# Optimization of Electricity Generation Technologies to Reduce Carbon Dioxide Emissions in Egypt
## 2 Materials and Methods
### MESSAGE Modeling
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Input Values** \\ \hline Base Year & 2015 \\ \hline Modelling Period & 2015 to 2060 \\ \hline Time Step & One year \\ \hline Discount Rate Used & 8.75\% [15] \\ \hline Resources Used & Oil, Gas, Nuclear, Hydro, Solar and Wind \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Input parameters of the general tab in MESSAGE. Figure 1: Energy chain in Egypt. Energy-demand projections drive the energy system. It is the most important parameter in the optimization process. In this study, data on the electricity demand from 1980 to 2015 (base year) are collected and statically analyzed by two models to select the most conservative model as an input. The first model is the average annual growth rate (AAGR) of 6.2453% for the electricity demand. The value of AAGR was considered depending on the normal distribution of the annual growth rate from 1980 to 2015 [16]. Figure 2a summarizes the statistical analysis of the annual growth rate for electricity consumption. The average value is 0.062453, and median value is 0.060406. Figure 2b shows the fluctuation of the annual growth rate from 1980 to 2015, and the average value of annual growth rate. From a literary review, we obtained the value of the average annual growth rate of 4.46%. This value was calculated by applying the computable general equilibrium model. This model was developed by the international food policy research institute to use in the analysis of agriculture plans. However, the model can be used to determine electricity demand [17]. The second model used was the polynomial regression analysis. We used the previous data collected to generate three regression models. The first model was a linear regression model with \(R^{2}=96.4\%\), the second model was a quadratic regression model with \(R^{2}=98.7\%\) and the final model was a cubic regression model with \(R^{2}=99.0\%\). Figure 2: Analysis of annual growth rate for electricity consumption. (**a**) Shows the results of Anderson-Darling normality test and summarize the statistical parameter for annual growth rate from 1980–2018. (**b**) Shows the fluctuation of annual growth rate and red dot line presents the average value that used in the simulation. Figure 3 shows the predication of the future electricity demand for the five models according to the data collected from 1980 to 2015. The average annual growth rate (AAGR) of 6.2453% is highest curve and more conservative than others. Therefore, the constant growth rate was used in MESSAGE to identify the future electricity demand. Based on Egyptian Vision 2030, we developed five energy mix scenarios. The first scenario is reference case. It represents the current situation without considering any new policies to protect the environment or secure energy, and answers the question \"what is the future of energy in Egypt if we continue with the current situation?\" The second scenario imposed a limit on CO\({}_{2}\) emission from only gas power plants and no constraints on CO\({}_{2}\) emission from oil power plants. The third scenario imposed a limit on CO\({}_{2}\) emission from both oil and gas power plants. The upper limit of CO\({}_{2}\) emission is 205 Mton. This value represents 10% less than the value of the base year (2015) [9]. The fourth scenario imposed a limit on CO\({}_{2}\) emission and constraint on electricity production from natural gas. The upper limit of electricity generation by natural gas is 27% of the maximum electricity generated in the first scenario [9]. The fifth scenario is separated into two parts: wind energy and solar energy. In this scenario, we assumed a case where Egypt will not include nuclear technology in its future energy mix, but instead rely on renewable sources of energy in addition to fossil fuels.