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Multi-criteria analysis of renewable energy technologies performance in diverse geographical locations of Saudi Arabia

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Abstract

Currently, more than 90% of the electricity produced in the Kingdom of Saudi Arabia originates from fossil fuels. Under the Vision 2030 initiative, the Kingdom aims to derive 50% of its energy from renewable sources by 2030. This study presents a comprehensive evaluation and ranking of renewable energy technologies for a selection of cities across the country using an integrated methodology that combines the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The focus is on four renewable sources, namely Photovoltaic (PV), Concentrated Solar Power (CSP), Wind Energy, and Fuel Cell, each with a capacity of 10 MW. The performance of these renewable sources is assessed in five different cities of Ar’Ar, Ha’il, Riyadh, Najran, and Al Baha. The evaluation considers multiple criteria that encompass climate, environment, and social aspects, providing a holistic assessment of the alternatives. The AHP method is employed to determine the relative weights of the criteria, ensuring consistency in the pairwise comparisons. The TOPSIS method is then applied to rank the alternatives based on their performance scores. The results highlight the preferences and relative performance of the different renewable energy technologies across the considered cities. Fuel Cell technology emerges as the most favorable option in all the cities with a score higher than 0.68, demonstrating superior capacity factor (92.4%), minimal environmental impact, and reliable power generation but with a negative net present value (NPV) of − 12.22 M$. Wind Energy and CSP technologies followed in ranking, indicating their competitiveness and suitability as renewable energy options by producing in excess of 25,000 MWh/year. PV technology demonstrates competitiveness across all cities with the lowest levelized cost of electricity (LCOE) (4.33 C/kWh) and quickest payback period (12.4 years). The findings of this study provide valuable insights for decision-makers and stakeholders involved in the selection of appropriate renewable energy technologies. The rankings serve as a valuable tool in informing decision-making processes and facilitating the transition to sustainable energy systems.

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Data availability

Data will be provided upon reasonable request from authors.

Abbreviations

MCDM:

Multi criteria decision making

TOPSIS:

Technique for order of preference by similarity to ideal solution

WT:

Wind turbine

NPV:

Net present value

GHG:

Green house gases

CO:

Carbon monoxide

SO2 :

Sulphur dioxide

SI:

Solar irradiance

A :

Surface area of module

DC:

Direct current

ΔWe:

Power output

\(\sum_{i=1}^{1}W\mathrm{sub},i\) :

Power consumption

PWTG :

Power output of a wind turbine

Ρ:

Air density

AHP:

Analytic hierarchy process

PV:

Photovoltaics

CSP:

Concentrated solar power

LCOE:

Levelized cost of electricity

CO2 :

Carbo dioxide

NO2 :

Nitrogen dioxide

GIS:

Geographic information system

C an :

Total annualized cost

P max:

Panel power

AC:

Alternating current

Qsolar:

Thermal energy input

\(\sum_{i=1}^{n}Q\mathrm{add},i\) :

Thermal energy load in the boiler

P WTG, STP :

Pout at standard temperature and pressure

ρ̥,:

Density of air at normal temperature and pressure

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Acknowledgements

The authors are grateful to the Prince Faisal bin Khalid bin Sultan Research Chair in Renewable Energy Studies and Applications (PFCRE) at Northern Border University for its support and assistance.

Funding

The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research work through the project number ‘‘NBU-FFR-2023-0136’’.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, College of Engineering, Northern Border University, Ar’Ar, Saudi Arabia

    Abdulaziz Alanazi

  2. Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka, Saudi Arabia

    Mohana Alanazi

  3. Department of Energy Engineering Technology, University of Technology, Nowshera, Pakistan

    Imad Hassan & Shayan Tariq Jan

  4. Renewable Energy Research Laboratory (RENERAL) Electrical Engineering Section, British Malaysian Institute, University Kuala Lumpur, Selangor, Malaysia

    Imad Hassan

Authors
  1. Abdulaziz Alanazi
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  2. Imad Hassan
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  3. Shayan Tariq Jan
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  4. Mohana Alanazi
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Contributions

Conceptualization: AA, IHSJ; Data curation: MA, IH and SJ; Formal analysis: IH and SJ Investigation: MA and SJ; Methodology, IH and SJ; Project administration: AA and MA; Resources: AA and MA; Supervision: AA and MA; Validation: AA and MA; Visualization, MA; Writing—original draft: IH and SJ; Writing—review & editing: AA and MA.

Corresponding author

Correspondence to Shayan Tariq Jan.

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Appendix 1

Appendix 1

AHP method for determining criteria weights

To evaluate the values in light of the chosen criteria, it is necessary to ascertain the weights of the criteria. The AHP method is employed for this purpose. Details of the methodology and process behind AHP can be found in foundational literature (Alanazi and Alanazi 2023; Mohammad et al. 2021; Mu and Pereyra-Rojas 2018). The fundamental idea behind this method is to create a comparison decision matrix that enables comparisons between each criterion and its pair using certain computations and procedures to produce constant weights for each criterion.

TOPSIS Method for ranking alternatives

Once the criteria weights have been established, the subsequent task involves ranking the alternatives in order of preference. To accomplish this, the TOPSIS method was employed. The TOPSIS method utilizes the concept of ideal and anti-ideal solutions to assess the degree of proximity of each alternative to the ideal choice. This method serves as a valuable tool in MCDM, offering a systematic approach to evaluating and ranking alternatives based on their overall performance. Details of the methodology and process behind TOPSIS can be found in foundational literature (Bilgili et al. 2022; Solangi et al. 2021; Rani et al. 2020).

AHP calculation

Table 14 shows the pairwise values of the comparison matrix. It shows a comparison matrix that represent the relative importance/preference of one element compared to another element. The values are assigned on a scale, ranging from 1 to 9, where 1 indicates equal importance/preference, and higher values indicate increasing preference or importance. The priority of each criterion was carefully selected after carrying out extensive literature review of renewable energy technologies.

Table 14 Pairwise values of Comparison matrix
Full size table

Once the pairwise comparison matrix is defined, the normalized pairwise matrix Table 15 is derived. The Normalized Pairwise Matrix is a mathematical transformation of the pairwise comparison matrix that ensures the values are normalized and consistent across all elements. The pairwise values in the comparison matrix are divided by the sum of the values in their respective columns. This normalization process ensures that the values represent relative importance or preference ratios that add up to 1 for each column.

Table 15 Normalized Pairwise Matrix
Full size table

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Alanazi, A., Hassan, I., Jan, S.T. et al. Multi-criteria analysis of renewable energy technologies performance in diverse geographical locations of Saudi Arabia. Clean Techn Environ Policy 26, 1165–1196 (2024). https://doi.org/10.1007/s10098-023-02669-y

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