Optimal Contracted Capacity of Power Consumer with Self-Owned Generating Units

• Main Authors: Pai-Chun Peng, 彭百君
• Other Authors: Ying-Yi Hong
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/27367471112176943380

博士 === 中原大學 === 電機工程研究所 === 101 === Following rapid development of high-technology industry and manufacturing restructuring for international industrial competition, electric power demand is growing rapidly. On the other side, with the trend of energy conservation and carbon reduction, reliable power supply and its efficiency are quite important. To reduce the impact of a severe power event that may cause huge losses and to avoid the possible interrupted power supply of the utilities, many customers have their self-owned generating units (SOGUs) in attempt to improve the power reliability and 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. By taking into account the corresponding operations of self-owned generating units, the dissertation focuses on developing an optimization planning system that may determine the optimal contracted capacity with the utilities to obtain the lowest total power expenditure. The dissertation proposes the improved Taguchi method and the cultural differential computation algorithm (CDCA) for the optimal contract capacity determination through evolution analysis to achieve savings of total electrical power expenses. The improved Taguchi method, combining existing Taguchi method and particle swarm optimization (PSO) algorithm, searches the optimal solution through the quality analysis in orthogonal matrices, which is based on the PSO searching experiences during the evolution process. The CDCA determines the optimal contracted capacity by differential evolution (DE) that uses the arithmetic operators, such as mutation, crossover, and selection and by cultural algorithm (CA) that extracts and saves the domain knowledge or problem properties during the evolution process. To verify feasibility of the proposed methods, the dissertation employs the real data obtained from an optoelectronics factory in Taiwan, the 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 shows from the simulation results that 14.56% of electrical power expenses can be saved from the proposed improved Taguchi method as well as CDCA method, as compared with the method currently adopted by the factory. Also, in comparison with the other optimization methods, the proposed approaches have superior results as revealed in the numerical results.