Abstract
Saudi Arabia is moving towards economical and technological development and to be an active player in the dynamics of the international community. The economic growth in the country requires significant additional electrical power generation in order to supply this increasing demand. At present, fossil fuel (petroleum and natural gas) reserves in Saudi Arabia are reliable in terms of feeding the needs of its conventional power plants. Fossil fuels, however, are not sustainable, are subject to eventual depletion and plants using these fuels produce large amounts of CO2 emissions. The authors have examined other power generation alternatives with an eye towards achieving sustainability. These requirements have led them to propose a renewable energy source—radiant energy from the sun as the way to achieve long-term success. The region is blessed with an abundant solar flux throughout most of the year. Recognizing this, the authors propose building solar-powered electrical generation plants in the Saudi deserts, but are faced with the problem: “Which solar technology is the ideal choice for this application?” Several different technologies have been identified and analyzed. A mathematical model was constructed and used to make a selection of the optimal technology. The decision model employed to determine the optimal technology, taking account of the fact that in making such a technology selection subjective judgment is required, is the Hierarchal Decision Model (HDM). This research relied on data extracted from scientific journals and industry sources as the inputs for the Decision Model. The authors also validated their research and derived expert opinion weighting factors with international experts in the solar technology. They have applied the model to a case study to demonstrate the use of it.
TopMethodology Background
Hierarchical models have been used for evaluation of alternatives in many parts of the energy sector: Aminloei and Ghaderi (2010) for generation planning in Iranian power plants; Aragones-Beltran (2010) for the selection of photovoltaic solar power plant investment projects; Chatzimouratidis et al. (2008) for the evaluation of power plants’ impact on living standards; Chen (2009) for eco-efficiency; Dey (2002) for cross-country pipelines; Garcia et al. (2008) for evaluation of an electric distribution system; Jaber et al. (2008) for the evaluation of conventional and renewable energy sources for space heating in the household sector; Kahraman et al. (2009) for evaluation of renewable energy alternatives; Karger and Hennings (2009) for sustainability evaluation of decentralized electricity generation; Nagesha and Balachandra (2006) for energy efficiency, Onut et al. (2008) for energy resources for the Turkish manufacturing industry; Wijayatunga et al., (2006) for cleaner generation technologies; Quintero et al. (2008) for comparative analysis for fuel ethanol production from sugarcane and corn; Thorhallsdottir (2007) for evaluating and ranking national energy projects by environmental impact; and Vashishtha and Ramachandran (2006) for demand side management implementation strategies in the Indian power sector.
The HDM provides a powerful tool for valuing different types of energy generation technologies. The selection of evaluative criteria, was initiated by carrying out literature research. The selection could have been done only with research but, we went further and supplemented the literature data by consulting with electrical power generation experts in Saudi Arabia (SA). Our theory was that the local expertise would add value to picking the right criteria. Based on the feedback from these experts and the literature results, we have selected five criteria:
- •
Efficiency
- •
Environmental Impact
- •
Life Time
- •
Cost
Key Terms in this Chapter
Sub Criteria: These are the sub factors under the factors – third level in the hierarchy.
Mission: Overall objective of the decision model – top of the hierarchy.
Alternatives: These are the technologies being evaluated – fourth and lowest level in the hierarchy
Criteria: These are the factors impacting the mission – second level in the hierarchy.
Bibliometric Analysis: Use of publication trends to identify technology trends
Relative Weight: The combined impact/contribution level of an element on a level on all the elements at a higher level.
Pairwise Comparison Method: A martrix based method enabling to compare a pair of alternatives at a time and thus calculating the assigned weights based on the comparisons.
Analytic Hierarchy Process: A hierarchical decision process enabling to analyze the problem in hierarchies and calculate contributions of elements at one level to the level above.
Inconsistency: A metric calculating the variance in the weights calculated when the orientation of the elements in the matrix which is obtained through pairwise comparisons is changed. It indicates how consistent a decision maker is.