MCDM for Off-Grid Rural Electrification Projects: Application of Renewable Energy Analyser Two (REA2)

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1 MCDM for Off-Grid Rural Electrification Projects: Application of Renewable Energy Analyser Two (REA2) NAJIB ALTAWELL Centre for Energy, Petroleum and Mineral Law and Policy (CEPMLP) The University of Dundee, Carnegie Building, Dundee DD1 4HN, SCOTLAND, UK Abstract: - A viable commercial implementation for a rural electrification project will require a methodology that has been specifically designed for the purpose of optimising positive aspects of the project and minimising any possible shortcomings. The Renewable Energy Analyser Two (REA2) has been developed with the aim of selecting the most suitable renewable energy off-grid system. REA2 is divided into five different main factors: Technical And Energy Resources Aspects (TAERA), Financial And Commercial Aspects (FACA), Environmental And Human Health Issues (EAHHI), Law, Regulations And Government Role (LRAGR), and Socio-Economic-Cultural Aspects (SECA). Each main factor is then divided into a number of sub-factors in order to cover a wider range of variables, which may directly and/or indirectly influence the final outcome. A hierarchy has been allocated within the five main factors: -Poor+, -Workable+, -Good+, -Excellent+ and Ideal. Results from the methodology using Rice Husks power station s project data from India shows that the levels results for the main factors are: TAERA and FACA= -Good, LRAGR= Workable, EAHHI and SECA= Good. The overall project level result is Good. Key-Words: - Methodology, rural electrification, main factors, sub-factors, off-grid 1 Introduction Methodology in essence is the ladder which enables those committed to the project to achieve what they are aiming for, i.e. to achieve the project aims and objectives. However, the project should in the first place comply with a number of basic supporting factors, such as national and local regulations and adequate funding. The combination of these supporting factors should be the starting basis for the process of constructing a successful working methodology for a viable rural electrification outcome. Having obtained all the necessary initial elements, the type of methodology required should then be considered in detail. Regarding rural electrification projects, such as offgrid system, MCDM is probably one of the only suitable approaches that can be implemented, as it involves variable and non-variable factors simultaneously. These factors, such as system requirements, energy resources, technical knowhow, finance and budgeting, tariff and pricing, system life cycle and maintenance are usually contained in any rural electrification project. However, devising approaches where factors related to the success or failure of a scheme - such as a rural electrification project - should be tackled first in depth before sorting out other varieties of issues related to the proposed new methodology. Working on the proposed methodology can allow examination of many of the off-grid (OG) project issues before embarking on the actual mechanism of the methodology itself. If data is needed to help in the formation of the methodology in its early stages, then a thorough examination of the proposed location should be made. If no decision has already been made concerning the above, a sample model location will be sufficient, as a starting point. The initial data obtained should provide some basic guidelines on the main issues such as cost, sustainable energy resources to be used, the general outlook for the economical viability of the proposed project, and the demand for the electricity supply by the local population. 2 MCDM Methods MCDM has two different sections: 1. Multiobjective decision making (MODM) optimisation of objectives; 2. Multi-attribute decision making (MADM) - evaluation of alternatives. There are three main techniques for MCDM: 1. Outranking (pairwise/global for the purpose of comparison but this is not possible to apply when there are a large number of alternatives); 2. Multi-attribute utility (linear additive/simple multiplicative models for the purpose of getting close to the standard ISBN:

2 evaluations); 3. Mathematical programming (to be used on continuous context). Subsequent to the above, further techniques are occasionally added to the list, such as preference disaggregation approach PDA (decision theory). Usually in MODM, a number of objective functions are worked upon and optimized while keeping in mind all the constraints associated with achieving the above. This means the alternatives are not predetermined in the MODM, as the solution will be selected on the basis of the overall efficiency and suitability for the decision makers. Therefore MODM examines problems by applying an approach where the decision space is continuous [1]. The MADM approach concerns discrete decision spaces, i.e. alternatives will be considered first in relation to a number of recognised attributes. MODM and MADM may contain different methods, such as distance based, priority based, outranking, as well as can be used in trying to tackle certain