To minimise transmission completion time in energy-harvesting rechargeable communication systems, various transmission scheduling schemes have been proposed. However, they did not consider the issue where a transmitter may fail before completing the transmission. To resolve this issue, transmission scheduling for broadcasting with energy-harvesting switching transmitters is studied. The broadcasting communication system consists of two transmitters and two users. The transmitters harvest energy independently and work alternately. The transmission scheduling problem is formulated as minimising the transmission completion time, under the energy causality constraint, the data transmission constraint, and the transmitter switching constraint. Transmitter switching is addressed by proving that a transmitter should not stop transmission before switching to the other transmitter and the switching threshold should be zero. Then, an optimal transmission policy is proposed based on earlier works on rate region and the transmitter switching mechanism. The proposed optimal transmission policy can achieve a larger rate region than several kinds of suboptimal transmission policies.

With the integration of distributed generation and the construction of cross-regional long-distance power grids, power systems become larger and more complex. They require faster computing speed and better scalability for power flow calculations to support unit dispatch. Based on the analysis of a variety of parallelization methods, this paper deploys the large-scale power flow calculation task on a cloud computing platform using resilient distributed datasets (RDDs). It optimizes a directed acyclic graph that is stored in the RDDs to solve the low performance problem of the MapReduce model. This paper constructs and simulates a power flow calculation on a large-scale power system based on standard IEEE test data. Experiments are conducted on Spark cluster which is deployed as a cloud computing platform. They show that the advantages of this method are not obvious at small scale, but the performance is superior to the stand-alone model and the MapReduce model for large-scale calculations. In addition, running time will be reduced when adding cluster nodes. Although not tested under practical conditions, this paper provides a new way of thinking about parallel power flow calculations in large-scale power systems.

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With the advancement of battery technology, energy harvesting communication systems attracted great research attention in recent years. However, energy harvesting communication systems with multiple transmitters and multiple receivers have not been considered yet. In this paper, the problem of broadcasting in a communication system with multiple energy harvesting transmitters and multiple receivers is studied. First, regarding the transmitters as a 'whole transmitter' [1], the optimal total transmission power is obtained and the optimal power allocation policy in [2] is extended to our system setup, with the aim of minimizing the transmission completion time. Then, a simpler power allocation policy is developed to allocate the optimal total transmission power to the data transmissions.As transmitter switching can provide flexibility and robustness to an energy harvesting communication system, especially when a transmitter is broken or the energy harvested by a transmitter is insufficient, a transmitter switching policy is further developed to choose a suitable transmitter to work whenever necessary. The results show that the proposed power allocation policy performs close to the optimal one and outperforms some heuristic ones in terms of transmission completion time. Besides, the proposed transmitter switching policy outperforms some heuristic ones in terms of number of switches.