Setting Priorities for Evaluating the Use of Renewable ...

Setting Priorities for Evaluating the Use of Renewable Power Generation: The Case of Saudi Arabia Hassan Al Garnia*, Anjali Awasthia a

Concordia University, CIISE, 1455 De Maisonneuve Blvd. W., Montreal H3G 1M8, Canada

Abstract: This research evaluates five renewable energy resources simultaneously using multi-criteria decision analysis. It formulates a decision tool to assess five renewable energy sources namely solar photovoltaic, concentrated solar power, wind energy, biomass, and geothermal as alternatives and employs eight different quantitative criteria to evaluate these alternatives. TOPSIS model is developed to prioritize the alternatives and assess their potential for electricity generation. Saudi Arabia, a major oil producer and global supplier, is considered as a case study. All the considered factors and their figures are elicited from literature and international energy organizations. The results of TOPSIS technique reveal that solar PV and solar thermal have the most potential to develop a sustainable power generation system. Additionally, the wind, geothermal and biomass energy are determined to be third, fourth and fifth respectively.

Keywords: Renewable Energy Selection, Electricity Generation, Multicriteria Decision Making, TOPSIS, Saudi Arabia

1. Introduction The global energy sector is under a historical transition to ease the dependence on fossil fuels which are leading to daunting challenges such as massive air pollution and increasing demand due to higher economic growth and growing population. One important stride to tackle these issues is by integrating renewable energy sources (RES) in energy mix portfolio for more sustainable energy future. This changeover is no longer voluntary, but has become a critical need to keep pace with growing energy demand. Oil-dependent countries including Saudi Arabia, which is a major oil producer and exporter as well as the largest oil consumption country in the middle east, has a protracted and arduous way ahead regarding energy production and consumption. Saudi Arabia consumes more than 3 million bbl/d of oil primarily for power generation, water desalination, and transportation as shown in the Sankey diagram in figure 1 [1]. It uses a major amount of crude oil for power generation which is mainly driven by the rapid growth of population besides the economic development, particularly in the industries sector. Moreover, the prolonged hot weather during Summer causing the significant usage of air conditioning which consumes more than 60% of the total consumed electric power in the country [2]. To tackle this issue, several measures have been taken by the decision makers including the establishments of the Saudi Energy Efficiency Centre in 2010 to publicize rationalization awareness and boost energy consumption efficiency which will preserve the national wealth of energy resources [3]. Furthermore, the King Abdullah City for Atomic and Renewable Energy (K.A.CARE) was established to make remarkable diversifications regarding energy resources. Saudi Arabia has pushed back its long-term RES plans to 2040 instead of 2032 due to the need for more evaluation and prioritization in which domestic RE sources to use for the portfolio. In June 2016, the government has removed subsidies for power generation and made a new adjustment for consumption tariff which causes more than 60% increase in some service

category [4]. Undoubtedly, the prioritization of RES involves multiple conflicting aspects and objectives. Therefore, considering multicriteria decision making (MCDM) tools are adequate to tackle a complex problem such as energy planning [5,6]. In this paper, five RES were evaluated using decision support framework under interrelated measures for selecting the most sustainable power generation technologies. The technique for order of preference by similarity to ideal solution (TOPSIS) will be considered in this paper as a MCDM model whereas the case of Saudi Arabia will be studied.

Figure 1. The balance chart for energy production, consumption and exports (IEA,2016).

2. Renewable Energy Potential in Saudi Arabia RES including: solar, wind, biomass and geothermal are clean, natural, free and easily replenished sources. Due to the plenty of oil availability in Saudi Arabia, the deployment of the RES has not yet exploited on a utility-scale across the country. However, Saudi Arabia is one of the most enriched countries with natural resources with high potential to utilize the abundant RES to meet a significant part of the Kingdom’s energy needs and deliver an energy efficient future [7]. The

