”, Florian Jones for control reading.

Berlin, November 2017

Alexander Rothoff

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Interviews Dr. Anga, Jean-Marc, Executive Director at the International Cocoa Organization, telephone interview 20.07.15 Beales, Alan, Senior Consultant at ForestFinest Consulting GmbH, Skype interview 16.11.15 Ebai, Sona, West Africa Regional Representative at Tree Global, previously Chief of Party at World Cocoa Foundation, Skype interview 23.11.15 Nengerman, Henk, Senior Consultant at PUM, telephone interview 24.11.15

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Appendix I - Fact sheet “Cocoa liquor mini-plant”

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Appendix II - Energy needs of machinery Table AII-1. Installed nominal power of machinery, split on energy requirements.

Machinery Electric power [kW] Possible heat power [kW] Electric Cooled water Cooled air Hot air Hot air Hot water drives (<10°C) (<20°C) (>350°C) (<100°C) (<100°C)

Chiller Chiller (Heating (Heating (Heating power! power! power) power) power) Dryer* 2.00 10.50 Feeder1 1.10 Winnower2 1.60 Feeder1 1.10 Roaster2 1.00 11.00 Cooler2 0.50 Feeder1 1.10 Pre-milling2 5.50 Intermediate milling2 2.20 Pump3 0.25 Fine milling2 4.00 Pump3 0.25 Stirring tank2 0.55 Pump3 0.25 Batch temperer2 0.67 8.00 2.00 Blocking/Storage2 2.00 Piping (heating) ** 0.12 0.50 Lights (LED) 2 0.08 Total 22.27 8.00 2.00 11.00 10.50 2.50 *see Box AII-1 **see Box AII-2 Table AII-2. Average requirement on electric power, including roaster burner, electric chillers and warm water during production time and standby.

Machinery Installed Load Average electric Hot water Standby electric coeff. consumption need during 1,2,6 power during production production Electricity Water [kW] [%] [kWh/h] [kWh/h] [kWh/h] [kWh/h] Dryer 2.00 100 2.0 0.00 Feeder, pumps 4.05 75 3.04 0.12 Winnower, pre-grinder 7.10 100 7.10 0.00 Intermediate milling, 7.54 75 5.66 0.66 0.83 0.50 Tank, Tempering Chillers 3.30 100 3.30 0.66 Roaster, drives 1.50 75 1.13 0.00 Roaster, burner 11.00 65 7.15 0.00 Lights, LED 0.08 100 0.08 0.03 Total 36.57 29.46 0.66 1.63 0.50

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Box AII-1. Energy requirement for beans drying process.

Energy consumption: Drying Condensation of approx. 3kg Water/h => 2 kWh principle: Heating approx. 8 kg of water from 30°C to 70°C Product Air => 0.4 kWh Heating of approx. 112kg beans from 30°C to 70°C 30 60 Inlet product / => 2.5 kWh Outlet air [°C] Heating recirc. air, 575 kg* from 60°C to 80°C 70 80 Outlet product / => 3.2 kWh/h Inlet air [°C] Heating fresh air, 575 kg* from 30°C to 80°C => 7.1 kWh/h (50% recirculation of air) 35 Surrounding air temp. [°C] Heating need = 10.5 kWh/h (electric heating) 120 1138 Throughput [kg/h]

η=max. 72% with burner (source: Stela Laxhuber GmbH / 8.4 Water content [kg] DLG Prüfbericht 6263 F) Heating need = 14.6 kWh/h (LPG burner) 6.3 6.3 Energy consumption [kW] *Airflow: Calculated with an absorption of moisture(A) of approx. 3.2 Heating 15 g/m³ and the necessary heat energy(B). recirculated air (50%) [kW] A) Drying from 7% to 4% => 250 – 300 m³/h 7.1 Heating fresh air (50%) [kW] B) Heat energy from air, cooling from 80°C to 60°C 10.3 kWh Total energy => 1,150m³/h demand

Box AII-2. Energy requirement for heating jacketed product piping.

Jacketed piping Øa Length: approx. 10 m / Øi = 32 mm, Øa =50 mm Øi

Surface, Product-Heating medium: Øi*π*l = 1.00 m² 40°C

Surface, Heating medium-surrounding air: Øa*π*l = 1.57 m² Max. need during production stillstand: Product temp. min. 40°C 50°C (-> Water temp. min. 50°C) 15°C Surrounding air temp. min.15°C Cooling of product due to air circulation: used air circulation6: 0.2 m/s Applied air layer thickness as active heat transfer component: 5 mm => Air circulation: 1.57 * 0.005 /(10/0.2) * 3,600 = 0.57 m³/h Heat losses during heat transfer: Heating water – piping material - Air: Heat transfer coefficient (Water-Stainless steel-Air) 7: 0,0073 kW/m²K ∆T (heating water – surrounding air) = 50-15 = 35K

P = 0.0073 * 1.57 * 35 = 0.40kW Sources: 1) LAU Förderanlagen GmbH & Co 2) BEAR Mühlen & Behälter GmbH 3) SIVAG GesmbH 4) www.haus-bau-planung.de/lexikon, “luftbewegung” 5) www.engineeringtoolbox.com, ”heat-transfer-coefficients” 6) Motor Challenge, A program of the U.S. Department of Energy, Fact Sheet: Determining Electric Motor Load and Efficiency.

