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Weifeng Zhong
Key Laboratory of Ministry of Education, School of Automation, Guangdong University of Technology, Guangzhou 510006, China

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Journal article
Published: 08 April 2020 in IEEE Transactions on Smart Grid
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This paper proposes a combinatorial auction approach for multi-resource allocation of an energy storage (ES) shared by multiple electricity end users in a residential community. Through the auction, a user buys a group of ES resources, including capacity, energy, charging power, and discharging power, from the ES operator. With the ES resources, users store grid energy during low-electricity-price hours, so that they can consume the cheap stored energy during high-electricity-price hours to reduce their electricity bills. In the auction, users submit their resource demands and corresponding bid prices, based on which the ES operator determines the winners and the final payments that the winners must pay. To solve the NP-hard winner determination problem, a fully polynomial time approximation scheme (FPTAS) is developed, which can optimize social welfare but may violate resource supply constraints. To deal with the constraint violation, the ES operator may buy extra energy outside the system to meet the winners’ actual demands. Further, a distributed implementation of the auction is designed to offload the auction computation onto the users while preventing the users from manipulating the auction outcomes in the course of computation. The proposed distributed auction can ensure that all users faithfully complete the assigned computation tasks in an ex-post Nash equilibrium. A real time-of-use (TOU) electricity tariff and actual home load data are used in the simulation, in which the proposed auction approach is evaluated in terms of social welfare and computational efficiency.

ACS Style

Weifeng Zhong; Kan Xie; Yi Liu; Chao Yang; Shengli Xie. Multi-Resource Allocation of Shared Energy Storage: A Distributed Combinatorial Auction Approach. IEEE Transactions on Smart Grid 2020, 11, 4105 -4115.

AMA Style

Weifeng Zhong, Kan Xie, Yi Liu, Chao Yang, Shengli Xie. Multi-Resource Allocation of Shared Energy Storage: A Distributed Combinatorial Auction Approach. IEEE Transactions on Smart Grid. 2020; 11 (5):4105-4115.

Chicago/Turabian Style

Weifeng Zhong; Kan Xie; Yi Liu; Chao Yang; Shengli Xie. 2020. "Multi-Resource Allocation of Shared Energy Storage: A Distributed Combinatorial Auction Approach." IEEE Transactions on Smart Grid 11, no. 5: 4105-4115.

Journal article
Published: 10 February 2020 in IEEE Transactions on Industrial Informatics
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ACS Style

Weifeng Zhong; Kan Xie; Yi Liu; Chao Yang; Shengli Xie; Yan Zhang. Distributed Demand Response for Multienergy Residential Communities With Incomplete Information. IEEE Transactions on Industrial Informatics 2020, 17, 547 -557.

AMA Style

Weifeng Zhong, Kan Xie, Yi Liu, Chao Yang, Shengli Xie, Yan Zhang. Distributed Demand Response for Multienergy Residential Communities With Incomplete Information. IEEE Transactions on Industrial Informatics. 2020; 17 (1):547-557.

Chicago/Turabian Style

Weifeng Zhong; Kan Xie; Yi Liu; Chao Yang; Shengli Xie; Yan Zhang. 2020. "Distributed Demand Response for Multienergy Residential Communities With Incomplete Information." IEEE Transactions on Industrial Informatics 17, no. 1: 547-557.

Journal article
Published: 03 December 2019 in IEEE Transactions on Smart Grid
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This paper proposes an online control approach for real-time energy management of distributed energy storage (ES) sharing. A new ES sharing scenario is considered, in which the capacities of physical ESs (PESs) are reallocated to users, so that each user manages its own virtual ES (VES) without knowing detailed operations of the PESs. To optimize the ES sharing system in real time, an online algorithm is developed based on Lyapunov optimization framework. The advantage of the online algorithm is that it makes decisions only based on the realization of current system states, without having to predict future uncertain system states such as electricity price, user load, and renewable generation. In performance analysis, it is proven that the online solution is feasible and has a provable performance guarantee. Based on the analysis, an approach for optimal offline parameter selection is proposed to guarantee the online control performance. For practical need of privacy protection, a distributed implementation of the online control is proposed via alternating direction method of multipliers (ADMM). In the distributed implementation, users are allowed to manage their VESs locally without sending their private data to anyone. In simulation, actual real-time data of electricity price, home load, and home renewable generation is used. Results show that the proposed distributed online control approach can provide a near-optimal solution, compared with other benchmarks.

ACS Style

Weifeng Zhong; Kan Xie; Yi Liu; Chao Yang; Shengli Xie; Yan Zhang. Online Control and Near-Optimal Algorithm for Distributed Energy Storage Sharing in Smart Grid. IEEE Transactions on Smart Grid 2019, 11, 2552 -2562.