following sub-sections introduced the RES technologies announced by K.A.CARE as potential alternatives for the RE portfolio plan of Saudi Arabia: 2.1. Solar radiation in the Kingdom is considered as one of the highest rates globally with an average global horizontal irradiance (GHI) of 2 MWh/m2/year [8]. Rahman et al. [9] studied long-term mean values of sunshine duration and global solar radiation on horizontal surfaces over 41 cities in the kingdom. The results demonstrate the overall mean of yearly sunshine duration in the Kingdom is 3,248 hours, and the GHI varies between a minimum of 1.63 MWh/m2/year at Tabuk in the northwest of the country and a maximum of 2.56 MWh/m2/year at Bisha in the southwest. Since 1960, remarkable experience has been acquired and many lessons learned in the area of solar energy from various studies and research programs conducted in the kingdom [8]. 2.2. Wind energy proposed by many researchers as a potential source of energy in Saudi Arabia as in many locations, the annual mean wind speed exceeds 4 m/s at the height of 20 m [10]. Eltamaly et al. [10] studied five locations in Saudi Arabia and found that the best place to install wind turbines is Dhahran at the cost of 5.85 US cents/kWh. The projected wind energy potential in Saudi Arabia is around 20 TWh/year [11]. Also, there is potential to develop wind power in the western coastal region, including at Al-Wajh, Jeddah, Yanbu, and Jizan. Yanbu has presented relatively high potential for wind energy generation compared to other places [12]. 2.3. Geothermal resources in the kingdom remain unexploited for electricity generation, heating, or any other purposes. Though, the outcome of research shows that there is sufficient geothermal energy to contribute to many direct applications. Hot springs and sedimentary aquifers are the primary geothermal resources in the kingdom [13].

2.4. Biomass energy remains idle despite estimated potential of 3 Mtoe/year [14]. The kingdom had a waste-to-energy potential estimated to be 1.75 kg per capita per day in 2012 the abundant of municipality solid waste (MSW). An enormous amount of biomass could be transformed into usable energy for more sustainable electricity generation [14].

3. Methodology Energy planning is a field that encompasses multiple aspects which not adequately addressed by some single phase assessment indicators such as cost to benefit analysis. As a result of this, MCDM techniques which have a rapidly growing literature in recent years to tackle problems involving long-term energy source ranking as opposed to the classic single-dimensional index are proposed. MCDM is used to evaluate the overall system mix for power suppliers to establish the bestproposed alternatives for sustainable development [33]. Through literature investigations, the proposed model takes into account eight effective and interrelated decision criteria associated with the energy planning including resource availability, efficiency, land requirement, emission impact, job creation, energy cost, capital cost and operation and maintenance (O&M) cost as shown in the hierarchy in Figure 2. Due to the lack of data on the resource availability of two alternatives in Saudi Arabia including geothermal and biomass; based on a review of K.A.CARE vision and associated studies to geothermal and biomass energy availability in Saudi Arabia, assumptions has been made so that geothermal followed by biomass represent the lowest level of resources availability. Biomass and geothermal electric generation were estimated as 200 kWh/m2/year and 100 kWh/m2/year, respectively. Various MCDM techniques have been reported in the literature to deal with energy planning applications including the analytical hierarchy process (AHP), the technique for order of preference by similarity to ideal solution (TOPSIS), elimination and choice expressing reality

(ELECTRE), and preference ranking organization method for enrichment evaluation (PROMETHEE) [6,15–18]. Informative literature review on application of MCDM approaches in the renewable energy area can be found in Pohekar et al. and Wang et al. [19,20].

Figure 2. The associated decision criteria and alternatives in a hierarchy structure

In this paper the TOPSIS method will be applied in order to evaluate the potential RES including: Solar photovoltaic (PV), solar thermal, wind, biomass and geothermal with respect to different criteria including: resource availability, efficiency, land requirement, emission impact, job creation, energy cost, capital cost and operation and maintenance (O&M) cost which have been extracted from literature in the field of renewable energy planning [6,15–21]. The proposed approach is introduced in Figure 3. We initiate by defining the alternatives and decision criteria to facilitate the problem. Afterward, performance scores of the alternatives on decision criteria are presented. The quantitative data of these alternatives are elicited from literature as well as from international energy organizations such as international energy agency (IEA), international renewable energy agency (IRENA) which will enrich the model since there is no subjectivity as shown in Tables 1 and 2. Subsequently, the weights of the decision criteria could

be considered. In this paper, primary equal weights deliberation will take a place which would deliver clear outcomes of alternatives on each criterion.

