Software Defined Networking for Flexible and Green Energy Internet

Transcription

1 Integrated Communications, Control, and Computing Technologies for Enabling Autonomous Smart Grid Software Defined Networking for Flexible and Green Energy Internet Weifeng Zhong, Rong Yu, Shengli Xie, Yan Zhang, and Danny H. K. Tsang Energy Internet is a vision of future power systems, which will achieve highly efficient interconnection among various types of energy resources, storages, and loads and enable P2P energy delivery on a large scale. To enhance the flexibility and efficiency of Energy Internet, this article employs the methodology of SDN in Energy Internet and proposes a SDEI architecture. Abstract Energy Internet is a vision of future power systems, which will achieve highly efficient interconnection among various types of energy resources, storage, and loads, and enable P2P energy delivery on a large scale. To enhance the flexibility and efficiency of Energy Internet, this article employs the methodology of SDN in Energy Internet and proposes an SDEI architecture. In the SDEI, the control, data, and energy planes are separated. The control plane dynamically reconfigures the data and energy planes and achieves flexible cooperation between them. We then focus on applying an SDN approach to energy router networking, and build a hierarchical energy control architecture that can implement the programmability of energy flow and allow P2P energy delivery from a high-level view. Additionally, we present a case study of future EVs, for which two applications within the SDEI framework are proposed to improve energy efficiency and green energy utilization. Introduction In the future, different types of electrical elements will increasingly join power grids, inlcuding renewable resources, energy storage, electric vehicles (EVs), smart appliances, and so on. These elements, with different scales and characteristics, are distributed at different levels and areas of power grids, which significantly challenges largescale energy management in practice. In the academic community, various kinds of energy control and management schemes have been proposed for future smart grids. Recently, a new vision of an energy system, called Energy Internet [1 3], has drawn considerable attention. Borrowing attractive features from the Internet, Energy Internet promises to provide highly efficient interconnection of distributed energy resources and support peer-to-peer (P2P) energy transactions on a large scale. Energy routers (also called power flow routers or grid routers), which execute physical energy control and enable energy routing, are the key components of Energy Internet [1, 2, 4, 5]. Abe et al. [1] propose IP addresses being assigned to energy routers, and energy delivery schemes are similar to data packet transmission in the Internet. Huang et al. [2] propose that a grid management system be embedded in each energy router, and energy routers coordinate with each other in a distributed manner. However, we believe that electrical energy is completely different from data packets. The Internet operates in a distributed fashion, and hence it has no exact solution in problem analysis and provides only best effort services. Internet-style distributed control may be unsuitable for achieving quick response and dynamic optimization in terms of practical energy management. In addition, energy routers are designed to have multiple functions, integrating executors of physical energy control, energy-related data services (e.g., pricing and demand response), and decision making in energy management [1, 2, 4]. However, this integration is not beneficial to the optimization of each function. For example, the decision made by one single energy router cannot be globally optimized because it is unrealistic for one energy router to acquire complete information of a power system. Furthermore, the existing literature does not provide effective coordination mechanisms and networking approaches for multiple energy routers, and these are supposed to be the most important issues for providing P2P energy delivery in Energy Internet. Considering the above problems of the existing designs of Energy Internet, this article employs the methodology of software defined networking (SDN) in Energy Internet to make it more flexible and greener. We regard SDN as a general networking approach aimed at using software to develop networks and making resources programmable in networks. The proposed architecture of the software defined Energy Internet (SDEI) has three separate planes: control, data, and energy planes. In the energy plane, energy routers only preserve basic intelligence for energy control execution. The control and data planes are responsible for decision making on global energy management and energy-related data services, respectively. We apply the SDN approach to energy router networking with the practical consideration of an energy router s characteristics and power grid topology. This enables the control plane to have a global view of Energy Internet and dynamically optimize energy router configuration. Digital Object Identifier: /MCOM CM Weifeng Zhong, Rong Yu, and Shengli Xie are with Guangdong University of Technology and Guangdong Key Laboratory of IoT Information Technology; Yan Zhang is with the University of Oslo and Simula Research Laboratory; Danny H. K. Tsang is with Hong Kong University of Science and Technology. Shengli Xie is the corresponding author for this article /16/$ IEEE IEEE Communications Magazine December 2016

