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This paper presents an improved differential evolution (IDE) method for the solution of large-scale unit commitment (UC) problems. The objective of the proposed scheme is to determine the generation schedule which minimizes the total operating cost over a given time horizon subject to a variety of constraints. Through its use of enhanced acceleration and migration processes, the IDE method limits the population size required in the search procedure and is therefore an ideal candidate for the solution of large-scale combinatorial optimization problems. The effectiveness of the proposed approach is verified by performing a series of simulations based upon the practical Tai-Power System (TPS) and various other power systems presented in the literature. In general, the results show that the IDE scheme outperforms existing deterministic and stochastic optimization methods both in terms of the quality of the solutions obtained and the computational cost. Furthermore, it is found that the magnitude of the cost savings achieved by the IDE scheme compared to that obtained by the other optimization techniques increases as the number of generating units within the power system increases. Therefore, the proposed scheme represents a particularly effective technique for the solution of large-scale UC problems.

Energy cost is the main constraint in modern wireless communication system. A powerful scheme due to optimal energy cost is provided for a single node server in this paper. In wireless communications, the total energy for transmitting packets can be reduced by proper regulating the service rate due to different packet sizes. In our study, a generic method applying the Lagrange Multiplier methods for optimizations is proposed. We show the energy cost is a convex function and it is easy to achieve the optimization. Our contribution focused on minimizing the total energy cost induced by the transmission energy in a single server with multi-queues. The methodology presented in this paper can effectively save the energy cost due to energy consumption in wireless communication systems.