complex areas [2]: 1. WSM (weighted sum method/model); 2. WPM (weighted product method/model); 3. AHP (analytical hierarchy process); 4. PROMETHEE (preference ranking organisation method for enrichment evaluation; 5. ELECTRE ELimination Et Choix Traduisant la REalité (ELimination and Choice Expressing Reality The ); 6. TOPSIS (the technique for order preference by similarity to ideal solutions); 7. CP (compromise programming); 8. MAUT (multiattribute utility theory). A. Approach Methodology Points System, or as it is sometimes referred to a Scoring Methods, can be applied to ascertain the best option to be used in decision making within renewable energy (RE) projects and their final applications. The decision making process may start, for example, with the most suitable location for the RE system to be installed, type of RE system needed, environmental issues for the chosen location, system natural resources locally available, local community issues. The main factors and their sub-factors will be allocated points (or figure values), according to their priority and importance within the project working agenda, as well as within the aim(s) and objective(s) of the project itself. REA2 will examine each factor within the contexts of the other main factors and their sub-factors, where practical issues can be compared to an ideal project scenario (IP), i.e. a standard or reference project for a variety of issues connected to rural electrification. To use MCDM, a number of steps should be considered first - similar to the Pyramid stages used for the construction of a selection method for biomass materials in REA1 [3]. An IP should be defined in accordance to the aim(s) and objective(s) of the proposed rural electrification project. Methodology terms and definitions should be constructed to define each factor within the methodology and their actual role within the project. B. Proposed Model Steps Step 1 : Define the location needed - Step 2 : Define the financing needed - Step 3 : Define the system needed Step 4: Discuss the REP with the local community Step 5 : Define the criteria of electricity supply and related aspects Step 6 : Select a decision making tool - Step 7 : Evaluate alternatives against criteria/standard - Step 6 : Construct the decision matrix - Step 7 : Determine the importance weight of criteria to be employed Step 8 : Select the most suitable RE/OG implementation approach, i.e. validate solutions against problem statement. 3 Ideal Project IP (Standard Reference) The IP is a measuring yard stick to help achieve the best possible outcome for each step and stage during the project construction. By comparing each step and each stage with the step and stage of the IP standard reference, a simpler and clearer comprehensive approach for a successful project construction can be achieved (Figure 1). Figure 1 Illustration of the relationship between IP and lower levels within REA2 methodology (source: Author) A. Stage One Regulations, finance, environment and costumes related to the establishment of RE/OG systems and rural electrification in general have been examined under the main factors and their sub-factors. ISBN:

3 Factors and sub-factors are examined and then filtered in order to be listed under relevant main headlines. All the sub-factors under each headline are checked against each other. If there is no conflict, then the factors will be checked against the other factors under different headlines. If there are no conflicts against other factors, then stage two will be considered. If there are conflicts within a main headline sub-factor, then a solution will be proposed by the decision makers who are overseeing the above process. These main headlines taken will be checked accordingly. If the solution is viable and accepted by the team, then other conflicts under different headlines can be treated in a similar manner. construction/structure will list the main factors and the sub-factors. C. Stage Three Stage three of the methodology construction is about choosing a recently completed highly successful rural electrification OG project, or even better, an imaginary OG IP - where various types of renewable energy off-grid systems can be applied. This chosen ideal project will be used as a reference for the proposed new project. The reference or ideal project (IP) will be given any numerical value (for example, 5), in order to establish a value for the purpose of comparison. The IP scoring value will not be possible to reach, in practical terms, as it represents the perfect imaginary project (Figure 3). Figure 2 Project s main factors born out from the decision makers appointed by the RE project planning committee (source: Author) Main headlines can be: Technical and Energy Resources Aspects, Financial and Commercial Aspects, Environmental and Human Health Issues, Law, Regulations and Government Role, and Socio-Economic-Cultural Aspects (Figure 2). B. Stage Two Depending on the type of rural electrification project, as well as on the type of commercial energy business that will be formed, a decision can be made with regard to the allocation of hierarchal structure, i.e. in the form of percentage values