2 The contributions of the article are as follows: We propose the SDEI architecture in which control, data, and energy planes are separated, and each plane has its own functionality. Three types of energy routers at different levels of power systems are presented. We then stick to the SDN principles and propose a hierarchical energy control architecture for the networking of multiple energy routers. Two applications based on the SDEI framework are proposed. Numerical results show that the two applications can collectively improve energy efficiency and renewable utilization. Control plane Data plane Data center SD data controller SD energy controller Energy router Energy plane Thermal power Software Defined Energy Internet Architecture Figure 1 shows the proposed SDEI architecture that includes data flow control and energy flow control. SDN approach is adopted in both controls following the four principles of SDN [6]: Separation of control and data (or energy) Logically centralized control Programmability Open interfaces Thus, an essential feature of the SDEI is the separation of the control, data, and energy planes. The separation divides the infrastructure of Energy Internet into three classes: controllers, network devices, and grid devices. The technologies in the three planes can be developed independently. Different devices from different vendors can communicate with each other through open interfaces. This makes the upgrade of the infrastructures more flexible. The data plane is responsible for the provision of energy-related data services, while the energy plane is responsible for physical energy flow control. The control plane dynamically reconfigures the data and energy planes to achieve programmability and flexible cooperation between them. New energy services and applications can be conveniently implemented on top of the control plane. The bottom plane in Fig. 1 is referred to as a layer of users. In this article, we consider future EVs in the user layer and present two applications for them. In the following, we separately describe the features of the control, data, and energy planes in the SDEI. To avoid confusion, hereinafter, communications networks are called networks, and power networks are called grids. Control Plane The control plane is an interface for software control of Energy Internet. The control flows are organized in a logically centralized manner. In the control plane, there are software defined (SD) data controllers and SD energy controllers responsible for data flow control and energy flow control, respectively. The two types of SD controllers also communicate with each other to achieve coordination between the data and energy planes. Data Flow Control: The future network will be upgraded to be a cloud computing environment in which the resources at data centers and network edges are shared and programmed by users. By adopting an SDN approach [6, 7], the SD data controller in the control plane is allowed to dynamically reconfigure network elements Base station EV plane Control flow Switch Wireless charger Unmanned EV Data flow Charging station Energy flow Figure 1. Software defined Energy Internet architecture with future EVs. and optimize energy-related data management according to the dynamic network topology and traffic. In the data flow control, energy routers are end devices and are not controlled by the SD data controller. Energy Flow Control: This article mainly focuses on energy flow control for which we propose to employ an SDN approach to control energy flows among energy routers. Energy routers are grid devices and controlled by the SD energy controller. In [2], Internet-style distributed energy control for Energy Internet is proposed. However, we believe that the logically centralized control of SDN is more suitable for practical energy management. The reasons are as follows. Energy Reliability: Data packets can be sent even if senders do not know networks conditions. If data packets are dropped or lost, they can be duplicated and retransmitted easily. The Internet provides best effort services while Energy Internet focuses on the reliability of services [3]. Power is produced and consumed transiently in power grids, and the balance between power generation and load should be maintained at all times. Once power is generated or consumed, it somehow impacts the whole power environment. Thus, to maintain the reliability of the entire Energy Internet, centralized energy control is useful to ensure that the energy consumption and production of each individual are observable and controllable. Energy Efficiency: A data packet may contain the addresses of senders and receivers, packet size, protocols, and so on. Electrical energy is a form of energy that generally does not carry any Renewable Storage Household IEEE Communications Magazine December

