Original Research ArticleSelection among renewable energy alternatives based on a fuzzy analytic hierarchy process in Indonesia
Introduction
Although the world has experienced many energy crises, especially in the last two decades, the amount of energy consumption has continued to increase. In the European Union, energy demand in the period from 2000 to 2030 is expected to continue to increase by 0.5%/year, whereas energy demand in Asia is expected to continue to increase by 3%/year [1]. The world average of the rate of expected increasing energy demand is 1.8%/year in the period from 2000 to 2030 [1].
The primary source of energy in the world is fossil fuels, which supply 81% of the total energy mix [1]. The demand of fossil fuel steadily increased during the period from 2007 to 2030, with the largest growth in coal, followed by gas and oil, with growth factors of 53%, 42% and 24%, respectively [2].
One impact of fossil energy use is the deterioration of the environment through pollutant releases, which mainly consist of CO2 emissions. As a result of significant fossil fuel use, the amount of CO2 emissions in the world is expected to more than double in the period from 1990 to 2030, from 21 to 45 Gt of CO2 [1]. Currently, 70% of the CO2 emissions originate from industrial countries, and the remainder originates from developing countries [1].
Energy cost has also exhibited an increasing trend over the past two decades, and it is predicted to rise much faster in the future due to the shortage of oil resources, high demand for oil and political conflict in the major oil-producing countries.
Because there are many problems related to the use of fossil fuels, such as price and environmental impact, people are making an effort to develop alternatives. Renewable energy is one of these alternative energy sources. Worldwide, the use of renewable energy to generate electricity is expected to increase from 2% in 1990 to 4% in 2030 [1]. Renewable energy sources are also predicted to contribute 4% of the energy mix in 2030 [1]. In some regions, such as North America, 55% of the electricity are projected to be produced from renewable energy sources by 2030 [3]. In the Commonwealth of Independent States (CIS) and the Central and Eastern European (CEEC) regions, renewable energy sources are predicted to produce 50% of the total amount of electricity used [3]. In Asia, the use of renewable energy sources is predicted to account for over 40% of the total amount of energy used in 2030 [3].
Similar to most countries in the world, the energy mix in Indonesia mainly consists of fossil fuel sources. Although demand for fossil fuels continues to increase, the fossil fuel production continues to decrease. Crude oil production decreased from 517 million barrels in 2000 to 344 million barrels in 2010 [4]. In 2030, Indonesian production is predicted to be reduced to 120 million barrels [4].
To substitute for the continuous decrease of fossil fuel production, the Indonesian government, in its national energy plan of 2005–2025, has called for a high share of renewable energy in its energy mix, with renewable energy targeted to provide approximately 15% of the energy need in 2025 [4].
A successful transition from fossil energy to renewable energy requires careful planning and selection of the most appropriate renewable energy sources. However, researches on the selection of the most appropriate renewable energy sources in Indonesia have not attracted much attention from researchers. One paper related to the selection of renewable energy in Indonesia was found in the literature. Rumbayan and Nagasaka [5] conducted a study to determine the priority locations for the development of renewable energies in Indonesia. Three kinds of renewable energies, solar, wind, and geothermal, were used as the main criteria. While the sub criteria were the level of availability of the renewable energies’ source. They also determined the weight of members of the main criteria (wind, solar and geothermal) but there was no explanation of the weight determination procedure. So far, there are no publications found in the literature regarding the most appropriate selection among the options for renewable energy sources in Indonesia. This issue of selecting the most appropriate renewable energy sources in Indonesia will be addressed in this study. This study is limited only to the renewable energy to be used commercially in the form of electrical energy. The uses of renewable energy for household needs are not included in this study.
In selecting the best option among a set of alternatives, a set of criteria is required. Each criterion does not have the same degree of importance; they should be weighted to indicate their relative importance. The alternatives are then awarded a performance score based on their performance related to the criteria. The total performance score of an alternative is the summation of the scores of the alternative for a particular criterion multiplied by the weight of the relevant criterion. The best alternative is the one that has the highest total performance score.
The weighting of the criteria is primarily performed by adopting the weighting procedure used in the Analytics Hierarchy Process (AHP), which was introduced by Saaty [6]. The AHP has been used successfully to solve energy problems, such as the problems described by Wang et al. [7], Kagazyo et al. [8], Ramathan and Ganesh [9] and Kablan [10].
Beccali et al. [11] used an AHP-based procedure known as Elimination and Choice Translating Reality (ELECTRE) for creating a renewable energy diffusion strategy. Xiaohua and Zhenmin [12] used an index to evaluate the sustainable development of rural energy and used the AHP to calculate the weighting of each index involved. Jaber et al. [13] also used the AHP to evaluate space heating systems running on conventional and renewable energy sources in Jordan. Kone and Buke [14] used ANP, a general form of AHP where interdependence and feedback among decision elements are calculated, to find the best fuel mix for electricity generation in Turkey.