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Appendix III - Hypothetical decision maker

As this study lacks a real case decision maker and the method used relies on the preferences of the decision maker, these preferences have had to be shaped artificially. There are certainly different ways to create such hypothetical preferences and in this example a profit maximization target has been chosen. This means that also non- financial objectives have been forced into a financial frame which is a disadvantage. In this case, it is justified by the profit maximization target and feedback during interviews.193 On the other hand, these rather single-track preferences enable a relatively transparent background for the motivation of priority settings and assessments, which could make it easier to understand the process. The resulting financial approach to value consequences has led to that the consequence differences have been analysed as annual monetary values. This has been possible thanks to the possibility of being able to express the consequences as financial values.194 The resulting financial figures assist the hypothetical decision maker to create a priority list for the swing rating in that the criteria with the highest annual value of the consequence difference gains the highest priority. The resulting priority list for the swing rating in Section 8.2.1 looks as follows: LCOE LOLH CFOE

The focus lies on the cost of energy, which directly influences the operating cost and thus the profitability of the SSCPP. The value of the difference between the worst and the best consequence indicates that this value, within this line-up, has the greatest impact on the profitability of the plant. The reliability, which is crucial for the SSCPP to avoid unscheduled stops and following problems, is ranked secondly due to its lower impact on the annual profit. This ranking, however, presumes that not processed beans can be sold at market value. The environmental impact ranks last due to the low financial impact of the used emission rights trading value of GHG despite the great consequence range. The swing weighting assessments have been done with help from the above priority list and the calculated annual values of the consequence differences below. Cost (LCOE) The LCOE can be related to the annual production costs by applying the average energy consumption of the SSCPP for one year. For an annual energy cost estimation, an average power demand of 41 kW has been used for the production time and 2 kW for the standby time.195

Annual energy consumption, production: 260[푑푎푦푠] ∗10[ℎ]∗41[푘푊] = 106,600 푘푊ℎ Annual energy consumption, standby: (260[푑푎푦푠] ∗14[ℎ] +105[푑푎푦푠] ∗ 24[ℎ]) ∗ 2[푘푊] = 12,320 푘푊ℎ

By applying the worst and the best LCOE consequence the financial value of this difference can be calculated:

193 Interview: Dr. Anga, J.-M. 194 Even though this possibility remains theoretical when it comes to the CFOE-related consequences. This is also explained in Section 5.2. 195 The used figures on the power demand, rely on calculated data within the Excel© sheet for the installed power in Appendix II.

79

Worst case cost: 118,920[푘푊ℎ] ∗ 0.24[€⁄푘푊ℎ] = 28,540.80 € Best case cost: 118,920[푘푊ℎ] ∗ 0.13[€⁄푘푊ℎ] = 15,459.60 € Difference: 13,081.20 € Environmental impact (CFOE) The possibility to turn the differences in CO2-emissions into a financial value exists even though it remains theoretical. Even if there exists a program to benefit less CO2 causing power generation alternatives, the possibility to apply this in a case like this is low. For a power supply of this size and for an SSCPP this possibility remains very difficult and is associated with certain obligations. Firstly, the benefitting power supply must be able to replace an energy source that causes higher CO2-emissions. Secondly it is very time consuming and expensive for one single small power producer to apply for the reward with CER (Carbon Emission Credits).196 As a result, this means that a generalizing financial value of the different CFOE consequences cannot be presented. In the exclusive case of a situation where the power supply of an SSCPP can be connected with a local power grid and partly replaces “diesel-generator-electricity”, a benefit of earning CER´s could be possible. To make it realistic, however, a group of multiple, similarly-powered SSCPP´s would need to join their efforts to get their application through.197 Due to this, for the CFOE consequences, we settle with calculating the accumulated annual CO2eqv.-emissions for the worst and the best consequence:

Worst case: 118,920[푘푊ℎ] ∗ 1.22[ ] = 145,082 푘푔 of CO2eqv. annually Best case: 118,920[푘푊ℎ] ∗ 0.12[ ] = 14,270 푘푔 of CO2eqv. annually

The resulting difference in CO2eqv.-emissions is hard to grasp without any references. To exemplify the above difference, it is slightly less than the per capita CO2eqv.- emissions in Canada during 2014.198 Despite of the mentioned problems in applying CER for a small energy producer, a CO2-emission allowance trade-value exists on the stock market. This value is about 5.37 €/tCO2eqv. which if applicable would transform the above difference in about 700.- € annually.199 Reliability (LOLH) The reliability index has a direct impact on the production throughput in such a way that a power loss leaves the production standing still during the time of the outage. The difference between the best and the worst consequence is 156 h per year, with the worst consequence being 189 h and the best 33 h annually. This can be estimated as an annual loss of income by relating the difference in outage time to the production output (see Table 1 in Section 2.2) to obtain a production loss expressed in metric tons (mt).