AMA Style

Weifeng Zhong, Kan Xie, Yi Liu, Chao Yang, Shengli Xie, Yan Zhang. Online Control and Near-Optimal Algorithm for Distributed Energy Storage Sharing in Smart Grid. IEEE Transactions on Smart Grid. 2019; 11 (3):2552-2562.

Chicago/Turabian Style

Weifeng Zhong; Kan Xie; Yi Liu; Chao Yang; Shengli Xie; Yan Zhang. 2019. "Online Control and Near-Optimal Algorithm for Distributed Energy Storage Sharing in Smart Grid." IEEE Transactions on Smart Grid 11, no. 3: 2552-2562.

Journal article
Published: 30 April 2019 in Energies
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This paper studies capacity allocation of an energy storage (ES) device which is shared by multiple homes in smart grid. Given a time-of-use (TOU) tariff, homes use the ES to shift loads from peak periods to off-peak periods, reducing electricity bills. In the proposed ES sharing model, the ES capacity has to be allocated to homes before the homes’ load data is completely known. To this end, an online ES capacity allocation algorithm is developed based on the online convex optimization framework. Under the online algorithm, the complex allocation problem can be solved round by round: at each round, the algorithm observes current system states and predicts a decision for the next round. The proposed algorithm is able to minimize homes’ costs by learning from home load data in a serial fashion. It is proven that the online algorithm can ensure zero average regret and long-term budget balance of homes. Further, a distributed implementation of the online algorithm is proposed based on alternating direction method of multipliers framework. In the distributed implementation, the one-round system problem is decomposed into multiple subproblems that can be solved by homes locally, so that an individual home does not need to send its private load data to any other. In simulation, actual home load data and a TOU tariff of the United States are used. Results show that the proposed online approach leads to the lowest home costs, compared to other benchmark approaches.

ACS Style

Kan Xie; Weifeng Zhong; Weijun Li; Yinhao Zhu. Distributed Capacity Allocation of Shared Energy Storage Using Online Convex Optimization. Energies 2019, 12, 1642 .

AMA Style

Kan Xie, Weifeng Zhong, Weijun Li, Yinhao Zhu. Distributed Capacity Allocation of Shared Energy Storage Using Online Convex Optimization. Energies. 2019; 12 (9):1642.

Chicago/Turabian Style

Kan Xie; Weifeng Zhong; Weijun Li; Yinhao Zhu. 2019. "Distributed Capacity Allocation of Shared Energy Storage Using Online Convex Optimization." Energies 12, no. 9: 1642.

Journal article
Published: 21 February 2019 in IEEE Access
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Vehicular networks are facing the challenges to support ubiquitous connections and high quality of service for numerous vehicles. To address these issues, mobile edge computing (MEC) is explored as a promising technology in vehicular networks by employing computing resources at the edge of vehicular wireless access networks. In this paper, we study the efficient task offloading schemes in vehicular edge computing networks. The vehicles perform the offloading time selection, communication, and computing resource allocations optimally, the mobility of vehicles and the maximum latency of tasks are considered. To minimize the system costs, including the costs of the required communication and computing resources, we first analyze the offloading schemes in the independent MEC servers scenario. The offloading tasks are processed by the MEC servers deployed at the access point (AP) independently. A mobility-aware task offloading scheme is proposed. Then, in the cooperative MEC servers scenario, the MEC servers can further offload the collected overloading tasks to the adjacent servers at the next AP on the vehicles' moving direction. A location-based offloading scheme is proposed. In both scenarios, the tradeoffs between the task completed latency and the required communication and computation resources are mainly considered. Numerical results show that our proposed schemes can reduce the system costs efficiently, while the latency constraints are satisfied.

ACS Style

Chao Yang; Yi Liu; Xin Chen; Weifeng Zhong; Shengli Xie. Efficient Mobility-Aware Task Offloading for Vehicular Edge Computing Networks. IEEE Access 2019, 7, 26652 -26664.

AMA Style

Chao Yang, Yi Liu, Xin Chen, Weifeng Zhong, Shengli Xie. Efficient Mobility-Aware Task Offloading for Vehicular Edge Computing Networks. IEEE Access. 2019; 7 ():26652-26664.

Chicago/Turabian Style

Chao Yang; Yi Liu; Xin Chen; Weifeng Zhong; Shengli Xie. 2019. "Efficient Mobility-Aware Task Offloading for Vehicular Edge Computing Networks." IEEE Access 7, no. : 26652-26664.

