Cultured Differential Computation Algorithm for Optimal Contracted Capacity of Power Consumer with Self-Owned Generating Units

• Main Authors: Shr-Ching Guo, 郭士慶
• Other Authors: Hong-Tzer Yang
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/96160103488385066960

碩士 === 中原大學 === 電機工程研究所 === 95 === To reduce the damages caused by voltage sag and interruption events, many customers have their self-owned generating units in attempt to improve the power supply quality. With the power tariff structure of the utilities and the cost functions of self-owned generating units considered at the same time, expenses due to the utility power consumed and the operation of self-owned generating units are highly related to the contracted capacity. Taking into account the corresponding operations of self-owned generating units, the thesis is thus aimed at determining the optimal contracted capacity with the utilities to obtain the lowest total power expenditure. The differential computation algorithm provides fast and robust converging characteristics in searching the optimal solution through operations of mutation, crossover, and selection. The cultural algorithm can extract and save the domain knowledge or problem properties during the evolution process. The domain knowledge in cultural algorithm can be added to the mutation operation in differential computation algorithm to make the searching more efficient. Accordingly, the thesis proposes the cultured differential computation algorithm to determine the optimal contracted capacity in order to reach the goal of saving the total power expenses. To verify feasibility of the proposed method, the thesis employs the real data obtained from an optoelectronics factory in Taiwan, data which include the amounts of power consumption from the utilities, capacities and cost functions of self-owned generating units, and load demand forecasting in the months of planning period. It is shown from the simulation results that 14.56% of electrical power expenses can be saved from the proposed cultured differential computation algorithm as compared with the method currently adopted by the factory. Also, in comparison with the other optimization methods, the proposed approach has superior results to the other existing optimization methods as revealed in the numerical results.