to each of the main headlines. This means each headline will be allocated a weighing percentage value according to the importance of each main headline. The overall percentage value (Weighing Value) for each main headline will be sub-divided among all the subfactors in accordance with their importance and priority. However, as has been mentioned previously, this choice is optional and, for the sake of simplicity, it has not been used in this report. In any case, if the project planners decided to opt for this kind of approach while using REA2 methodology, then this option can be added easily with some minor changes to the present structure of REA2 methodology. Stage two of the methodology Figure 3 Methodology scaling approach for factors scoring mechanism (source: Author) Standard scoring values should be allocated to each of the sub-factor levels, such as Poor, Workable, Good, Excellent and Ideal, as in the following form: The scoring for the factor -Poor+ (-P+) = 1; The scoring for the factor -Workable+ (-W+) = 2; The scoring for the factor -Good+ (-G+) = 3; The scoring for the factor -Excellent+ (-E+) = 4; The scoring for the factor Ideal (I) = 5 Boundaries & Values If the average scoring is more than two units lower or higher, then the sign or + will be added, respectively, otherwise the scoring within the two units above or two units below will be given the level without the minus ( ) or positive (+) signs. For any value more than two units above or below, then the level will change either to a higher level or to a lower one, accordingly. The Ideal level will not have this kind of grading, as it is meant to be the absolute 100% perfect solution and therefore, it is ISBN:

4 impossible to reach. More factors (or less) can be added and the actual value, rather than the average value, will be allocated according to the facts and/or the estimations by the project committee/decision makers. Using the following simple equation, the percentage of negative aspects, or more accurately the distant (D) from the Ideal Project (IP) can be calculated. IP = TAV D where IP = Ideal Project (IP); D = Project s shortages, or distant from IP; TAV = Total Average Value (for the project) 4 REA2 Subjective and Objective Values The subjective values can be reduced to a minimum as and when the listings of the factors and their subfactors have been defined individually under the terms and definition of the methodology. In this way, actual clarifications can be made between subjective and objective values of all the factors/sub-factors employed within REA2. Depending on the definition and interpretation by the decision makers of the actual meaning of each factor/sub-factor, the final value in the form of a scoring unit will be interpreted accordingly. As has been mentioned previously, there are five main factors within the REA2. Unlike the two main factors - Socio-Economic-cultural and Financial And Commercial Aspects - the remaining three factors are more likely to be objective rather than subjective, i.e. more than two thirds of the methodology is objective and less than one third of the methodology can potentially be subjective in their values. TAERA, LRAGR, and EAHHI are inclined to be objective, while FACA and SECA are more closely on the subjective side: The business affiliated factors/sub-factors are a combination of a large number of various categories related to different areas and fields, such as market, legislation, business, products and land & water. The aim of having such a large selection of categories is that by knowing and understanding the influence of these factors on the project, the scoring of these factors will give a good indication as to how and why choosing one particular type of value is better than choosing another. This basic but important process of selection can perform a useful before and after function in any off-grid project, regardless of whether they are on a large or small commercial scale. The current market and subfactors are a reflection of the present prices related to the energy resources (e.g. biomass materials) and the hardware availability and prices and their maintenance costs, i.e. the overall cost and availability to convert certain energy resources into electricity. The buying and selling of shares related to the energy business and related areas, plus, of course, other additional factors such as any international political situation which may directly or indirectly impact upon the market, natural disasters, artificial market manipulation, and so on are all influencing factors. Each of the above possibilities, if we refer to what might happen in the future regarding the energy market, may have some subjective valuation based on a prediction as experienced by current events which researchers and dealers in the market believe could shape the future market of the RE industry [4]. What the business/market affiliated factors/sub-factors in the methodology aims for is the removal, or at least the reduction of these kinds of subjective predictions. Only a small part of the business/market affiliated factors/sub-factors can be related to the market. The rest of the factor s data can be