## 4 Simulation Results and Discussion
### Case Studies  
In order to analyze the behavior of the prosumer, three case studies were introduced according to the system architecture, which has been shown in Figure 1. The three considered case studies are as follows.  
* Case 1: Day-ahead scheduling of the prosumer considering predicted weather data.
* Case 2: Day-ahead scheduling of the prosumer considering ESSs depreciation cost and predicted weather data.
* Case 3: Day-ahead scheduling of the prosumer considering real weather data.  
As discussed in previous sections, no power loss was assumed in the mentioned objective function since the grid of the prosumer had relatively short lines in comparison to the transmission or distribution lines. Parameters of PV and WT are shown in Table 2, as well as technical parameters of the energy storage system are depicted in Table 3.  
In this study, residential buildings with net metering devices were included in a time of use (TOU) pricing program with different hourly rates [45]. TOU rates were used as prosumer payments to the utility. These rates were based on real electricity prices utilized in Iran's electricity market during the summer season. However, the rates differ from region to region depending on their varying climates. In a particular case, the city of Kerman (30\({}^{\circ}\)28'39\"N, 57\({}^{\circ}\)08'34\"E) was included in tropical region 2 (out of 4 tropical regions) due to its hot and dry weather during the summer season. Table 3 depicts the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Weather Parameter** & **Training** & **Testing** & **Validation** & **All** \\ \hline Solar irradiance & 0.957 & 0.948 & 0.954 & 0.956 \\ Temperature & 0.989 & 0.988 & 0.987 & 0.988 \\ Wind speed & 0.227 & 0.229 & 0.232 & 0.230 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Linear regression results for all the predicted parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**PV Parameter** & **Value** & **WT Parameter** & **Value** \\ \hline Module Nominal Power & 225 W & \(P_{num}\) & 5 kW \\ \(\alpha\) & \(-\)0.38\% & \(V_{ci}\) & 2 m/s \\ \(NOCT\) & 45 C & \(V_{r}\) & 12 m/s \\ \(T_{ref}\) & 25 C & \(V_{c}\) & 25 m/s \\ \(\eta_{pvarted}\) & 15\% & & \\ \(A_{po}\) & 1.244 & & \\ \(N_{po}\) & 30 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Technical parameters photovoltaic (PV) and wind turbine (WT) units [44].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **SB** & **PHEV** & **Unit** \\ \hline \(Vnom\) & 12 & 12 & V \\ \(SOC_{0}\) & 5 & 4 & kW \\ \(SOC_{max}\) & 10 & 8 & kW \\ \(pmin\) & 0 & 0 & kW \\ \(pmax\) & 9 & 7 & kW \\ \(pmin\) & 0 & 0 & kW \\ \(pmax\) & 8 & 6 & kW \\ \(\eta_{charge}\) & 0.93 & 0.9 & \% \\ \(T_{discharge}\) & 0.95 & 0.9 & \% \\ \(B_{SB}\), \(B_{PHEV}\) & 0.6 & 0.2 & \$ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Technical parameters of energy storage systems (ESSs).  
In this paper, GAMS (GAMS Development Corporation, Washington, DC, USA) software was deployed to solve MILP problems through CPLEX (IBM ILOG CPLEX Optimization Studio, New York, USA) solver. In addition, a 64-bit personal computer with 8 GB RAM and Core i5 (Intel) CPU was used during all simulations. Predicted values of weather parameters were linked from the MATLAB (MathWorks, Massachusetts, USA) environment to GAMS through the GDXMRW interface [47].