The process of research presented in this paper is illustrated in
Figure 1. The flow chart in
Figure 1 outlines the process of identifying key RE challenges in India. The use of MCDM for decision problems is common in research and in some practical cases such as in power system, energy-based applications like biomass facility location using MCDM [
37], Q-GIS MCDM-based approach for potential site selection [
38,
39], an overview of AHP [
40], MCDM-based application towards RE [
41,
42]. Here, in this approach, the TFN-based fuzzy model is used for ranking. Thirteen challenges that governments and private firms face in the RE domain, which are slowing down the transition from conventional to non-conventional energy sources, have been identified.
The challenges considered for evaluation are: availability of power (C1), power quality issues (C2), size of plant (C3), transmission (C4), information barrier (C5), scantiness in government support (C6), investors should have patience and perseverance (C7), lack of environmental concern among investors (C8), resource location (C9), drawbacks of renewable energy sources (C10), training courses not properly utilized (C11), high initial cost (C12), and lack of low-interest loans (C13), as summarized in
Figure 2 under different factors.
Table 1 shows the selected factors with their literature references.
The traditional renewable energy sources, especially wind, solar and hydro energy, have long been used by man to generate electricity. Since many decades, even biomass energy has been in existence in a much raw form. On the other hand, sources like tidal and geothermal energy seems also promising as the latest for promoting a developing nation like India. As of the most benefitted energy system hydro energy, it is quite a popular energy resource within the nation. The supply of abundant amount of water and river resources as well as moderate costs of establishment and electricity generation further add up to its popularity and resourceful usage. As of geothermal energy, that as well is a largely unexplored genre of energy generation and further research is needed. Wind energy has also gained large popularity within the nation, especially within the Southern parts of India with continuous coastal supply of optimum wind to generate a continuous amount of electricity. For an agriculture-oriented country like India, biomass energy can also prove a good adoption, with enough scope and potential for generation of biomass in the form of agricultural wastes. With enormous potential, nuclear energy, despite low generation costs comes with many consequences. Apart from environmental hazard, establishment cost itself for a nuclear power plant is tremendously high.
The following subsections present detailed definitions, metrics, and the acquired data for each criterion used in the model.
3.1. Challenges for RE Power Generation in India
An important concern in RE is the dependency on natural resources over which humans do not have control and which are intermittent. For instance, solar energy depends on the amount of sunshine, and it is not available at night. Wind energy depends on the amount of wind. Wind turbines do not rotate without enough wind, so there are periods of zero power supply to the grid. On the other hand, too much wind flow is not suitable for the generator, and a moderate amount is needed for continuous generation of electricity. Due to the uncertainty in energy production associated with the RE, integration with the grid is complex.
A constant supply of high-quality power is needed for efficiency and stability of the network. When the power supply quality is good, the system works properly with high reliability and low costs. A low quality of power may have major adverse effects on the industrial processes and on the power grid. This may cause additional costs and equipment failure. A poor power quality is associated with current/voltage harmonics, disorder in frequency, less power factor, variations in voltage and transmission line transits.
Determining an optimal size of plant has been a big challenge for RE firms. Because, due to constraints in storing electricity on a large scale, energy produced more than demand requirement goes unutilized. Therefore, the plant should not be too large. On the other hand, too small of a plant may not justify the investment and the demand.
For good utilization of RE sources, proper transmission infrastructure is required. Present power transmission infrastructure was mostly built during the twentieth century, and it was suited to nuclear plants and large fossil fuel plants. This often means that infrastructure objects and lines are not located near renewable energy sources and generation sites.
The challenge of information barrier is reflected in the following. The awareness of RE is constantly growing, but the society is still largely not informed about the benefits of RE and the need for it. In establishing a RE plant in India, both capital allowances and investment support by the government are available. However, many of the potential applicants are not aware of the incentives available, or they need assistance in applying for the incentives.
The Indian government’s target of installing 175 GW of clean energy capacity by 2022 is not likely to be met. The impediments in achieving this target are related to land acquisition, tariff caps and an import duty on solar cells and other modules. The government should support the research and development of the technologies, including those in renewable energy storage. If the problem of energy storage gets overcome, the RE sector would grow, and this could reduce the dependence on the conventional energy sources.
The Indian RE industry has been maturing. It is growing, the perceived risks are diminishing, and the expected yields are medium to high. RE is becoming more attractive than the other infrastructure sub-sectors, particularly the fossil fuel power generation. It is currently considered as risk free in terms of returns on investment.
In principle, investors are concerned with financial benefits and some investors are interested only in quick returns. Some investors have a certain degree of concern for the environment as well. This situation could improve to some extent by educating the investors more about the RE, but more significantly, with financial incentives and regularity action and enforcement by the government.
A geographic location of a RE source has multiple effects. In most cases, RE plants need to be in specific locations, governed by the availability of the resource, such as sunlight, wind, or water flow, as well as the suitability of the terrain and other location factors. The distance between the RE sources and the grid increases the cost and decreases the efficiency of the system. A grid that includes RE plants generally requires a larger area then a grid including conventional sources only.