3 Network functions virtualization technology can be used to construct a virtual network for the data exchange of the demand response. The SD data controller should be able to optimize the configuration of the virtual network that should be secure and able to provide optimized data routing to ensure the fast convergence of demand response algorithms. AC bus Source ER-D HV side LV side ER-T (a) DC bus ER-T ER-D Control plane Data plane Power system analysis Energy market Energy services SD energy controller Energy plane ER-T ER-D ER-C Renewable Storage ER-C (b) ER-C EV Charger EV plane ER-C (d) AC bus Software system Charger Device Physical energy flow controller Operating state High-level instruction (c) Direct configuration Figure 2. Energy routers: a) transmission level; b) distribution level; c) consumption level; d) hierarchical energy control architecture for coordination of energy routers in the SDEI. data. Producers inject power into grids, and consumers simultaneously absorb the power from the grids. The consumers cannot identify from which producers the power comes. Therefore, it is obvious that centralized energy control with a full view of power systems is beneficial to achieve efficient energy routing and scheduling. Data Plane In the SDEI, various types of data will be generated and transmitted in a P2P fashion in the data plane [8]. The behaviors of the P2P data flows should be controlled by the SD data controller. For example, in the energy plane, energy routers generate data for power system analysis, which requires low delay. Energy routers also receive data of software update, which can be delay-tolerant but needs considerable bandwidth. The SD data controller should be aware of data content to satisfy different requirements for different data. Moreover, demand response can be performed among the users within a distribution grid to maintain power balance. Network functions virtualization technology can be used to construct a virtual network for the data exchange of the demand response. The SD data controller should be able to optimize the configuration of the virtual network, which should be secure and able to provide optimized data routing to ensure the fast convergence of demand response algorithms. Energy Plane In Energy Internet, distributed renewable resources and energy storages will be widely deployed at the user side, and P2P energy trading will also be allowed. The following problems may arise in P2P energy transaction. First, the buyers and sellers may be located in different distribution grids. Thus, the energy received by the buyers may not be the energy provided by the sellers. Second, the consumption of the buyers and the production of the sellers may not happen at the same time, for which energy storage is needed to buffer energy. Third, the buyers and sellers may have different electricity requirements, including DC or AC, three phases or single phase, different voltage levels, and so on. Thus, the SD energy controller should be able to calculate optimal configuration of energy routers to ensure the power quality of all users under the dynamic energy trading. By 70 IEEE Communications Magazine December 2016

4 doing so, the consumers feel that energy is directly delivered by the sellers in a P2P way. In addition, the SD energy controller should be aware of the change of grid topology, and reduce generation cost, storage cost, and transmission loss. SDN for Energy Router Networking Power flow follows the Kirchhoff law so, strictly speaking, P2P energy flow is only understood from a perspective of energy market or top-level energy management. The concept of P2P energy flow does not exist in a legacy grid since there is no information sharing for the whole power system. In the SDEI, if the control plane can acquire global information of the energy plane, dynamically ensure power quality, and hide physical power control details from users, it is possible to provide users with P2P energy flow services in a high-level view. This is similar to Internet. Users can exchange information in the application layer without understanding the lower physical layer, where raw bits in a form of electrical signals are transmitted between network nodes. In this section, we provide details of using an SDN approach for energy routers networking. We follow the principles of SDN and build a hierarchical control architecture for energy routers to support P2P energy flow. In this hierarchical architecture, energy routers are categorized into three types corresponding to transmission, distribution, and consumption levels, respectively. As shown in Figs 2a 2c, the building blocks of an energy router are a common bus, physical energy flow controllers, and a software system. Energy Routers at the Transmission Level Figure 2a shows an energy router at the transmission level (ER-T), in which physical energy flow controllers can be unified power flow controllers (UPFCs) or static synchronous compensators (STATCOMs) [4] that are able to control the active and reactive powers of transmission lines. ER-Ts serve as the buses in transmission systems and connect with one another. There is a software system in an ER-T to centrally control its physical energy flow controllers and lower layer. ER-Ts are the key components in backbone grids, so they should have high reliability of energy control to ensure power quality and enable HV power routing in a wide area. Energy Routers at the Distribution Level An energy router at the distribution level (ER-D) may replace a traditional transformer of a distribution grid, as shown