Annual difference of production loss: (189[ℎ] − 33[ℎ]) ∗ 100[푘푔] = 15.6 푚푡

By applying values for the selling of beans and the selling of cocoa liquor the annual profit-loss can be estimated:200

196 Tenenbaum et.al., 2014, p.141 197 Tenenbaum et.al., 2014, p.142 198 www.statista.com, 11.07.17 199 http://www.finanzen.net/rohstoffe/co2-emissionsrechte, 11.07.17 200 The profit loss estimation in this case relies on the assumption that beans are available in any case. Therefore, the difference in income is related to if beans are sold raw as bulk or if they are sold as processed cocoa liquor.

80

Selling cocoa liquor: 15.6[푚푡] ∗ 3,669[$]201 = $ 57,236.40 Selling cocoa beans: 15.6[푚푡] ∗ 120% ∗ 2,763[$]202 = $ 51,723.36203 Difference ($): $ 5,513.04 Difference (€): 5,127.13 €204

201 The price of cocoa liquor is based on the average price of cocoa liquor that was exported from Côte d´Ivoire during 2015. Source: International Trade Centre, www.intracen.org 202 The used price of beans comes from the stock market trading value on the 14.11.16. Source: ICCO, www.icco.org. In reality this price cannot be achieved for smaller amounts within the country due to trading intermediates. A more realistic selling price will be lower. 203 The 120 % comes from the weight losses during production as described in Section 2.2. 204 Used Dollar to Euro exchange rate: 0,93 (30.03.2017)

81

Appendix IV - Power supply and energy cost by alternatives

4

[%] [%] 4,7% 4,1% 3,0% 4,3% 95,3% 61,6% 38,4% 77,1% 22,9% 69,6% 23,3% 34,1% 24,6% 36,9% 100,0% operation operation share share during

0% 5% 0% 5% 0% 53% 47% 79% 21% 74% 21% 74% 21% 100% 100% Installed share [%]

heating

electricity

[kW] electricitygrid from electricitygrid from backup electricitygrid from LPG Dieselgenerator Heat recovery Solar electricity Solar heat Solar water heating Backup Solar Solar air Solar water heating Powergrid [kWh]

Total installed power

49,60 49,60 54,21 49,60 49,60 49,60 Annual average of supplied supplied of average Annual energy

Solar 36,60 36,60 electric

heat Solar 2,50 2,50 (water)

(air) heat Solar 10,50 10,50

Heat recov. 10,50

LPG burner [kW] 25,58

Electr. power 49,60 49,60 28,63 39,10 Summarized installed installed Summarized power

2,5 2,5 2,5 2,5 2,5 Hot water (<100°C)

0,5 10,5 14,6 10,5 10,5 10,5 Hot air (<100°C)

[kW]

11,0 11,0 11,0 11,0 11,0 Hotair (>350°C) Possible heating Possibleheating power

air 0,7 0,7 0,7 0,7 0,7 Cooled (<20°C)

2,7 2,7 2,7 2,7 2,7 water Cooled (<10°C)

22,3 22,3 22,3 22,3 22,3 power Electric Electric power [kW] Electricpower

the main powersystem supply

1,2

3 1. 1. Power supply alternatives by and energy type, including annual averages consideringwhen backup running times.

-

power (Generator) power -

SeaLxue mH/DGPübrct66 Liquid gas, specifications: https://www.tytogaz.de Stela Laxhuber DLG GmbH / Prüfbericht6263 Prüfbericht F, DLG e.V5664 =max. 72% = approx. 40% Table AIVTable Sources: 1) 2) 3) Calculated4) basedthe timeon annual outage of Power supply Power alternative 1) 100% electric (grid) electric 100% 1) +grid power 1b) generator backup electric 2) (LPG) gas liquid Generator 5) recovery Heat electricity Solar 4) heat Solar Backup electricity Solar 3) heat Solar backup grid Power η η Airheating: 60m² Water heating:/ 6,5m²/500l

82

0.160 0.173 0.127 0.137 0.188 0.235 [€/kWh] [€/kWh] LCOE LCOE combined

0.16 0.16 0.43 0.17 0.06 0.18 0.04 0.18 0.23 0.04 0.43 0.23

LCOE of of LCOE individual energy source

79,972 126,558 655,867 939,161 494,680 583,886 494,680 2,539,003 2,539,003 1,563,188 1,258,258 1,005,384 [kWh] Accumulated Accumulated energy

2 003,63 2003,63 9247,51 2003,63 [€] 36 447,81 447,81 36 317,01 30 603,84 78 229,21 16 380 850,52 850,52 380 850,52 380 478,26 234 142,71 140 260,84 184 Accumulated Accumulated cost

lower dueto higherfailure rate lower dueto higherfailure rate lower dueto higherfailure rate

110,723 116,242 110,723 110,936 117,222 116,375 [kWh] Energy, Energy, total

35 35 35 35 20 35 20 20 20 35 10

[yrs] Lifetime

5,519.04 5,519.04 3,487.49 [kWh] 68,168.81 68,168.81 42,554.15 40,955.70 32,096.00 37,883.90 81,638.71 32,096.00 89,683.58 26,691.02 110,722.96 110,722.96 110,722.96 Annual Annual / energy subsystem

130,00 130,00 600,00 130,00 707,74 1 589,45 1589,45 1967,04 6111,46 5100,00 [€] 16 608,44 608,44 16 608,44 16 225,32 10 328,44 21 Annual cost Annual

[€] 25 200,00 25 200,00 25 203,00 18 200,00 25 000,00 12 200,00 25 685,00 19 000,00 93 685,00 19 203,00 18 753,00 51 217 217 000,00 Investment

/

2. Costof energy different for alternatives.the grid grid -

-

Table AIV Table Power supply Power alternative 1) 100% Power grid Power 100% 1) /grid Power 1b) generator diesel Power grid 2) LPG Power grid 3) heat* Solar, PV Solar, off Solar, 4) heat* Solar, generator backup / DieselGenerator 5) exchanger heat 83

Appendix V - Investment and annual costs of alternatives Table AV-1. Investment and annual costs of power supply alternatives.

