In serverless vehicular edge computing (SVEC), a variety of vehicular services are encapsulated into the containers deployed on accessible edge computing nodes such as roadside edge servers and nearby vehicular terminals, aiming to bring remarkable benefits to the service management, e.g., simplifying the infrastructure management, improving the resource utilization, and dynamically scaling up or down in response to the resource demand. However, there still exists a challenging service scheduling problem between the requester vehicles and available SVEC processors, due to the the dynamic vehicle mobility and heterogeneous edge computing environment. In the problem, a set of request vehicles can locally process the service requests, or offload them to a nearest edge server and peripheral vehicular terminals. We particularly consider the essential difference of the edge server and hardware-constrained vehicular terminals in the storage capacity and computing capabilities for running the containers, and aim to minimize the total service cost of all requester vehicles subject to the mobility constraints of the vehicles. To address the problem, we propose a diffusion-based deep reinforcement learning (DRL) approach to quickly learn a high-accuracy solution. Numerical results demonstrate that compared with the baseline DRL approaches, the proposed approach has great advantages in both the learning accuracy and convergence rate.

The rapid development of autonomous driving technologies and the expansion of the Internet of Things (IoT) have intensified the demand for timely and accurate vehicular perception, highlighting the potential of leveraging vehicular edge computing (VEC) systems to support perception tasks. Unmanned aerial vehicles (UAVs), owing to their flexible mobility and line-of-sight advantages, have emerged as promising IoT-enabling aerial platforms to enhance both vehicular perception and computation capabilities in VEC environments. In this paper, we propose an accuracy-oriented and computation-efficient framework for air-ground cooperative sensing and computing, wherein a UAV cooperates with a group of connected and autonomous vehicles (CAVs) to collect sensing data of the objects around them, followed by data fusion and computation for object classification. We formulate a joint optimization problem involving UAV trajectory planning and sensing task placement, aiming to minimize the sensing accuracy error and task processing delay. The joint optimization problem is reformulated as a Markov decision process (MDP), where a penalty term for constraint violations is incorporated into the reward function to ensure feasibility. Furthermore, we develop an improved twin delayed deep deterministic policy gradient (TD3)-based algorithm for UAV-assisted cooperative sensing and computing to derive an efficient UAV trajectory control and subtask placement strategy. Results demonstrate that the proposed algorithm achieves superior performance compared to baselines in terms of convergence speed, training stability, and cost-saving, validating its applicability in dynamic UAV-assisted VEC environments.

This article addresses the predefined-time optimal motion control problem of an autonomous surface vehicle (ASV) with disturbances under dynamic self-triggered frameworks via reinforcement learning (RL). Initially, to eliminate the influence of disturbance on the ASV, a predefined-time second-order integral sliding mode control (SOISM) strategy is formulated by establishing a novel integral sliding mode (ISM) function and a terminal sliding mode function. Subsequently, a predefined-time approximate optimal motion (AOM) control strategy is further developed to ensure the ASV maintains a stable state. Furthermore, a single critic network is used to obtain an approximate solution of the Hamilton-Jacobi-Bellman (HJB) equation. The above two strategies are established under the dynamic self-triggered framework, which relies on the current information to predict the next updating time, effectively reducing the computational and communication burden while avoiding the continuous monitoring of the ASV state. In the theoretical analysis, the main challenges lie in the design of Lyapunov functions and triggered conditions to ensure the stability of the sliding mode dynamics and the disturbed ASV. By applying the Lyapunov stability principle and designing two novel Lyapunov functions and triggered conditions that both contain dynamic variables, we demonstrate that the developed control strategies can ensure the stability within the specified time frame. Ultimately, simulation results verify the efficacy of the proposed motion control approach.

