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.

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.

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.

This article proposes a cooperative energy market model for an active Distribution Network (DN) by using the theory of Generalized Nash Bargaining (GNB). The proposed energy market has three types of participants: the DN operator, buyers, and sellers. Two energy trading manners are allowed at the same time: 1) each of buyers/sellers trades energy with the DN operator; 2) buyers and sellers trade energy in a Peer-to-Peer (P2P) manner and pay network usage fees to the DN operator. In the market, the DN operator actively manages voltage and reactive power (Volt-VAR) via controlling on-load tap changers of transformers and shunt capacitors. The payments among participants are subject to price constraints. The GNB problem for the proposed market is formulated and then decomposed into two subproblems: social welfare maximization problem (P1) and energy trading problem (P2). Both P1 and P2 are nonconvex problems. Next, linearization techniques are employed and a grid propagation algorithm is developed, transforming P1 and P2 into their equivalent Mixed-Integer Linear Programming (MILP) problems. In simulation, the proposed market model is compared with other GNB-based market models. The results show that the proposed one can significantly increase social welfare through Volt-VAR control and also can maximize the extent of fairness of profit allocation under the price constraints.

Recently, plug-in hybrid electric vehicles (PHEVs) have attracted considerable attention as a sustainable transport system and also an essential component of the smart grid. With the rapid growth of PHEVs penetration, the charging and discharging of PHEVs will pose a significant impact on the residential electricity distribution network. For this reason, the management of PHEV charging and discharging has become one of the key issues in the research of PHEVs. In most existing work, PHEVs are supposed to operate individually for charging and discharging in the grid. However, we argue that, by leveraging the cooperation among PHEVs, the grid will efficiently stimulate PHEV users to charge in load valley and discharge in load peak. As a consequence, the electricity load is well balanced. Meanwhile, the PHEV users also achieve higher profit. The PHEV charging and discharging cooperation is a win-win strategy for both the grid and the PHEV users. We formulate and resolve the PHEV charging and discharging cooperation in the framework of coalition game. The simulation results indicate that the peak-valley difference in electricity load of the grid is significantly reduced. Besides, the PHEV users have better satisfaction in the vehicle battery status and the economic profit.