Alternative Component Installation Remark Operating Remark [€] [€] 1) 100% power Trafo 60kVA 10 000,00 1 grid Low voltage station 2 500,00 2 Compensation unit 1 700,00 2 Main cable 4 000,00 3 Housing 2 000,00 1 Installation 5 000,00 4 16 608,44 kWh cost Total 25 200,00 16 608,44 1b) Power grid + Power grid, see 1) 25 200,00 alt.1) 16 608,44 kWh cost backup generator Backup generator 18 203,00 5 1 428,45 diesel cost 161,00 Maintenance, yearly + inj.pump overhaul every 10yrs based on power grid failure rate, 14, 15 Total 43 403,00 18 197,89 2) Power grid + Power grid, see 1) 25 200,00 alt.1) 10 225,32 kWh cost LPG LPG Tank, 6m³ 2 000,00 6 Fundament 500,00 7 Valves 2 000,00 7 Evaporator 1 500,00 7 Approval 1 500,00 7 Additional cost 2 000,00 4 Installation 2 500,00 4 1 967,04 LPG cost Total, LPG 12 000,00 1 967,04 Total Alt.2 37 200,00 12 192,36 3) Solar electricity PV 50 kWp, see 4) 93 000,00 alt.4) 100,00 cleaning, 12 w. power grid excl. Battery, backup Generator 500,00 inverter renewal, 8 Solar heat, see 4) 19 685,00 alt.4) 130,00 cleaning, filter change, 10 Grid, see 1) 25 200,00 alt.1) 6 111,46 kWh cost Total Alt. 3 137 885,00 6 841,46 4) Solar electricity Air heating panels 12 155,00 10 w. battery + 45m² backup generator Mounting mtrl 2 040,00 10 Support structure 1 190,00 13 20,00 Filter, 10 Installation 2 300,00 12 60,00 Cleaning, 10

Flat collectors 5m² 1 000,00 11 Pump, pipings, 500,00 11 valves Installation 500,00 11 50,00 Cleaning, 8 Total, Solar heat 19 685,00 130,00

84

PV-system 100kWp 144 000,00 12 Support structure 8 000,00 13 Batteries 40kWh 40 000,00 8 5 000,00 Battery & Inverter renewal after 10 yrs, 8 Installation 25 000,00 12 100,00 Cleaning, 12 Total, PV 217 000,00 100,00 585,74 diesel cost Generator 16 800,00 5 42,00 filter change, 14 oversized tank 903,00 5 80,00 oil change, 14 Installation 500,00 4 Total 18 203,00 707,74 Total Alt. 4 254 888,00 5 937,74 5) Diesel Generator 16 800,00 5 generator oversized tank 903,00 5 external tank 668,00 5 19 902,44 diesel cost pump system 1 142,00 5 126,00 filter change, 14 connection tubing 240,00 5 240,00 oil change, 14 Installation 1 500,00 4 1 000,00 annual injection pump overhaul, 14, 15 Batteries 23kWh 23 000,00 8 Total, Generator 44 253,00 21 268,44

Heat exchanger 4 000,00 1 By-pass 500,00 1 Piping 1 500,00 9 10,00 Filter, 1 Installation 1 500,00 4 50,00 Cleaning, 1 Total, Heat exch. 7 500,00 60,00 Total Alt.5 51 753,00 21 328,44 Sources: 1) BEAR Mühlen & Behälter GmbH 2) Elektrotechnische Werke Fritz Driescher & Söhne GmbH 3) Obeta GmbH 4) Estimation based on previous projects 5) Feeser Group GmbH 6) www.energieverbraucher.de 7) Baltimir Group OÜ 8) Solarladen.de 9) Armaturenwerk Hötensleben GmbH 10) Grammer Solar GmbH 11) Solarthermie.net 12) Sunset Energietechnik GmbH 13) www.oeko-energie.de 14) Maintenance guide Iveco F32 15) www.umbscheiden.de

85

Appendix VI - Carbon Footprint of energy sources Table AVI-1. Review of values on the carbon footprint of different energy sources.