obtained easily and accurately without the need for a prediction or a subjective conclusion. i.e. the majority of the data stability and accuracy is based on how the data has been obtained from the original source. For example, if data is related to land issues, then we can easily obtain all the relevant data about land availability and water supply, for a particular project in any part of the UK, safely - or in any politically stable country in the world for that matter. Other sub-factors, such as Energy input, Metering system, Fossil fuel cost and many others can be obtained through recently available documented data (e.g. data about present existing systems, knowledge about the present fuel available on the market, and so on) and from data related to the actual building process of the project during and after the completion of every stage. The market factor data can be obtained by dividing the market into smaller sections using the market segmentation method [5]. 5 Methodology Scoring & Figures Values A. Tables Percentage Values The IP has the highest value (100%), i.e. the lower the scoring value, the higher the: e.g. cost, risk, input barriers and so on. At the same time, the higher the score, the lower the negative aspects associated with the sub-factors value, such as the example factors mentioned above. To illustrate, a system cost can be given 22 units (22%) is double of the original price plus 22% of the overall total price. ISBN:

5 While as the level moves up the scale, i.e. from Poor to Workable, then it is only 50% higher than the original price. Moving further upward within the scale, 64 units is only half of the percentage in the price rising and the 85 is an increase of quarter percent, consequently the 100 is 0 % increase in the price (Figure 4). listing all the sub-factors first. Comparison between values is then listed between each sub-factor textual values (Table 1). Table 1 Examples of SECA sub-factors with their textual values converted to numerical values covering all levels (P to I) Figure 4 The relationship between the percentage values and other variable factors within REA2 (source: Author) B. Converting Textual Values into Figures For the purpose of obtaining actual data in the form of figures, all the sub-factors textual values should be converted into numerical values. The process of doing this kind of conversion has been referred to as Business Process Modelling (BPM) [6]. The accuracy of conversion textual values into figures is reliant upon the type of method used. This means that the more the textual values are close to the fact on the ground, the more likely the final figures will be able to represent them. However, the process itself can be highly subjective if mere estimations were the only approach used in the conversion process. To avoid (and/or reduce) that kind of subjectivity, all figures should be obtained directly from the source, rather than inserting these kinds of value in the form of textual valuations then estimating the numerical values individually. Since all the data provided during the visit to the Rice Husks power station were in the form of estimated data and/or estimated value textual information, then the only way is to use all the subfactors in the form of estimated textual values then convert them into figures using the above highly unreliable approach. The above conversion will not provide the accuracy needed in representing the overall facts for the Rice Husks power station, nor for any other OG project for that matter. For this reason, REA2 conversion has been achieved by The above has been followed by another listing, where numerical values were given to the textual values with comparison for the newly given numerical data of the sub-factors. Therefore, to convert textual values into numerical figures, the listing of all the sub-factors opposite of each other is the first step. Each factor will be compared to each other (relative to their values), i.e. whether the sub-factor is higher or lower in value than the opposite sub-factor. A complete table will have the value of each sub-factor marked as either higher or lower than the opposite counterpart. This process will be repeated again and again until each sub-factor has its own value which differs from the rest. The possibility of having two sub-factors of the same value is remote, in particular when there is three or four (or higher) digit number in each value. The next step will be to insert a column opposite to the sub-factor column with numerical values range, e.g. from units. By counting the units and sub-units opposite each sub-factor, numerical values will be obtained for all the sub-factors within the main headlines of the REA2 methodology. ISBN:

6 6 Methodology Tables The scoring values should be entered by the actual project planners and those working on the ground, i.e. in the process of before during and after constructing the project. This is clearly the best way to provide actual data (or realistic estimated data) representing the value of each sub-factor. It is also to indicate that the listing of the sub-factors is open to changes in the form of adding further sub-factors, removing some of the present factors and/or adding priority listing within the sub-factors themselves. The priority listing is connected to the hierarchy (percentage values) system mentioned briefly in the previous section (Tables 2, 3 and 4). Table 2 Examples of sub-teara factors with their scoring values and named levels Technical and Energy Resources Aspects (TAERA) Sub- Factor 1 (-P+) 2 (-W+) 3 (-G+) 4 (-E+) (5) (I) A (e.g. Energy Resources) B C (e.g. up to 17 sub-factors) Table 3 Listing of TEARA factors with examples of possible textual range and values Technical and Energy Resources Aspects (TAERA) Sub- Factor 1 (-P+) 2 (-W+) 3 (-G+) 4 (-E+) (5) (I) A (e.g. Energy Resources) <20 % / year 20-50% /year 51 70% / year 71 95% /year % /year The interpretation of the exact meaning and the actual scoring value for each sub-factor is entirely in the hand of the person (or persons) who are given the task to fill these tables. This, as has been discussed earlier on, could mean that some of the values provided in the methodology tables can be subjective in nature. The value of each level, e.g. Poor, is multiplied with the number of sub-factors. This has been done in order to obtain the overall number of that category for that particular level. Table 4 Converting all the textual and range values for TERA into figures Technical and Energy Resources Aspects (TAERA) Sub- Factor 1 (-P+) 2 (-W+) 3 (-G+) 4 (-E+) (5) (I) A (e.g. Energy Resources) A. TAERA Tables: As an example for representing Rice Husks power station project, seventeen subfactors have been listed dealing directly and indirectly with various technical and energy resources aspects. Sub-factors can be generated by examining some of the general and basic headlines concerning the following: 1. Present systems; 2. Emerging systems; 3. Technology issues; 4. Byproducts; 5. Type of energy resources needed 6. Availability of energy crops all year round; 7. The balance between energy input and energy output; 8. Technical training. B. TAERA Table Calculation: The TAERA example has provided seventeen factors. Using estimated data from the Rice Husks power station project, the sub-factor values (figures) for TAERA have been allocated. C. TAERA initial scoring: Poor = (1x17 number of sub-factors ) = 17 Workable = 2x17 = 34; Good = 3x17 = 51 Excellent = 4x17 = 68 Ideal = 5x17 = 85 D. TAERA scoring with the sub-factor value: The TAERA Poor Level sub-factor average individual value: The total value of the level value x subfactor value e.g. 17 (level Poor value) x 32 (32 is the already calculated sub-factor numerical value for this level of the rice husk project, i.e. energy resources = 32) = 544 Energy Resources for the Poor level. E. FACA Tables: The energy issue is the cost of energy input versus the cost of energy output. The cost of energy input should be approximately equivalent or less than the cost of the energy output, whenever this is applicable. This is because commercial viability requires this kind of approach on both short and long term business basis. As an example, 22 sub-factors have been provided. The following are some of the points which may generate further information related to the sub-factor of this category: ISBN:

7 1. Banking rules/regulations related to the financing of OG rural electrification projects; 2. Recent reports concerning the local population access to finance and an up-to-date report(s) concerning the level of standard of living for the average family with no access to electricity; 3. Availability of subsidy and its value for the proposed project; 4. A list of energy sources annual cost; 5. Cost of license related to the project construction and future operations. F. FACA Table Calculation: The FACA example has provided twenty two sub-factors. Using estimated data from the Rice Husks power station project, the sub-factor values (figures) for FACA have been allocated. G. FACA initial scoring: Poor = 1x22 = 22 Workable = 2x22 = 44 Good = 3x22 = 66 Excellent = 4x22 = 88 Ideal = 5x22 = 110 H. FACA scoring with the sub-factor: The FCA Poor Level sub-factor average individual value: e.g. 22x22 (22 is the already calculated sub-factor numerical value of the Rice Husks project for this level, i.e. System cost = 22) = 484 System cost I. EAHHI Tables: Environmental issues may not influence the proposed work and construction on the project, if and when tight regulations are not part of the local and/or national government. This is especially the case within some of the developing countries. The environment and human health issues resulting from an off-grid project are highly connected to each other, in that by benefiting the local population, i.e. providing electricity - the environment as part of the natural process in providing sustainable energy resources - should be looked after and not abused on the account that regulations and laws in a particular location in a developing country do not apply to the proposed project. Listing at least twelve main sub-factors, within the Rice Husks project example, the environmental and human health impact, therefore, have been considered seriously. The environmental issues related to the Rice Husks project