Alternative

Efficiency (%)

Solar PV Solar Thermal Wind Geothermal Biomass

12 21 35 16 25

Resource Availability (kwh/m2/year) 2130 2200 570 100 200

Land use average (𝒎𝟐 /𝑮𝑾𝒉)

Emissions (𝒕𝑪𝑶𝟐 equivalent/MWh)

150 40 200 100 25

0.07 0.02 0.04 0.04 0.1

Table 1. Efficiency [22,23] , resource availability [24–26] , Land requirement [27] and emission level [28]

Alternative

Capital cost (USD /MW)

O&M cost (USD /KW-year)

Energy cost (USD/kWh)

Total JobYear/GWh

Solar PV Solar Thermal

3,873 5,067

39.55 67.26

0.270 0.230

0.87 0.23

Wind Geothermal Biomass

2,213 6,243 8,312

24.69 132 460.47

0.08 0.07 0.05

0.17 0.25 0.21

Table 2., Capital costs [29] , operations and maintenance costs [29] , energy cost [30] and employment creation [31]

TOPSIS algorithm which developed by Hwang and Yoon [32] is based on the concept of ranking the ideal alternative that has the shortest distance from the positive ideal solution (PIS) and the farthest distance from the negative ideal solution (NIS). The final ranking is obtained using the closeness index. Figure 4 illustrates the associated steps of general TOPSIS algorithm which could be in explained as follows: Step 1: formulate the performance matrix which includes the values of 𝑋𝑖𝑗 where X is the rating of alternative 𝐴𝑖 with respect to criteria 𝐶𝑗 as per equation 1.

[𝐶1

𝐶2

𝐶𝑛 ]

. ..

𝑋11 𝑋12 . .. 𝐴1 𝑋21 𝑋22 . . . 𝐴2 . . ... 𝐷= . . . . ... [𝐴𝑚 ] [𝑋𝑚1 𝑋𝑚2 . . .

𝑋1𝑛 𝑋2𝑛 … . , 𝑖 = 1,2 … . , 𝑚; 𝑗 = 1,2, … . . , 𝑛 (1) …. 𝑋𝑚𝑛 ]

Figure 3. The proposed approach of renewable energy sources evaluation using TOPSIS

1 2 3 4 5 6 7

• Establish a performance matrix • Normalize the decision matrix • Calculate the weighted normalized decision matrix • Determine the PIS and NIS • Calculate the separation measures • Calculate the relative closeness to the ideal solution • Rank the Preferanace order

Figure 4. TOPSIS general steps

Step 2: compute the normalized performance of matrix D according to equation 2. 𝑟𝑖𝑗 = 𝑋𝑖𝑗 / (∑𝑛𝑖=1 𝑋𝑖𝑗 2 )

(2)

Step 3: construct the weighted performance matrix by multiplying by its associated criteria weights as: 𝑣𝑖𝑗 = 𝑤𝑖𝑗 ∗ 𝑟𝑖𝑗

(3)

Step 4: determine the PIS and NIS as follows: + }, 𝐴+ = {(𝑚𝑎𝑥 𝑣𝑖𝑗 | 𝑗 ∈ 𝐼) , (𝑚𝑖𝑛 𝑣𝑖𝑗 | 𝑗 ∈ 𝐼 ′ ) , 𝑖 = 1,2, … , 𝑚} = {𝑣1+ , 𝑣2+ , … , 𝑣𝑚 𝑖

𝑖

− }, 𝐴− = {(𝑚𝑖𝑛 𝑣𝑖𝑗 | 𝑗 ∈ 𝐼) , (𝑚𝑎𝑥 𝑣𝑖𝑗 | 𝑗 ∈ 𝐼 ′ ) , 𝑖 = 1,2, … , 𝑚} = {𝑣1− , 𝑣2− , … , 𝑣𝑚 𝑖

𝑖

(4)

where I = {j = 1, 2, … , n} and I′ = {j = 1, 2, … , n} are set of benefit and cost criteria respectively. Step 5: determine the separation measures from PIS and NIS: 𝑆𝑖+ = √∑𝑛𝑗=1(𝑣𝑖𝑗 − 𝑣𝑗+ ) 2

, 𝑖 = 1,2 … … 𝑚

𝑆𝑖− = √∑𝑛𝑗=1(𝑣𝑖𝑗 − 𝑣𝑗− ) 2

, 𝑖 = 1,2 … … 𝑚

(5)

Step 6: calculate the relative closeness 𝐶𝑖 of each alternative to the ideal solution. The closer alternative to 1 is the most feasible alternative: 𝐶𝑖 =

𝑆𝑖− 𝑆𝑖+ + 𝑆𝑖−

, 𝐶𝑖 ∈ {1,0}

(6)

Step 7: rank the preference of the potential alternatives according to the relative closeness index.