Dispersed computing has emerged as a promising paradigm that leverages underutilized resources from massive Internet of Things devices (IoTDs) to enhance the computing capacity at the network edge. However, existing works about the dispersed computing overlook the heterogeneous computing environment with parallel and serial computations and task reliability requirements for the hardware-constrained IoTDs, and they lack multi-objective optimization approaches to optimize the task offloading. To address the challenges, we propose a comprehensive scheme to achieve a delay-aware and economic-aware dispersed computing paradigm by using a multi-objective optimization approach. Particularly, we consider parallel processing at an edge server and serial processing at the lightweight IoTDs, and leverage the task redundancy to satisfy the task reliability requirements on the IoTD side. We further formulate a constrained multi-objective optimization problem (CMOP) aiming at jointly optimizing the task assignment, bandwidth allocation, and CPU frequency allocation to simultaneously minimize the total delay cost and the total charge cost of the tasks. To address the CMOP, we propose an improved constrained multi-objective evolutionary algorithm that employs a dual-population cooperative mechanism between two populations and a repairing constraint-handling technique. The dual-population cooperative mechanism can balance convergence toward Pareto optimality and solution diversity maintenance. The repairing constraint-handling technique is designed to guide solutions toward feasible regions, achieving efficient exploration of complex constrained search spaces. Simulation results demonstrate the superiority of our algorithm in seeking the better-converged and better-distributed Pareto optimal solutions to well address the tradeoffs between the two objectives.

In long-distance and long-duration flight missions of unmanned aerial vehicles (UAVs), optimal scheduling of flight speed and energy replenishment is crucial to ensure flight efficiency and safety. This paper focuses on a UAV-based patrol inspection system, where a UAV is scheduled to visit multiple task nodes that are geographically distributed in the communication coverage of a base station (BS). The UAV hovers at each task node, performing data collection and data processing. The BS is equipped with a mobile edge computing (MEC) server and a battery swapping station, offering computation and energy support to the UAV. A decision-making model customized for the UAV is proposed, jointly optimizing flight speed selection, battery swapping, and task offloading to minimize the UAV's total operational cost in its flight. By introducing virtual nodes in the flight network, we construct a unidirectional extended graph, based on which the original nonconvex cost minimization problem is reformulated to a tractable mixed-integer convex problem. Further, a fast heuristic based on analytical target cascading (ATC) is developed to obtain suboptimal solutions to large-scale problems. Results demonstrate that the proposed model can lower the UAV's total operational cost by providing greater flexibility in terms of speed selection and battery swapping, and the proposed heuristic shows high computational efficiency for large-scale network scenarios.

The growing adoption of electric vehicles (EVs) and expansion of Internet of Things (IoT) in-vehicle applications enhance vehicle intelligence and connectivity but also drive higher demand for both charging and computing services. Charging and computing stations (CCSs), integrating bidirectional chargers and edge computing servers and allowing optimal joint energy-computation management, has been taken as an effective solution to address this demand. This article introduces a new concept called charging and computing service vehicle (CCSV) fleets, which are equipped with high-capacity batteries and edge servers, serving as mobile resources to support the stationary CCSs at different locations in a wide area. We propose a two-timescale model integrating temporal-spatial scheduling, charging/discharging management, and computation task offloading of the CCSV fleets. Our goal is to minimize the total system cost by optimizing the energy-computation coordination between the mobile CCSV fleets and the stationary CCSs. We construct an extended time-space network (TSN) with congestion nodes, providing a clearer depiction of the time-varying congestion conditions in the traffic network. For practical implementation, we develop a heuristic based on the convex-concave procedure (CCP) and penalty alternating direction method (PADM) to solve the problem quickly. Simulation results in a traffic network based on Guangzhou city demonstrate that the proposed model effectively leverages the mobility and multidimensional resources of the CCSV fleets to reduce the system cost significantly.