Journal article
Published: 16 August 2018 in IEEE Access
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Energy hub integrates various energy conversion and storage technologies, which can yield complementarity among multiple energy and provide consumers with stable energy services, such as electricity, heating, and cooling. This enables energy hub to be an ideal energy system design for smart and green buildings. This paper proposes a distributed auction mechanism for multi-energy scheduling of an energy hub that serves numbers of building energy users. In the auction, users first submit their demand data to the hub manager. Then, the hub manager allocates energy to users via optimization of energy scheduling based on the users' data. The auction mechanism is designed to be incentive compatible, meaning that users are incentivized to truthfully submit their demand data. Next, to mitigate the computational burden of the hub manager, a distributed implementation of the auction is developed, in which an algorithm based on alternating direction method of multipliers (ADMM) is adopted to offload auction computation onto the users. Distributed computation offloading may bring in new chances for users to manipulate the auction outcome since the users participate part of the auction computation. It is proven that the proposed distributed auction mechanism can achieve incentive compatibility in a Nash equilibrium, which indicates that rational users will faithfully report demand data and complete the assigned computation as well. Finally, simulation results based on a household energy consumption dataset are presented to evaluate the energy scheduling performance and to verify the incentive compatibility of the auction mechanism.

ACS Style

Weifeng Zhong; Chao Yang; Kan Xie; Shengli Xie; Yan Zhang. ADMM-Based Distributed Auction Mechanism for Energy Hub Scheduling in Smart Buildings. IEEE Access 2018, 6, 45635 -45645.

AMA Style

Weifeng Zhong, Chao Yang, Kan Xie, Shengli Xie, Yan Zhang. ADMM-Based Distributed Auction Mechanism for Energy Hub Scheduling in Smart Buildings. IEEE Access. 2018; 6 ():45635-45645.

Chicago/Turabian Style

Weifeng Zhong; Chao Yang; Kan Xie; Shengli Xie; Yan Zhang. 2018. "ADMM-Based Distributed Auction Mechanism for Energy Hub Scheduling in Smart Buildings." IEEE Access 6, no. : 45635-45645.

Article
Published: 26 February 2018 in Mobile Networks and Applications
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Energy trading mechanism for microgrids has an inherent two-layer architecture, in which the energy trading at the first layer is between a microgrid aggregator and consumers (e.g., households) within a microgrid, and the second layer is referred to as the wide area energy trading among multiple microgrids. This paper employs a two-layer game approach to achieve optimal and elastic energy trading for microgrids and improve utilization of green energy. First, a non-cooperative game is developed inside a microgrid, in which the relationship among household users is non-cooperative, and they adjust load schedules to optimize their utilities while trading energy with the microgrid aggregator. Second, a multileader-multifollower Stackelberg game is employed for the energy trading among microgrids. The role of a microgrid (as an energy buyer or seller) in the energy market is based on the result of the first game, and it can elastically adjust its energy trading strategy by charging or discharging the energy storage device. The existence and uniqueness of the equilibriums for the two games are proven. We also present algorithms that can reach the equilibriums where players achieve optimal utilities. Simulation results show that the proposed two-layer energy trading is able to significantly improve the utilization of microgrids’ green energy.

ACS Style

Wenhui Zhou; Jie Wu; Weifeng Zhong; Haochuan Zhang; Lei Shu; Rong Yu. Optimal and Elastic Energy Trading for Green Microgrids: a two-Layer Game Approach. Mobile Networks and Applications 2018, 24, 950 -961.

AMA Style

Wenhui Zhou, Jie Wu, Weifeng Zhong, Haochuan Zhang, Lei Shu, Rong Yu. Optimal and Elastic Energy Trading for Green Microgrids: a two-Layer Game Approach. Mobile Networks and Applications. 2018; 24 (3):950-961.

Chicago/Turabian Style

Wenhui Zhou; Jie Wu; Weifeng Zhong; Haochuan Zhang; Lei Shu; Rong Yu. 2018. "Optimal and Elastic Energy Trading for Green Microgrids: a two-Layer Game Approach." Mobile Networks and Applications 24, no. 3: 950-961.