Energy CO2eqv.g/kWh Sources/remarks: source In the median values the highest and the lowest values have been excluded. For Photovoltaics Min Median Max a median value for each column has been calculated with the highest and the lowest value being neglected. Solar heat 9.8 17.8 25.8 CO2 Ökobilanz für Energieträger (2005), www.energyglobe.info 21.8 Pehnt, Dynamic life cycle assessment (LCA) of renewable energy technologies (2005) median 19.8 Photovoltaics 77.0 90.0 103.0 CO2 Ökobilanz für Energieträger (2005), www.energyglobe.info 80.0 120.0 160.0 Lübbert, CO2-Bilanzen verschiedener Energieträger im Vergleich (2007) 50.0 72.5 95.0 World Nuclear Association (referred to by Lübbert) 101.0 Fritsche, Öko-institut (2007) 99.0 Pehnt, Dynamic life cycle assessment (LCA) of renewable energy technologies (2005) 95.0 S.M. Gmünder et al., Life cycle assessment of village electrification based on straight jatropha oil in Chhattisgarh, India, p.6, Biomass and Bioenergy (2009) 40.0 45.0 50.0 National Renewable Energy Laboratory, Life Cycle Greenhouse Gas Emissions from Solar Photovoltaics (2012), www.nrel.gov median 63.5 81.3 98.6 high median 90.0 Diesel 1,150.0 1,475.0 1,800.0 S.M. Gmünder et al., Life cycle assessment of village electrification based on straight jatropha generator oil in Chhattisgarh, India, p.6, Biomass and Bioenergy (2009) Fleck, Huot, Comparative life-cycle assessment of a small wind turbine for residential off-grid

use (2009) 1,692.0 median 1,583.5 Natural gas 737.0 841.0 945.0 CO2 Ökobilanz für Energieträger (2005), www.energyglobe.info single cycle 630.0 Lübbert, CO2-Bilanzen verschiedener Energieträger im Vergleich (2007) 608.0 889.0 1,170.0 World Nuclear Association (referred to by Lübbert) median 865.0 combined cycle 410.0 420.0 430.0 Lübbert, CO2-Bilanzen verschiedener Energieträger im Vergleich (2007) 450.0 484.5 519.0 World Nuclear Association (referred to by Lübbert) 428.0 Fritsche, Öko-institut (2007) median 444.2 Hydro-power 3.5 4.1 4.7 CO2 Ökobilanz für Energieträger (2005), www.energyglobe.info 4.0 8.5 13.0 Lübbert, CO2-Bilanzen verschiedener Energieträger im Vergleich (2007) 3.0 7.0 11.0 World Nuclear Association (referred to by Lübbert) 40.0 Fritsche, Öko-institut (2007) 10.0 11.5 13.0 Pehnt, Dynamic life cycle assessment (LCA) of renewable energy technologies (2005) median 9.0 Note: The highest and the lowest value has been excluded in the overall median value Bio-mass 11.0 20.0 28.9 CO2 Ökobilanz für Energieträger (2005), www.energyglobe.info (electric) 14.6 15.8 17.0 Pehnt, Dynamic life cycle assessment (LCA) of renewable energy technologies (2005) median 17.9 Natural gas 258.0 290.0 322.0 CO2 Ökobilanz für Energieträger (2005), www.energyglobe.info (heating) 181.2 http://www.eia.gov/environment/emissions/co2_vol_mass.cfm median 235.6 LPG* 235.0 www.stovesonline.co.uk/fuel-CO2-emissions.html 227.0 230.5 234.0 A. Herold, Comparison of CO2 emission factors for fuels used in Greenhouse Gas Inventories and consequences for monitoring and reporting under the EC emissions trading scheme (2003) 215.0 218.5 222.0 http://www.eia.gov/environment/emissions/co2_vol_mass.cfm median 224.5 Note: The highest and the lowest value has been excluded in the overall median value corrected value 321.6 see Table AVI-2 below LPG Tank + 2,300 kg of steel Assumed 1.800 kg (Tank) + 500 kg (piping, support) installation pipings Carbon footprint, 1,400 kg/t, steel Metallurgist, Vol. 57, Nos. 9–10, January, 2014 (Russian Original Nos. 9–10, September– steel October, 2013) Carbon footprint, 3,220 kgCO2 Yu. N. Chesnokov,V. G. Lisienko, and A. V. Lapteva, EVALUATING THE CARBON FOOTPRINT installation FROM THE PRODUCTION OF STEEL IN AN ELECTRIC-ARC FURNACE *For LPG the values are not related to a specific application with a complete LCA. The figures are related to the amount of CO2 that is released during combustion for heating.

86

Table AVI-2. Correction of CFOE-value for LPG heating.

Correction of CFOE for LPG [gCO2eqv./kWh]

non LCA non LCA non LCA LCA LCA A. Herold EIA.gov Stovesonline LPG Footprint* Ökobilanz.de natural gas 205.0 181.2 89 % 290.0 LPG 230.5 218.5 234.0 100 % electricity (UK) 460.0 594.0

LPG corrected 1 303.9 based on a compare of electricity between "stovesonline.co.uk" and "LPG Footprint" LPG corrected 2 325.8 based on a compare with the percentage between "Ökobilanz" and "LPG Footprint" LPG corrected 3 335.0 based on a compare between "Ökobilanz", "A. Herold" and "eia.gov" *Atlantic Consulting, LPG’s Carbon Footprint Relative to Other Fuels (2009)

Table AVI-3. Energy split in Côte d´Ivoire.

