# State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System
## 4 Enabling Technologies for Transaction-Based Energy
### Information Communication Technologies
#### 4.3.3 Multi-Agent System For Inter-Prosumer Communication  
The realization of the TE concept between prosumers depends on the availability of the essential communication infrastructures to guarantee reliable information dissemination [10]. This is because actors need to update their energy profiles including their availability and demand requirements and communicate them to other actors via two-way communication systems. In addition, communication technologies will enable each actor to monitor its energy profile, as well as the energy available in the market. Communication technologies that can be adopted for MG communication have been discussed in Section 4.3.1. Furthermore, this work examines some control and communication strategies adopted in the literature during energy exchange. One major approach to integrating distributed communication among prosumers is through multi-agent system (MAS) technology, where each actor can be modelled as an autonomous agent capable of interacting through messaging. An example of MAS interaction is shown in Figure 3, where each prosumer represents an agent. Each agent communicates in its local grid and can also communicates with other agents outside its local neighbourhood. MAS has been used in the literature to model communication in MG control [3; 8; 54]. Agents' communication can be categorized as P2P or non-P2P [55]. Non-P2P is synonymous with centralized control, where there is no direct information flow between agents; instead, a central entity exists for information sharing. For example, the model in [54] used a central database as information storage. Each agent acquires information about other agents, as well as updates its own status using the database. Furthermore, [PERSON]. [32] implemented energy markets in DR program-based open automated demand response (OpenADR) programs. Conversely, agents communicate directly in the P2P model, for instance using a distributed P2P multi-agent framework to manage power sharing in MG [56]. The proposed algorithm is based on graph theory, in particular the Ziegler-Nichols method, which was applied to electric vehicles (EV). The performance analysis of the framework shows that information exchange improves the system performance. The study in [58] modelled agents' communication using the round robin (RR) technique; however, the major setback of the RR technique is an increase in communication steps with increasing numbers of agents, i.e., it is not scalable. Alternatively, researchers in [59] implemented agent-based communication using a minimum spanning tree (MST) algorithm for MG control. In MST, communication steps between agents to disseminate information are a function of the minimum path formation. However, the drawback here is that communication between the agents can only start after the formation of the tree; in addition, with every addi  
Figure 3: An example of multi-agent system interaction (adapted from [56; 57]). to be reconstructed. In view of this, [PERSON]. [55] proposed a new communication algorithm based on a foundation of intelligent physical agents (FIPA) for MAS P2P communication. The proposed algorithm has fewer communication steps, faster response and reduced complexity compared to other existing P2P architectures such as RR and MST. One disadvantage of their proposed architecture is that the agents must always be even in number when, in some cases, an odd number of communicating agents can exist. Another important aspect of prosumer communication is control. MAS have been studied in the literature for MG managements [8; 56]. MAS can treat the MG as a cluster of energy markets; for instance, [PERSON]. [3] modelled each agent to perform various functions such as scheduling, market clearing and coordination. The agents are assigned different objectives with the aim to maximize energy generation or reduce the load/demand. The study illustrated in [60] presented an approach to control multiple DG based on MAS to reduce network cost and emissions.