Graph/Game Theory-Based Energy Routing Methods in the Energy Internet: A Review

With the maturation of technologies such as communication, sensors and networks, very large amounts of data are generated that are available for processing and analysis. With the popularizing process of the Internet of Things (IoT), available data resources will become even more plentiful and diversified in the near future. To provide feasible solutions in data science for the key features of the energy internet, such as energy interconnection and routing, a big data architecture could be utilized in the energy internet infrastructure to provide large-scale analysis of massive various types of data. In this chapter, the utilization of big data in the energy internet infrastructure is explored. A three-layer big data architecture for usage in the energy internet is presented. The characteristics of data utilized in the energy internet and the potential requirements of the energy internet for the big data architecture are studied. Then, analytics methods that could be executed in the energy internet big data infrastructure are introduced. Real-time and offline analyses, as two types of analysis modes for different requirements of application scenarios, are described. Several well-known open-source big data tools are discussed. In addition, the open challenges of utilizing big data in the energy internet are proposed.

The Energy Internet (EI) is recently proposed as a new energy system framework for future smart grid where all distributed electrical devices are interconnected via energy routers (ERs). With the novel EI, the traditional unidirectional power supply is transformed into a multi-source networked supply, which generates the problem of energy routing. Although various methods for energy routing have been proposed, a comparison of the existing energy routing algorithms is not existent and is necessary to facilitate future research. In this paper, energy routing algorithms in EI are discussed and classified based on the application scenario and the way of power transmission. Finally, several classical energy routing algorithms are selected for detailed comparison by extensive simulations.

The Energy Internet (EI) has been proposed as an evolution of the power system in order to improve its efficiency in terms of energy generation, transmission and consumption. It aims to make the use of renewable energy effective. Herein, the energy router has been considered the crucial element that builds the net structure between the different EI components by connecting and controlling the bidirectional power and data flow. The increased use of renewable energy sources in EI has contributed to the creation of a new competitive energy trading market known as peer-to-peer energy trading, which enables each component to be part of the trading process. As a consequence, the concept of energy routing is increasingly relevant. In fact, there are three issues that need to be taken into account during the energy routing process: the subscriber matching, the energy-efficient path and the transmission scheduling. In this work, we first proposed a peer-to-peer energy trading scheme to ensure a controllable and reliable EI. Then, we introduced a new energy routing approach to address the three routing issues. A subscriber matching mechanism is designed to determine which producer/producers should be assigned for each consumer by optimizing the energy cost and transmission losses. This mechanism provides a solution for both mono and multi-source consumers. An improved ant colony optimization-based energy routing protocol was developed to determine a non-congestion minimum loss path. For the multi-source consumer case, an energy particle swarm optimization algorithm was proposed to choose a set of producers and to decide the amount of energy that should be collected from each producer to satisfy the consumer request. Finally, the performance of the proposed protocol, in terms of power losses, cost and computation time was compared to the best existing algorithms in the literature. Simulation results show the effectiveness of the proposed approach.

Due to the large-scale and unequal distribution of energy resources, it has become common for numerous power system elements such as producers, consumers, microgrids, and so on to be interconnected in a network structure via energy routers (ER), referred to as the Energy Internet (EI). In EI, the trading mechanism is called a peer to peer energy trading (P2PET). It allows prosumers to trade their excess energy for additional income. This new trading scheme progressively transformed the energy supply mode from single-source to multisource and multipath. As a result, the concept of energy routing is becoming particularly crucial in the realisation of P2PET system. The minimum loss path (MLP) with power system constraints is the fundamental objective in the design of P2PET. In this work, we present a Discrete-Artificial Bee Colony algorithm (D-ABC). It is an optimization approach, which is an effective variant of ABC algorithm to solve the MLP problem with capacity constraints in EI. In addition, for the congestion management, two different schemes are applied and compared. Some numerical simulations with varied MLP scenarios are used to compare the the proposed algorithm's effectiveness to existing algorithms in the literature.