In this paper, the model-free dynamic event-driven safe (MFDEDS) control of unknown nonaffine systems with state and input constraints is investigated via adaptive dynamic programming. To begin with, by introducing a dynamic compensator and performing system transformation, the safe control problem with state and input constraints is transformed into an optimal regulation problem of an unconstrained system. Afterwards, an integral reinforcement learning algorithm is applied to the unconstrained system to derive an optimal safe control policy independent of the original system model, which achieves model-free approximate optimal control for the original system. To conserve computing and communication resources, a novel game-based dynamic event-driven mechanism is established, which models the control policy and the event-driven error as players in a zero-sum game, with the aim of obtaining the worst event-driven error to maximize the triggering interval. Furthermore, an approximate solution to the Hamilton-Jacobi-Bellman equation is derived by constructing a single-critic learning structure, which results in an approximate optimal safe control policy. Theoretical analysis demonstrates that the proposed MFDEDS control scheme ensures the closed-loop system is asymptotically stable. Ultimately, the efficacy of the developed approach is corroborated through two simulation examples.

In this paper, the event-triggered robust hierarchical synchronization (ETRHS) control of unmanned surface vehicles (USVs) is investigated via reinforcement learning. In the ETRHS control problem, there exists one dominant USV and many following USVs. The dominant USV chooses a motion control policy based on the responses of all following USVs, and then each following USV takes corresponding optimal responses to the dominant USV’s policy. This paper converts the ETRHS control problem to an event-triggered optimal synchronization control problem by designing novel value functions for the dominant and following USVs. Subsequently, critic-only structures are established and the ETRHS control laws of all USVs are obtained to form the Stackelberg equilibrium. In order to reduce the computing and communication burden, a novel event-triggering condition is designed for each USV, and the corresponding control law is updated when the condition is triggered. Theoretical analysis demonstrates that the developed reinforcement learning-based ETRHS controllers guarantee all following USVs synchronize with the dominant USV even when dynamic uncertainties exist. Finally, simulation results verify the effectiveness of the developed reinforcement learning-based ETRHS control scheme.

Vehicle-road collaboration is an effective means of improving perception capacities and enhancing safety of intelligent connected vehicles (ICVs). A larger volume of perception data increases the accuracy and robustness of environmental understanding, but it also introduces heavier computation loads. Aiming to reduce data size while meeting perception requirements, this paper studies joint data preprocessing and offloading in vehicle-road collaboration. In the preprocessing stage, we assign different priorities to the detected objects based on their types and distances from the perceiving vehicles. We allow discarding some low-priority objects that may not need immediate attention to reduce computation loads in subsequent data processing. After object selection and downsampling on video frames, the downsized perception data is offloaded and processed collectively by ICVs and roadside units (RSUs). A nonconvex mixed-integer problem is formulated, maximizing the sum of priorities of the selected objects while satisfying constraints of time delay, bandwidth, and computing resources. A fast heuristic based on the penalty alternating direction method (PADM) and modified annealed feasibility pump (MAFP) is developed to solve the problem. Results show that the proposed method is more computationally efficient than the commercial solver in solving the priority maximization problem. Also, it can significantly reduce perception data size, enabling efficient use of the limited communication and computing resources to timely complete more high-priority tasks.

Rapid advancements in wireless communication have led to a dramatic upsurge in data volumes within mobile edge networks. These substantial data volumes offer opportunities for training Artificial Intelligence-Generated Content (AIGC) models to possess strong prediction and decision-making capabilities. AIGC represents an innovative approach that utilizes sophisticated generative AI algorithms to automatically generate diverse content based on user inputs. Leveraging mobile edge networks, mobile AIGC networks enable customized and real-time AIGC services for users by deploying AIGC models on edge devices. Nonetheless, several challenges hinder the provision of high-quality AIGC services, including issues related to the quality of sensing data for AIGC model training and the establishment of incentives for big data sharing from mobile devices to edge devices amidst information asymmetry. In this paper, we initially define a Quality of Data (QoD) metric based on the age of information to quantify the quality of sensing data. Subsequently, we propose a contract theoretic model aimed at motivating mobile devices for big data sharing. Furthermore, we employ a Proximal Policy Optimization (PPO) algorithm to determine the optimal contract. Numerical results demonstrate the efficacy and reliability of the proposed PPO-based contract model.