Journal article
Published: 05 January 2018 in IEEE Transactions on Smart Grid
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This paper proposes a topology-aware vehicle-to-grid (V2G) energy trading mechanism that uses charging/discharging powers of electric vehicles (EVs) to regulate voltage deviations of an active distribution system. To incentivize EVs to trade charging/discharging energy, auction theory is employed to guarantee three economic properties: truthfulness, individual rationality, and social cost minimization. The mechanism is designed according to the topology of an active distribution system, in which multiple microgrids (MGs) are networked through a distribution network (DN). Two types of V2G auctions are proposed for the MG and DN, respectively. In each auction, the auctioneer optimizes control of EVs’ charging/discharging by solving a social cost minimization problem subject to constraints of power network topology. Further, the mechanism uses analytic target cascading (ATC) framework, allowing the MG and DN to set up their own V2G auctions separately, and meanwhile enabling them to coordinate with each other to determine DN-MG power exchange. It is theoretically proved that the mechanism’s three economic properties hold in both the cases of grid-tied MGs and islanding MGs. Simulation results show that the proposed mechanism can regulate voltages of the distribution system to a secure range. Theoretic analysis of the economic properties is verified as well.

ACS Style

Weifeng Zhong; Kan Xie; Yi Liu; Chao Yang; Shengli Xie. Topology-Aware Vehicle-to-Grid Energy Trading for Active Distribution Systems. IEEE Transactions on Smart Grid 2018, 10, 2137 -2147.

AMA Style

Weifeng Zhong, Kan Xie, Yi Liu, Chao Yang, Shengli Xie. Topology-Aware Vehicle-to-Grid Energy Trading for Active Distribution Systems. IEEE Transactions on Smart Grid. 2018; 10 (2):2137-2147.

Chicago/Turabian Style

Weifeng Zhong; Kan Xie; Yi Liu; Chao Yang; Shengli Xie. 2018. "Topology-Aware Vehicle-to-Grid Energy Trading for Active Distribution Systems." IEEE Transactions on Smart Grid 10, no. 2: 2137-2147.

Journal article
Published: 28 December 2017 in IEEE Transactions on Industrial Informatics
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In green cities, one of the most promising energy system designs is the multi-energy system, which is capable of integrating different energy resources to supply stable energy for users. To schedule diverse energy efficiently, the energy trading among different energy entities is a big issue in multi-energy systems. This paper proposes auction mechanisms for energy trading in a smart multi-energy district, in which the district manager sells electricity, natural gas, and heating energy to users and meanwhile trades with outer energy networks. Two auction mechanisms are designed under the day-ahead and real-time markets, respectively. For each auction, energy allocation is optimized by solving a social welfare maximization problem, which is strictly subject to constraints of physical multi-energy system models. It is theoretically proven that both auctions are able to guarantee the properties of economic efficiency, truthfulness, and individual rationality. With these properties, users are incentivized to participate into the auctions with fairness. Finally, real data are adopted to evaluate the performance of the proposed mechanisms. The theoretic analysis of the properties is verified as well.

ACS Style

Weifeng Zhong; Kan Xie; Yi Liu; Chao Yang; Shengli Xie. Auction Mechanisms for Energy Trading in Multi-Energy Systems. IEEE Transactions on Industrial Informatics 2017, 14, 1511 -1521.

AMA Style

Weifeng Zhong, Kan Xie, Yi Liu, Chao Yang, Shengli Xie. Auction Mechanisms for Energy Trading in Multi-Energy Systems. IEEE Transactions on Industrial Informatics. 2017; 14 (4):1511-1521.

Chicago/Turabian Style

Weifeng Zhong; Kan Xie; Yi Liu; Chao Yang; Shengli Xie. 2017. "Auction Mechanisms for Energy Trading in Multi-Energy Systems." IEEE Transactions on Industrial Informatics 14, no. 4: 1511-1521.

Conference paper
Published: 01 May 2017 in 2017 IEEE International Conference on Communications (ICC)
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One of the major advantages of smart grid is to allow a large number of electric vehicles (EVs) to participate in energy dispatch as elastic energy storage devices via vehicle-to-grid (V2G) technology. As mechanism design for V2G energy trading can stimulate energy interaction between EVs and grids, it is really significant to V2G systems. This paper focuses on efficient mechanism design for energy trading in a two-layer V2G architecture, which includes a grid-aggregator layer and aggregator-EV layer. We propose two auction mechanisms for the two layers, respectively, and discuss three essential economic properties of the mechanisms, i.e., truthfulness, individual rationality, and efficiency. Then, based on these two mechanisms, we illustrate the detailed operation procedure of the two-layer V2G energy trading architecture. Performance evaluation shows that the proposed auction mechanisms greatly reduce social costs, i.e., enhance efficiency, while guaranteeing truthfulness and individual rationality.

ACS Style

Weifeng Zhong; Kan Xie; Yi Liu; Chao Yang; Shengli Xie. Efficient auction mechanisms for two-layer vehicle-to-grid energy trading in smart grid. 2017 IEEE International Conference on Communications (ICC) 2017, 1 -6.

