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Multicriteria Decision-Making for Evaluating Solar Energy Source of Saudi Arabia

doi: 10.3390/su151310228
Saudi Arabia generates more than 98% of its electricity through hydrocarbon resources. To reduce the consumption of fossil fuel resources and protect the environment, the government of Saudi Arabia is planning to make renewable energy an essential part of its energy mix. In this study, due to the country’s abundant solar potential, solar energy has been selected as the energy source to generate renewable energy in Saudi Arabia. The two solar energy technologies, photovoltaic (PV) and solar thermal, have been analyzed in three different locations within the country. Multi-criteria decision-making (MCDM) techniques were used to rank the cities for each of the technologies. The SAW(Simple Additive Weighting)-AHP(Analytic Hierarchy Process) MCDM method based on climate, environmental, technical, economic, and social has been adopted to analyze the suitability of each technology for all locations. To assign weights to the criteria AHP method was used, while to rank the technologies, SAW was used. The results show that for the PV technology, Abha ranked 1st with a performance score of 91%, making it the most suitable location, followed by Jeddah with 83%. While for solar thermal technologies, Jeddah is the most suitable location, with a performance score of 96%, followed by Abha with 91%. The PV systems generated a maximum of 11,019 MWh in Abha, while the solar thermal produced maximum of 14,000 MWh in Jeddah. Overall, solar thermal technology outperformed PV technology in Saudi Arabia due to the country’s higher temperature. The analysis of photovoltaic and solar thermal technologies in this study provides valuable insight for the government of Saudi Arabia in identifying the best site for solar energy technologies in the country.
- Northern Border University Saudi Arabia
- Al Jouf University Saudi Arabia
- Northern Border University Saudi Arabia
- Al Jouf University Saudi Arabia
improved clouded leopard optimization algorithm, reconfiguration, Environmental effects of industries and plants, TJ807-830, adaptive inertia weight, TD194-195, photovoltaic allocation, Renewable energy sources, Environmental sciences, distribution network; multi-objective and coordinated framework; reconfiguration; photovoltaic allocation; adaptive inertia weight; improved clouded leopard optimization algorithm, GE1-350, multi-objective and coordinated framework, distribution network
improved clouded leopard optimization algorithm, reconfiguration, Environmental effects of industries and plants, TJ807-830, adaptive inertia weight, TD194-195, photovoltaic allocation, Renewable energy sources, Environmental sciences, distribution network; multi-objective and coordinated framework; reconfiguration; photovoltaic allocation; adaptive inertia weight; improved clouded leopard optimization algorithm, GE1-350, multi-objective and coordinated framework, distribution network
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).15 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