Collaborative Vehicular Edge Computing (CVEC) employs an edge server on the roadside unit and volunteer vehicles as processors to provide vehicle-to-infrastructure (V2I) offloading and vehicle-to-vehicle (V2V) offloading for requester vehicles in computation offloading. Since the processors have heterogeneous computing capabilities, we study a hybrid task scheduling problem to minimize the total service cost of all requester vehicles subject to feasible constraints. More specifically, the service cost of a requester vehicle is formulated as the product of the priority value and weighted sum of the delay and energy consumption of processing the task. We derive delay constraints of the V2V and V2I offloading according to the mobility of the vehicles. Furthermore, we present a deep reinforcement learning approach to solve the above problem in the dynamic vehicular environment. Particularly, we adopt the state-of-the-art Rainbow algorithm to accelerate the convergence and achieve better performance. Finally, we provide numerical results to demonstrate that our approach outperforms the baseline approaches in achieving the faster and more accurate learning.

Vehicular crowdsensing employs vehicles as mobile sensing nodes to collect road environmental information and process the collected data. To perform the delay-tolerant crowdsensing tasks in convenience, vehicles with computational demands can offload the data process tasks to a proximal edge server (ES) in a probabilistic manner after entering a parking lot. The ES determines how to price the offloading services to maximize the expected total revenue, causing a joint probabilistic data offloading and service pricing problem between the vehicles and ES. To address the problem, we adopt a Stackelberg game approach to study the interaction between them. Specifically, the ES plays as the leader to determine the uniform price for all offloading vehicles, and to equally allocate the computing resource among them. The vehicles play as the followers to optimize their offloading probabilities to minimize the expected weighted sum of task delay, energy consumption and service fee. We employ the backward induction method to analyze the unique Stackelberg equilibrium. Subsequently, a distributed algorithm is designed to reach the Stackelberg equilibrium without prior knowledge of the vehicles. Numerical results demonstrate that compared with the baseline schemes, our scheme has an advantage in improving the economic benefits of the ES.

With the advancement in battery technology and the rise of on-board computing capabilities, electric vehicles (EVs) can serve as both energy prosumers and computing nodes. The mobility of EVs allows them to perform wide-area multi-resource exchange in both electricity networks and edge computing networks. We call such a paradigm an Electric Vehicle Charging and Computing Network (EVCCN). It is considered that the EVCCN is composed of multiple charging and computing stations (CCSs) in different locations. Each CCS integrates EV chargers and an edge server, offering the interfaces for EVs to bidirectionally trade both energy and computing resources. We propose a customized model jointly optimizing the path selection, charging/discharging, and task offloading in different CCSs to minimize an EV’s travel cost (i.e., the money spent on the EV’s trip). In the proposed model, the EV consumes energy and generates data on its way to the destination, subject to travel time, energy, and data constraints. The cost minimization problem is formulated as a nonconvex mixed-integer problem from a user-centric perspective. To solve it fast in practice, we construct a new action-expanded network to simply the model and develop a heuristic based on piecewise McCormick to quickly obtain a near-optimal solution. Simulation results show that our heuristic is computationally efficient for large traffic networks compared with global solvers. We also present results in a traffic network based on Guangzhou city, which shows that our model can save 33.99% in the travel cost compared with a baseline model.