AMA Style

Weifeng Zhong, Kan Xie, Yi Liu, Chao Yang, Shengli Xie. Efficient auction mechanisms for two-layer vehicle-to-grid energy trading in smart grid. 2017 IEEE International Conference on Communications (ICC). 2017; ():1-6.

Chicago/Turabian Style

Weifeng Zhong; Kan Xie; Yi Liu; Chao Yang; Shengli Xie. 2017. "Efficient auction mechanisms for two-layer vehicle-to-grid energy trading in smart grid." 2017 IEEE International Conference on Communications (ICC) , no. : 1-6.

Conference paper
Published: 01 April 2017 in 2017 IEEE International Conference on Energy Internet (ICEI)
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In the future Energy Internet, one of the challenging problems faced by microgrids will be the multiple energy management including outer energy exchange and inner energy schedule. This paper proposes a game theory based dual energy scheduling method for microgrids in Energy Internet. The dual energy scheduling problem is formulated as a non-cooperative game, where household users within a microgrid schedule electricity and gas usages to minimize their total energy costs. The energy router in the microgrid is responsible for providing users with information that is needed in the game process, and executing multi-energy control according to the game results. The existence and uniqueness of Nash equilibrium for the proposed game model are discussed, and an iterative algorithm for reaching the equilibrium is presented. Simulation results show that the proposed game-based approach can effectively reduce both electricity and gas consumption costs of household users in a microgrid.

ACS Style

Jie Wu; Wenhui Zhou; Weifeng Zhong; Yuhua Cheng; Jinhua Liu. Dual Energy Scheduling for Microgrids in Energy Internet: A Non-Cooperative Game Approach. 2017 IEEE International Conference on Energy Internet (ICEI) 2017, 48 -52.

AMA Style

Jie Wu, Wenhui Zhou, Weifeng Zhong, Yuhua Cheng, Jinhua Liu. Dual Energy Scheduling for Microgrids in Energy Internet: A Non-Cooperative Game Approach. 2017 IEEE International Conference on Energy Internet (ICEI). 2017; ():48-52.

Chicago/Turabian Style

Jie Wu; Wenhui Zhou; Weifeng Zhong; Yuhua Cheng; Jinhua Liu. 2017. "Dual Energy Scheduling for Microgrids in Energy Internet: A Non-Cooperative Game Approach." 2017 IEEE International Conference on Energy Internet (ICEI) , no. : 48-52.

Journal article
Published: 16 December 2016 in IEEE Communications Magazine
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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.

ACS Style

Weifeng Zhong; Rong Yu; Shengli Xie; Yan Zhang; Danny H. K. Tsang. Software Defined Networking for Flexible and Green Energy Internet. IEEE Communications Magazine 2016, 54, 68 -75.

AMA Style

Weifeng Zhong, Rong Yu, Shengli Xie, Yan Zhang, Danny H. K. Tsang. Software Defined Networking for Flexible and Green Energy Internet. IEEE Communications Magazine. 2016; 54 (12):68-75.

Chicago/Turabian Style

Weifeng Zhong; Rong Yu; Shengli Xie; Yan Zhang; Danny H. K. Tsang. 2016. "Software Defined Networking for Flexible and Green Energy Internet." IEEE Communications Magazine 54, no. 12: 68-75.

Journal article
Published: 15 November 2016 in IEEE Transactions on Smart Grid
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Demand response (DR) plays a significant role in enhancing the reliability of future smart grids. Electric vehicles (EVs) can be exploited to facilitate DR because their batteries, as a form of flexible energy storage, can be controlled to consume energy from or feed energy back to the grid depending on user needs. However, EVs’ mobility is inherently probabilistic, which presents a challenge for system stability particularly. This paper analyzes the stability of DR in which mobile EVs participate. Using the methodology of dynamical complex networks, we present a DR model of vehicle-to-grid (V2G) mobile energy network in which the EVs generally move across different districts represented as network nodes. EV fleets, therefore, transport energy and energy storage capacity among these nodes in general. A difference equation system is developed to model the DR dynamics of the nodes, which mutually affect each other. A DR algorithm is proposed to control the demand for EV charging and discharging. It is proved that the stability of the algorithm is robust to internode coupling. Numerical results show that incoming EVs that bring new energy and storage into a district can impact the DR stability. Real-world traces of vehicle mobility are used in simulations to illustrate the DR model.

ACS Style

Weifeng Zhong; Rong Yu; Shengli Xie; Yan Zhang; David K. Y. Yau. On Stability and Robustness of Demand Response in V2G Mobile Energy Networks. IEEE Transactions on Smart Grid 2016, 9, 3203 -3212.