As an important energy Internet technology, energy hub can realize energy routing and information interconnection of power grid within a certain geographical range. Virtual energy hubs can connect multiple isolated energy hubs and expand the scale of energy Internet. However, the problem of optimal allocation of multiple energy sources considering uncertainty remains to be solved. This paper presents a stochastic programming method for device capacity and topology of multi-zone energy Internet. Firstly, based on the virtual energy hub, the integrated energy system with multi-area interconnection is modeled. Then, based on the extended energy transfer model and mixed power flow model, a multi-energy network association model considering virtual energy hub is established. A two-layer stochastic optimization algorithm for path planning is proposed. The outer layer determines the energy network candidate set, and the inner layer determines the cost optimal device capacity under uncertain scenarios. The simulation results show that the proposed method can give the installed capacity and topology structure of multi-area energy Internet, and the proposed planning algorithm greatly improves the planning efficiency. The relevant planning results are helpful to improve energy efficiency, reduce carbon emission, and improve the economic benefits of energy Internet.

In order to cope with the energy crisis, the concept of an energy internet (EI) has been proposed as a novel energy structure with high efficiency which allows full play to the advantages of multi-energy coupling. In order to adapt to the multi-energy coupled energy structure and achieve flexible conversion and interaction of multi-energy, the concept of energy routing centers (ERCs) is proposed. A two-layered structure of an ERC is established. Multi-energy conversion devices and connection ports with monitoring functions are integrated in the physical layer which allows multi-energy flow with high flexibility. As for the EI with several ERCs connected to each other, energy flows among them are managed by an energy routing controller located in the information layer. In order to improve the efficiency and reduce the operating cost and environmental cost of the proposed EI, an optimal multi-energy management-based energy routing design problem is researched. Specifically, the voltages of the ERC ports are managed to regulate the power flow on the connection lines and are restricted on account of security operations. An artificial neural network (ANN)-based reinforcement learning algorithm was proposed to manage the optimal energy routing path. Simulations were done to verify the effectiveness of the proposed method.

The energy internet is a new emerging concept which is mainly associated with peer to peer energy trading in a smart grid power network. To facilitate this, support for bidirectional power flow in addition to a bidirectional communication flow is needed. Thus energy routing functions/algorithms and energy routing devices are the core entities constituting the energy internet. In this paper we are mainly concerned with energy routing algorithms. These energy routing algorithms are designed based on various techniques such as graph theory, game theory and autonomous systems. Given the infancy of this area of research, in this paper, we review the state of the art of energy routing algorithms. We first introduce the concept of energy routing and then, we present a comprehensive literature survey of existing energy routing algorithms. Furthermore, we provide a discussion of our findings and future directions.

Energy Internet (EI) is proposed as the evolution of smart grid, aiming to integrate various forms of energy into a highly flexible and efficient grid that provides energy packing and routing functions, similar to the Internet. As an essential part in EI system, a scalable and interoperable communication infrastructure is critical in system construction and operation. In this article, we survey the recent research efforts on EI communications. The motivation and key concepts of EI are first introduced, followed by the key technologies and standardizations enabling the EI communications as well as security issues. Open challenges in system complexity, efficiency, reliability are explored and recent achievements in these research topics are summarized as well. © 2016 IEEE.

Energy Internet has been considered as a promising alternative to the traditional power grid. In order to improve the energy efficiency of an Energy Internet, energy routers are treated as a significant power conversion device to increase the penetration of renewable sources. Different from previous studies which only consider the shortest path for energy routers, a virtual energy routing strategy involving renewable energy integration is proposed in this paper to reduce the network loss, considering both transmission and conversion efficiency. To address above, the discrete particle swarm optimization (DPSO) algorithm is studied and applied. The effectiveness of the proposed virtual energy routing strategy is validated by an Energy Internet platform integrated with photovoltaic (PV) generation and energy-storage.