To provide a dependency-aware application, multiple unmanned aerial vehicles (UAVs) are employed to serve a ground user with a set of interdependent tasks. This leads to a new computing paradigm called as multi-UAV-enabled aerial edge computing (MU-AEC). For the large-scale application of MU-AEC, both the task-centric objective and UAV-centric objective should be simultaneously considered. Thus, we focus on the joint interdependent task scheduling and energy balancing for MU-AEC by using a multiobjective optimization approach, which enables a decision maker to identify the optimal solutions corresponding to the best feasible tradeoffs between the two objectives. A constrained multiobjective optimization problem involving two objectives: 1) the makespan minimization of all tasks and 2) energy balancing among different UAVs, is formulated. In the solution methodology, we propose a constrained decomposition-based multiobjective evolution algorithm. To quickly seek more superior solutions, a local search mechanism by utilizing the objective information, and an improved genetic operator are proposed for remarkable performance improvements. Finally, numerical results demonstrate that compared with the baseline algorithms, our algorithm achieves both advantages in increasing the convergence and diversity of the solutions.

This paper studies optimal day-ahead scheduling of a battery swapping and charging system (BSCS) for electric vehicles (EVs) from a new perspective of multiple decision makers. It is considered that the BSCS locally incorporates the battery swapping and charging processes, and the two processes are managed by two operators, called a battery swapping operator (BSO) and a battery charging operator (BCO), respectively. Our main contribution is to propose a bilevel model where the BSO acts as the leader to receive and serve the battery swapping requests from EV users, and the BCO acts as the follower to interact with the grid and control battery charging and discharging power. We reformulate the bilevel optimization problem into an equivalent single-level problem that is a nonconvex mixed-integer nonlinear program (MINLP), and its size can easily become very large. To solve the problem efficiently, we develop a new heuristic composed of two parts, i.e., an estimation of the integer solution and an algorithm based on the alternating direction method (ADM). The results show that the proposed heuristic performs well in solving large-scale problems, providing close-to-optimal solutions quickly. In addition, compared to a social welfare maximization model that follows most existing related works, the proposed bilevel model can increase the number of swapped-out batteries by 35% and the batteries’ average energy state by 6%, improving the quality of battery swapping services.

With the vehicle-to-grid and computing capabilities, a parked electric vehicle (EV) has a dual role, namely being an energy prosumer as well as a computing node for accommodating computation-offloading services. This dual-role feature of EVs yields a new computing paradigm named Electric Vehicle Edge Computing (EVEC). To ease the implementation of EVEC, we propose a fine-grained EV management approach to jointly provide parking guidance for EVs and control their charging/discharging power in parking lots. We formulate a bilevel optimization problem where the top-level problem optimizes the matching between EVs and parking lots from the perspective of computation offloading, and the bottom-level problem optimizes the control of EV charging/discharging power from the view of power networks. We transform the bilevel optimization problem into a single-level form, which is a nonconvex mixed-integer nonlinear programming problem, and we further tackle it by linearization techniques. Finally, we provide numerical results to demonstrate the efficiency and effectiveness of our approach.

By considering dynamic operational conditions in high-speed railway, this paper focuses on the on-line generation problem of train speed profile with energy-saving. This problem is formulated via the model predictive control framework in a moving-horizon manner, such that the real-time running conditions (e.g., temporary speed restrictions) can be involved in the on-line scheduling process of the train. At each control step, a hybrid scheme combining the energy-efficient and time-optimal train control strategies is proposed to ensure the feasibility of the optimal train control problem within the prediction horizon. The optimal control problem in the horizon is transformed into a multi-phase optimal control problem, which is then solved efficiently on-line using the pseudospectral method. By repeatedly solving the train control problem at each step, the energy-efficient train speed trajectory for the whole trip involving dynamic operational conditions can be obtained on-line. In addition, a delay recovery process is designed to re-schedule the train operation if the delay time during the trip exceeds a given threshold value. Finally, numerical examples using data for a real high-speed railway line are given to demonstrate the effectiveness and robustness of the proposed approach.

