## 5 Evaluation and Simulation Results  
The data for this study were obtained within Shinhyeocheon village from Grida Energy for the c2x project, an area situated in South Korea. This work involves analysis of the data collected over twelve months, as shownin Table 2, with the recorded data involving specific information on energy generation and consumption for three houses. The data also provide an assessment of how P2P affects household electricity cost and value. Figure 2 displays the energy generation and consumption for three households for a twelve-month period. Further, we can see that there exists variation in energy generation and consumption every year in different months. For instance, household 2 shows a highenergy generation profile in summer, from the months of June to August, with a small drop in July. However, when it comes to the energy demand, it more or less stays almost constantly the same, with a slight surge in the winter season, that is, from December to February. To assess the economic benefits of P2P energy trading, we compared the costs before and after implementing P2P trading for each household. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Month**} & \multicolumn{2}{c}{**Household 1**} & \multicolumn{2}{c}{**Household 2**} & \multicolumn{2}{c}{**Household 3**} \\ \cline{2-7}  & **Generation** & **Demand** & **Generation** & **Demand** & **Generation** & **Demand** \\ \hline April & 373.62 & 214.7 & 372.5 & 309.4 & 380.31 & 311.21 \\ May & 383.17 & 238.7 & 370.36 & 295.81 & 387.49 & 329.34 \\ June & 375.94 & 343.56 & 384.42 & 230.02 & 384.25 & 252.23 \\ July & 282.97 & 490.24 & 281.64 & 256.3 & 289.2 & 272.7 \\ August & 47.39 & 70.34 & 394.43 & 327.03 & 375.25 & 250.1 \\ September & 277.58 & 270.71 & 324.64 & 286.26 & 264.25 & 291.59 \\ October & 408.48 & 229.56 & 391.83 & 314.52 & 399.46 & 217.54 \\ November & 291.6 & 235.93 & 283.25 & 343.22 & 290.24 & 221.73 \\ December & 188.33 & 238.07 & 187.29 & 354.49 & 202.67 & 220.94 \\ January & 255.277 & 256.12 & 259.22 & 341.93 & 271.62 & 232.48 \\ February & 222.34 & 212.96 & 222.44 & 356.03 & 226.78 & 200.07 \\ March & 375.52 & 209.27 & 362.19 & 345.73 & 378.35 & 231.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Monthly energy generation and demand for three households from April to March. Figure 2: Demand and generation for three households. In order to evaluate the economic incentive of P2P energy trading, the differential costs for each household before and after the P2P implementation are calculated. These comparisons are captured in Figure 3. Each column represents a different household. The first row of graphs shows the monthly cost comparison before and after P2P trading. The solid lines represent the costs before P2P trading, where households only buy electricity from the grid. The dashed lines represent the costs after P2P trading, which incorporates trading surplus energy with peers and obtaining discounted grid electricity. For most months, the cost after P2P trading is lower than before, which indicates that P2P trading reduces the energy expenses for households, particularly during high-surplus months. The middle row highlights the cost savings achieved through P2P trading. These graphs highlight the monthly cost savings resulting from P2P trading. The difference between before and after P2P trading costs is plotted, indicating how much each household saves each month due to energy trading. Some households have more consistent savings, while others experience peak savings during months when they generate more surplus energy. The bottom row shows the monthly benefits gained from trading surplus energy with peers. The benefit is the income households earn by selling their excess energy to other households. This benefit fluctuates depending on the surplus generation in each month. For example, during months of high energy generation, households tend to earn more from P2P trading.