• Energy Research

In this work, we consider the practical issues of resource allocation problem in OFDMA two-way relay networks: the inaccuracy of channel-state information (CSI) available to the transmitters. Instead, only the estimated channel status is known by the transmitters. In this context, a joint optimization of subcarrier pairing and allocation, relay selection and transmit power allocation is formulated in the OFDMA two-way amplify-and-forward relay networks. Moreover, to ensure the Quality of Service (QoS) requirement, the energy consumption must be minimized without compromising the QoS. Therefore, by applying convex optimization techniques, energy efficient algorithms are developed with the objective to minimize the total transmit power while guaranteeing the required data rates. Through simulation studies, energy consumption performance of the systems under the proposed schemes are investigated. It is shown that the proposed scheme can improve the energy consumption performance without scarifying the QoS.

Abstract Due to random patterns of demand response from the consumer side and the unreliable nature of renewable energy resources, load flow balancing and transient stability become challenging issues in power systems. They are even more challenging in cases of multiple interval three phase (L-L-L) faults (MITPFs), which arise in power systems due to power quality disturbances. The intent of this work is to examine the influence of MITPFs on renewable energy resources (RERs) for load flow balancing and transient stability analysis. Wind turbine power dispatchability and uncertainty have a significant impact on load flow balancing and transient stability, especially in cases of occurrence of MITPFs. Probabilistic modeling is performed in this paper to formulate the complexity of randomness for load flow balancing through a smart node and transient stability analysis through a unified power flow controller. The proposed probabilistic algorithm is based on the deviations between generation and demand response patterns due to an MITPF. An autocorrelation expansion is applied to approximate the randomness of probabilistic variables between the forecast generation and actual response pattern. Future contingencies can be predicted before disruptive changes arise due to the occurrence of an MITPF using the above probabilistic analysis. Simulation results show that the proposed algorithm outperforms existing alternatives and can achieve near optimal performance for a wide range of load variations and power quality disturbances in renewable-integrated power grids.

The rapidly-increasing high data rate wireless networks and the fast-growing smartphone techniques are continually changing our daily behaviors and coloring our life. However, in order to fully experience the high rate broadband multimedia services, prolonging the battery life of user equipment is critical for the mobile users, especially for the smartphone users. In this work, the problem of offloading the cellular data via a collaborative mobile cloud in an energy efficient manner is investigated. The CMC is formed by a group of users interested in downloading the same content from the operator. Through Device-to-Device communications, the users inside CMC are able to cooperate during downloading procedure and offload data from Base Station for other CMC members. When considering wireless power transfer and MIMO wireless channel, an efficient algorithm is presented to address how to optimally schedule the data offloading and radio resources to obtain the energy efficiency as well as fairness among mobile users. Specifically, the proposed framework takes energy saving maximization, Quality of Service (QoS) requirement as well as user fairness into consideration. Performance evaluations demonstrate that significant energy saving gain can be achieved by the proposed data offloading scheme.

When integrating device-to-device (D2D) communications with densely deployed cellular networks, both energy efficiency (EE) and quality of service (QoS) will be severely degraded by strong intracell and intercell interference. To optimize EE while guaranteeing QoS provisioning, a three-stage energy-efficient resource allocation algorithm is proposed, which combines centralized interference mitigation and distributed power allocation algorithms by exploiting multi-cell cooperations, nonco-operative game, nonlinear fractional programming, and Lagrange dual decomposition. Simulation results have demonstrated that the proposed algorithm achieves a nearly zero infeasibility ratio, and improves EE performance significantly for both cellular and D2D user equipments (UEs) compared to the previous distributed scheme.

The explosive growth of mobile date traffic and ubiquitous mobile services cause an high energy consumption in mobile devices with limited energy supplies, which has become a bottleneck for deploying device-to-device (D2D) communication. Simultaneous wireless information and power transfer (SWIPT), which enables mobile devices to harvest energy from the radio frequency signals, has emerged as a promising solution to improve the energy efficiency (EE) performance. In this paper, we address joint power control and spectrum resource allocation problem in SWIPT-based energy-harvesting D2D underlay networks. First, we formulate joint optimization problem as a 2-D matching between D2D pairs and cellular user equipments (CUEs), and propose a preference establishment algorithm based on Dinkelbach method and Lagrange dual decomposition. Second, we propose an energy-efficient stable matching algorithm by exploring the Gale-Shapley algorithm, which is able to maximize the EE performance of D2D pairs and the amount of energy harvested by CUEs simultaneously. Third, we provide in-depth theoretical analysis of the proposed matching algorithm in terms of stability, optimality, and complexity. Simulation results demonstrate that the proposed algorithm can bring significant EE performance gains compared with some heuristic algorithms.

The topic of network lifetime has been attracting much research attention because of its importance in prolonging the standing operation of battery-restricted wireless sensor networks, and the rechargeable wireless sensor network has emerged as a promising solution. In this paper, we propose a joint energy supply and routing path selection algorithm to extend the network lifetime based on an initiative power supply. We develop a two-stage energy replenishment strategy to supplement the energy consumption of nodes as much as possible. Furthermore, the influence of charging factors on the selection of next-hop nodes in data routing is considered. The simulation results show that our algorithm effectively prolong the network lifetime, and different demands of network delay and energy consumption can be obtained by dynamically adjusting parameters.