Semi-decentralized energy routing algorithm for minimum-loss transmission in community energy internet

Due to the capacity limitation and the renewable energy's uncontrollability, multiple energy hubs (EHs) are connected by energy routers (ERs) to form a network structure and further build the future energy internet. With concave objective functions and/or nonlinear operation constraints, the centralized optimal dispatch of interconnected EHs is difficult to solve. In order to reduce the complexity of centralized optimal dispatch on large-scale systems and protect the information privacy of different EHs, a hierarchical optimization strategy is put forward for the ER-based energy internet. Specifically, the lower level is the optimal dispatch within EHs, where the dispatch model of EH is further optimized by considering variable dispatch factors, and the information privacy of different EHs is preserved. The upper level is the energy routing control between EHs, where the end-to-end power transmission according to contract path is realized through the controllability of ERs and energy routing protocols. Finally, the effectiveness of the hierarchical optimization strategy and model are verified by simulations.

The Energy Internet is a hot topic for building networks that can transmit energy like the Internet. This paper mainly discusses the composition and energy scheduling for the electric vehicle energy transmission network. The network mainly includes energy points and charging stations connected by energy routing. The energy point will generate energy and the charging station will provide energy. However, in some cases, the charging station cannot obtain energy point, which leads to its energy reserve crisis. In this paper, we introduce an algorithm to dispatch energy to alleviate the energy crisis. On the basis of some graph theory, the routing problem when energy input is lost is analyzed by comparing two algorithms from the energy point to charging station. In addition, we discuss this issue in dynamic situations to better observe changes in energy transfer over time and use real- world transportation data onto simulation. The simulation results show that the algorithm can effectively delay the emergence of crisis charging stations.

The concept of energy Internet (EI) is very promising due to its ability to integrate distributed generators and storage devices. Energy routers (ERs) enable highly efficient, reliable, and flexible power system operation. To dynamically control and optimize energy flow in EI, an extensive communication is required. Considering the diversity of generators and ERs, it is crucial that such communication is achieved in a standardized and interoperable way. In this paper, an improved energy routing algorithm and its impact in EI operation are demonstrated. Furthermore, a full communication solution is developed for ERs and their interaction with grid operator. This paper proposes an International Electrotechnical Commission (IEC) 61850 communication-based power routing algorithm in microgrids. Optimal selection of source and routing path is achieved according to the proposed algorithm and communicated to the ERs via IEC 61850 based communication. The effectiveness of the proposed IEC 61850 communication-based routing algorithm is verified on network emulation platform comprising of IEC 61850 emulator tools and network simulator tool.

Green smart home technologies are expected to comprise a significant portion in the future Energy Internet (EI) with the introduction of new home appliances and the integration of distributed energies. As the key interface equipment to undertake power conversion, energy integration and management, energy router plays a significant role in EI. In this way, a home energy router and energy management strategy for AC/DC hybrid sources and consumers is proposed in this paper. The architecture, communication system and energy management are introduced and discussed. The proposed home energy router could provide six kinds of interfaces. Each interface can realize the plug-and-play of sources and consumers. The energy flow will autonomously choose the optimized path between energy sources and consumers by using the home energy management system. As sources and consumers in the proposed architecture could be in different AC or DC forms, and voltage levels or qualities, more flexible path selection could be provided for power supply compared to traditional configuration. By using the distributed hierarchical energy management, energy routing could be achieved to obtain different functions such as peak load shifting, peak load shaving, smoothing power fluctuation and islanded operation, electricity plan following, etc. The proposed home energy router could also be improved and applied to the smart buildings and other extensive occasions.

The endeavor to increase the integration of distributed power generation systems (DPGS) into the electric power system brings about new research areas, one of them is the energy internet concept. Similarly to the information internet, the energy internet would transfer packets as the units of energy. To avoid congestions in the packet dispatching process, routers and routing methods have been suggested in literature. Our proposal is to use multiple channels simultaneously. In this paper, the possibility of three-phase multi-frequency energy transfer is investigated. This task is performed by using grid-forming and grid-feeding converters. To have a balanced three-phase multi-frequency power system, the third harmonic component of the fundamental frequency is added to the power system as a zero-sequence signal. The control system consists of two parts. The grid-forming controller is responsible for the voltage and the respective frequency. The grid-feeding controller controls the powers and current of the grid-feeding converter. For doing so, both control systems should be able to decouple the frequencies from each other. As a result, the active and reactive powers can be transferred separately at different frequencies.