can be summarised under the following points: 1. Emission in general, is one of the main causes of global warming, if not controlled. This is particularly so where a variety of biomass materials are being burned collectively; 2. Problems related to air pollution, air quality and water pollution resulted from power generating companies; 3. Inefficient combustion of rice husks materials from power generating stations can have a major effect on the environment and human health. Reduction of emission, therefore, such as the emission of NOx and SO 2, is vital in protecting the environment. J. EAHHI Table Calculation: The EAHHI example has provided twelve sub-factors. Using estimated data from the Rice Husks power station project, the sub-factor values (figures) for EAHHI have been allocated. K. EAHHI initial scoring table: Poor = 1x12 = 12 Workable = 2x12 = 24 Good = 3x12 = 36 Excellent = 4x12 = 48 Ideal = 5x12 = 60 L. EAHHI scoring with the sub-factor: The EAHHI Poor Level sub-factor average individual value: e.g. 12x15 = 180 (15 is the already calculated subfactor numerical value for this level of the rice husk project, i.e. Possible immediate environmental impact = 15). M. LRAGR Tables: Generating sub-factors from this category means that knowledge of the local area legal system and its regulatory role, concerning a proposed off-grid project, is vital. The above knowledge and understanding will help in listing all the necessary sub-factors related to this category. Policies related to the method of electricity generations, training, health and safety, marketing, recruitment and various other internal and external procedures, which the business embarks upon under the umbrella of the company policies, are all influencing factors for the success or failure of the newly proposed OG electrification project. Law, regulations and government role (LRAGR) are important ingredients for the success or failure of any electrification project. The negativity resulting from the above is when restrictive rules, regulations and bureaucracies are the norm. For the Rice Husks project example, 13 sub-factors have been provided. The following could help in locating some of the required sub-factors: 1. Various publications related to the national and local government connected to the off-grid rural electrification projects planning and operations; 2. License conditions and regulations related to it (if any); 3. Possible future roles, regulations and laws - concerning rural electrification projects OG in the pipeline. N. LRAGR Table Calculation: The LRAGR example has provided thirteen sub-factors. ISBN:

8 Using estimated data from the Rice Husks power station project, the sub-factor values (figures) for LRAGR have been allocated. O. LRAGR initial scoring table: Poor = 1x13 = 13; Workable = 2x13 = 26; Good = 3x13 = 39; Excellent = 4x13 = 52; Ideal = 5x13 = 65 P. LRAGR scoring with the sub-factor: The LRGR Poor Level sub-factor average individual value: e.g. 13x23 = 299 (23 is the already calculated subfactor numerical value for this level of the Rice Husks project, i.e. Tariff setting regulations = 23). Q. SECA Tables: This part of the methodology carries with it the knowledge and understanding of the local population from three prospective: social, economical and cultural issues. To gain comprehensive outlook to a particular local community - in relation to the above three issues - the involvement of the local population in some of the decision making, is vital. The eleven subfactors listed are some of the points suggested during a recent visit to a local community in India. In addition to the above 11 sub-factors, there are other issues which may not have been mentioned in this paper. However, by examining the following headlines, other sub-factors may emerge: 1. Language, religion, customs, educational level and local politics; 2. Access to transportation and accessible roads to the proposed location; 3. Farming, local products and possible business opportunities; 4. Present usage of energy/electricity (if any), and how reliable they are, on daily basis; 5. Gender quality and segregations R. SECA Table Calculation: The SECA has provided eleven factors. Using estimated data from the Rice Husks power station project, the sub-factor values (figures) for SECA have been allocated. S. SECA initial scoring table: Poor = 1x11 = 11; Workable = 2x11 = 22; Good = 3x11 = 33; Excellent = 4x11 = 44; Ideal = 5x11 = 55 T. SECA scoring with the sub-factor: The SECA Poor Level sub-factor average individual value: e.g. 11x20 = 220 (20 is the already calculated subfactor numerical value for this level of the rice husk project, i.e. Local Economy = 20). 