# Hybrid Energy Routing Approach for Energy Internet
## 1 Introduction
These energy routing algorithms need to solve three main problems: the subscriber matching problem by determining the best producer for each consumer; the efficient energy routing problem by selecting the best path with the minimum power loss between these producer-consumer pairs; and finally, scheduling these energy transmissions to prevent congestion or overflow problems. Current literature indicates that the different energy routing algorithms proposed to solve the energy routing are usually based on graph theory, game theory, mixed integer programming and autonomous systems. In the graph theory-based routing algorithms, the grid is presented as a graph and routing methods are applied to find the lowest cost path such as: the protocol proposed by [PERSON] et all in paper [20]. In this work, the proposed energy routing protocol finds all the possible paths between the consumer and producer in the grid, and then determines the best path among them. This solution is not practical, especially in large grids, as the proposed protocol assumes that the transmission capacity of \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Category of Comparison** & **Internet** & **Energy Internet** \\ \hline Transmission & Data (Information) & Energy (electricity) \\ Transmission lines & Wired and wireless connections & Power lines (grid) \\ Routing devices & Network routers and switches & Energy routers and smart meters \\ Transmission loss & No losses & Loss exist \\ Objective of routing & Efficient data transmission & Efficient energy transmission \\  & & Minimize the power losses \\ Challenges & Shortest transmission path & Subscriber matching \\  & Congestion management & Energy Efficient transmission path \\  & Demand dominated & Demand dominated \\ Criteria (characteristics) & The source of data packet is predefined & The source of energy is not defined \\  & Regenerate the waste packet & Cannot regenerate the wasted energy \\ \hline \end{tabular}
\end{table} 
Table 1: A comparison in routing design between the Internet and Energy Internet. transmission lines is large enough. In paper [21], the authors proposed a distributed routing algorithm based on Dijkstra algorithm. This study did not investigate how the prosumer-consumer pairs are created. The authors in [22] proposed a hierarchical energy routing protocol where a master node determines all efficient paths between each pair in the grid, which affects the security and efficiency of the grid, and could cause the one point failure of the system. However, game theory has been widely used in power systems to solve different issues such as the demand response optimization problem including the work in [23] where a Stackelberg game approach was proposed in order to reduce the peak load and balance the supply-demand in the smart grid. The proposed approach determines the efficient consumption scheduling for consumers based on the real-time pricing. The authors in [24] used the reinforcement learning with the game theory and proposed an approach to solve the demand response problem in SG--in which a reinforcement learning mechanism was used to determine the best company for each consumer based on the electricity price and availability, and game theory based-demand response management technique to schedule the consumer consumption. Several game theory based-approaches [25; 26; 27; 28] have been proposed to solve the energy routing problem in the peer-to-peer energy trading market. In these approaches, producers and consumers play a game (such us Stackelberg game, Coalition game...etc.) to create the producer/prosumer-consumer pairs with the maximization of their benefits. An energy routing algorithm based on an autonomous system is proposed in [29] where each system entity acts as an agent in the trading process. Authors in [30] proposed an energy trading platform with a mixed integer programming model for the optimal sharing energy between different houses in the EI in order to create a total profit for all participant houses.