in Fig. 2b. An ER-D is able to step down electricity voltage. Its physical energy flow controllers can be DC-DC or AC-DC bidirectional converters. The software system in an ER-D focuses on the energy management in a distribution grid. Especially in an islanding mode, an ER-D controls the discharge of energy storage to meet critical demand, and it also dynamically balances energy supply and demand to ensure the power quality in a distribution grid. Energy Routers at the Consumption Level Figure 2c shows an energy router at the consumption level (ER-C). In a home scenario, ER-Cs may replace smart meters. Different from a smart meter, an ER-C has an additional physical energy flow controller to control the external power flow. On the user device side, different devices use different physical energy flow controllers. Generally, the controllers should be able to turn devices on/ off or adjust their input/output powers. ER-Cs may also be installed in buildings or charging stations. Each ER-C has embedded intelligence to manage the energy flow of devices within the consumers premises. Hierarchical Energy Control Architecture When the SD energy controller has a clear view of the energy plane, energy control strategies can be optimized globally, and the power balance of the entire power system can be maintained. However, there are a huge number of grid devices, and each of them requires accurate configuration. If the control command for each individual device were made by the SD energy controller, it would be unrealistic to obtain a globally optimal solution immediately under a dynamic power environment. Therefore, detailed operations of grid devices (especially at the user side) should be abstracted properly in upper control layers. Figure 2d shows a hierarchical energy control architecture for energy router networking. The SD energy controller is located at the top of the hierarchical architecture, so it is able to acquire a global abstracted view of power grids and program energy routers. The software systems in energy routers have basic intelligence to manage their lower layers, but they have to follow the control instructions from their upper layers and hide implementation details. As shown in Fig. 2d, there are three types of control flows: high-level instruction, operation state, and direct configuration. Energy routers receive high-level instructions that may contain abstracted energy control information (e.g., amount and quality of energy) but do not provide any detailed configuration information for physical energy flow controllers. The SD energy controller centrally determines the information of high-level instructions. Operation states of energy routers should be fed back to their upper layers based on the real-time conditions of their lower layers. According to the received high-level instructions, software systems generate control commands to directly configure physical energy flow controllers. The direct configuration may contain detailed control information, including voltage, reactive power, and so on. All energy routers receive different instructions and respond accordingly to maintain the dynamic stability of power systems. Open interfaces are required to enable the communications among the SD energy controller and energy routers. The hierarchical energy control architecture has the following features of SDN. Separation of Control and Energy: The function of decision making is separated from energy routers. Energy routers only have the basic intelligence to execute physical energy control. The SD energy controller takes charge of decision making on global energy management. Logically Centralized Control: The operation of each energy router is subject to the control of its upper layer. The SD energy controller has the ability to perform logically centralized control on energy routers. When the SD energy controller has a clear view of the energy plane, energy control strategies can be optimized globally, and the power balance of the entire power system can be maintained. However, there are a huge number of grid devices, and each of them requires to be configured accurately. IEEE Communications Magazine December

5 Control plane Data plane Road traffic database EV plane SD data controller Energy market Route and charging planning Driver Power system analysis Figure 3. Mobility management system architecture in the SDEI. EV SD energy controller Energy plane Energy router Renewable resource Energy storage Programmability: The SD energy controller can make decisions on energy management and program energy routers through control flows to make them deliver energy according to the decisions, and can conceptually form P2P energy flows. Open Interface: The SD energy controller and energy routers communicate with each other via open interfaces. New energy services and applications can be implemented on top of the control plane without knowing the details of physical energy flow control. Meanwhile, the hierarchical energy control architecture is based on the characteristics of the three types of energy routers and the topology of power grids. In this sense, it is feasible to apply an SDN approach to energy router networking, and the SDN approach improves the flexibility of power systems. As shown in Fig. 2d, energy routers also generate or receive data, such