Electric power mix, Côte d´Ivoire Share by source % Energy source gCO2eqv./kWh 18% Bio-mass 18 bio 17.9 15% Hydro-power 15 hydro 9 42% single cycle, natural gas 42 single cycle 865 25% combined cycle, natural gas 25 combined cycle 444.2 Source: Azito starts up phase 3, African Energy Issue 304, Total 478.92 9 July (2015)

87

Appendix VII - Solar energy yield

186 177 162 165 162 194 202 198 178 170 161 186 ] ------2144 -

kWh

[

Daily difference /SSCP Alt.3 (prod.time) kWh kW

] 13 14 16 15 16 12 12 12 14 15 16 13 14 167

36123 kWh/h [

] output - 158 169 186 183 186 148 138 143 168 177 187 158 2001

Average kWh/day [ Alt.PV 3

29 29 29 29 29 29 29 29 29 29 29 29

]

kW [ average average

95 295 295 295 295 295 295 295 295 295 2 295 295

kWh/ (daily) (daily) prod.time prod.time

23 23 23 23 23 23 23 23 23 23 23 23 274 kWh/day (nighttime)

7223 7223 7223 7223 7223 7223 7223 7223 7223 7223 7223 7223 86682

(total) (total) kWh/month SSCPP kWh/a incl.kWh/a standby

4884 4736 5772 5476 5772 4440 4292 4440 5032 5476 5624 4884 62,8% 60828 38178 kWh/month

9 9 9 10 10 10 10 10 10 10 10 10 117

[h] [h] olar s power power

33 32 39 37 39 30 29 30 34 37 38 33 411 1. energy PV yield Alternative 3. for - kWh/ module module

Table AVII Table Alt. 3: PowerAlt. solar + grid Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total, electric Total,usable Use (%) Eff.

88

3 3 3 2 2 3 3 2 ]

kW /a [ Power loss due to PV failure h/a h hrs

0 0 1 1 1 1 1 1 0 0 1 0 3

- - - - 7,2

70,3 77,5 ]

h [ Ave.daily running time, generator due tolow irradiation

23 23 23 23 23 23 23 23 23 23 23 23 [h]: accum. accum. design Battery Battery capacity

23 23 23 23 23 23 23 23 23 23 23 23

failure min. Battery Battery capacity

27 29 32 31 32 25 24 24 29 30 32 27 max. Battery Battery capacity Time due to low irradiationTimeduelowto Timedueto Total,time/a

0 6 0 2 } - differenceAlt.4 /SSCP 49 29 65 81 73 31 16 49 ------

378 - kWh [ Daily

9 0 30 12 14 14 44 59 52 14 16 30 ------232 - w. Battery difference during failure loss) (10%

5 3 15 34 28 34 31 47 39 18 37 15 - - - - - 146 -

w. Battery difference Alt.4 /SSCP (prod .time)

3 1 2 2 2 4 6 5 1 1 3 3 9

------Hourly Hourly surplus / / deficit kWh/a Average,kWh

] 27 29 32 31 32 25 24 24 29 30 32 27

341 28,4 /h [kWh 71919

] output -

323 346 381 374 381 303 283 293 343 362 384 323 4096 [kWh /day PV

29 29 29 29 29 29 29 29 29 29 29 29 kW kW average average

295 295 295 295 295 295 295 295 295 295 295 295 time time prod. prod. kWh/ (daily)

23 23 23 23 23 23 23 23 23 23 23 23 274

day day time) kWh/ (night-

7223 7223 7223 7223 7223 7223 7223 7223 7223 7223 7223 7223 86682 kWh/ (total) month SSCPP kWh/a kWh/a incl. standby

65,8% 81 964 w. battery w.

9697 9091 8788 9091 59,4% 10000 11818 11212 11818 10303 11212 11515 10000 73 974 73 kWh/ 124545 month month

9 9 9 10 10 10 10 10 10 10 10 10

117 [h] [h] solar solar power power grid -

33 32 39 37 39 30 29 30 34 37 38 33 2. energy yield PV Alternativefor 4. - 411 kWh/

module

Table AVIITable Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total, electric Total, usable [kWh] Use Eff. (%) Alt. 4: SolarAlt. off

89

1880 2009 1976 1936 1630 1042 1155 1050 1091 1509 1646 1679 Difference SSCP / Airheat

1 1 1 1 1 1 1 1 1 1 1 1 1 kW during kW during standby

12 12 12 12 12 12 12 12 12 12 12 12 kWh kWh during offdays, Water

14 14 14 14 14 14 14 14 14 14 14 14

kW during kW during days prod. incl. nighttime standby

1 1 1 1 1 1 1 1 1 1 1 1 1 kW during kW Time prod.