AMA Style

Weifeng Zhong, Rong Yu, Shengli Xie, Yan Zhang, David K. Y. Yau. On Stability and Robustness of Demand Response in V2G Mobile Energy Networks. IEEE Transactions on Smart Grid. 2016; 9 (4):3203-3212.

Chicago/Turabian Style

Weifeng Zhong; Rong Yu; Shengli Xie; Yan Zhang; David K. Y. Yau. 2016. "On Stability and Robustness of Demand Response in V2G Mobile Energy Networks." IEEE Transactions on Smart Grid 9, no. 4: 3203-3212.

Article
Published: 01 November 2016 in Chinese Journal of Electronics
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A game theory based scheduling method for household electricity consumption in smart grid is proposed in this paper. A non-cooperative game model is employed during the scheduling. All household consumers compete with each other and control their loads to maximize their payoffs. For solving the scheduling problem, we formulate the Utility optimization (UO) model, in which the electricity cost reduction and the improvement of consumers' comfort and preference are considered simultaneously. The System optimization (SO) model only minimizes the electricity cost when scheduling the consumers' electricity consumption. Then we compare and analyze the two models in numerical simulation. The existence and the uniqueness of Nash equilibrium for proposed game model are proved. Simulation results show that the UO model provides an effective scheduling approach to achieve higher comfort and preference and at the same time decrease the energy cost.

ACS Style

Wenhui Zhou; Jie Wu; Weifeng Zhong; Sheng Zou. Electricity Consumption Scheduling with Consumers' Comfort and Preference in Smart Grid. Chinese Journal of Electronics 2016, 25, 1151 -1158.

AMA Style

Wenhui Zhou, Jie Wu, Weifeng Zhong, Sheng Zou. Electricity Consumption Scheduling with Consumers' Comfort and Preference in Smart Grid. Chinese Journal of Electronics. 2016; 25 (6):1151-1158.

Chicago/Turabian Style

Wenhui Zhou; Jie Wu; Weifeng Zhong; Sheng Zou. 2016. "Electricity Consumption Scheduling with Consumers' Comfort and Preference in Smart Grid." Chinese Journal of Electronics 25, no. 6: 1151-1158.

Journal article
Published: 25 February 2016 in IEEE Transactions on Neural Networks and Learning Systems
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Research on the smart grid is being given enormous supports worldwide due to its great significance in solving environmental and energy crises. Electric vehicles (EVs), which are powered by clean energy, are adopted increasingly year by year. It is predictable that the huge charge load caused by high EV penetration will have a considerable impact on the reliability of the smart grid. Therefore, fair energy scheduling for EV charge and discharge is proposed in this paper. By using the vehicle-to-grid technology, the scheduler controls the electricity loads of EVs considering fairness in the residential distribution network. We propose contribution-based fairness, in which EVs with high contributions have high priorities to obtain charge energy. The contribution value is defined by both the charge/discharge energy and the timing of the action. EVs can achieve higher contribution values when discharging during the load peak hours. However, charging during this time will decrease the contribution values seriously. We formulate the fair energy scheduling problem as an infinite-horizon Markov decision process. The methodology of adaptive dynamic programming is employed to maximize the long-term fairness by processing online network training. The numerical results illustrate that the proposed EV energy scheduling is able to mitigate and flatten the peak load in the distribution network. Furthermore, contribution-based fairness achieves a fast recovery of EV batteries that have deeply discharged and guarantee fairness in the full charge time of all EVs.

ACS Style

Shengli Xie; Weifeng Zhong; Kan Xie; Rong Yu; Yan Zhang. Fair Energy Scheduling for Vehicle-to-Grid Networks Using Adaptive Dynamic Programming. IEEE Transactions on Neural Networks and Learning Systems 2016, 27, 1697 -1707.

AMA Style

Shengli Xie, Weifeng Zhong, Kan Xie, Rong Yu, Yan Zhang. Fair Energy Scheduling for Vehicle-to-Grid Networks Using Adaptive Dynamic Programming. IEEE Transactions on Neural Networks and Learning Systems. 2016; 27 (8):1697-1707.

Chicago/Turabian Style

Shengli Xie; Weifeng Zhong; Kan Xie; Rong Yu; Yan Zhang. 2016. "Fair Energy Scheduling for Vehicle-to-Grid Networks Using Adaptive Dynamic Programming." IEEE Transactions on Neural Networks and Learning Systems 27, no. 8: 1697-1707.

