## 3 Problem Formulation  
### DR Optimization Model  
A dynamic price or incentive-based hourly DR is extremely invasive to consumers since they are less likely to have enough time to reschedule their demands well ahead of time and be more stressful for residential consumers. Hence, a day ahead DR model is more appropriate for residential consumers, having enough time to adjust the loads impacting their electricity bills the most, and incentivizing them to increase their efficiency in energy usages, such as optimizing their heating and cooling loads. The proposed DR \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Periods** & **Period of Peak** \\ \hline
**Start** & **End** & **Demand Tariff** \\ \hline
2021-01-01—00:00 & 2021-01-31—24:00 & (17:00–20:00) \\
2021-02-01-00:00 & Wintertime End 24:00 & (17:30–20:30) \\ Summertime starts—00:00 & 2021-06-30—24:00 & (18:30–21:30) \\
2021-07-01—00:00 & 2021-08-15—24:00 & (18:00–21:00) \\
2021-08-16—00:00 & 2021-09-30—24:00 & (17:30–20:30) \\
2021-10-01—00:00 & Summertime End—24:00 & (18:00–21:00) \\ Wintertime starts—00:00 & 2021-12-31—24:00 & (17:00–20:00) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Peak tariff periods for 2020. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Bulk Supply Price**} \\ \cline{2-5}
**Bulk Consumers** & **Day** & **Night** & **Peak** & **Peak Demand (MW)** & **Peak Demand \%** \\  & **(Fills/kWh)** & **(Fills/kWh)** & **(JD/kW/Month)** & & \\ \hline JEPCO & 71.90 & 61.88 & 2.98 & 2129.9 & 61.26\% \\ EDCO & 74.02 & 64.07 & 2.98 & 580.8 & 16.70\% \\ IDECO & 58.20 & 48.29 & 2.98 & 622.7 & 17.91\% \\ PC1 & 237 & 170 & 2.98 & 71.6 & 2.06\% \\ PC2 & 124 & 109 & 2.98 & 72.1 & 2.07\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Bulk supply prices and peak demand 2019.  
model in this study is depicted in Figure 4, which is supposed to be implemented by the GO targeting the residential consumers. Precise day-ahead information on electrical demand, generation availability, generation costs, and the expected amount of renewable energy generated are highly crucial to implementing the DR system [17]. The GO (NEPCO) aims to reduce the cost of energy production, especially at peak periods, to reduce the expensive energy purchased from IPP3 and IPP4, while increasing the demand in off-peak periods to maximize its net profit. The DR system can influence consumer behavior from peak to off-peak and allow the grid higher flexibility acting as a safety margin in cases of high expected demand. NEPCO's main responsibility is to implement a precise day-ahead hourly demand prediction to estimate the demand every hour for the next day. Since the GO, as per grid regulations, provides the fuel for the power plants, the day-ahead cost of energy purchase is correlated to fuel prices known by the GO; therefore, the generation costs are given. Furthermore, the GO utilizes the PEM to predict the effects of different prices on the residential consumers and decide the best price to achieve the required goals relating to peak demand reduction and off-peak filling by increasing the prices at peak loads and reducing them at off-peak periods. It is noted that the prices are announced to the distribution companies at the start of each day, where they announce them to their consumers through specially designed schemes. In this study, it is assumed that the generation schedule is given through unit commitment analysis done by the GO by communicating with the power suppliers, where the renewable energy plants also provide the renewable generation predictions.