7 7 7 7 7 7 7 7 7 7 7 7

kWh during time, prod. Water

400 400 400 400 400 400 400 400 400 400 400 400 4796

kWh/ month, Water

11 11 11 11 11 11 11 11 11 11 11 11 kW, AirkW,

105 105 105 105 105 105 105 105 105 105 105 105

kWh/ prod. Air day,

2275 2275 2275 2275 2275 2275 2275 2275 2275 2275 2275 2275

27300 kWh/ month, Air SSCP

17 18 17 17 16 14 14 14 14 16 16 16 kWh/day

519 535 531 526 488 415 429 416 421 473 490 494 84%

5738 4796 kWh/ month Water heating heating Water modules

14 14 14 14 13 11 11 11 11 12 13 13 kWduring production time

136 140 139 138 128 109 112 109 110 124 129 130 energyAlternative for 3 and 4.

kWh/day

ing 59%

4155 4284 4251 4211 3905 3317 3430 3325 3366 3784 3921 3954 45904 27300 kWh/ month Air heating Airheating modules

3. heat Solar

- 5 5 5 5 5 4 4 4 4 5 5 5

1739 GHI (kWh/GHI m²/day)*

Table Table AVII Month Jan Feb Mrz Apr Mai Jun Jul Aug Sep Okt Nov Dez Total Total,usable Use Eff. (%)

90

91

Appendix VIII - PV simulation

92

93

Appendix IX - Production scenarios

Production scenario II - 12h per day/300 days/year

Below the consequences table and the assessment of weight coefficients according to the swing weighting method for the 12 h/day-scenario is presented. For the swing weighting procedure, the same approach as for the original scenario, with financial values, has been used.

Table IX-1. Consequences table for the power supply alternatives with a 12 h/day production time.

Power supply alternative LCOE CFOE LOLH [kgCO2Eq./ [h] [€/kWh] kWh] 1) 100% electricity from local power 0.16 0.48 218 grid 1b) Power grid + back-up generator 0.17 0.53 72 2) Electricity from local power grid + 0.12 0.42 218 gas (LPG) for drying and roasting 3) Electricity from local power grid + PV-panels + solar heat for drying and 0.12 0.20 218 hot water 4) PV-panels w. batteries + solar heat for drying and hot water + back-up with 0.16 0.17 52 generator 5) Generator (diesel) + heat recovery 0.22 1.20 67 for drying

LCOE For this profit loss estimation an average power demand of 41 kW has been used for production time and 2 kW for standby time.205

Annual energy consumption, production: 300[푑푎푦푠] ∗12[ℎ] ∗41[푘푊] = 147,600 푘푊ℎ Annual energy consumption, standby: (300[푑푎푦푠]∗12[ℎ]+65[푑푎푦푠] ∗ 24[ℎ]) ∗ 2[푘푊] = 10,320 푘푊ℎ

By applying the worst and the best LCOE consequence the financial value of this difference can be calculated:

Worst case cost: (157,920[푘푊ℎ]) ∗ 0.22[€⁄푘푊ℎ] = 34,742.40 € Best case cost: (157,920[푘푊ℎ]) ∗ 0.12[€⁄푘푊ℎ] = 18,950.40 € Difference: 15,792.00 €

CFOE Worst case: (157,920[푘푊ℎ]) ∗ 1.20[ ] = 189,504 푘푔 of CO2eqv. annually Best case: (157,920[푘푊ℎ]) ∗ 0.17[ ] = 26,846 푘푔 of CO2eqv. annually

205 The used figures on the power need rely on calculated data within the Excel© sheet for installed power.

94

Despite of the mentioned problems in applying for CER for a small energy producer, a CO2-emission allowance trade-value exists on the stock market. This value is about 5.37 €/tCO2 which (if applicable) would transform the above difference in about 850.- € annually.206

LOLH Annual difference of production loss: (218[ℎ] − 52[ℎ]) ∗ 100[푘푔] = 16.6 푚푡

By applying values for the selling of beans and the selling of cocoa liquor the annual profit-loss can be estimated:207 Selling cocoa liquor: 16.6[푚푡] ∗ 3,669[$]208 = $ 60,905.40 Selling cocoa beans: 16.6[푚푡] ∗ 120% ∗ 2,763[$]209 = $ 55,039.00210 Difference ($): $ 5,866.40 Difference (€): 5,455.80 €211

Table IX-2. Weight assessment table for 12 h/day-scenario. Swinging Consequences Rank Rate Weight attribute Benchmark 0.22 €/kWh / 1.20 kgCO2Eq./kWh / 218 h 4 0 - (all worst) LCOE 0.12 €/kWh / 1.20 kgCO2Eq./kWh / 218 h 1 100 0.67 CFOE 0.22 €/kWh / 0.17 kgCO2Eq./kWh / 218 h 3 10 0.07 LOLH 0.22 €/kWh / 1.20 kgCO2Eq./kWh / 52 h 2 40 0.27

Table IX-3. Overall utility for 12 h/day-scenario. Utility values Power supply alternative LCOE´ CFOE´ LOLH´ Overall Utility (Swing weighting) 1) 100% power grid 0.62 0.70 0.00 0.46 1b) Power grid + 0.53 0.65 0.88 0.63 back-up generator 2) Power grid + LPG 0.96 0.76 0.00 0.69 3) Solar + power 1.00 0.97 0.00 0.73 grid 4) Solar + Battery + 0.63 1.00 1.00 0.76 Generator 5) Diesel Generator 0.00 0.00 0.91 0.24

206 http://www.finanzen.net/rohstoffe/co2-emissionsrechte, 11.07.17 207 The profit loss estimation in this case relies on the assumption that beans are available in any case. Therefore, the difference in income is related to if beans are sold raw as bulk or if they are sold as processed cocoa liquor. 208 The price of cocoa liquor is based on the average price of cocoa liquor that was exported from CI during 2015. Source: International Trade Centre, www.intracen.org 209 The used price of beans comes from the stock market trading value on the 14.11.16. Source: ICCO, www.icco.org. In reality this price cannot be achieved for smaller amounts within the country due to trading intermediates. A more realistic selling price will be lower. 210 The 120% comes from the weight losses during production as described in Section 2.2. 211 Used Dollar to Euro exchange rate: 0,93 (30.03.2017)

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Production scenario III - 24h per day/300 days/year

Below the consequences table and the assessment of weight coefficients according to the swing weighting method for the 24 h/day-scenario is presented. For the swing weighting procedure, the same approach as for the original scenario, with financial values, has been used.