as data on power system analysis, market, and service. However, the data flow does not directly influence the energy control of energy routers. The data is transmitted in the data plane, and the SD data controller is responsible for the related routing and processing problems. Case Study: Future Electric Vehicles In this section, we describe a scenario of future EVs, for which two possible applications based on the SDEI architecture are presented. In the future, advanced vehicle technologies will completely change the energy consumption behaviors of EVs. Vehicle-to-grid technology allows EVs to provide auxiliary services (e.g., frequency regulation) when charging. Wireless power transfer technology enables EVs to charge batteries when they are in motion. With unmanned vehicle technology, EVs can automatically find their parking lots and charge without any human intervention. The impact of EVs on power systems will not be just simple daily charging loads, but fast-fluctuating, fast-moving, uncertain loads. In the future, EV mobility and traffic will cause particular energy consumption behaviors and in turn impact power systems significantly. In the following, we present two applications within the SDEI framework for future EVs: a mobility management system and energy service provider cooperation. The implementation of the applications depends on flexible cooperation between the data and energy planes in the SDEI. Mobility Management System Figure 3 shows the architecture of the mobility management system in the SDEI. The component of route and charging planning provides EVs with route recommendation and charging strategy. Route planning and charging planning are coupled problems. In route planning, we consider not only road traffic but also battery states. We need to make sure if the battery has enough energy to reach the destination. If not, some roads with wireless charging should be selected when the driver does not want static charging. From some candidate routes, we prefer to choose a route with a cheaper price of wireless energy. Therefore, the route and charging planning component should simultaneously analyze the information from EVs, the power system, the energy market, and road traffic, as shown in Fig. 3. The component of power system analysis that knows dynamic EV load information communicates with the control plane. The SD energy controller will be informed in a timely manner to optimize resource allocation in the energy plane for meeting the EVs demand. Further, the state of the energy plane will be fed back to the mobility management system and influence the route and charging planning. Therefore, the proposed mobility management is a closed-loop control process. The SD data controller should dynamically optimize the allocation of computing and network resources in the data plane to improve the users experience of service (EoS) and the system s quality of service (QoS). For example, virtual machines migration according to EV mobility can reduce communications delay and routing cost. Road traffic data can be cached in local network edges, which reduces the data traffic load of accessing a central database. In addition, fifth generation (5G) mobile networks, especially their radio access networks, bring diverse kinds of wireless technologies, such as device-to-device (D2D) and LTE. The SD data controller should manage the heterogeneous wireless environments and optimize the allocation of computing and spectrum resources to satisfy the mobile data traffic from EVs [9]. Energy Service Provider Cooperation Supported by the SDEI architecture, we introduce a new role in Energy Internet, called the energy service provider (ESP). For ESPs, a power grid is only a network that they can use for energy delivery. ESPs possess renewable resources and energy storage to provide their customers with energy services. This is similar to a cloud computing scenario. Cloud service providers have computing, memory, and bandwidth resources, and provide users with cloud services through the Internet. ESPs resources are located in fixed positions in the grid. Customers are dispersed in the grid, and they can choose ESPs without limitation in geographic locations. This will be convenient 72 IEEE Communications Magazine December 2016

6 for EVs since they will not have to change their ESPs when they plug in at different locations at different times. ESPs share their resources with each other when the cooperation brings extra benefits. Figure 4 shows a three-layered diagram of ESP cooperation. The ESPs are at the top management layer and communicate with each other using high-level languages. The ESPs make decisions on energy delivery and resource sharing. At the middle layer, the SD energy controller generates configuration instructions based on the ESPs decisions and controls energy routers. As a result, at the bottom layer, conceptual P2P energy flows can be drawn from the view of ESPs. The following costs and constraints should be considered during ESP cooperation. First, when ESPs do not have enough energy supply and storage, they may need help from the main grid and pay extra bills. Second, there are control and communications overheads in the control and data planes, respectively. Energy transmission and storage cause energy loss in the energy plane. Third, if the power