# Hybrid Energy Routing Approach for Energy Internet
## 7 Simulation and Results
### Results Discussion  
In order to validate the performance of our proposed scheme, as well as to show its efficiency in solving problems related to energy routing, namely subscriber matching, efficient routing path and transmission scheduling, we compared the obtained results of different experiments to previous works in the literature.  
As illustrated in Table 24, starting with the mono-source consumer case, works in [20; 21] have proposed an energy routing protocols based on graph traversal and Dijkstra algorithms, respectively, to determine a non congestion minimum loss path between producer-consumer pairs. First, the two proposed protocols did not examine how pairs were formed. The graph traversal method in [20] finds that all possible paths between the producer and consumer pairs then determines the path with the minimum power loss. This method generates an optimal solution but with a height execution time especially in the case of large-scale networks. IACO-ERP offers the best solution compared to [20] with the same execution time for small-scale networks and a better execution time for large-scale networks as shown in Table 25. In addition, we proposed a subscriber matching mechanism that decides which producer should be chosen for the consumer while reducing the costs and losses of energy.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Consumer**} & **Consumer** & **Transaction Power** & **Cost** & **Energy Efficient** & **Power Loss (kw)** & **Transmission Capacity** & **Fitness Value** & **Selected Producer** \\ \cline{3-10}  & **Consumer** & **P** & **(kw)** & **(USD/kw.h)** & **Path** & **Loss (kw)** & **(15)** & **0.420106** & \\ \hline \multirow{4}{*}{**D26**} & \multirow{2}{*}{\(D30\)} & \multirow{2}{*}{\(6\)} & \multirow{2}{*}{\(0.54\)} & \multirow{2}{*}{\(30\to 27\to 25\to 26\)} & \multirow{2}{*}{\(0.300212\)} & \multirow{2}{*}{\(15\)} & \multirow{2}{*}{\(0.420106\)} & \multirow{2}{*}{\(D30\)} \\  & & & & & & \(3\to 4\to 12\to 15\) & & & \\  & \multirow{2}{*}{\(D26\)} & \multirow{2}{*}{\(D3\)} & \multirow{2}{*}{\(6\)} & \multirow{2}{*}{\(0.84\)} & \multirow{2}{*}{\(\begin{array}{l}\to 23\to 24\to 25\to \\ 26\end{array}\)} & \multirow{2}{*}{\(0.420846\)} & \multirow{2}{*}{\(12\)} & \multirow{2}{*}{\(0.630423\)} & \multirow{2}{*}{\(D30\)} \\  & & & & & & & & \\ \hline \multirow{4}{*}{**D24**} & \multirow{4}{*}{\(D8\)} & \multirow{2}{*}{\(11.95\)} & \multirow{2}{*}{\(0.513852\)} & \multirow{2}{*}{\(\begin{array}{l}8\to 28\to 27\to 25\)} \\ \to 24\to 12\to 15\) & & & & & & & \\ \to 23\to 24\to 25\to 26\For the multi-source consumer, the work in [20] proposed a source selection algorithm that determines the amount of power to obtain from each producer to satisfy the heavy load demand. The proposed algorithm used the greedy search topology where it tests all possible (interval values) cases to define the appropriate amount of power that needs to be received from each producer in the aim of minimizing the power loss. This method provides a solution with a high execution time which speedily augments with the growing of the system. The execution time of cases 4 and 5 is 217 s and 513 s, respectively, whereas using the _EPSOA_ algorithm helped in achieving the best solution in a significantly reduced time while minimizing the cost and power loss at the same time. Our proposed scheme produced the same result for cases 4 and 5 in 51 s and 109 s seconds of execution time, respectively. In comparison with fully distributed peer-peer energy trading, the use of the broker minimized the number of exchange messages in the system.