■ Cognitive-radio-based Smart Grid Networks
The current centrally controlled power grid is undergoing a drastic change in order to deal with increasingly diversified challenges, including environment and infrastructure. The next-generation power grid, known as the smart grid, will be realized with proactive usage of state-of-the-art technologies in the areas of sensing, communications, control, computing, and information technology. In a smart power grid, an efficient and reliable communication architecture plays a crucial role in improving efficiency, sustainability, and stability. We first propose an unprecedented cognitive radio based communications architecture for the smart grid, which is mainly motivated by the explosive data volume, diverse data traffic, and need for QoS support. The proposed architecture is decomposed into three subareas: cognitive home area network, cognitive neighborhood area network, and cognitive wide area network, depending on the service ranges and potential applications. Next, we focus on dynamic spectrum access and sharing in each subarea. We also identify a very unique challenge in the smart grid, the necessity of joint resource management in the decomposed NAN and WAN geographic subareas in order to achieve network scale performance optimization. Illustrative results indicate that the joint NAN/WAN design is able to intelligently allocate spectra to support the communication requirements in the smart grid.
Fig. 1 Cognitive radio based smart grid network.
■ Vehicle-to-Grid (V2G) Mobile Energy Network
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 our work, we focus on the exploration of EV mobility to impact DRM in V2G systems in smart grid. We first present a dynamical 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.
Fig. 2 V2G mobil energy network.
Fig. 3 DRM performance in once simulation (one hour).
Fig. 4 Average DRM performance in one day simulation (18 hours).
■ Demand Response Management in Smart Grid
The feature of two-way communications in smart grid enables the implementation of demand response management (DRM) to collect and control the energy consumption of the users. We consider an energy consumption management for households (users) in a residential smart grid network. In each house, there are two types of demands, essential and flexible demands, where the flexible demands are further categorized into delay-sensitive and delay-tolerant demands. We formulate an optimization problem with queuing analysis to minimize the total electricity cost and the operation delay of flexible demands by obtaining the optimal energy management decisions. Based on adaptive dynamic programming, a centralized algorithm is proposed to solve the optimization problem. In addition, a distributed algorithm is designed for practical implementation and the neural network is employed to estimate the pricing or demands when such system information is not known. Simulation results show that the proposed schemes can provide effective management for household electricity usage and reduce the operation delay for the flexible demands.
Fig. 5 Proposed DRM scheduling model.
Fig. 6 Performance Evaluation.