Electricity Generation Scenarios For Jordan (2018-2035)

Research Article
Farah Dawoud., Ahmed Al-Salaymeh and Osama Abuzeid
DOI: 
http://dx.doi.org/10.24327/ijrsr.2019.1002.3190
Subject: 
science
KeyWords: 
Jordan, Conventional power plants Renewable power plants , Electricity generation ,Economic model Environmental model,Social model Optimization, Scenarios
Abstract: 

The planning for strong, productive and sustainable electricity generation is complex and challenging in Jordan. Therefore in this paper, a new approach for scenario planning has been conducted to give an effective tool for evaluating and examining the electricity generation technologies, for both conventional and renewable technologies, in Jordan. The aim of the scenarios is to prepare and implement innovative and robust plans for the electricity generation sector. Consequently, strengthen the readiness of the country to response to the growing demand and the emergency’s circumstance. Accordingly, four scenarios, for the electricity generation from renewable and conventional power plants in Jordan for the years (2018 – 2035), have been developed by building economic, environmental and social models. These four scenarios have been developed based on the most two key uncertainties that make a crucial threat on the electricity generation sector in Jordan, which are; the economic and the geopolitical uncertainties. Finally, this paper identified the best power generation technologies form conventional and renewable systems, through conducting optimization process by using GAMS software for the three aforementioned models. The results of the optimization found that the best technologies in generating electricity are the GCC power plant, from conventional technologies and the PV utility and the wind turbine from the renewable technologies during the years (2018 – 2035). Accordingly, their average optimal shares in generating the electricity for the years (2018) and (2035) are for the GCC power plant (70%) and (10%), for the PV utility (19%) and (71%) and for the Wind turbine (11%) and (19%) respectively.