Table IX-4. Consequences table for the power supply alternatives with a 24 h/day production time.

Power supply alternative LCOE CFOE LOLH [kgCO2Eq./ [h] [€/kWh] kWh] 1) 100% electricity from local power 0.15 0.48 218 grid 1b) Power grid + back-up generator 0.16 0.51 72 2) Electricity from local power grid + 0.11 0.41 218 gas (LPG) for drying and roasting 3) Electricity from local power grid + PV-panels + solar heat for drying and 0.13 0.34 218 hot water 4) PV-panels w. batteries + solar heat for drying and hot water + back-up with 0.18 0.83 67 generator 5) Generator (diesel) + heat recovery 0.24 1.18 67 for drying

LCOE For this profit loss estimation an average power demand of 41 kW has been used for production time and 2 kW for standby time.212

Annual energy consumption, production: 300[푑푎푦푠] ∗24[ℎ] ∗41[푘푊] = 295,200 푘푊ℎ Annual energy consumption, standby: (65[푑푎푦푠] ∗ 24[ℎ]) ∗ 2[푘푊] = 3,120 푘푊ℎ

By applying the worst and the best LCOE consequence the financial value of this difference can be calculated:

Worst case cost: (298,320[푘푊ℎ]) ∗ 0.24[€⁄푘푊ℎ] = 71,596.80 € Best case cost: (298,320[푘푊ℎ]) ∗ 0.11[€⁄푘푊ℎ] = 32,815.20 € Difference: 38,781.60 €

CFOE Worst case: (298,320[푘푊ℎ]) ∗ 1.18[ ] = 352,018 푘푔 of CO2eqv. annually Best case: (298,320[푘푊ℎ]) ∗ 0.34[ ] = 101,428 푘푔 of CO2eqv. annually

Despite of the mentioned problems in applying for CER for a small energy producer, a CO2-emission allowance trade-value exists on the stock market. This value

212 The used figures on the power need rely on calculated data within the Excel© sheet for installed power.

96 is about 5.37 €/tCO2 which (if applicable) would transform the above difference in about 1,350.- € annually.213

LOLH Annual difference of production loss: (218[ℎ] − 67[ℎ]) ∗ 100[푘푔] = 15.1 푚푡

By applying values for the selling of beans and the selling of cocoa liquor the annual profit-loss can be estimated:214 Selling cocoa liquor: 15.1[푚푡] ∗ 3,669[$]215 = $ 55,402.00 Selling cocoa beans: 15.1[푚푡] ∗ 120% ∗ 2,763[$]216 = $ 50,065.60217 Difference ($): $ 5,336.40 Difference (€): 4,962.90 €218

Table IX-5. Weight assessment table for 24 h/day-scenario. Swinging Consequences Rank Rate Weight attribute Benchmark 0.22 €/kWh / 1.18 kgCO2Eq./kWh / 265 h 4 0 - (all worst) LCOE 0.11 €/kWh / 1.18 kgCO2Eq./kWh / 265 h 1 100 0.80 CFOE 0.22 €/kWh / 0.34 kgCO2Eq./kWh / 265 h 3 5 0.04 LOLH 0.22 €/kWh / 1.18 kgCO2Eq./kWh / 67 h 2 20 0.16

Table IX-6. Overall utility for 24 h/day-scenario. Utility values Power supply alternative LCOE´ CFOE´ LOLH´ Added Utility (Swing weighting) 1) 100% power grid 0.67 0.83 0.00 0.57 1b) Power grid + 0.63 0.79 0.97 0.69 back-up generator 2) Power grid + 1.00 0.91 0.00 0.84 LPG 3) Solar + power 0.85 1.00 0.00 0.72 grid 4) Solar + Battery + 0.50 0.42 1.00 0.58 Generator 5) Diesel Generator 0.00 0.00 1.00 0.16

213 http://www.finanzen.net/rohstoffe/co2-emissionsrechte, 11.07.17 214 The profit loss estimation in this case relies on the assumption that beans are available in any case. Therefore, the difference in income is related to if beans are sold raw as bulk or if they are sold as processed cocoa liquor. 215 The price of cocoa liquor is based on the average price of cocoa liquor that was exported from CI during 2015. Source: International Trade Centre, www.intracen.org 216 The used price of beans comes from the stock market trading value on the 14.11.16. Source: ICCO, www.icco.org. In reality this price cannot be achieved for smaller amounts within the country due to trading intermediates. A more realistic selling price will be lower. 217 The 120% comes from the weight losses during production as described in Section 2.2. 218 Used Dollar to Euro exchange rate: 0,93 (30.03.2017)

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