4. Results and discussion The determination of the preference ranking of RES has several aspects including technical, environmental and economical and this paper includes effective criteria from these prospective. Setting priorities for evaluating the use of renewable power generation for the case of Saudi Arabia is established. The alternatives are considered from K.A.CARE which is in authority for energy diversification in Saudi Arabia. The criteria corresponding weights could be obtained from experts. However, in this study only equal weights considered as shown in Table 3 which introduces the

normalized scores using equation 2 and 3. Table 4 shows the PIS and NIS of decision criteria scores for all alternatives using equation 4.

PV Solar thermal Wind Geothermal Biomass

Res. Av 0.410 0.423 0.110 0.019 0.038

Effic. 0.110 0.193 0.321 0.147 0.229

Land req. 0.291 0.078 0.388 0.194 0.049

Emissions 0.259 0.074 0.148 0.148 0.370

Job 0.503 0.133 0.098 0.145 0.121

Energy cost 0.386 0.329 0.114 0.100 0.071

Capit. Cost 0.151 0.197 0.086 0.243 0.323

O&M cost 0.055 0.093 0.034 0.182 0.636

Energy cost

Capit. Cost

O&M cost

Table 3. The normalized performances of the decision criteria

Ideal solution

Res. Av

Effic.

Land req.

Emissions

Job

PIS

0.423

0.321

0.049

0.074

0.503

0.071

0.086

0.034

NIS

0.019

0.110

0.388

0.370

0.098

0.386

0.323

0.636

Table 4. The positive ideal solution (PIS) and negative ideal solution (NIS) under criteria

As mentioned in methodology section that a preferable alternative is the one which poses a closer value to 1, thus the results show that solar PV was deemed as best RES followed by solar thermal. The rank attained in ascending order as follows: Biomass, geothermal, wind, solar thermal and solar PV. The overall measurement results are summarized in Table 5. Moreover, it is noteworthy, that the main finding of this research is that TOPSIS can be a powerful technique to provide the decision maker with the insightful decision to consider the RES alternatives for energy mix portfolio. Also, TOPSIS has shown its flexibility to deal with different parameters with various units as well as accepting different decision weight criteria. Distance to the ideal d+

PV

Solar thermal

Wind

Geothermal

Biomass

0.491

0.486

0.620

0.629

0.899

d-

0.840

0.818

0.771

0.620

0.479

Ca

0.631 1

0.627 2

0.554 3

0.496 4

0.348 5

Ranking

Table 5. Distance from the ideal solutions for the potential alternatives

Comparing to the study by Al Garni et al. [21] who applied AHP as a MCDM technique for the same case study, these results are in line with the first three alternatives. However, in this research the geothermal energy surpasses biomass energy because geothermal was closer the PSI and farther from NIS. Moreover, in this study we consider only equal weight scenario wherein [21] different weights were given to the decision criteria. The main limitation of the experimental result is that more accurate data is required for the resource availability of geothermal and biomass availability in Saudi Arabia. The criteria weight could play a significant role in the outcomes where experts could provide their inputs to enhance the decision-making framework model. The model also shows that each alternative resource of solar PV and solar thermal presents relatively short distance to the ideal positive while they show the farthest distance from the negative solutions.

5. Conclusions In the renewable energy planning development, the prioritization of RES is a complex and important process. Such decision should consider different and multiple factors that affect the performance or the cost of the RES project. This study is an attempt to address the issue of assessment and evaluation of RES and presents a MCDM for setting priorities for evaluating the renewable power generation sources considering Saudi Arabia as a case study. Saudi Arabia is entirely dependent on fossil fuel for electricity and noticeably the power demand is going to rise in future. To construct a sustainable energy mix portfolio, the integration of RES with the fossil fuel become inevitability rather than a facultative. Notably, more deployment of RES should go to solar PV and solar thermal as they preferred options over others due to the higher performance under technical, environmental and economic factors. Adoption of TOPSIS shows its strength as a useful MCDM technique to prioritize different RES under interrelated weighted criteria.

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