7 REA2 Commercial Applications In order to present the practical aspects of the REA2 methodology, i.e. for the business community within the energy fields, printed tables of REA2 - with additional spaces within each table - will be used by them in order to obtain a primary result before embarking on an advance stage. If any of the printed main factors and/or sub-factors is not needed, then the scoring value will not be added (or alternatively inserting zero in the space provided will be sufficient to complete the overall scoring). As a way of providing a guideline in completing the REA2 tables, the commercial aspects of the off-grid (OG) project may examine some or all of the following factors - from a business point view [3]: 1. Present systems; 2. Emerging systems; 3. Technology issues; 4. By-products. In relation to the Rice Husks, the following should be considered: 1. Present prices; 2. Prices tendency; 3. Land and water issues. Regulatory risks are usually brought about by the fact that markets are created by policy mechanisms [7], i.e. by the central and local governments, as policy priorities usually replace old rules with new ones. This kind of change in regulations may not benefit new contracts or the renegotiation of the present contracts. 8 REA2 Methodology Summary In the example chosen, it has been estimated that there are twenty two factors that related to the financial and commercial aspects - from various angles. The overall scoring result for the FACA factor is 55 which is equal to -Good on the scaling level. The TAERA factor has seventeen sub-factors, which scored 46, keeping it on a similar level (- Good) as the previous factor. With thirteen sub-factors, the LRAGR scored the lowest in comparison to the other main factors, i.e. 28, which is in the centre of the Workable level. EAHHI, with only twelve sub-factors, scored within the central level of Good. Finally, the SCEA with eleven estimated factors achieved a similar level as the previous two main factors. Obtaining the distant (D) from the ideal project (IP) is one of the important aspects of REA2 methodology, as it provides reasonable indicators to the overall project viability. The final results should provide further guidelines on whether or not to ISBN:

9 proceed with the proposed OG project work and implementation (Tables 5 and 6). Table 5 Example results for rice husk project (RHP) for the values of the main levels and the distant of each level from its IP equivalent factor Main Factors TAERA FACA EAHHI LRAGR SECA Main Factors Level (- Good) (-Good) (-Good) (+Workable) (+Good) Table 6 Final results for the RHP compared to IP RHP Level RHP Value IP Value Distant Between RHP and IP (-Good) Conclusion Aspects of the OG systems have been considered during the construction of REA2 methodology. This means creating a list of various factors and sub-factors associated directly and indirectly with the overall make-up of an OG project. The REA2 methodology presented in this paper has a direct and practical approach in dealing with any OG system. This may mean non-academic business, as well as non-business personnel should be able to apply it directly without any outside help or training. The methodology has provided a skeleton for listing various factors associated with rural electrification projects. The calculation to obtain the level of scoring is simple to use. This means that there are no complicated mathematical equations or programming approaches. The methodology is universal in its approach and application, i.e. a commercial energy business has the option and the flexibility to fit the methodology for their own type of functionalities, business dealings and calculations. This kind of approach, therefore, resembles a skeleton which can fit into any situation related to any commercial and noncommercial energy schemes. The methodology will give the opportunity for those who are using it to tailor it for their own particular use. The creation of an IP within the methodology, as a standard reference, has provided the prospect of unlimited comparison to be made regardless of the location, finance and approach. The IP and the sub-factors scoring will be always in the hands of the decision makers, while at the same time, the conversion of textual values, will largely determine the accuracy of some of the results within the overall project data. References: [1] Triantaphoyllou E. (1997) A sensitivity analysis approach for some deterministic multi-criteria decision making methods. Decision Science vol.28, No. 1, pp [2] Pohekar S.D. and Ramachandran M. (2004) Application of multi-criteria decision making to sustainabkle energy planning A review. ScienceDirect, Renewable and Sustainable Energy Reviews, 8(2004) [3] Altawell N. (2009) A systematic approach to biomass and energy crop streams for cofiring. PhD thesis, The University of Nottingham. [4] Capoor K. and Ambrosi P. (2006) State and trends of the carbon market The World Bank, IETA. [5] Tonks G.D. (2004) Identifying market segments in consumer markets: variable selection and data interpretation. Track, working paper - department of marketing, the Management School, University of Lancaster. [6] Beck K., Joseph J., Goldszmidt G. (2005) Learn business process modelling basics for the analysts y/ws-bpm4analyst/ [7] Agnolucci P. (2005) Factors influencing the likelihood of regulatory change in renewable electricity markets. Policy studies institute, 5th BIIE academic conference, St John s College, Oxford.Fletcher S., Lyon K. and Rayner M. with assistance from John Todd and Philip Jennings of Murdoch University. (2005) Biomass energy systems. Edited and updated by Mark McHenry. ml ISBN: