Browsing by Author "Cao, Zhigang"
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Item Charging Scheduling of Electric Vehicles With Local Renewable Energy Under Uncertain Electric Vehicle Arrival and Grid Power Price(IEEE Transactions on Vehicular Technology, 12/20/2013) Zhang, Tian; Chen, Wei; Han, Zhu; Cao, ZhigangIn this paper, we consider delay-optimal charging scheduling of the electric vehicles (EVs) at a charging station with multiple charge points. The charging station is equipped with renewable energy generation devices and can also buy energy from power grids. The uncertainty of the arrival of the EV, the intermittence of the renewable energy, and the variation of the grid power price are taken into account and described as independent Markov processes. Meanwhile, the required charging energy for each EV is random. The goal is to minimize the mean waiting time for EVs under the long-term constraint on the cost. We propose queue mapping to convert the EV queue to the charging demand queue, and we prove the equivalence between the minimization of the two queues' average length. Then, we focus on the minimization for the average length of the charging demand queue under the long-term cost constraint. We propose a framework of Markov decision process (MDP) to investigate this constrained stochastic optimization problem. The system state includes the charging demand queue length, the charging demand arrival, the energy level in the storage battery of the renewable energy, the renewable energy arrival, and the grid power price. Additionally, the number of charging demands and the allocated energy from the storage battery compose the 2-D policy. We derive two necessary conditions of the optimal policy. Moreover, we discuss the reduction of the 2-D policy to be the number of charging demands only. We give the sets of system states for which charging no demand and charging as many demands as possible are optimal, respectively. Finally, we investigate the proposed policies numerically.Item Hierarchic Power Allocation for Spectrum Sharing in OFDM-Based Cognitive Radio Networks(IEEE Transactions on Vehicular Technology, 2/4/2014) Zhang, Tian; Chen, Wei; Han, Zhu; Cao, ZhigangIn this paper, a Stackelberg game is built to model the joint power allocation of the primary user (PU) network and the secondary user (SU) network hierarchically in orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) networks. We formulate the PU and SUs as the leader and the followers, respectively. We consider two constraints: the total power constraint and the interference-to-signal ratio (ISR) constraint, in which the ratio between the accumulated interference and the received signal power at each PU should not exceed a certain threshold. First, we focus on the single-PU-multi-SU scenario. Based on the analysis of the Stackelberg equilibrium (SE) for the proposed Stackelberg game, an analytical hierarchic power-allocation method is proposed when the PU can acquire the additional information to anticipate SUs' reactions. The analytical algorithm has two steps. First, the PU optimizes its power allocation by considering the SUs' reactions to its action. In the power optimization of the PU, there is a subgame for power allocation of SUs given the fixed transmit power of the PU. The existence and uniqueness for the Nash equilibrium (NE) of the subgame are investigated. We also propose an iterative algorithm to obtain the NE and derive the closed-form solutions of the NE for the perfectly symmetric channel. Second, the SUs allocate the power according to the NE of the subgame given the PU's optimal power allocation. Furthermore, we design two distributed iterative algorithms for the general channel even when private information of the SUs is unavailable at the PU. The first iterative algorithm has a guaranteed convergence performance and the second iterative algorithm employs asynchronous power update to improve time efficiency. Finally, we extend to the multi-PU-multi-SU scenario, and a distributed iterative algorithm is presented.