In the AHP methods, items are ranked or weighted by comparing the items in a pairwise manner. The score for the pairwise comparison is awarded based on the relative importance of the first item to second item of the pair. The score is given sometime intuitively; therefore, it is influenced by and depends on human perception, which always contains vagueness and imprecision. To eliminate the imprecise nature of human judgment, scores in the form of fuzzy numbers are used instead of the precise or crisp one used in the original AHP. Using fuzzy number in AHP was introduced by van Laarhoven and Pedrycz [15] who extended AHP method with triangular fuzzy number. Fuzzy numbers were also used in AHP by Jung [16] to get the weight of criteria in the AHP structure. Since, the fuzzy AHP has been used to solve various multi-criteria decision problems. Mikhailov [17] used fuzzy AHP for partnership selection in a virtual enterprise. Gungor et al. [18] used a fuzzy AHP to address personal selection problems.
Fuzzy AHP is also widely used for various purposes in the field of energy. Ansari et al. [19] used fuzzy AHP methods for the selection of the best renewable energy to generate electricity in India. Lee et al. [20] used fuzzy AHP to determine weight of five types of hydrogen energy technologies. Four criteria consisting of; economic impact, commercial potential, inner capacity and technical spin off were used in the determination of the weights. Zeng et al. [21] used fuzzy AHP to develop a model for building energy conservation assessment. They proposed seven assessment factors which have 22 sub-factors. In the assessment, a group of experts was asked to give a score for performance of the building on each sub-factor. Then a group of experts are also asked to make pairwise comparisons among the sub-factors to determine weights of the sub-factors. The same was done for the main factors. Degree of membership of each of these main factors and sub factors was calculated based on the assessment scores and the weights of factors and sub-factors. Kahraman and Kaya [22] used fuzzy AHP to find the weights of main criteria and sub-criteria that are needed to determine the rank of renewable energy. Once the weights of the main criteria and sub-criteria were determined, experts measure the performance of each renewable energy source on each sub-criterion and give a performance score to the renewable energy source. Ranking of the renewable energy sources was determined as summation of all the results of multiplication between the performance score of each of the renewable energy sources regarding the particular criterion and the weight of the criterion.
AHP is also often combined with other methods such as TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), DEA (Data Envelopment Analysis) or VIKOR (VIsekriterijumsko KOmpromisno Rangiranje). Choudary and Shankar [23] used combination of fuzzy AHP and TOPSIS to select appropriate location for thermal power plants. Fuzzy AHP was used to determine weights of criteria, while the TOPSIS was used to determine performance score of alternatives on each criterion and to determine the ranking of the alternatives. Similar to this work, Kaya and Kahraman [24] used combination of VIKOR and AHP to select best energy alternative for the city of Istanbul. They used AHP to find weight of criteria and VIKOR was used to find the best among the alternatives.
To produce an accurate result, the comparison in AHP procedure should not be made by one person but by a group of experts. The members of the group of experts must have different backgrounds and points of view to enrich the result. The opinions of each member of the group of experts are then aggregated to form a group opinion. There are some methods for the aggregation of fuzzy numbers that have been introduced [25], [26], [27]. One of popular methods among these aggregation methods is the similarity index (SAM) method of Hsu and Chen [27]. They proposed to aggregate the fuzzy numbers as the summation of the results of the multiplications between the fuzzy numbers and corresponding weight that measures the significance of the relevant fuzzy number. The weight was determined as the proportion of the consistent area to the total area of the fuzzy number involved in the aggregation. In the case of fuzzy numbers that do not have an intersection, the consistent area is zero and the result is a zero weight; SAM methods do not work in this condition. To solve this problem, Hsu and Chen [27] proposed to use the Delphi method [6], [28], in which the fuzzy number is adjusted so that the intersection is established; however, this adjustment causes distortion of the expert opinions.
A new aggregation procedure is proposed in this work in which the weight factor is determined based on the distance among the fuzzy numbers instead of the consistence area, as used in the SAM methods, so that the zero weight case is avoided. The new aggregation method is used as a part of a fuzzy AHP-based selection procedure for the selection of the best sources among the alternative renewable energy sources for Indonesia. The selection is aimed to find renewable energy source that is best to generate electricity commercially. A set of evaluation criteria adequate for the situation in Indonesia is also proposed. Finally, the best renewable energy for Indonesia is presented.
Section snippets
Material and methodology
Selection of renewable energy sources is a complex process. This selection requires a number of tools that include data on the availability of energy sources, selection criteria and methods used in the selection process. The tools are described briefly in this section.
Selection among the renewable energy alternatives for Indonesia
In this work, the five renewable energy sources that are typically used to generate electricity in Indonesia were taken into consideration. These energy sources are geothermal, wind, solar, biomass and hydro power.
Determination of the most appropriate renewable energy source was carried out using the steps described in the section ‘Methodology for the selection of renewable energy sources’. Following these steps, each member of the group of experts first determines a score via a pairwise
4. Conclusion
Five types of renewable energy options in Indonesia have been evaluated to determine the most appropriate one. An AHP based selection methodology is proposed. This methodology involves a new procedure for the aggregation of expert opinions using the five selection criteria and fifteen sub-criteria that are appropriate for Indonesia.
Experts involved in the assessment found that the quality of the energy source is the most important main criterion, followed by the economic as the second most
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