Journal article
Published: 26 October 2015 in IEEE Transactions on Industrial Informatics
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Vehicle-to-grid (V2G) technology enables bidirectional energy flow between electric vehicles (EVs) and power grid, which provides flexible demand response management (DRM) for the reliability of smart grid. EV mobility is a unique and inherent feature of the V2G system. However, the inter-relationship between EV mobility and DRM is not obvious. In this paper, we focus on the exploration of EV mobility to impact DRM in V2G systems in smart grid. We first present a dynamic complex network model of V2G mobile energy networks, considering the fact that EVs travel across multiple districts, and hence EVs can be acting as energy transporters among different districts. We formulate the districts' DRM dynamics, which is coupled with each other through EV fleets. In addition, a complex network synchronization method is proposed to analyze the dynamic behavior in V2G mobile energy networks. Numerical results show that EVs mobility of symmetrical EV fleet is able to achieve synchronous stability of network and balance the power demand among different districts. This observation is also validated by simulation with real world data.

ACS Style

Rong Yu; Weifeng Zhong; Shengli Xie; Chau Yuen; Stein Gjessing; Yan Zhang. Balancing Power Demand Through EV Mobility in Vehicle-to-Grid Mobile Energy Networks. IEEE Transactions on Industrial Informatics 2015, 12, 79 -90.

AMA Style

Rong Yu, Weifeng Zhong, Shengli Xie, Chau Yuen, Stein Gjessing, Yan Zhang. Balancing Power Demand Through EV Mobility in Vehicle-to-Grid Mobile Energy Networks. IEEE Transactions on Industrial Informatics. 2015; 12 (1):79-90.

Chicago/Turabian Style

Rong Yu; Weifeng Zhong; Shengli Xie; Chau Yuen; Stein Gjessing; Yan Zhang. 2015. "Balancing Power Demand Through EV Mobility in Vehicle-to-Grid Mobile Energy Networks." IEEE Transactions on Industrial Informatics 12, no. 1: 79-90.

Conference paper
Published: 01 October 2015 in 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing
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Power Line Communications (PLC) is regarded as the promising home networking technology for smart grid application since the PLC technology enables data transmission via the existing power line. The cooperation between PLC and Wireless Local Area Network (WLAN) will be an effective hybrid solution for Home Area Networks (HANs). In this paper, we consider that Transmission Control Protocol (TCP) packets are transmitting in heterogenous communications network based on PLC and WLAN. The existing TCP congestion control has low effectiveness due to the error-prone power line channel. Therefore, we propose an adaptive rate control to improve TCP performance in the hybrid PLC/WLAN HANs. We exploit the communications mechanism of WLAN to improve the PLC link utilization. The methodology of Heuristic Dynamic Programming (HDP) is employed to obtain the optimal rate policy. The bottleneck link capacity is estimated by neural network training. The sending rate is adapted according to the long-term quality of service (QoS) of applications. Simulation results illustrate that the proposed rate control effectively filters out the negative impact from the instantaneous impulse noise in PLC channel and outperforms the existing TCP.

ACS Style

Weifeng Zhong; Rong Yu; Yan Zhang; Yue Gao; Stein Gjessing. Adaptive Rate Control in Smart Grid Heterogeneous Communications Networks. 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing 2015, 12 -17.

AMA Style

Weifeng Zhong, Rong Yu, Yan Zhang, Yue Gao, Stein Gjessing. Adaptive Rate Control in Smart Grid Heterogeneous Communications Networks. 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing. 2015; ():12-17.

Chicago/Turabian Style

Weifeng Zhong; Rong Yu; Yan Zhang; Yue Gao; Stein Gjessing. 2015. "Adaptive Rate Control in Smart Grid Heterogeneous Communications Networks." 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing , no. : 12-17.

Conference paper
Published: 01 June 2015 in 2015 IEEE International Conference on Communications (ICC)
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Vehicle-to-grid (V2G) technology enables bidirectional energy flow between electric vehicles (EVs) and grid, which provides powerful demand response, balancing the electricity demand and supply in smart grid. Mobility is the key feature of EVs, which is also a significant challenge for V2G systems. In order to model the EV mobility in V2G systems, we propose a complex networking modeling for V2G mobile energy network. Each district has a V2G system. EVs travel among different districts. The EV fleets transport energy and impact the V2G systems of districts. The theory of complex network synchronization is employed to analyze the dynamics of the mobile energy network. Numerical results show the energy transportation of EV fleets may achieve synchronous stability of demand level of different districts, balancing the demand response in the mobile energy network.

ACS Style

Weifeng Zhong; Rong Yu; Yan Zhang; Jiawen Kang; Haochuan Zhang; Shengli Xie. Dynamic demand balance in vehicle-to-grid mobile energy networks. 2015 IEEE International Conference on Communications (ICC) 2015, 5589 -5594.

