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Abstract

Comparing Qatar with some developed countries such as Unite States and Europe, electricity price in Qatar is ultra-low because most of electricity is produced from low-cost gas and subsides are offered by the goverment. With facing this truth, it is very important to keep in mind that current electricity tariff is neither sustainable nor energy-saving due to oil price down, and also it will cause more and more electricity waste and carbon emission. Therefore, it is necessary for stakeholders and policymakers such as Kahramaa to evaluate what is the best tariff for electricity tariff for energy-saving and renewable energy initiative of Qatar. This paper investigate and addresses this issue by developing an approach on how to design and validate an efficient electricity tariff for renewable energy in Qatar. Firstly, negative impacts on the public and other stakeholders are investigated for the current electricity tariff. Based on demand analysis, a numerical pricing model of electricity for suppliers and users is built, and its quantitative economics are designed, considering renewables as a key utility function factor. Based on the designed pricing model of electricity, a convex quadratic minimization with linear constraints is defined. Optimization solving algorithms based on Artificial Intelligence such as Genetic Algorithm (GA) and Ant Colony Algorithm (ACA) are utilized to search the best solution to maximize the utility function. Finally, the dynamics of the pricing model is investigated and validated based on simulation scenarios. The main contribution of this study is to help stakeholders such as Kaharama to evaluate the effectiveness of current electricity tariff and advise a better pricing solution under consideration for renewable energy initiative of Qatar. Also, the quantitative model can also work as an efficient and dynamic evaluation tool and approach for stakeholders and policy-makers.

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/content/papers/10.5339/qfarc.2016.EEPP2075
2016-03-21
2020-05-26
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http://instance.metastore.ingenta.com/content/papers/10.5339/qfarc.2016.EEPP2075
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