With the popularization of electric vehicles (EVs), EV charging demand is becoming an important load in the building. Considering the mobility of EVs from building to building and their uncertain charging demand, it is of great practical interest to control the EV charging process in a microgrid of buildings to optimize the total operation cost while ensuring transmission security between the microgrid and the main grid. We consider this important problem in this article and make the following contributions. First, we formulate this problem as a Markov decision process (MDP) to capture the uncertain supply and EV charging demand in the microgrid of buildings. Besides reducing the total operation cost of buildings, the model also considers the power exchange limitation to ensure transmission security. Second, this model is reformulated within an event-based optimization (EBO) framework to mitigate the impact of large state and action space. A randomized parametric event-based control policy is proposed for the microgrid controller to determine the charge ratio for each building. Then, a selecting-to-charging principle is implemented for each building controller to determine the charge control of each EV in the building. Third, a constrained gradient-based policy optimization method is developed to find the optimal event-based control policy. Furthermore, an adjustment mechanism is also introduced to ensure transmission security during optimization. Numerical experiments are conducted to analyze the structure and the performance of the event-based control policy for EV charging in a microgrid of three buildings.

The electric vehicle (EV) utilizing vehicle-to-grid (V2G) technology can serve as an important mobile storage in a microgrid of building. Since more and more buildings are equipped with charging piles and distributed renewable energy, it can potentially improve the operation efficiency of building microgrid and reduce the impact of EVs to the grid by V2G scheduling considering the uncertain supply and EV charging demand in the building. We consider this important problem in this paper and make the following contributions. First, we formulate this V2G scheduling problem as a constrained Markov decision process (CMDP). The objective function is to improve the overall building energy operation cost while constraining the expected cycling time to ensure the participation enthusiasm of the EV users. Second, a simulation-based primal-dual approach is developed to decompose the original problem into a continuous optimization subproblem on the supply side and a discrete optimization subproblem on the demand side. The demand side optimization can be further decoupled as a distributed single EV scheduling problem where simulation-based regularized rollout method can be applied to improve from existing base policies. Third, the structural property of the problem and the cost improvement of the proposed method are analyzed to speed up the optimization and ensure the solution quality. Numerical experiments based on real building load data and EV data are conducted to demonstrate the performance of this method. Note to Practitioners—With the rapid adoption of EVs in the microgrid of building, there goes the challenge of how to reduce their charging impact to the microgrid. The V2G scheduling between EVs and building microgrid can provide a promising way to reduce the impact and improve the building operation efficiency. However, it suffers from the uncertain EV charging demand and the conflict between synthesized coordination and ensuring the participation enthusiasm of drivers. In this paper, we propose a constrained V2G scheduling model to address this issue. After formulating the problem as a constrained Markov decision process, a simulation-based primal-dual approach is developed to decompose the problem where the derived control policy in the supply side can be deployed in the building energy controller and the derived control policy in the demand side can be deployed in the controller of each charging pile. Case studies show the performance of the proposed method and its win-win property for the building and EV users.

Due to the mobility and vulnerability of satellite nodes, the topology of satellite-terrestrial integrated networks (STINs) is highly dynamic. It requires a flexible reconstruction strategy to avoid severe performance loss. However, the current reconstruction strategies usually result in a heavy computational burden, especially in the case of a shortage of satellite resources. Hence, it is of great practical interest to design a flexible topology reconstruction strategy to balance the computational efficiency and network performance of STINs. We investigated this important problem in this paper and made the following contributions. First, a deep Q-learning model is proposed to dynamically optimize the node classification results and flexibly determine the range of network reconstruction, which could enhance the efficiency of the network recovery. Second, a modified SVM classification model is proposed to classify the node type with the hyperplane parameters dynamically adjusted, which can increase the accuracy of the classification result and improve the convergence efficiency of the reconstruction algorithm. Third, an artificial bee colony algorithm is proposed to flexible recover the performance of the damaged network, which considers the age of information and range of the topology cross iteration. Numerical results demonstrate that compared with existing algorithms, the average end-to-end delay decreased by about 13.68%, and other performance indicators of STINs where improved to a certain extent.

Multiaccess edge computing (MEC) provides users with a network environment and computing storage capacity at the edge of the network, ensuring a deterministic service with low delivery delay. This paper introduces a new satellite‐ground integrated collaborative caching network architecture based on MEC and studies the caching strategy. On the ground side, the edge nodes (ENs) are deployed to the user side to form a hierarchical collaborative cache mode centered on the base station. On the satellite side, we utilize intelligent satellite ENs to precache and multicast the highly popular contents, reducing the initial content delivery delay. Under the constraints of the user demand and storage capacity, we study the deployment and cache scheme of ENs and establish the delivery delay minimization problem. To solve the problem, we propose a content update decision parameter for content cache update and transform the problem into improving the hit rate of ENs. Simulation results show that the proposed MEC network architecture and content caching scheme can increase the caching system hit rate to 64% and reduce the average delay by 32.96% at most.

Compared with the stable states of the ground networks, the space-ground integrated networks (SGIN) have limited resources, high transmission delay, and vulnerable topology, which make traditional caching strategies unable to adapt to the complex space network environment. An intelligent and efficient caching strategy is required to improve the edge service capabilities of satellites. Therefore, we investigate these problems in this paper and make the following contributions. First, the content value evaluation model based on classification and regression tree is proposed to solve the problem of what to cache by describing the cache value of content, which considers the multidimensional content characteristics. Second, we propose a cache decision strategy based on the node caching cost model to answer where to cache. This strategy modified the genetic algorithm to adapt the 0-1 knapsack problem under SDN architecture, which greatly improved the cache hit rate and the network service quality. Finally, we propose a cache replacement strategy by establishing an effective service time model between the satellite and ground transmission link, which solves the problem of when to replace. Numerical results demonstrate that the proposed strategy in SGIN can improve the nodes cache hit rate and reduce the network transmission delay and transmission hops.