level of a grid device (e.g., an energy router) reaches its upper bound, ESPs cannot increasingly deliver energy via this grid device. Management layer ESP1 Control flow layer Energy flow layer ESP2 ESP3 Numerical Results We present simulation results to show the benefits gained from the mobility management system and ESP cooperation. As shown in Fig. 5, we consider a road layout that has 12 road sections equipped with on-road wireless charging panels. There is a 3-bus system that powers the wireless charging systems [10]. The proposed architecture of an ER-T is adopted at each bus. There is a conventional generator (G) connecting to each ER-T. It is assumed that there are EVs starting at the top left point and ending at the bottom right point in this area. The EVs are charging while traveling. The EVs are divided into three groups of ESP customers, and each group of them has the same EV number. The EVs have uniform speeds, and the duration of the trip is one time unit. The parameter settings in the simulation are shown in Fig. 6a. We consider four cases in the experiment, that is, case 1: free traveling, case 2: only mobility management, case 3: only ESP cooperation, and case 4: both mobility management and ESP cooperation. There are three settings of EV population: 90, 180, and 270. In the experiments, when there is a shortage of renewable energy, conventional generators will serve the charging loads. The supply from renewable resources and conventional generations equals the consumption of EV charging load and power loss. Figure 6b shows the power loss caused by the energy transmission between energy routers (line 12, line 23, and line 13). Figure 6c shows the use of conventional generation in the EV charging. In case 1, the EVs randomly select routes, so some EVs routes may be far away from their ESPs renewable resources, which causes extra energy routing between ER-Ts and in turn results in high power loss. Also, in case 1, the ESPs only deliver renewable energy to their own customers without cooperation, which causes low renewable utilization and in turn increases the use of conventional generation. In case 2, the mobility management system recommends that the Figure 4. Three-layered architecture of energy service provider cooperation. An ESP, its resources, and its customers use the same color. ER-T 1 ER-T 2 G ESP 1 ESP 3 ESP 2 G Line 12 Line 23 Line 13 Figure 5. On-road wireless charging systems powered by a three-bus system. An ESP s renewable resources and its customers (EVs) use the same color. If renewable resources can directly power road sections via an energy router, they use the same color. G ER-T 3 IEEE Communications Magazine December

7 Renewable generation rate EV charging Resistance ESP 1 ESP 2 ESP 3 rate Line 12 Line 23 Line (a) Power loss EVs 180 EVs 270 EVs Conventional generation EVs 180 EVs 270 EVs Free Only mobility management (b) Only ESP cooperation Both mobility management and ESP cooperation 0 Free Only mobility management (c) Only ESP cooperation Both mobility management and ESP cooperation Figure 6. a) Simulation parameter settings; simulation results: b) power loss; c) conventional generation under the four cases. EVs travel on the road sections that are close to their ESPs renewable resources. Thus, power loss from energy routing can be reduced. Further, lower power loss improves the utilization of renewable energy. In case 3, the EVs travel freely while the ESPs share renewable energy with each other. This means that renewable energy is used to meet the local EV charging demands no matter to which ESP the EVs belong. Therefore, this case also avoids long-distance energy routing and improves renewable utilization. In case 4, considering renewable energy sharing among ESPs, route planning is based on the overall distribution of renewable resources. Obviously, mobility management and ESP cooperation can collectively reduce power loss and conventional generation. Conclusion In this article, we propose the SDEI architecture, in which logically centralized controllers can dynamically program the data and energy planes to enable data and energy to flow in P2P fashion. We present three types of energy routers and adopt an SDN approach for energy router networking to form the hierarchical energy control architecture. Two applications based on the SDEI architecture are developed for future EVs. At last, we mention two potential challenges in developing the SDEI. The first challenge is to implement real-time energy control. For the most time-critical energy control information, the transmission delay should be less than 3 ms [5]. Thus, the SD energy controller should have powerful computing capacity to calculate optimal control strategies with high speed, and the interfaces or protocols among energy routers should have high efficiency to deliver control information with low delay. The second challenge comes from the scalability of the SDEI. When the number of energy routers grows dramatically, multiple SD energy controllers may be used to process the massive control information of energy routers. In this case, we should solve the coordination problem among multiple SD energy controllers and ensure the efficiency of the coordination. The scalability problem also comes when an SDN approach is adopted in communications networks [11]. Acknowledgment This work was supported in part by the National Natural Science Foundation of China under Grants , , U , U , , and U , the Science and Technology Program of Guangdong Province under Grant 2015B , Special-Support Project of Guangdong Province under Grant 2014TQ01X100, High Education Excellent Young Teacher Program of Guangdong Province under Grant YQ , Science and Technology Program of Guangzhou under Grant 2014J (Zhujiang New Star Program), and the projects /F20 funded by the Research Council of Norway. References [1] R. Abe, H. Taoka, and D. McQuilkin, Digital Grid: Communicative Electrical Grids of the Future, IEEE Trans. Smart Grid, vol. 2, no. 2, 2011, pp [2] A. Q. Huang et al., The Future Renewable Electric Energy Delivery and Management (Freedm) System: The Energy Internet, Proc. IEEE, vol. 99, no. 1, 2011, pp [3] L. Tsoukalas and R. Gao, From Smart Grids to an Energy Internet: Assumptions, Architectures and Requirements, Proc IEEE Int l. Conf. Electric Utility Deregulation and Restructuring and Power Technologies, 2008, pp [4] J. Lin et al., Architectural Design and Load Flow Study of Power Flow Routers, Proc IEEE Int l. Conf. Smart Grid Communications, 2014, pp [5] Y. Xu et al., Energy Router: Architectures and Functionalities Toward Energy Internet, Proc IEEE Int l. Conf. Smart Grid Communications, 2011, pp [6] M. Jarschel et al., Interfaces, Attributes, and Use Cases: A Compass for SDN, IEEE Commun. Mag., vol. 52, no. 6, 2014, pp [7] A. Cahn et al., Software-Defined Energy Communication Networks: From Substation Automation to Future Smart Grids, Proc IEEE Int l. Conf. Smart Grid Commun., 2013, pp [8] H. Jiang et al., Energy Big Data: A Survey, IEEE Access, vol. 4, 2016, pp IEEE Communications Magazine December 2016

8 [9] X. Huang et al., Software Defined Networking with Pseudonym Systems for Secure Vehicular Clouds, IEEE Access, vol. 4, 2016, pp [10] C.-H. Ou, H. Liang, and W. Zhuang, Investigating Wireless Charging and Mobility of Electric Vehicles on Electricity Market, IEEE Trans. Ind. Electron., vol. 62, no. 5, 2015, pp [11] A. Dixit et al., Towards an Elastic Distributed SDN Controller, Proc. ACM SIGCOMM Comp. Commun. Rev., vol. 43, no. 4. ACM, 2013, pp Biographies Weifeng Zhong (wf_zhong@163.com) received his B.E. and M.E. degrees from Guangdong University of Technology (GDUT), China, in 2013 and 2016, respectively. He is currently working toward his Ph.D. degree in control science and engineering at GDUT. His research interests include vehicle mobility, vehicle-to-grid, and Energy Internet. He spent six months studying at Hong Kong University of Science and Technology as a postgraduate visiting internship student in Rong Yu [M] (yurong@ieee.org) received his Ph.D. degree from Tsinghua University, Beijing, China, in He is currently a full professor at GDUT. His research interests mainly focus on information networking and data, including vehicular network, mobile cloud computing, smart grid, the Internet of Things, and cognitive radio. He is a coauthor of over 100 international journal and conference papers, and a co-holder of over 30 patents. He is currently serving as the Deputy Secretary General of the Internet of Things (IoT) Industry Alliance of Guangdong and Deputy Head of the IoT Engineering Center of Guangdong. Shengli Xie [SM] (shlxie@gdut.edu.cn) received his M.S. degree in mathematics from Central China Normal University, Wuhan, in 1992, and his Ph.D. degree in control theory and applications from South China University of Technology, Guangzhou, in He is presently a full professor and head of the Institute of Intelligent Information Processing at GDUT. His research interests include wireless networks, automatic control, and blind signal processing. He is an author or co-author of two books and more than 150 scientific papers in journals and conference proceedings. He received the second prize in China s State Natural Science Award in 2009 for his work on blind source separation and identification. Yan Zhang [SM] (yanzhang@ieee.org) is a full-time full professor at the University of Oslo, Norway. He is also chief scientist at Simula Research Laboratory, Norway. He received his Ph.D. degree from the School of Electrical & Electronics Engineering, Nanyang Technological University, Singapore. He is an Associate Editor or on the Editorial Board of a number of well established scientific international journals. He is a Senior Member of IEEE ComSoc, IEEE VT Society, IEEE PES, and IEEE Computer Society. He is a Fellow of IET. Danny H. K. Tsang [F 12] (eetsang@ece.ust.hk) received his Ph.D. degree from the University of Pennsylvania in He joined the Department of Electronic & Computer Engineering at Hong Kong University of Science and Technology in summer 1992 and is now a professor in the department. He was a Guest Editor for the IEEE Journal of Selected Areas in Communications Special Issue on Advances in P2P Streaming Systems, an Associate Editor for the Journal of Optical Networking published by the Optical Society of America, and a Guest Editor for the IEEE Systems Journal. He currently serves as a Technical Editor for IEEE Communications Magazine. His current research interests include cloud computing, cognitive radio networks, coordinated electric vehicle charging, and smart grids. IEEE Communications Magazine December