RE has some drawbacks. When it comes to hydro-energy, establishing hydro-electric plants can cause extinction of living organisms. Reduction in sedimentation deposition causes a decrease in land fertility, raises the risk of dam structure failure and disturbs a normal river flow, which affects the water plants and animals and increases the risk of flood. Local people may lose their homes along with source of income. Greenhouse gases emission may rise due to decomposition of immersed biomass. When it comes to wind farms, the movement of mechanical parts of the turbine may result in vibrations due to wind disturbance, and potential change in wind speed. The wind blades are a threat to flying creatures and can cause the loss of habitat. The burning of biomass may lead to loss in biodiversity. If crops are used as biomass this may reduce the amount available for produce. In the case of biomass waste, the burning of biomass waste for power generation may reduce the amount available for other biomass waste uses. The establishment of solar power plants may cause ecosystem imbalance. Toxic heavy metals and rare earth minerals are required to produce photovoltaic cells. Some locations experience high variations in solar radiation throughout the year, resulting in uneven power production throughout the year. The use of geothermal energy can cause water and air pollution; and it can induce micro-seismicity and land subsidence.
Training courses to educate workers about green and sustainable practices are not well developed, and the courses that exist are not used by the management.
One of the greatest obstacles for the adoption of renewable energy is cost. The cost of building RE power generation plants is quite high. On the other hand, their operation costs are low, as the sources are free, and maintenance is low. Hence, the main cost associated with the RE projects is their initial cost. Because of this, the lenders perceive these projects as high-risk, which affects the borrowing rates, and this in turn makes it more difficult to justify an investment. The cost also refers to the costs associated with R&D investments.
Lack of low-interest loans. Banks are risk-averse, and since the environmentally friendly and sustainable industry has not yet proven to be highly profitable, the availability of bank loans for sustainable projects is limited, and their interest rates are high. The challenges identified were grouped in the following categories: Technical, Involvement and Support, Financial and Others, as shown in
Table 1.
3.2. Ranking of the Challenges Using Triangular Fuzzy Number (TFN)
Fuzzy set theory (FST) was proposed in 1965 [
43] to deal with uncertainty of a solution where input information is not complete [
44]. A degree of judgment is involved in decision-making, represented with a set of three numbers [
44,
45].
In a generalized fashion, a triangular fuzzy number (TFN) is represented as shown in Equation (1), where d1 indicates minimum boundary limit for the judgment’s uncertainty, d2 represents its median value, and d3 represents its maximum limit [
44]. The TFN technique is extremely useful in situations where information has some level of subjectivity [
44,
46].
The function of fuzzy membership can be given as:
where ‘
A’ represents a real number.
The operations of addition and division of any two TFNs,
and
, are as follows:
The scale of preference using the TFN method to compare two items is shown in
Table 2 [
47,
48].
Table 1.
Challenges for RE power generation.
Table 1.
Challenges for RE power generation.
NO. | Factor | Challenges | Symbol | Reference |
---|
1. | Technical | Availability of power | C1 | [49,50] |
| | Power quality issues | C2 | [51,52] |
| | Size of plant | C3 | [53,54] |
| | Transmission | C4 | [55,56] |
2. | Others | Information barrier | C5 | [49,50] |
| | Scantiness in Government support | C6 | [55,57] |
| | Investors should have patience and perseverance | C7 | [58,59] |
| | Lack of environmental concern among investors | C8 | [60,61] |
3. | Involvement and Support | Resource location | C9 | [62,63] |
| | Drawbacks of renewable energy sources | C10 | [53,58] |
| | Training courses not properly utilized | C11 | [55,58] |
4. | Financial | High initial cost | C12 | [50,63] |
| | Lack of low-interest loans | C13 | [58,59] |
The total integral value method, using fuzzy numbers [
48], is chosen for ranking of the challenges. The steps involve the calculations with TFNs
, and they are as follows:
Step 1: A decision matrix for pairwise comparisons of all the considered criteria (in this case, the 13 challenges) is made, applying the comparison scheme from
Table 2. The authors prepared a questionnaire corresponding to the decision matrix and using the TFN method nine-point linguistic scale. The comparisons represent the opinions of experts from related fields of study, along with circumstantial preference of decision maker. The responses of the three experts are shown in decision matrices, in
Table A1,
Table A2 and
Table A3 in the
Appendix A. Inputting these values into Equations (4)–(11), described below, the weight of each challenge was found.
Step 2: A synthetic extent value is given as:
Further, it was found that the previous research in this area proposed a few alterations and simplifications to the Equation (5) for the synthetic extent value. Implementing these modifications gives a re-arranged equation [
43]:
Step 3: After obtaining the synthetic extent value, using the comparison values and the calculations above, the degree of possibility of each respective criterion is obtained as follows:
The maximum point of interaction (
Q) is given within the specified boundaries of
and
as shown in
Figure 3. Here the membership value range is from 0 to 1 as per the standard followed for assigning membership value in fuzzy logic.
Step 4: Making the following assumptions:
The weight vector
C* can be obtained as:
Step 5: The Equation (10) shown below is usually used to find normalized weight vectors for the challenges. However, later modifications of the total integral value system [
43], by applying the methodology described, result in Equation (11), as shown below:
where
value has a range between 0 and 1, and it represents the degree of optimism from the perspective of the decision-maker. The value adopted for
β in this study is 0.5 [
43].