AMA Style

Weifeng Zhong, Rong Yu, Yan Zhang, Jiawen Kang, Haochuan Zhang, Shengli Xie. Dynamic demand balance in vehicle-to-grid mobile energy networks. 2015 IEEE International Conference on Communications (ICC). 2015; ():5589-5594.

Chicago/Turabian Style

Weifeng Zhong; Rong Yu; Yan Zhang; Jiawen Kang; Haochuan Zhang; Shengli Xie. 2015. "Dynamic demand balance in vehicle-to-grid mobile energy networks." 2015 IEEE International Conference on Communications (ICC) , no. : 5589-5594.

Journal article
Published: 22 April 2015 in IEEE Transactions on Neural Networks and Learning Systems
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As the next-generation power grid, smart grid will be integrated with a variety of novel communication technologies to support the explosive data traffic and the diverse requirements of quality of service (QoS). Cognitive radio (CR), which has the favorable ability to improve the spectrum utilization, provides an efficient and reliable solution for smart grid communications networks. In this paper, we study the QoS differential scheduling problem in the CR-based smart grid communications networks. The scheduler is responsible for managing the spectrum resources and arranging the data transmissions of smart grid users (SGUs). To guarantee the differential QoS, the SGUs are assigned to have different priorities according to their roles and their current situations in the smart grid. Based on the QoS-aware priority policy, the scheduler adjusts the channels allocation to minimize the transmission delay of SGUs. The entire transmission scheduling problem is formulated as a semi-Markov decision process and solved by the methodology of adaptive dynamic programming. A heuristic dynamic programming (HDP) architecture is established for the scheduling problem. By the online network training, the HDP can learn from the activities of primary users and SGUs, and adjust the scheduling decision to achieve the purpose of transmission delay minimization. Simulation results illustrate that the proposed priority policy ensures the low transmission delay of high priority SGUs. In addition, the emergency data transmission delay is also reduced to a significantly low level, guaranteeing the differential QoS in smart grid.

ACS Style

Rong Yu; Weifeng Zhong; Shengli Xie; Yan Zhang; Yun Zhang. QoS Differential Scheduling in Cognitive-Radio-Based Smart Grid Networks: An Adaptive Dynamic Programming Approach. IEEE Transactions on Neural Networks and Learning Systems 2015, 27, 435 -443.

AMA Style

Rong Yu, Weifeng Zhong, Shengli Xie, Yan Zhang, Yun Zhang. QoS Differential Scheduling in Cognitive-Radio-Based Smart Grid Networks: An Adaptive Dynamic Programming Approach. IEEE Transactions on Neural Networks and Learning Systems. 2015; 27 (2):435-443.

Chicago/Turabian Style

Rong Yu; Weifeng Zhong; Shengli Xie; Yan Zhang; Yun Zhang. 2015. "QoS Differential Scheduling in Cognitive-Radio-Based Smart Grid Networks: An Adaptive Dynamic Programming Approach." IEEE Transactions on Neural Networks and Learning Systems 27, no. 2: 435-443.

Conference paper
Published: 01 April 2015 in 2015 5th International Conference on Information Science and Technology (ICIST)
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Smart grid, the future of power grid, aims at making progress on electricity reliability and emission reduction. In recent years, electric vehicles (EVs) are adopted increasingly due to their zero discharge and high efficiency. For the reliability of smart grid, the charging control with high penetration of EVs is needed to prevent from overload and power loss. In this paper, the adaptive price control is proposed for EV charging. The aggregator manages the EV batteries and regulates the charging demand with the consideration of energy supply limit by using price control. We consider that the information of EV mobility is unknown in advance, which will impact the performance of price control. Thus, the technique of Neuro-dynamic Programming (NDP) is leveraged to obtain optimal price policy by processing online learning. Numerical results show that our adaptive price control can tune the EV charging demand to approach the expected level by learning from the EV charging process and the EV mobility.

ACS Style

Weifeng Zhong; Chuan Lu; Rong Yu. Adaptive price control for electric vehicle charging in smart grid. 2015 5th International Conference on Information Science and Technology (ICIST) 2015, 292 -296.

AMA Style

Weifeng Zhong, Chuan Lu, Rong Yu. Adaptive price control for electric vehicle charging in smart grid. 2015 5th International Conference on Information Science and Technology (ICIST). 2015; ():292-296.

Chicago/Turabian Style

Weifeng Zhong; Chuan Lu; Rong Yu. 2015. "Adaptive price control for electric vehicle charging in smart grid." 2015 5th International Conference on Information Science and Technology (ICIST) , no. : 292-296.