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Top1. Introduction
Energy Internet (EI) is a decentralized energy supply-demand sides by developing a revolutionary paradigm of smart grids into the Internet. Internet of Things (IoT) is the paradigm that creates an internet-connected devices, where all the everyday devices capture data from EI environment and adapt it to both the supply and demand side need. EI is one of the scenarios in which IoT is applied to achieve efficient energy use. An IoT-based energy management system is a crucial component in building EI architectures, as it not only enables new energy-related value-added services but also intelligently facilitates the integration of various energy sources and efficient operation control. Due to the complexity of the IoT-based energy management environment, which includes distribution networks, participants (i.e. producers, pronsumers and users) transaction, and so on, numerous types of information need to be transferred in real time. For example, electricity peak smoothing requires information about energy profiles as well as users' acceptable level. The mechanism of transactive energy is mainly to cope with fluctuating energy sources and consumption can effective energy resource management and allow a dynamic balance of multi-energy supply and active demand. These requirements prompt the EI to employ new information and communication technologies, such as 5G and edge computing technology need an effective way to offload computational task to the edge side of 5G networks. As edge computing can provide the computing services at the network “edge” near EI devices, it allows for more secure and efficient data processing in real time, achieving better performance and results. The edge computing is collected by sensors and sent to the aggregators to be associated to the preferences set by the EI’s participants or devices. These preferences can be modified by the participants of the consensus process who take collaborative decisions by following the consensus algorithm taking into account the gathered data and engendered decisions.
Moreover, with the increase of collected data from EI, it becomes a key point to effectively improve the computing power of edge nodes in edge computing to monitor and transmit data. The economic dispatch problem (EDP) is one of the most fundamental decisions in power systems, so it also exists in EI. EDP is essentially an optimization decision that assigns the required power generation to a number of generating units to meet the active load demand. The objective of economic dispatch(ED) is to minimize the total power generation cost, subject to several constraints (Han and Gooi, 2007). In the last, ED is usually conducted in a central controller, who is able to access global information. However, EI envisions an incremental number of network nodes, flexible architecture, and plug-and-play(PnP) characters, and it is only with some decentralized edge computing controllers that better performance is achieved. Decentralized control doesn’t mean these controllers have no relationship among them. Oppositely, they not only have individual goal, but also hold the reliable exchange of the information with neighbors. Therefore, every decentralized controller can be modeled as an intelligent agent and a consensus algorithm is an iterative interaction rule that specifies the information exchange between an agent and all of its neighbors in the EI network.