This article proposes distributed demand response (DR) approaches for a multienergy residential community, which is equipped with various energy conversion and storage devices to serve multiple residential loads (e.g., electricity, natural gas, and heating loads). In the proposed DR approaches, each of the energy devices and loads is an individual decision-maker and also a node in a randomly connected communication network. The DR approaches are tolerant to incomplete information which is caused by random inaction of nodes and links in the network. At first, in order to coordinate nodes' behaviors in distributed DR, different information transmission mechanisms among nodes are employed. Particularly, Steiner tree broadcast, in which nodes are networked according to their energy types, is proposed to lower the nodes' computational complexity and the network's communication overhead. Based on the information transmission mechanisms, the initial DR problem is transformed into network problems that are solvable in a random network. Then, based on the randomized alternating direction method of multipliers, distributed algorithms are designed to optimally solve the network problems in the presence of incomplete information. In simulation, real-world datasets of multiple energy loads and prices are used, and three proposed DR approaches are compared in terms of convergence performance and communication overhead.

By providing computation and storage resources at the edge of the wireless access networks, edge computing(EC) has been regarded as a provisioning solution to enable the efficient, reliable and cost-effective two-way energy and information flows in smart grids. In this paper, we propose the framework of EC-based smart grid networks, in which EC servers are deployed at the gateways between the remote cloud center and the terminal smart meters. The EC servers perform energy scheduling and renewable energy generation(RG) output forecasting, based on the collected power demand data of terminal devices and monitoring data of RG equipments. According to the inherent collaboration features of the monitoring data offloading, that the outputs of the same size/type RG equipments in a limited area are the same in a short time, we consider an efficient cooperative task offloading and resource allocation scheme. Not all of the monitoring data should be offloaded. Then, an optimal problem is formulated, the transmission powers and channels, computation resource allocation and task offloading fraction of devices are jointly optimized. Numerical results show that our proposed schemes reduce the system cost efficiently, while the latency constraints are ensured.

This paper studies a new model of energy storage (ES) sharing in a residential community in which some homes have physical ESs (PESs) but some do not. The non-PES homes can buy ES capacity from PES homes, creating virtual ESs (VESs). Based on the transaction results between PESs and VESs, an online algorithm is developed for real-time energy management of ES sharing among the homes. During online control, non-negative long-term utilities of homes and practical charging/discharging constraints of PESs and VESs are considered. The advantage of the proposed algorithm is that system state forecasting, such as home load, renewable generation, and grid price, is not required. The algorithm only needs current system states to make a control decision. Theoretic analysis shows that the worst-case system cost under the algorithm is upper bounded, guaranteeing the online solution is near-optimal. In the simulation, real-time data of grid price and home power use is employed, and the proposed algorithm is benchmarked against a greedy algorithm and a theoretic lower bound.

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.

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.

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.

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.

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.

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.

Plug-in hybrid electric vehicles (PHEVs) are receiving growing attention to achieve a sustainable transport system and society. Due to the limited vehicle battery capacity, PHEVs perform charging and re-charging from time to time. It is visioned that the charging load with high PHEVs penetration will pose a considerable impact on the residential distribution network. Therefore, implementation of coordinated PHEVs charging becomes necessary for smart grid. For maintaining the household load, the limited energy supply may not fulfill all PHEVs charging load at any time. Thus, the fairness of energy scheduling among PHEVs should be considered. In this paper, charging fairness (CF) and discouraging-charging fairness (DCF) are proposed to guarantee the charging opportunity of each PHEV and fast recovery of PHEV driving ability. We formulate the problem of the fair energy scheduling in residential distribution network as a Semi Markov Decision Process (SMDP). The technique Neuro-Dynamic Programming (NDP) is exploited to solve the corresponding problem in SMDP. In the scheduling process, Entropy Weight Method (EWM) is proposed to consider three key metrics: CF, DCF and cable power loss. Simulation results illustrate that the proposed scheduling scheme is able to avoid a number of peak load caused by PHEVs charging and at the same time reduce power loss without affecting traveling plan.

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.